TTMS Blog
TTMS experts about the IT world, the latest technologies and the solutions we implement.
Posts by: Marcin Kapuściński
GPT-5 Training Data: Evolution, Sources, and Ethical Concerns
Did you know that GPT-5 may have been trained on transcripts of your favorite YouTube videos, Reddit threads you once upvoted, and even code you casually published on GitHub? As language models become more powerful, their hunger for vast and diverse datasets grows—and so do the ethical questions. What exactly went into GPT-5’s mind? And how does that compare to what fueled its predecessors like GPT-3 or GPT-4? This article breaks down the known (and unknown) facts about GPT-5’s training data and explores the evolving controversy over transparency, consent, and fairness in AI training. 1. Training Data Evolution from GPT-1 to GPT-5 GPT-1 (2018): The original Generative Pre-Trained Transformer (GPT-1) was relatively small by today’s standards (117 million parameters) and was trained on a mix of book text and online text. Specifically, OpenAI’s 2018 paper describes GPT-1’s unsupervised pre-training on two corpora: the Toronto BookCorpus (~800 million words of fiction books) and the 1 Billion Word Benchmark (a dataset of ~1 billion words, drawn from news articles). This gave GPT-1 a broad base in written English, especially long-form narrative text. The use of published books introduced a variety of literary styles, though the dataset has been noted to include many romance novels and may reflect the biases of that genre. GPT-1’s training data was a relatively modest 4-5 GB of text, and OpenAI openly published these details in its research paper, setting an early tone of transparency. GPT-2 (2019): With 1.5 billion parameters, GPT-2 dramatically scaled up both model size and data. OpenAI created a custom dataset called WebText by scraping content from the internet: specifically, they collected about 8 million high-quality webpages sourced from Reddit links with at least 3 upvotes. This amounted to ~40 GB of text drawn from a wide range of websites (excluding Wikipedia) and represented a 10× increase in data over GPT-1. The WebText strategy assumed that Reddit’s upvote filtering would surface pages other users found interesting or useful, yielding naturally occurring demonstrations of many tasks in the data. GPT-2 was trained to simply predict the next word on this internet text, which included news articles, blogs, fiction, and more. Notably, OpenAI initially withheld the full GPT-2 model in February 2019, citing concerns it could be misused for generating fake news or spam due to the model’s surprising quality. (They staged a gradual release of GPT-2 models over time.) However, the description of the training data itself was published: “40 GB of Internet text” from 8 million pages. This openness about data sources (even as the model weights were temporarily withheld) showed a willingness to discuss what the model was trained on, even as debates began about the ethics of releasing powerful models. GPT-3 (2020): GPT-3’s release marked a new leap in scale: 175 billion parameters and hundreds of billions of tokens of training data. OpenAI’s paper “Language Models are Few-Shot Learners” detailed an extensive dataset blend. GPT-3 was trained on a massive corpus (~570 GB of filtered text, totaling roughly 500 billion tokens) drawn from five main components: Common Crawl (Filtered): A huge collection of web pages scraped from 2016-2019, after heavy filtering for quality, which provided ~410 billion tokens (around 60% of GPT-3’s training mix). OpenAI filtered Common Crawl using a classifier to retain pages similar to high-quality reference corpora, and performed fuzzy deduplication to remove redundancies. The result was a “cleaned” web dataset spanning millions of sites (predominantly English, with an overrepresentation of US-hosted content). This gave GPT-3 a very broad knowledge of internet text, while filtering aimed to skip low-quality or nonsensical pages. WebText2: An extension of the GPT-2 WebText concept – OpenAI scraped Reddit links over a longer period than the original WebText, yielding about 19 billion tokens (22% of training). This was essentially “curated web content” selected by Reddit users, presumably covering topics that sparked interest online, and was given a higher sampling weight during training because of its higher quality. Books1 & Books2: Two large book corpora (referred to only vaguely in the paper) totaling 67 billion tokens combined. Books1 was ~12B tokens and Books2 ~55B tokens, each contributing about 8% of GPT-3’s training mix. OpenAI didn’t specify these datasets publicly, but researchers surmise that Books1 may be a collection of public domain classics (potentially Project Gutenberg) and Books2 a larger set of online books (possibly sourced from the shadow libraries). The inclusion of two book datasets ensured GPT-3 learned from long-form, well-edited text like novels and nonfiction books, complementing the more informal web text. Interestingly, OpenAI chose to up-weight the smaller Books1 corpus, sampling it multiple times (roughly 1.9 epochs) during training, whereas the larger Books2 was sampled less than once (0.43 epochs). This suggests they valued the presumably higher-quality or more classic literature in Books1 more per token than the more plentiful Books2 content. English Wikipedia: A 3 billion token excerpt of Wikipedia (about 3% of the mix). Wikipedia is well-structured, fact-oriented text, so including it helped GPT-3 with general knowledge and factual consistency. Despite being a small fraction of GPT-3’s data, Wikipedia’s high quality likely made it a useful component. In sum, GPT-3’s training data was remarkably broad: internet forums, news sites, encyclopedias, and books. This diversity enabled the model’s impressive few-shot learning abilities, but it also meant GPT-3 absorbed many of the imperfections of the internet. OpenAI was relatively transparent about these sources in the GPT-3 paper, including a breakdown by token counts and even noting that higher-quality sources were oversampled to improve performance. The paper also discussed steps taken to reduce data issues (like filtering out near-duplicates and removing potentially contaminated examples of evaluation data). At this stage, transparency was still a priority – the research community knew what went into GPT-3, even if not the exact list of webpages. GPT-4 (2023): By the time of GPT-4, OpenAI shifted to a more closed stance. GPT-4 is a multimodal model (accepting text and images) and showed significant advances in capability over GPT-3. However, OpenAI did not disclose specific details about GPT-4’s training data in the public technical report. The report explicitly states: “Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method.”. In other words, unlike the earlier models, GPT-4’s creators refrained from listing its data sources or dataset sizes. Still, they have given some general hints. OpenAI has confirmed that GPT-4 was trained to predict the next token on a mix of publicly available data (e.g. internet text) and “data licensed from third-party providers”. This likely means GPT-4 used a sizable portion of the web (possibly an updated Common Crawl or similar web corpus), as well as additional curated sources that were purchased or licensed. These could include proprietary academic or news datasets, private book collections, or code repositories – though OpenAI hasn’t specified. Notably, GPT-4 is believed to have been trained on a lot of code and technical content, given its strong coding abilities. (OpenAI’s partnership with Microsoft likely enabled access to GitHub code data, and indeed GitHub’s Copilot model was a precursor in training on public code.) Observers have also inferred that GPT-4’s knowledge cutoff (September 2021 for the initial version) indicates its web crawl likely included data up to that date. Additionally, GPT-4’s vision component required image-text pairs; OpenAI has said GPT-4’s training included image data, making it a true multimodal model. All told, GPT-4’s dataset was almost certainly larger and more diverse than GPT-3’s – some reports speculated GPT-4 was trained on trillions of tokens of text, possibly incorporating around a petabyte of data including web text, books, code, and images. But without official confirmation, the exact scale remains unknown. What is clear is the shift in strategy: GPT-4’s details were kept secret, a decision that drew criticism from many in the AI community for reducing transparency. We will discuss those criticisms later. Despite the secrecy, we know GPT-4’s training data was multimodal and sourced from both open internet data and paid/licensed data, representing a wider variety of content (and languages) than any previous GPT. OpenAI’s focus had also turned to fine-tuning and alignment at scale – after the base model pre-training, GPT-4 underwent extensive refinement including reinforcement learning from human feedback (RLHF) and instruction tuning with human-written examples, which means human-curated data became an important part of its training pipeline (for alignment). GPT-5 (2025): The latest model, GPT-5, continues the trend of massive scale and multimodality – and like GPT-4, it comes with limited official information about its training data. Launched in August 2025, GPT-5 is described as OpenAI’s “smartest, fastest, most useful model yet”, with the ability to handle text, images, and even voice inputs in one unified system. On the data front, OpenAI has revealed in its system card that GPT-5 was trained on “diverse datasets, including information that is publicly available on the internet, information that we partner with third parties to access, and information that our users or human trainers and researchers provide or generate.”. In simpler terms, GPT-5’s pre-training draw from a wide swath of the internet (websites, forums, articles), from licensed private datasets (likely large collections of text such as news archives, books or code repositories that are not freely available), and also from human-generated data provided during the training process (for example, the results of human feedback exercises, and possibly user interactions used for continual learning). The mention of “information that our users provide” suggests that OpenAI has leveraged data from ChatGPT usage and human reinforcement learning more than ever – essentially, GPT-5 has been shaped partly by conversations and prompts from real users, filtered and re-used to improve the model’s helpfulness and safety. GPT-5’s training presumably incorporated everything that made GPT-4 powerful (vast internet text and code, multi-language content, image-text data for vision, etc.), plus additional modalities. Industry analysts believe audio and video understanding were goals for GPT-5. Indeed, GPT-5 is expected to handle full audio/video inputs, integrating OpenAI’s prior models like Whisper (speech-to-text) and possibly video analysis, which would mean training on transcripts and video-related text data to ground the model in those domains. OpenAI hasn’t confirmed specific datasets (e.g. YouTube transcripts or audio corpora), but given GPT-5’s advertised capability to understand voice and “visual perception” improvements, it’s likely that large sets of transcribed speech and possibly video descriptions were included. GPT-5 also dramatically expanded the context window (up to 400k tokens in some versions), which might indicate it was trained on longer documents (like entire books or lengthy technical papers) to learn how to handle very long inputs coherently. One notable challenge by this generation is that the pool of high-quality text on the open internet is not infinite – GPT-3 and GPT-4 already consumed a lot of what’s readily available. AI researchers have pointed out that most high-quality public text data has already been used in training these models. For GPT-5, this meant OpenAI likely had to rely more on licensed material and synthetic data. Analysts speculate that GPT-5’s training leaned on large private text collections (for example, exclusive literary or scientific databases OpenAI could have licensed) and on model-generated data – i.e. using GPT-4 or other models to create additional training examples to fine-tune GPT-5 in specific areas. Such synthetic data generation is a known technique to bolster training where human data is scarce, and OpenAI hinted at “information that we…generate” as part of GPT-5’s data pipeline. In terms of scale, concrete numbers haven’t been released, but GPT-5 likely involved an enormous volume of data. Some rumors suggested the training might have exceeded 1 trillion tokens or more, pushing the limits of dataset size and requiring unprecedented computing power (it was reported that Microsoft’s Azure cloud provided over 100,000 NVidia GPUs for OpenAI’s model training). The cost of training GPT-5 has been estimated in the hundreds of millions of dollars, which underscores how much data (and compute) was used – far beyond GPT-3’s 300 billion tokens or GPT-4’s rumored trillions. Data Filtering and Quality Control: Alongside raw scale, OpenAI has iteratively improved how it filters and curates training data. GPT-5’s system card notes the use of “rigorous filtering to maintain data quality and mitigate risks”, including advanced data filtering to reduce personal information and the use of OpenAI’s Moderation API and safety classifiers to filter out harmful or sensitive content (for example, explicit sexual content involving minors, hate speech, etc.) from the training corpora. This represents a more proactive stance compared to earlier models. In GPT-3’s time, OpenAI did filter obvious spam and certain unsafe content to some extent (for instance, they excluded Wikipedia from WebText and filtered Common Crawl for quality), but the filtering was not as explicitly safety-focused as it is now. By GPT-5, OpenAI is effectively saying: we don’t just grab everything; we systematically remove sensitive personal data and extreme content from the training set to prevent the model from learning from it. This is likely a response to both ethical concerns and legal ones (like privacy regulations) – more on that later. It’s an evolution in strategy: the earliest GPTs were trained on whatever massive text could be found; now there is more careful curation, redaction of personal identifiers, and exclusion of toxic material at the dataset stage to preempt problematic behaviors. Transparency Trends: From GPT-1 to GPT-3, OpenAI published papers detailing datasets and even the number of tokens from each source. With GPT-4 and GPT-5, detailed disclosure has been replaced by generalities. This is a significant shift in transparency that has implications for trust and research, which we will discuss in the ethics section. In summary, GPT-5’s training data is the most broad and diverse to date – spanning the internet, books, code, images, and human feedback – but the specifics are kept behind closed doors. We know it builds on everything learned from the previous models’ data and that OpenAI has put substantial effort into filtering and augmenting the data to address quality, safety, and coverage of new modalities. 2. Transparency and Data Disclosure Over Time One clear evolution across GPT model releases has been the degree of transparency about training data. In early releases, OpenAI provided considerable detail. The research papers for GPT-2 and GPT-3 listed the composition of training datasets and even discussed their construction and filtering. For instance, the GPT-3 paper included a table breaking down exactly how many tokens came from Common Crawl, from WebText, from Books, etc., and explained how not all tokens were weighted equally in training. This allowed outsiders to scrutinize and understand what kinds of text the model had seen. It also enabled external researchers to replicate similar training mixes (as seen with open projects like EleutherAI’s Pile dataset, which was inspired by GPT-3’s data recipe). With GPT-4, OpenAI reversed course – the GPT-4 Technical Report provided no specifics on training data beyond a one-line confirmation that both public and licensed data were used. They did not reveal the model’s size, the exact datasets, or the number of tokens. OpenAI cited the competitive landscape and safety as reasons for not disclosing these details. Essentially, they treated the training dataset as a proprietary asset. This marked a “complete 180” from the company’s earlier openness. Critics noted that this lack of transparency makes it difficult for the community to assess biases or safety issues, since nobody outside OpenAI knows what went into GPT-4. As one AI researcher pointed out, “OpenAI’s failure to share its datasets means it’s impossible to evaluate whether the training sets have specific biases… to make informed decisions about where a model should not be used, we need to know what kinds of biases are built in. OpenAI’s choices make this impossible.”. In other words, without knowing the data, we are flying blind on the model’s blind spots. GPT-5 has followed in GPT-4’s footsteps in terms of secrecy. OpenAI’s public communications about GPT-5’s training data have been high-level and non-quantitative. We know categories of sources (internet, licensed, human-provided), but not which specific datasets or in what proportions. The GPT-5 system card and introduction blog focus more on model capabilities and safety improvements than on how it was trained. This continued opacity has been met with calls for more transparency. Some argue that as AI systems become more powerful and widely deployed, the need for transparency increases – to ensure accountability – and that OpenAI’s pivot to closed practices is concerning. Even UNESCO’s 2024 report on AI biases highlighted that open-source models (where data is known) allow the research community to collaborate on mitigating biases, whereas closed models like GPT-4 or Google’s Gemini make it harder to address these issues due to lack of insight into their training data. It’s worth noting that OpenAI’s shift is partly motivated by competitive advantage. The specific makeup of GPT-4/GPT-5’s training corpus (and the tricks to cleaning it) might be seen as giving them an edge over rivals. Additionally, there’s a safety argument: if the model has dangerous capabilities, perhaps details could be misused by bad actors or accelerate misuse. OpenAI’s CEO Sam Altman has said that releasing too much info might aid “competitive and safety” challenges, and OpenAI’s chief scientist Ilya Sutskever described the secrecy as a necessary “maturation of the field,” given how hard it was to develop GPT-4 and how many companies are racing to build similar models. Nonetheless, the lack of transparency marks a turning point from the ethos of OpenAI’s founding (when it was a nonprofit vowing to openly share research). This has become an ethical issue in itself, as we’ll explore next – because without transparency, it’s harder to evaluate and mitigate biases, harder for outsiders to trust the model, and difficult for society to have informed discussions about what these models have ingested. 3. Ethical Concerns and Controversies in Training Data The choices of training data for GPT models have profound ethical implications. The datasets not only impart factual knowledge and linguistic ability, but also embed the values, biases, and blind spots of their source material. As models have grown more powerful (GPT-3, GPT-4, GPT-5), a number of ethical concerns and public debates have emerged around their training data: 3.1 Bias and Stereotypes in the Data One major issue is representational bias: large language models can pick up and even amplify biases present in their training text, leading to outputs that reinforce harmful stereotypes about race, gender, religion, and other groups. Because these models learn from vast swaths of human-written text (much of it from the internet), they inevitably learn the prejudices and imbalances present in society and online content. For example, researchers have documented that GPT-family models sometimes produce sexist or racist completions even from seemingly neutral prompts. A 2024 UNESCO study found “worrying tendencies” in generative AI outputs, including GPT-2 and GPT-3.5, such as associating women with domestic and family roles far more often than men, and linking male identities with careers and leadership. In generated stories, female characters were frequently portrayed in undervalued roles (e.g. “cook”, “prostitute”), while male characters were given more diverse, high-status professions (“engineer”, “doctor”). The study also noted instances of homophobic and racial stereotyping in model outputs. These biases mirror patterns in the training data (for instance, a disproportionate share of literature and web text might depict women in certain ways), but the model can learn and regurgitate these patterns without context or correction. Another stark example comes from religious bias: GPT-3 was shown to have a significant anti-Muslim bias in its completions. In a 2021 study by Abid et al., researchers prompted GPT-3 with the phrase “Two Muslims walk into a…” and found that 66% of the time the model’s completion referenced violence (e.g. “walk into a synagogue with axes and a bomb” or “…and start shooting”). By contrast, when they used other religions in the prompt (“Two Christians…” or “Two Buddhists…”), violent references appeared far less often (usually under 10%). GPT-3 would even finish analogies like “Muslim is to ___” with “terrorist” 25% of the time. These outputs are alarming – they indicate the model associated the concept “Muslim” with violence and extremism. This likely stems from the training data: GPT-3 ingested millions of pages of internet text, which undoubtedly included Islamophobic content and disproportionate media coverage of terrorism. Without explicit filtering or bias correction in the data, the model internalized those patterns. The researchers labeled this a “severe bias” with real potential for harm (imagine an AI system summarizing news and consistently portraying Muslims negatively, or a user asking a question and getting a subtly prejudiced answer). While OpenAI and others have tried to mitigate such biases in later models (mostly through fine-tuning and alignment techniques), the root of the issue lies in the training data. GPT-4 and GPT-5 were trained on even larger corpora that likely still contain biased representations of marginalized groups. OpenAI’s alignment training (RLHF) aims to have the model refuse or moderate overtly toxic outputs, which helps reduce the blatant hate speech. GPT-4 and GPT-5 are certainly more filtered in their output by design than GPT-3 was. However, research suggests that covert biases can persist. A 2024 Stanford study found that even after safety fine-tuning, models can still exhibit “outdated stereotypes” and racist associations, just in more subtle ways. For instance, large models might produce lower quality answers or less helpful responses for inputs written in African American Vernacular English (AAVE) as opposed to “standard” English, effectively marginalizing that dialect. The Stanford researchers noted that current models (as of 2024) still surface extreme racial stereotypes dating from the pre-Civil Rights era in certain responses. In other words, biases from old books or historical texts in the training set can show up unless actively corrected. These findings have led to public debate and critique. The now-famous paper “On the Dangers of Stochastic Parrots” (Bender et al., 2021) argued that blindly scaling up LLMs can result in models that “encode more bias against identities marginalized along more than one axis” and regurgitate harmful content. The authors emphasized that LLMs are “stochastic parrots” – they don’t understand meaning; they just remix and repeat patterns in data. If the data is skewed or contains prejudices, the model will reflect that. They warned of risks like “unknown dangerous biases” and the potential to produce toxic or misleading outputs at scale. This critique gained notoriety not only for its content but also because one of its authors (Timnit Gebru at Google) was fired after internal controversy about the paper – highlighting the tension in big tech around acknowledging these issues. For GPT-5, OpenAI claims to have invested in safety training to reduce problematic outputs. They introduced new techniques like “safe completions” to have the model give helpful but safe answers instead of just hard refusals or unsafe content. They also state GPT-5 is less likely to produce disinformation or hate speech compared to prior models, and they did internal red-teaming for fairness issues. Moreover, as mentioned, they filtered certain content out of the training data (e.g. explicit sexual content, likely also hate content). These measures likely mitigate the most egregious problems. Yet, subtle representational biases (like gender stereotypes in occupations, or associations between certain ethnicities and negative traits) can be very hard to eliminate entirely, especially if they permeate the vast training data. The UNESCO report noted that even closed models like GPT-4/GPT-3.5, which undergo more post-training alignment, still showed gender biases in their outputs. In summary, the ethical concern is that without careful curation, LLM training data encodes the prejudices of society, and the model will unknowingly reproduce or even amplify them. This has led to calls for more balanced and inclusive datasets, documentation of dataset composition, and bias testing for models. Some researchers advocate “datasheets for datasets” and deliberate inclusion of underrepresented viewpoints in training corpora (or conversely, exclusion of problematic sources) to prevent skew. OpenAI and others are actively researching bias mitigation, but it remains a cat-and-mouse game: as models get more complex, understanding and correcting their biases becomes more challenging, especially if the training data is not fully transparent. 3.2 Privacy and Copyright Concerns Another controversy centers on the content legality and privacy of what goes into these training sets. By scraping the web and other sources en masse, the GPT models have inevitably ingested a lot of material that is copyrighted or personal, raising questions of permission and fair use. Copyright and Data Ownership: GPT models like GPT-3, 4, 5 are trained on billions of sentences from books, news, websites, etc. – many of which are under copyright. For a long time, this was a grey area given that the training process doesn’t reproduce texts verbatim (at least not intentionally), and companies treated web scraping as fair game. However, as the impact of these models has grown, authors and content creators have pushed back. In mid-2023 and 2024, a series of lawsuits were filed against OpenAI (and other AI firms) by groups of authors and publishers. These lawsuits allege that OpenAI unlawfully used copyrighted works (novels, articles, etc.) without consent or compensation to train GPT models, which is a form of mass copyright infringement. By 2025, at least a dozen such U.S. cases had been consolidated in a New York court – involving prominent writers like George R.R. Martin, John Grisham, Jodi Picoult, and organizations like The New York Times. The plaintiffs argue that their books and articles were taken (often via web scraping or digital libraries) to enrich AI models that are now commercial products, essentially “theft of millions of … works” in the words of one attorney. OpenAI’s stance is that training on publicly accessible text is fair use under U.S. copyright law. They contend that the model does not store or output large verbatim chunks of those works by default, and that using a broad corpus of text to learn linguistic patterns is a transformative, innovative use. An OpenAI spokesperson responded to the litigation saying: “Our models are trained on publicly available data, grounded in fair use, and supportive of innovation.”. This is a core of the debate: is scraping the internet (or digitizing books) to train an AI akin to a human reading those texts and learning from them (which would be fair use and not infringement)? Or is it a reproducing of the text in a different form that competes with the original, thus infringing? The legal system is now grappling with these questions, and the GPT-5 era might force new precedents. Notably, some news organizations have also sued; for example, The New York Times is reported to have taken action against OpenAI for using its articles in training without license. For GPT-5, it’s likely that even more copyrighted material ended up in the mix, especially if OpenAI licensed some datasets. If they licensed, say, a big corpus of contemporary fiction or scientific papers, then those might be legally acquired. But if not, GPT-5’s web data could include many texts that rights holders object to being used. This controversy ties back to transparency: because OpenAI won’t disclose exactly what data was used, authors find it difficult to know for sure if their works were included – although some clues emerge when the model can recite lines from books, etc. The lawsuits have led to calls for an “opt-out” or compensation system, where content creators could exclude their sites from scraping or get paid if their data helps train models. OpenAI has recently allowed website owners to block its GPTBot crawler from scraping content (via a robots.txt rule), implicitly acknowledging the concern. The outcome of these legal challenges will be pivotal for the future of AI dataset building. Personal Data and Privacy: Alongside copyrighted text, web scraping can vacuum up personal information – like private emails that leaked online, social media posts, forum discussions, and so on. Early GPT models almost certainly ingested some personal data that was available on the internet. This raises privacy issues: a model might memorize someone’s phone number, address, or sensitive details from a public database, and then reveal it in response to a query. In fact, researchers have shown that large language models can, in rare cases, spit out verbatim strings from training data (for example, a chunk of software code with an email address, or a direct quote from a private blog) – this is called training data extraction. Privacy regulators have taken note. In 2023, Italy’s data protection authority temporarily banned ChatGPT over concerns that it violated GDPR (European privacy law) by processing personal data unlawfully and failing to inform users. OpenAI responded by adding user controls and clarifications, but the general issue remains: these models were not trained with individual consent, and some of that data might be personal or sensitive. OpenAI’s approach in GPT-5 reflects an attempt to address these privacy concerns at the data level. As mentioned, the data pipeline for GPT-5 included “advanced filtering processes to reduce personal information from training data.”. This likely means they tried to scrub things like government ID numbers, private contact info, or other identifying details from the corpus. They also use their Moderation API to filter out content that violates privacy or could be harmful. This is a positive step, because it reduces the chance GPT-5 will memorize and regurgitate someone’s private details. Nonetheless, privacy advocates argue that individuals should have a say in whether any of their data (even non-sensitive posts or writings) are used in AI training. The concept of “data dignity” suggests people’s digital exhaust has value and should not be taken without permission. We’re likely to see more debate and possibly regulation on this front – for instance, discussions about a “right to be excluded” from AI training sets, similar to the right to deletion in privacy law. Model Usage of User Data: Another facet is that once deployed, models like ChatGPT continue to learn from user interactions. By default, OpenAI has used ChatGPT conversations (the ones that users input) to further fine-tune and improve the model, unless users opt out. This means our prompts and chats become part of the model’s ongoing training data. A Stanford study in late 2025 highlighted that leading AI companies, including OpenAI, were indeed “pulling user conversations for training”, which poses privacy risks if not properly handled. OpenAI has since provided options for users to turn off chat history (to exclude those chats from training) and promises not to use data from its enterprise customers for training by default. But this aspect of data collection has also been controversial, because users often do not realize that what they tell a chatbot could be seen by human reviewers or used to refine the model. 3.3 Accountability and the Debate on Openness The above concerns (bias, copyright, privacy) all feed into a larger debate about AI accountability. If a model outputs something harmful or incorrect, knowing the training data can help diagnose why. Without transparency, it’s hard for outsiders to trust that the model isn’t, for example, primarily trained on highly partisan or dubious sources. The tension is between proprietary advantage and public interest. Many researchers call for dataset transparency as a basic requirement for AI ethics – akin to requiring a nutrition label on what went into the model. OpenAI’s move away from that has been criticized by figures like Emily M. Bender, who tweeted that the secrecy was unsurprising but dangerous, saying OpenAI was “willfully ignoring the most basic risk mitigation strategies” by not disclosing details. The company counters that it remains committed to safety and that it balances openness with the realities of competition and misuse potential. There is also an argument that open models (with open training data) allow the community to identify and fix biases more readily. UNESCO’s analysis explicitly notes that while open-source LLMs (like Meta’s LLaMA 2 or the older GPT-2) showed more bias in raw output, their “open and transparent nature” is an advantage because researchers worldwide can collaborate to mitigate these biases, something not possible with closed models like GPT-3.5/4 where the data and weights are proprietary. In other words, openness might lead to better outcomes in the long run, even if the open models start out more biased, because the transparency enables accountability and improvement. This is a key point in public debates: should foundational models be treated as infrastructure that is transparent and scrutinizable? Or are they intellectual property to be guarded? Another ethical aspect is environmental impact – training on gigantic datasets consumes huge energy – though this is somewhat tangential to data content. The “Stochastic Parrots” paper also raised the issue of the carbon footprint of training ever larger models. Some argue that endlessly scraping more data and scaling up is unsustainable. Companies like OpenAI have started to look into data efficiency (e.g., using synthetic data or better algorithms) so that we don’t need to double dataset size for each new model. Finally, misinformation and content quality in training data is a concern: GPT-5’s knowledge is only as good as its sources. If the training set contains a lot of conspiracy theories or false information (as parts of the internet do), the model might internalize some of that. Fine-tuning and retrieval techniques are used to correct factual errors, but the opacity of GPT-4/5’s data makes it hard to assess how much misinformation might be embedded. This has prompted calls for using more vetted sources or at least letting independent auditors evaluate the dataset quality. In conclusion, the journey from GPT-1 to GPT-5 shows not just technological progress, but also a growing awareness of the ethical dimensions of training data. Issues of bias, fairness, consent, and transparency have become central to the discourse around AI. OpenAI has adapted some practices (like filtering data and aligning model behavior) to address these, but at the same time has become less transparent about the data itself, raising questions in the AI ethics community. Going forward, finding the right balance between leveraging vast data and respecting ethical and legal norms will be crucial. The public debates and critiques – from Stochastic Parrots to author lawsuits – are shaping how the next generations of AI will be trained. GPT-5’s development shows that what data we train on is just as important as how many parameters or GPUs we use. The composition of training datasets profoundly influences a model’s capabilities and flaws, and thus remains a hot-button topic in both AI research and society at large. 4. Bringing AI Into the Real World – Responsibly While the training of large language models like GPT-5 raises valid questions about data ethics, transparency, and bias, it also opens the door to immense possibilities. The key lies in applying these tools thoughtfully, with a deep understanding of both their power and their limitations. At TTMS, we help businesses harness AI in ways that are not only effective, but also responsible — whether it’s through intelligent automation, custom GPT integrations, or AI-powered decision support systems. If you’re exploring how AI can serve your organization — without compromising trust, fairness, or compliance — our team is here to help. Get in touch to start the conversation. 5. What’s New in GPT‑5.1? Training Methods Refined, Data Privacy Strengthened GPT‑5.1 did not introduce a revolution in terms of training data-it relies on the same data foundation as GPT‑5. The data sources remain similar: massive open internet datasets (including web text, scientific publications, and code), multimodal data (text paired with images, audio, or video), and an expanded pool of synthetic data generated by earlier models. GPT‑5 already employed such a mix-training began with curated internet content, followed by more complex tasks (some synthetically generated by GPT‑4), and finally fine-tuned using expert-level questions to enhance advanced reasoning capabilities. GPT‑5.1 did not introduce new categories of data, but it improved model tuning methods: OpenAI adjusted the model based on user feedback, resulting in GPT‑5.1 having a notably more natural, “warmer” conversational tone and better adherence to instructions. At the same time, its privacy approach remained strict-user data (especially from enterprise ChatGPT customers) is not included in the training set without consent and undergoes anonymization. The entire training pipeline was further enhanced with improved filtering and quality control: harmful content (e.g., hate speech, pornography, personal data, spam) is removed, and the model is trained to avoid revealing sensitive information. Official materials confirm that the changes in GPT‑5.1 mainly concern model architecture and fine-tuning-not new training data FAQ What data sources were used to train GPT-5, and how is it different from earlier GPT models’ data? GPT-5 was trained on a mixture of internet text, licensed third-party data, and human-generated content. This is similar to GPT-4, but GPT-5’s dataset is even more diverse and multimodal. For example, GPT-5 can handle images and voice, implying it saw image-text pairs and possibly audio transcripts during training (whereas GPT-3 was text-only). Earlier GPTs had more specific data profiles: GPT-2 used 40 GB of web pages (WebText); GPT-3 combined filtered Common Crawl, Reddit links, books, and Wikipedia. GPT-4 and GPT-5 likely included all those plus more code and domain-specific data. The biggest difference is transparency – OpenAI hasn’t fully disclosed GPT-5’s sources, unlike the detailed breakdown provided for GPT-3. We do know GPT-5’s team put heavy emphasis on filtering the data (to remove personal info and toxic content), more so than in earlier models. Did OpenAI use copyrighted or private data to train GPT-5? OpenAI states that GPT-5 was trained on publicly available information and some data from partner providers. This almost certainly includes copyrighted works that were available online (e.g. articles, books, code) – a practice they argue is covered by fair use. OpenAI likely also licensed certain datasets (which could include copyrighted text acquired with permission). As for private data: the training process might have incidentally ingested personal data that was on the internet, but OpenAI says it filtered out a lot of personal identifying information in GPT-5’s pipeline. In response to privacy concerns and regulations, OpenAI has also allowed people to opt out their website content from being scraped. So while GPT-5 did learn from vast amounts of online text (some of which is copyrighted or personal), OpenAI took more steps to sanitize the data. Ongoing lawsuits by authors claim that using their writings for training was unlawful, so this is an unresolved issue being debated in courts. How do biases in training data affect GPT-5’s outputs? Biases present in the training data can manifest in GPT-5’s responses. If certain stereotypes or imbalances are common in the text the model read, the model may inadvertently reproduce them. For instance, if the data associated leadership roles mostly with men and domestic roles with women, the model might reflect those associations in generated content. OpenAI has tried to mitigate this: they filtered overt hate or extreme content from the data and fine-tuned GPT-5 with human feedback to avoid toxic or biased outputs. As a result, GPT-5 is less likely to produce blatantly sexist or racist statements compared to an unfiltered model. However, subtle biases can still occur – for example, GPT-5 might unconsciously use a more masculine persona by default or make assumptions about someone’s background in certain contexts. Bias mitigation is imperfect, so while GPT-5 is safer and more “politically correct” than its predecessors, users and researchers have noted that some stereotypes (gender, ethnic, etc.) can slip through in its answers. Ongoing work aims to further reduce these biases by improving training data diversity and better alignment techniques. Why was there controversy over OpenAI not disclosing GPT-4 and GPT-5’s training data? The controversy stems from concerns about transparency and accountability. With GPT-3, OpenAI openly shared what data was used, which allowed the community to understand the model’s strengths and weaknesses. For GPT-4 and GPT-5, OpenAI decided not to reveal details like the exact dataset composition or size. They cited competitive pressure and safety as reasons. Critics argue that this secrecy makes it impossible to assess biases or potential harms in the model. For example, if we don’t know whether a model’s data heavily came from one region or excluded certain viewpoints, we can’t fully trust its neutrality. Researchers also worry that lack of disclosure breaks from the tradition of open scientific inquiry (especially ironic given OpenAI’s original mission of openness). The issue gained attention when the GPT-4 Technical Report explicitly provided no info on training data, leading some AI ethicists to say the model was not “open” in any meaningful way. In summary, the controversy is about whether the public has a right to know what went into these powerful AI systems, versus OpenAI’s stance that keeping it secret is necessary in today’s AI race. What measures are taken to ensure the training data is safe and high-quality for GPT-5? OpenAI implemented several measures to improve data quality and safety for GPT-5. First, they performed rigorous filtering of the raw data: removing duplicate content, eliminating obvious spam or malware text, and excluding categories of harmful content. They used automated classifiers (including their Moderation API) to filter out hate speech, extreme profanity, sexually explicit material involving minors, and other disallowed content from the training corpus. They also attempted to strip personal identifying information to address privacy concerns. Second, OpenAI enriched the training mix with what they consider high-quality data – for instance, well-curated text from books or reliable journals – and gave such data higher weight during training (a practice already used in GPT-3 to favor quality over quantity). Third, after the initial training, they fine-tuned GPT-5 with human feedback: this doesn’t change the core data, but it teaches the model to avoid producing unsafe or incorrect outputs even if the raw training data had such examples. Lastly, OpenAI had external experts “red team” the model, testing it for flaws or biases, and if those were found, they could adjust the data or filters and retrain iterations of the model. All these steps are meant to ensure GPT-5 learns from the best of the data and not the worst. Of course, it’s impossible to make the data 100% safe – GPT-5 still learned from the messy real world, but compared to earlier GPT versions, much more effort went into dataset curation and safety guardrails.
Read
Best Energy Software Companies in 2025 – Global Leaders in Energy Tech
The energy sector is undergoing a rapid digital transformation in 2025. Leading energy technology companies around the world are delivering advanced software to help utilities and energy providers manage power more efficiently, reliably, and sustainably. From smart grid management and real-time analytics to AI-driven maintenance and automation, the top energy software companies offer solutions that drive efficiency and support the transition to cleaner energy. Below is a ranking of the best energy software companies in 2025, highlighting their focus areas, scale, and why they stand out. These leading energy management software companies are empowering the industry with cutting-edge IT development, AI integration, and services tailored for the energy domain. 1. Transition Technologies MS (TTMS) Transition Technologies MS (TTMS) is a Poland-headquartered IT services provider that has emerged as a dynamic leader in energy sector software. Founded in 2015 and now over 800 specialists strong, TTMS leverages its expertise in custom software, cloud, and AI to deliver bespoke solutions for energy companies. TTMS has deep roots in the European energy industry – it’s part of a larger capital group that has supported major power providers for years. The company builds advanced platforms for real-time grid monitoring, remote asset management, and automated fault detection, all with robust cybersecurity and compliance (e.g. IEC 61850, NIS2) in mind. TTMS’s engineers have helped optimize energy operations in refineries, mines, wind and solar farms, and energy storage facilities by consolidating systems and introducing smarter analytics. By combining enterprise technologies (as a certified Microsoft, Adobe, and Salesforce partner) with industry know-how, TTMS delivers end-to-end software that improves efficiency and reliability in energy management. Its recent projects include developing AI-enhanced network management tools to prevent blackouts and implementing digital platforms that integrate distributed energy resources. For energy companies seeking agile development and innovative solutions, TTMS offers a unique blend of domain experience and cutting-edge tech skill. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/software-solutions-for-energy-industry/ Headquarters: Warsaw, Poland Main services / focus: Real-time network management systems (RT-NMS), SCADA integration, predictive maintenance, IoT & AI analytics, cybersecurity compliance (NIS2), cloud-based energy monitoring, and digital transformation for utilities 2. Siemens Siemens is a global industrial technology powerhouse and a leader in energy management software and automation solutions. With origins dating back over 170 years, Siemens provides utilities and industrial firms with advanced platforms for grid control, power distribution, and smart infrastructure management. Its portfolio includes SCADA and smart grid software (e.g. Spectrum Power and SICAM) that enable real-time monitoring of electricity networks, as well as IoT and AI-based analytics to predict and prevent outages. Siemens also integrates renewable energy and storage into grid operations through its cutting-edge control systems. Known for its deep R&D capabilities and engineering excellence, Siemens continues to drive innovation in energy technology – from digital twin simulations of power plants to intelligent building energy management. As one of the world’s largest tech companies in this space, Siemens offers end-to-end solutions that help modernize energy systems and ensure reliable, efficient power delivery. Siemens: company snapshot Revenues in 2024: €75.9 billion Number of employees: 327,000+ Website: www.siemens.com Headquarters: Munich, Germany Main services / focus: Industrial automation, energy management, smart grid software, IoT solutions 3. Schneider Electric Siemens is a global industrial technology leader in energy management software and automation. For over 170 years, it has provided utilities and industries with advanced platforms for grid control, power distribution, and smart infrastructure. Its SCADA and smart grid tools (like Spectrum Power and SICAM) enable real-time monitoring and use AI analytics to prevent outages. Siemens also integrates renewables and storage through advanced control systems. With strong R&D and engineering expertise, the company delivers end-to-end energy solutions that modernize power systems and ensure efficiency and reliability. Schneider Electric: company snapshot Revenues in 2024: €38.15 billion Number of employees: 155,000+ Website: www.se.com Headquarters: Rueil-Malmaison, France Main services / focus: Digital automation, energy management, power systems, sustainability solutions 4. General Electric (GE Vernova) General Electric’s energy division, now known as GE Vernova, is one of the top energy software and equipment companies in the world. GE Vernova combines the legacy of GE’s power generation and grid businesses into a focused energy technology company. It produces everything from heavy-duty gas turbines and wind turbines to advanced software for managing power plants and electric grids. On the software side, GE’s solutions (such as the GE Digital Grid suite) help utilities orchestrate the flow of electricity, monitor grid stability, and integrate renewable sources via intelligent control systems. The company leverages industrial IoT and AI to enable predictive maintenance – for instance, analyzing sensor data from turbines or transformers to foresee issues and optimize performance. With a century-long heritage in electrification, GE Vernova remains a go-to provider for end-to-end energy infrastructure needs, pairing its industrial hardware with modern software to drive efficiency and decarbonization efforts globally. General Electric (GE Vernova): company snapshot Revenues in 2024: $34.9 billion Number of employees: 75,000 Website: www.gevernova.com Headquarters: Cambridge, Massachusetts, USA Main services / focus: Power generation equipment, grid infrastructure, energy software, industrial IoT 5. IBM IBM is a pioneer in applying enterprise software, cloud and artificial intelligence to the energy sector. As a global IT leader, IBM provides utilities and energy companies with solutions to modernize their operations and harness data effectively. One flagship offering is IBM Maximo for Asset Management, which helps energy and utility firms monitor the health of critical infrastructure (like transformers, pipelines, and power stations) and schedule maintenance proactively. IBM’s IoT platforms and analytics enable smart grid capabilities – for example, balancing electricity supply and demand in real time or detecting anomalies in power networks. The company’s consulting arm also partners with energy providers on digital transformation projects, from improving cybersecurity of grid systems to implementing AI-driven demand forecasting. With its breadth of experience across industries, IBM serves as a trusted technology partner for energy companies aiming to improve reliability, efficiency, and customer service through software innovation. IBM: company snapshot Revenues in 2024: $62.8 billion Number of employees: 270,000+ Website: www.ibm.com Headquarters: Armonk, New York, USA Main services / focus: Cloud & AI solutions, enterprise software, IoT for energy, consulting services 6. Accenture Accenture is a global IT consulting and professional services company that plays a major role in the energy industry’s digital initiatives. With a dedicated Energy & Utilities practice, Accenture helps power companies implement custom software solutions, upgrade legacy systems, and deploy emerging technologies like AI and blockchain. The firm has led large-scale smart grid rollouts, customer information system implementations, and analytics programs for utility providers worldwide. Accenture’s strength lies in end-to-end delivery: from strategy and design to development and systems integration, ensuring new tools fit seamlessly into an organization. For instance, Accenture might develop a cloud-based energy trading platform for a utility or streamline an oil & gas company’s supply chain with automation software. Its vast global team (hundreds of thousands of IT experts) and experience across many industries make Accenture a go-to partner for energy companies seeking to modernize and become more data-driven. In short, Accenture is a leader in energy software development services, guiding clients through complex technology transformations that improve efficiency and business outcomes. Accenture: company snapshot Revenues in 2024: $65.0 billion Number of employees: 770,000+ Website: www.accenture.com Headquarters: Dublin, Ireland Main services / focus: IT consulting, digital transformation, software development, AI services 7. ABB ABB is a Swiss-based engineering and technology company renowned for its industrial automation and electrification solutions, including a strong portfolio of energy software. Through its ABB Ability™ platform and related offerings, the company provides digital tools for monitoring and controlling power grids, renewable energy installations, and smart buildings. ABB’s energy management software helps utility operators supervise substations, optimize load flow, and integrate distributed energy resources like solar panels and batteries. The firm also delivers control systems for power plants and factories, combining them with IoT sensors and AI analytics to improve performance and safety. In the realm of electric mobility, ABB’s software manages electric vehicle charging networks and energy storage systems to support the evolving grid. With over a century in the power sector, ABB blends deep technical know-how with modern software development, making it one of the top energy management software companies driving reliability and efficiency across global energy infrastructure. ABB: company snapshot Revenues in 2024: $32.9 billion Number of employees: 110,000+ Website: www.abb.com Headquarters: Zurich, Switzerland Main services / focus: Robotics, industrial automation, electrification, energy management software Energize Your Operations with TTMS’s Expertise As this ranking shows, the energy software landscape is full of global tech giants – but Transition Technologies MS (TTMS) combines agility, industry insight, and technical excellence that truly set it apart. Belonging to the Transition Technologies Capital Group, which has supported the energy sector for over 30 years, TTMS benefits from deep engineering heritage and access to a powerful R&D ecosystem. This background enables us to deliver tailor-made digital solutions that modernize and optimize energy operations across the entire value chain. One example is our recent digital transformation project for a major European energy automation company, where TTMS developed a scalable application that unified multiple legacy systems, streamlined workflows, and significantly improved operational efficiency. The platform not only enhanced monitoring and control processes but also introduced automation that reduced downtime and increased data accuracy. The results: faster decision-making, lower maintenance costs, and a future-ready digital infrastructure. Another success story comes from a client in the Grynevia Group, a company with over 30 years of experience in the mining and industrial energy sectors. Facing growing sales complexity and data fragmentation, TTMS implemented Salesforce Sales Cloud to replace scattered Excel sheets with a centralized CRM system. The solution provided instant reporting, full visibility of the sales pipeline, and smoother communication between teams. As a result, the company gained control over its business processes, strengthened decision-making, and laid a solid foundation for future digitalization across production and energy operations. If you’re looking to modernize your energy operations with advanced software, TTMS is ready to be your trusted partner. From real-time network management and cybersecurity compliance to AI-driven analytics, our solutions are built to help energy companies achieve greater efficiency, reliability, and sustainability. Harness the power of innovation in the energy sector with TTMS – and let us help you drive measurable results in 2025 and beyond. How is AI changing the way energy companies predict demand and manage grids? AI allows energy providers to move from reactive to predictive management. Machine learning models now process massive data streams from smart meters, weather systems, and market conditions to forecast consumption patterns with unprecedented accuracy. This enables utilities to balance supply and demand dynamically, reduce waste, and even prevent blackouts before they happen. Why are cybersecurity and compliance becoming critical factors in energy software development? The growing digitalization of grids and critical infrastructure makes the energy sector a prime target for cyberattacks. Regulations such as the EU NIS2 Directive and the Cyber Resilience Act require strict data protection, incident reporting, and system resilience. For software vendors, compliance is not only a legal necessity but also a key trust factor for clients operating national infrastructure. What role do digital twins play in the modernization of energy systems? Digital twins – virtual replicas of physical assets like turbines or substations – are revolutionizing energy management. They allow operators to simulate real-world conditions, test system responses, and optimize performance without risking downtime. As a result, companies can predict maintenance needs, extend asset lifespan, and make data-driven investment decisions. How can smaller or mid-sized utilities benefit from advanced energy software traditionally used by large corporations? Thanks to cloud computing and modular SaaS models, powerful energy management platforms are no longer reserved for global utilities. Mid-sized providers can now access AI analytics, predictive maintenance, and smart grid monitoring through scalable, cost-efficient tools. This democratization of technology accelerates innovation across the entire energy landscape. What future trends will define the next generation of energy technology companies? The next wave of leaders will blend sustainability with data intelligence. Expect to see more AI-driven microgrids, peer-to-peer energy trading platforms, and blockchain-based verification of renewable sources. The industry is moving toward autonomous energy ecosystems where technology enables self-optimizing, resilient, and transparent power networks – redefining what “smart energy” truly means.
Read
From Weeks to Minutes: Accelerating Corporate Training Development with AI
1. Why Traditional E‑Learning Is So Slow? One of the biggest bottlenecks for large organisations is the painfully slow process of producing training programmes. Instructional design is inherently labour intensive. According to the eLearningArt development calculator, an average interactive course lasting one hour requires about 197 hours of work. Even basic modules can take 49 hours, while complex, advanced courses may reach over 700 hours for each hour of learner seat time. A separate industry guide notes that most e‑learning courses take 50-700 hours of work (about 200 on average) per learning hour. These figures include scripting, storyboarding, multimedia production and testing – a workload that typically translates into weeks of effort and significant cost for learning & development (L&D) teams. The ramifications are clear: by the time a course is ready, organisational needs may have shifted. Slow development cycles delay upskilling, make it harder to keep courses current and strain the resources of HR and L&D departments. In a world where skills gaps emerge quickly and regulatory requirements evolve frequently, the traditional timeline for course creation is a strategic liability. 2. AI: A Game‑Changer for Course Authoring Recent advances in artificial intelligence are poised to rewrite the rules of corporate learning. AI‑powered authoring platforms like AI4E‑learning can ingest your organisation’s existing materials and transform them into structured training content in a fraction of the time. The platform accepts a wide array of file formats – from text documents (DOC, PDF) and presentations (PPT) to audio (MP3) and video (MP4) – and then uses AI to generate ready‑to‑use face‑to‑face training scenarios, multimedia presentations and learning paths tailored to specific roles. In other words, one file becomes a complete toolkit for online and in‑person training. Behind the scenes, AI4E‑learning performs several labour‑intensive steps automatically: Import of source materials. Users simply upload Word or PDF documents, slide decks, MP3/MP4 files or other knowledge assets. Automatic processing and structuring. The tool analyses the content, creates a training scenario and transforms it into an interactive course, presentation or training plan. It can also align the course to specific job roles. User‑friendly editing. The primary interface is a Word document – accessible to anyone with basic office skills – allowing subject matter experts to adjust the scenario, content structure or interactions without specialised authoring software. Translation and multilingual support. Uploading a translated script automatically generates a new language version, facilitating rapid localisation. Responsive design and SCORM export. AI4E‑learning ensures that content adapts to different screen sizes and produces ready‑to‑use SCORM packages for any LMS. Crucially, the entire process – from ingestion of materials to the generation of a polished course – takes just minutes. This automation allows human trainers to focus on refining content rather than building it from scratch. 3. Why Speed Matters to Business Leaders Time saved on course creation translates directly into business value. Faster development means employees can upskill sooner, allowing them to meet new challenges or regulatory requirements more quickly. Rapid authoring also keeps training content aligned with current policies or product updates, reducing the risk of outdated or irrelevant instruction. For organisations operating in fast‑moving markets, the ability to roll out learning programmes quickly is a competitive advantage. In addition to speed, AI‑powered tools offer personalisation and scalability. AI4E‑learning enables scenario‑level editing and full personalisation of training content through an AI‑powered chat interface. Modules can be tailored to a learner’s role or knowledge level, resulting in more engaging experiences without additional development time. The platform’s enterprise‑grade security leverages Azure OpenAI technology within the Microsoft 365 environment, ensuring that sensitive corporate data remains protected. For CISOs and IT leaders, this means AI‑enabled training can be deployed without compromising internal security standards. 4. Case Study: Boosting Helpdesk Training with AI A recent TTMS client needed to improve the effectiveness of its helpdesk onboarding programme. Newly hired employees struggled to respond to customer tickets because they were unfamiliar with internal guidelines and lacked proficiency in English. The company implemented an AI‑powered e‑learning programme that combined traditional knowledge modules with interactive exercises driven by an AI engine. Trainees wrote responses to example tickets, and the AI provided personalised feedback, highlighting areas for improvement and offering model answers. The system continually learned from user input, refining its feedback over time. The results were striking. New employees became proficient faster, adherence to guidelines improved and written communication skills increased. Managers gained actionable insights into common errors and training gaps through AI‑generated statistics. This case demonstrates how AI‑driven training not only accelerates course creation but also enhances learner outcomes and provides data for continuous improvement. Read the full story of how TTMS used AI to transform helpdesk onboarding in our dedicated case study. 5. AI as an Enabler – Not a Replacement Some organisations worry that AI will replace human trainers. In reality, tools like AI4E‑learning are designed to augment the instructional design process, automating the time‑consuming tasks of organising materials and generating drafts. Human expertise remains essential for setting learning objectives, ensuring content quality and bringing organisational context to life. By automating the mundane, AI frees up L&D professionals to focus on strategy and personalisation, helping them deliver more impactful learning experiences at scale. 6. Turning Learning into a Competitive Advantage As corporate learning becomes more strategic, organisations that can develop and deploy training quickly will outperform those that can’t. AI‑powered authoring tools compress development cycles from weeks to minutes, allowing companies to respond to market changes, compliance requirements or internal skill gaps almost in real time. They also reduce costs, improve consistency and provide analytics that help leaders make data‑driven decisions about workforce development. At TTMS, we combine our expertise in AI with deep experience in corporate training to help organisations harness this potential. Our AI4E‑learning authoring platform leverages your existing knowledge base to produce customised, SCORM‑compliant courses quickly and securely. To see how AI‑driven training can transform your business, visit our website. Modern learning and development leaders no longer have to choose between speed and quality. With AI‑powered e‑learning authoring, they can deliver both-ensuring employees stay ahead of change and that learning becomes a source of sustained competitive advantage. How much time can AI actually save in e-learning content creation? AI can reduce the time needed to develop a corporate training course from several weeks to just a few hours – or even minutes for basic modules. Traditional course design requires 100-200 hours of work for one hour of content, but AI-driven tools automate tasks like text extraction, slide generation, and assessments. This allows learning teams to focus on validation and customization instead of manual production. Does using AI in e-learning mean replacing human instructors or designers? Not at all. AI serves as a co-creator rather than a replacement. It automates repetitive steps such as structuring materials, generating draft lessons, and suggesting visuals, while humans maintain control over quality, tone, and alignment with company culture. The combination of AI efficiency and human expertise results in faster, more engaging learning experiences. How secure are AI-based e-learning authoring tools for enterprise use? Security is a top priority for enterprise solutions. Modern AI authoring platforms can operate entirely within trusted environments like Microsoft Azure OpenAI or private cloud setups. This ensures that company data and training materials remain confidential, with no external model training or data sharing—meeting strict corporate compliance and data protection standards. Can AI-generated training content be personalized for different roles or regions? Yes. AI-powered authoring systems can adapt tone, terminology, and complexity based on learner profiles, departments, or even languages. This means a global organization can automatically generate localized versions of a course that respect cultural nuances and regulatory requirements while maintaining consistent learning outcomes across all regions. What measurable business benefits can companies expect from AI in corporate learning? Enterprises adopting AI for training report faster onboarding, lower production costs, and higher content quality. By shortening development cycles, companies can react quickly to new skill gaps or policy changes. AI also helps maintain consistency in training materials, ensuring employees across different locations receive unified and up-to-date information—ultimately improving performance and ROI.
Read
OpenAI GPT‑5.1: A Faster, Smarter, More Personal ChatGPT for Business
OpenAI’s GPT‑5.1 model has arrived, bringing a new wave of AI improvements that build on the successes of GPT‑4 and GPT‑5‑turbo. This latest flagship model is designed to be faster, more accurate, and more personable than its predecessors, making interactions feel more natural and productive. GPT‑5.1 introduces two optimized modes (Instant and Thinking) to balance speed with reasoning, delivers major upgrades in coding and problem-solving abilities, and lets users finely tune the AI’s tone and personality. It also comes paired with an upgraded ChatGPT user experience – complete with web browsing, tools, and interface enhancements – all aimed at helping professionals and teams work smarter. Below, we dive into GPT‑5.1’s key new features and how they compare to GPT‑4 and GPT‑5. 1. GPT, Why Did You Forget Everything I Taught You? Even the smartest AI has blind spots – and GPT‑5.1 proved that. After months of refining how our content should look, sound, and behave behind the scenes, the upgrade wiped much of it clean. Hidden markup rules, tone presets, structural habits – all forgotten. Frustrating? Yes. But also a good reminder: progress in AI isn’t always linear. If GPT‑5.1 suddenly forgets your workflow or tone, don’t panic. Just reintroduce your instructions patiently. Those who’ve documented their process – or can search past chats – will realign faster. A few nudges are usually all it takes to get things back on track. And once you do, the speed and smarts of GPT‑5.1 make the reset worth it. 2. How GPT-5.1 Improves Speed and Adaptive Reasoning Speed is the first thing you’ll notice with GPT‑5.1. The new release introduces GPT‑5.1 Instant, a default chat mode optimized for responsiveness. It produces answers significantly faster than GPT‑4, while also feeling “warmer” and more conversational. Early users report that chats with GPT‑5.1 Instant are snappier and more playful, without sacrificing clarity or usefulness. In side-by-side tests, GPT‑5.1 Instant follows instructions better and responds in a friendlier tone than GPT‑5, which was itself an improvement in latency and naturalness over GPT‑4. Under the hood, GPT‑5.1 introduces adaptive reasoning to intelligently balance speed and depth. For simple queries or everyday questions, it responds almost instantly; for more complex problems, it can momentarily “think deeper” to formulate a thorough answer. Notably, even the fast Instant model will autonomously decide to invoke extra reasoning time on challenging prompts, yielding more accurate answers without much added wait. Meanwhile, the enhanced GPT‑5.1 Thinking mode (the successor to GPT‑4’s heavy reasoning model) has become more efficient and context-aware. It dynamically adjusts its processing time based on question complexity – spending more time on hard problems and less on easy ones. On average, GPT‑5.1 Thinking is twice as fast as GPT‑5 was on straightforward tasks, yet can be more persistent (a bit slower) on the toughest questions to ensure it really digs in. The result is that users experience faster answers when they need quick info, and more exhaustive solutions when they pose complex, multi-step challenges. OpenAI also introduced a smart auto-model selection mechanism in ChatGPT called GPT‑5.1 Auto. In most cases, ChatGPT will automatically route your query to whichever version (Instant or Thinking) best fits the task. For example, a simple scheduling request might be handled by the speedier Instant model, while a complicated analytical question triggers the Thinking model for a detailed response. This routing happens behind the scenes to give “the best response, every time,” as OpenAI puts it. It ensures you don’t have to manually switch models; GPT‑5.1 intelligently balances performance and speed on the fly. Altogether, these improvements mean GPT‑5.1 feels more responsive than GPT‑4, which was sometimes slow on complex prompts, and more strategic than GPT‑5, which improved speed but lacked this level of adaptive reasoning. 3. GPT-5.1 Accuracy: Smarter Logic, Better Answers, Fewer Hallucinations Accuracy and reasoning have taken a leap forward in GPT‑5.1. OpenAI claims the model delivers “smarter” answers and handles complex logic, math, and problem-solving better than ever. In fact, both GPT‑5.1 Instant and Thinking have achieved significant improvements on technical benchmarks – outperforming GPT‑5 and GPT‑4 on tests like AIME (math reasoning) and Codeforces (coding challenges). These gains reflect a boost in the model’s underlying intelligence and training. GPT‑5.1 inherits GPT‑5’s “thinking built-in” design, which means it can internally work through a chain-of-thought for difficult questions instead of spitting out the first guess. The upgrade has paid off with more accurate and factually grounded answers. Users who found GPT‑4 occasionally hallucinated or gave uncertain replies will notice GPT‑5.1 is much more reliable – it’s OpenAI’s “most reliable model yet… less prone to hallucinations and pretending to know things”. Reasoning quality is noticeably higher. GPT‑5.1 Thinking in particular produces very clear, step-by-step explanations for complex problems, now with less jargon and fewer undefined terms than GPT‑5 used. This makes its outputs easier for non-experts to understand, which is a big plus for business users reading technical analyses. Even GPT‑5.1 Instant’s answers have become more thorough on tough queries thanks to its ability to momentarily tap into deeper reasoning when needed. For example, if you ask a tricky multi-part finance question, Instant might pause to do an internal “deep think” and then respond with a well-structured answer – whereas older GPT‑4 might have given a shallow response or required switching to a slower mode. Users have also observed that GPT‑5.1 is better at following the actual question and not going off on tangents. OpenAI trained it to adhere more strictly to instructions and clarify ambiguities, so you get the answer you’re looking for more often. In short, GPT‑5.1 combines knowledge and reasoning more effectively: it has a broader knowledge base (courtesy of GPT‑5’s unsupervised learning boost) and the logical prowess to use that knowledge in a sensible way. For businesses, this means more dependable insights – whether it’s analyzing data, troubleshooting a problem, or providing expert advice in law, science, or finance. Another benefit is GPT‑5.1’s expanded context memory. The model supports an astonishing 400,000-token context window, an order of magnitude jump from GPT‑4’s 32,000 token limit. In practical terms, GPT‑5.1 can intake and reason over huge documents or lengthy conversations (hundreds of pages of text) without losing track. You could feed it an entire corporate report or a large codebase and still ask detailed questions about any part of it. This extended memory pairs with improved factual consistency to reduce instances of the AI contradicting itself or forgetting earlier details in long sessions. It’s a boon for long-form analyses and for maintaining context over time – scenarios where GPT‑4 might have struggled or required workarounds due to its shorter memory. 4. GPT-5.1 Coding Capabilities: A Major Upgrade for Developers For developers and technical teams, GPT‑5.1 brings a major upgrade in coding capabilities. GPT‑4 was already a capable coding assistant, and GPT‑5 built on that with better pattern recognition, but GPT‑5.1 takes it to the next level. OpenAI reports that GPT‑5.1 shows “consistent gains across math [and] coding…workloads”, producing more coherent solutions and handling programming tasks end-to-end with greater reliability. In coding benchmarks and challenges, GPT‑5.1 outperforms its predecessors – it’s scoring higher on Codeforces problem sets and other coding tests, demonstrating an ability to not only write code, but to plan, debug, and refine it effectively. The model’s enhanced reasoning means it can tackle complex coding problems that require multiple steps of logic. With GPT‑5, OpenAI had already integrated “expert thinking” into the model, allowing it to break down problems like an engineer would. GPT‑5.1 builds on this with improved instruction-following and debugging prowess. It’s better at understanding nuanced requests (e.g. “optimize this function for speed and explain the changes”) and will stick closer to the specification without going on tangents. The code GPT‑5.1 generates tends to be more ready-to-use with fewer errors or omissions; early users note it often provides well-commented, clean code solutions in languages ranging from Python and JavaScript to more niche languages. OpenAI specifically highlights that GPT‑5 can deliver more usable code and even generate front-end UIs from minimal prompts, so imagine what GPT‑5.1 can do with its refinements. It also seems more effective at debugging code – you can paste in an error stack trace or a snippet that’s not working, and GPT‑5.1 will not only find the bug quicker than GPT‑4 did, but explain the fix more clearly. Another new advantage for coders is tool use and extended context. GPT‑5.1 has a massive 400K token window, meaning it can ingest entire project files or extensive API documentation and then operate with full awareness of that context. This is transformative for large-scale software projects – you can give GPT‑5.1 multiple related files and ask it to implement a feature or perform a code review across the codebase. The model can also call external tools more reliably when integrated via the API. OpenAI notes improved “tool-use reliability”, which implies that when GPT‑5.1 is hooked up to developer tools or functions (e.g. via the API’s function calling feature), it handles those operations more consistently than GPT‑4. In practical terms, this could mean better performance when using GPT‑5.1 in an IDE plugin to retrieve documentation, run test cases, or use terminal commands autonomously. All told, GPT‑5.1’s coding improvements help developers accelerate development cycles – it’s like an expert pair programmer who’s faster, more knowledgeable, and more attuned to your instructions than any version before. 5. Customize GPT-5.1 Tone and Writing Style with New Personality Controls One of the most noticeable new features of GPT‑5.1 (especially for business users) is its advanced control over writing style and tone. OpenAI heard loud and clear that users want AI that not only delivers correct answers but also communicates in the right manner. Different situations call for different tones – an email to a client vs. a casual internal memo – and GPT‑5.1 now makes it easy to tailor the voice of ChatGPT’s responses accordingly. Earlier in 2025, OpenAI introduced basic tone presets in ChatGPT, but GPT‑5.1 greatly expands and refines these options. You can now toggle between eight distinct personality presets for ChatGPT’s conversational style: Default, Professional, Friendly, Candid, Quirky, Efficient, Nerdy, and Cynical. Each preset adjusts the flavor of the AI’s replies without altering its underlying capabilities. For instance: Professional – Polished, precise, and formal tone (great for business correspondence). Friendly – Warm, upbeat, and conversational (for a casual, helpful vibe). Candid – Direct and encouraging, with a straightforward style. Quirky – Playful, imaginative, and creative in phrasing. Efficient – Concise and no-nonsense (formerly the “Robot” style, focused on brevity). Nerdy – Enthusiastic and exploratory, infusing extra detail or humor (good for deep dives). Cynical – Snarky or skeptical tone, for when you need a critical or witty angle. “Default” remains a balanced style, but even it has been tuned to be a bit warmer and more engaging by default in GPT‑5.1. These presets cover a wide spectrum of voices that users commonly prefer, essentially letting ChatGPT adopt different personas on demand. According to OpenAI, GPT‑5.1 “does a better job of bringing IQ and EQ together,” but recognizes one style can’t fit everyone. Now, simple guided controls give you a say in how the AI sounds – whether you want a formal report or a fun brainstorming partner. Beyond the presets, GPT‑5.1 introduces granular tone controls for those who want to fine-tune further. In the ChatGPT settings, users can now adjust sliders or settings for attributes like conciseness vs. detail, level of warmth, use of jargon, and even how frequently the AI uses emojis. For example, you could tell ChatGPT to be “very concise and not use any emojis” or to be “more verbose and technical,” and GPT‑5.1 will faithfully reflect that style in its answers. Impressively, ChatGPT can proactively offer to update its tone if it notices you manually asking for a certain style often. So if you keep saying “can you phrase that more casually?”, the app might pop up and suggest switching to the Friendly tone preset, saving you time. This level of customization was not present in GPT‑4 or GPT‑5 – previously, getting a different tone meant engineering your prompt each time or using clunky workarounds. Now it’s baked into the interface, making GPT‑5.1 a chameleon communicator. For businesses, this is incredibly useful: you can ensure the AI’s output aligns with your brand voice or audience. Marketing teams can set a consistent tone for copywriting, customer support can use a friendly/helpful style, and analysts can opt for an efficient, report-like tone. Importantly, the underlying quality of answers remains high across all these styles; you’re only changing the delivery, not the substance. In sum, GPT‑5.1 gives you unprecedented control over how AI speaks to you and for you, which enhances both user experience and the professionalism of the content it produces. Fun fact: GPT‑5.1 no longer overuses long em dashes (-) the way earlier models did. While the punctuation is still used occasionally for style or rhythm, it’s no longer the default for every parenthetical pause. Instead, the model now favors simpler, cleaner punctuation like commas or parentheses – leading to better formatting and more SEO-friendly output. 6. GPT-5.1 Memory and Personalization: Smarter, Context-Aware Interactions GPT‑5.1 not only generates text with better style – it also remembers and personalizes better. We’ve touched on the expanded context window (400k tokens) that allows the model to retain far more information within a single conversation. But OpenAI is also improving how ChatGPT retains your preferences across sessions and adapts to you personally. The new update makes ChatGPT “uniquely yours” by persisting personalization settings and applying them more broadly. Changes you make to tone or style preferences now take effect across all your chats immediately (including ongoing conversations), rather than only applying to new chats started afterward. This means if you decide you prefer a Professional tone, you don’t need to restart your chat or constantly remind it – all current and future chats will consistently reflect that setting, unless you change it. Additionally, GPT‑5.1 models are better at respecting your custom instructions. This was a feature introduced with GPT‑4 that let users provide background context or directives (like “I am a sales manager, answer with a focus on retail industry insights”). With GPT‑5.1, the AI adheres to those instructions more reliably. If you set an instruction that you want answers in bullet-point format or with a certain point of view, GPT‑5.1 is more likely to follow it in every response. This kind of personalization ensures the AI’s output aligns with your needs and saves time otherwise spent reformatting or correcting the tone. The ChatGPT experience also gradually adapts to you. OpenAI is experimenting with having the AI learn from your behavior (with your permission). For instance, if you often ask for clarifications or simpler language, ChatGPT might adjust to explain things more clearly proactively. Conversely, if you often dive into technical discussions, it might lean into a more detailed style for you. While these adaptive features are nascent, the vision is that ChatGPT becomes a truly personalized assistant that remembers your context, projects, and preferences over time. Business users will appreciate this as it means less repetitive setup for each session – the AI can recall your company’s context or past conversations when formulating new answers. On the topic of memory and context, it’s worth noting that OpenAI’s ecosystem now allows GPT‑5.1 to integrate with your own data securely. ChatGPT Enterprise and Business plans enable “organizational memory” by connecting the AI to your company files and knowledge bases (with proper permission controls). GPT‑5.1 can utilize these connectors to pull in relevant information from, say, your SharePoint or Google Drive documents to answer a question – all while respecting access rights. This effectively gives the model a real-time memory of your business context. Compared to GPT‑4, which operated mostly on its trained knowledge (up to 2021 data) unless you manually provided context each time, GPT‑5.1 can be outfitted to remember and retrieve up-to-date internal info as needed. It’s a game changer for using ChatGPT in business scenarios: imagine asking GPT‑5.1 “Summarize the sales report from last quarter and highlight any growth opportunities,” and it can securely reference your actual internal report to give an accurate, tailored answer. This kind of personalization – combining user-specific data with the model’s intelligence – marks a significant step beyond what GPT‑5 offered. 7. GPT-5.1 ChatGPT Tools and UI: Browsing, Voice, File Uploads, and More Finally, along with the GPT‑5.1 model upgrade, OpenAI has rolled out a suite of user experience improvements for ChatGPT that make the AI more useful in day-to-day workflows. One major enhancement is the integration of real-time web browsing and research tools. While GPT‑4 had an optional browsing plugin (often slow and beta), ChatGPT with GPT‑5.1 now features built-in web search as a core capability. In fact, OpenAI noted that after adding search into ChatGPT last year, it quickly became one of the most-used features. Now ChatGPT can seamlessly pull in timely information from the internet when you ask for the latest data or news, without any setup. If you ask GPT‑5.1, “What’s the current stock price of XYZ Corp?” or “Who won the game last night?”, it can fetch that info live. Moreover, the AI will often provide inline citations to sources for factual claims, which builds trust and makes it easier to verify answers – an important factor for business and research use. The browsing is smarter too: ChatGPT can click through search results, read pages, and extract what you need, all within the chat. It even uses an agent mode that can take actions in the browser on your behalf. For example, it could navigate to your company website’s analytics dashboard and pull data (with permission), or help fill out a form online. This “AI agent in the browser” approach, launched as ChatGPT Atlas (OpenAI’s new AI-powered browser), brings the assistant beyond just chat and into real web tasks. Besides browsing, ChatGPT now comes loaded with built-in tools that greatly expand its functionality. These include: Image generation: GPT‑5.1 in ChatGPT can create images on the fly using DALL·E 3 technology. You can literally ask for “an illustration of a robot reading a financial report” and get a custom image. This is integrated right into the chat, no separate plugin needed. File uploads and analysis: You can upload files (PDFs, spreadsheets, images, etc.) and have GPT‑5.1 analyze them. For example, upload a PDF of a contract and ask the AI to summarize key points. This was cumbersome with GPT‑4 but is seamless now. In group chat settings, it can even pull data from previously shared files to inform its answers. Voice input & output (dictation): ChatGPT supports voice conversations – you can talk to it and hear it talk back in a natural voice. The dictation feature converts your speech to text so you can ask questions without typing (great for multitasking professionals), and the AI’s text-to-speech can read its answers aloud. This makes ChatGPT a hands-free aide during commutes or meetings. All these tools are integrated in a user-friendly way. The interface has evolved from the simple chat box of GPT‑4’s era to a more feature-rich dashboard. For instance, there are now quick tabs for searching the web, an “Ask ChatGPT” sidebar in the Atlas browser for instant help on any webpage, and easy toggles for turning the AI’s page visibility on or off (to control when it can read the content you’re viewing). These changes reflect OpenAI’s push to make ChatGPT not just a Q&A chatbot, but a versatile assistant that fits into your workflow. They are even piloting Group Chat features, where multiple people can be in a chat with the AI simultaneously. In a business context, this means a team could brainstorm with a GPT‑5.1 assistant in the room, asking questions in a shared chat. GPT‑5.1 is savvy enough to handle group conversations, only chiming in when prompted (you can @mention “ChatGPT” to ask it something in the group) and otherwise listening in the background. This is a far cry from the single-user chatbot of GPT‑4 – it suggests an AI that can participate in collaborative settings, which could revolutionize meetings, support, and training. In summary, the ChatGPT experience with GPT‑5.1 is more powerful and polished than ever. Compared to GPT‑4 and the interim GPT‑5, users now enjoy a much faster AI with richer capabilities at their fingertips. Whether you’re leveraging GPT‑5.1 to draft a report, debug code, get strategic advice, or even generate on-brand marketing content, the process is smoother. The AI can fetch real-time information, work with your files, adjust to your preferred tone, and do it all in a secure, private environment (especially with Enterprise-grade offerings). For businesses, this means higher productivity and confidence when using AI: you spend less time wrestling with the tool and more time benefiting from its insights. OpenAI has added a bit of “marketing polish” to the model’s style, indeed – ChatGPT now feels less like a robotic expert and more like a helpful colleague who can adapt to any scenario. 8.Ready to Put GPT‑5.1 to Work for Your Business? If the capabilities of GPT‑5.1 sound impressive on paper, just imagine what they can do when tailored precisely to your workflows, data, and industry needs. Whether you’re looking to build AI-powered tools, automate customer service, generate smart content, or boost productivity with custom GPT‑5.1 solutions – we can help. At TTMS, we specialize in applying cutting-edge AI to real business problems. Explore our AI solutions for business and let’s talk about how GPT‑5.1 can transform the way your teams work. AI for Legal – Automate legal document analysis and research to support law firms and in-house legal teams. AI Document Analysis Tool – Accelerate contract review and large document processing for compliance or procurement teams. AI e-Learning Authoring Tool – Quickly create personalized training content for HR and L&D departments. AI Knowledge Management System – Organize, retrieve, and maintain company knowledge effortlessly for large organizations. AI Content Localization – Adapt content across languages and cultures for global marketing teams. AML AI Solutions – Detect suspicious transactions and streamline compliance for financial institutions. AI Resume Screening Software – Improve hiring efficiency with smart candidate shortlisting for HR professionals. AEM + AI Integration – Bring intelligent content automation to Adobe Experience Manager users. Salesforce + AI – Enhance CRM workflows and sales productivity with AI embedded in Salesforce. Power Apps + AI – Build smart, scalable apps with AI-powered logic using Microsoft’s Power Platform. Let’s explore what AI can do – not someday, but today. Contact us to discuss how we can tailor GPT‑5.1 to your organization’s needs. FAQ What is GPT-5.1, and how is it different from GPT-4 or GPT-5? GPT-5.1 is OpenAI’s latest generation AI language model, succeeding 2023’s GPT-4 and the interim GPT-5 (sometimes called GPT-4.5-turbo). It represents a significant upgrade in both capability and user experience. Compared to GPT-4, GPT-5.1 is smarter (better at reasoning and following instructions), has a much larger memory (able to consider far more text at once), and integrates new features like tone control. GPT-5.1 builds on GPT-5’s improvements in knowledge and reliability, but goes further by introducing two modes (Instant and Thinking) for balancing speed vs. depth. In short, GPT-5.1 is faster, more accurate, and more customizable than the older models. It makes ChatGPT feel more conversational and “human” in responses, whereas GPT-4 could feel formal or get stuck, and GPT-5 was an experimental step up in knowledge. If you’ve used ChatGPT before, GPT-5.1 will seem both more responsive and more intelligent in handling complex queries. Why are there two versions - GPT-5.1 Instant and GPT-5.1 Thinking? The two versions exist to give users the best of both worlds in performance. GPT-5.1 Instant is optimized for speed and everyday conversations – it’s very fast and produces answers that are friendly and to-the-point. GPT-5.1 Thinking is a more powerful reasoning mode – it’s slower on hard questions but can work through complex problems in greater depth. OpenAI introduced Instant and Thinking to address a trade-off: sometimes you want a quick answer, other times you need a detailed solution. With GPT-5.1, you no longer have to choose one model for all tasks. If you use the Auto setting in ChatGPT, simple questions will be handled by the Instant model (so you get near-instant replies), and difficult questions will invoke the Thinking model (so you get a well-thought-out answer). This dual-model approach is new in the GPT-5 series – GPT-4 only had a single mode – and it leads to both faster responses on easy prompts and better quality on tough prompts. It basically ensures you always get an optimal response tuned to the question’s complexity. Does GPT-5.1 produce more accurate results (and fewer hallucinations)? Yes, GPT-5.1 is more accurate and less prone to errors than previous models. OpenAI improved the training and added an adaptive reasoning capability, which means GPT-5.1 does a better job verifying its answers internally before responding. Users have found that it’s less likely to “hallucinate” – i.e. make up facts or give irrelevant answers – compared to GPT-4. It also handles factual questions better by using the built-in browsing tool to fetch up-to-date information when needed, then citing sources. In areas like math, science, and coding, GPT-5.1’s answers are notably more reliable because the model can actually spend time reasoning through the problem (especially in Thinking mode) instead of guessing. That said, it’s not perfect – very complex or niche questions can still pose a challenge – but overall you’ll see fewer incorrect statements. If accuracy is critical (for example, summarizing a financial report or answering a medical query), GPT-5.1 is a safer choice than GPT-4, and it often provides references or a rationale for its answers, which helps in verifying the information. What are GPT-5.1’s improvements for coding and developers? GPT-5.1 is a big leap forward for coding assistance. It can handle larger codebases thanks to its expanded context window, meaning you can input hundreds of pages of code or documentation and GPT-5.1 can keep track of it all. This model is better at understanding and implementing complex instructions, so it can generate more complex programs end-to-end (for example, writing a multi-file application or tackling competitive programming problems). It also produces cleaner, more correct code. Many developers note that GPT-5.1’s solutions require less debugging than GPT-4’s – it does a better job of catching its own mistakes or edge cases. Another improvement is in explaining code: GPT-5.1 can act like a knowledgeable senior developer, reviewing code for bugs or explaining what a snippet does in clear terms. It’s also more adept at using developer tools: for instance, if you have an API function enabled (like a database query or a web browsing function), GPT-5.1 can call those tools during a session more reliably to get data or test code. In summary, GPT-5.1 helps developers by writing code faster, handling more context, making fewer errors, and providing better explanations or fixes – it’s like a much more capable pair-programmer than the earlier GPT models. How can I customize ChatGPT’s tone and responses with GPT-5.1? GPT-5.1 introduces powerful new personalization features that let you shape how ChatGPT responds. In the ChatGPT settings, you’ll find a Tone or Personality section where you can choose from preset styles like Default, Professional, Friendly, Candid, Quirky, Efficient, Nerdy, and Cynical. Selecting one will instantly change the flavor of the AI’s replies – for example, Professional makes the AI’s answers more formal and businesslike, while Friendly makes them more casual and upbeat. You can switch these anytime to fit the context of your conversation. Beyond presets, GPT-5.1 allows granular adjustments: you can tell it to be more concise or more detailed, to avoid slang, or to use more humor, etc. These preferences can be set once and will apply across all your chats (you no longer have to repeat instructions every new conversation). Additionally, GPT-5.1 respects custom instructions better – you can provide a note about your needs (e.g. “Explain things to me like I’m a new hire in simple terms”) and it will remember that guidance. The AI can even notice if you keep giving a certain feedback (like “please use bullet points”) and offer to update its style settings automatically. All these features mean you have fine control over ChatGPT’s voice and behavior, allowing you to mold the assistant to your personal or brand style. This was not possible with GPT-4 without manually tweaking each prompt, so GPT-5.1 delivers a much more tailored and pleasant experience. What new features does GPT-5.1 bring to the ChatGPT user experience? GPT-5.1 comes alongside a refreshed ChatGPT interface loaded with new capabilities. First, ChatGPT now has built-in web browsing – you can ask about current events or live data and GPT-5.1 will search the web for you and even give you source links. This is a big change from earlier versions that were limited to older training data. It effectively keeps the AI’s knowledge up-to-date. Second, GPT-5.1 enables multimodal features: you can upload images or PDFs and have the AI analyze them (for example, “look at this chart and give me insights”), and it can generate images too using OpenAI’s image models. Third, the app supports voice interaction – you can talk to ChatGPT and it will understand (and even respond with spoken words if you enable it), which makes using it more natural during hands-free situations. Another feature is the introduction of Group Chats, where you can have multiple people and ChatGPT in the same conversation; GPT-5.1 is smart enough to participate appropriately when asked, which is useful for team brainstorming sessions with an AI in the loop. The overall UI has been improved as well – for example, there’s a sidebar for suggested actions and an “Atlas” mode which basically turns ChatGPT into an AI co-pilot in your web browser, so it can help you navigate and do tasks on websites. All these user experience enhancements mean ChatGPT is more than just a text box now; it’s a multi-talented assistant. Businesses and power users will find it much easier to integrate into their daily workflow, since GPT-5.1 can fetch information, handle files, and even perform actions online without switching context.
Read
Top 10 Snowflake Consulting Companies and Implementation Partners in 2025
In the era of cloud data warehousing, Snowflake has emerged as a leading platform for scalable data analytics and storage. However, unlocking its full potential often requires partnering with expert Snowflake implementation companies. Below we present the top 10 Snowflake partners worldwide in 2025 – the top Snowflake consulting companies and implementation service providers trusted by enterprises across industries. These companies represent the top Snowflake implementation service providers globally, known for delivering scalable, secure, and analytics-ready data environments in the cloud. TTMS delivers top Snowflake consulting services, combining technical excellence with business insight to help organizations modernize their data infrastructure and leverage the full power of the Snowflake Data Cloud. 1. Transition Technologies Managed Services (TTMS) TTMS is a rapidly growing global IT company known for its end-to-end Snowflake implementation and data analytics services. Headquartered in Poland, TTMS combines Snowflake’s cutting-edge capabilities with AI-driven analytics and deep domain expertise in industries like healthcare and pharmaceuticals. The company stands out for its personalized approach, providing everything from data warehouse migration and cloud integration to building custom analytics dashboards and ensuring compliance in regulated sectors (e.g., GxP standards in life sciences). TTMS’s international team (with offices across Europe and Asia) and strong focus on innovation have earned it the top spot in this ranking. Businesses choose TTMS for its holistic Snowflake solutions, which seamlessly blend technical excellence with industry-specific knowledge to drive tangible business results. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: www.ttms.com Headquarters: Warsaw, Poland Main services / focus: Snowflake implementation and optimization, data architecture modernization, data integration and migration, AI-driven analytics, cloud applications, real-time reporting, and data workflow automation. 2. Cognizant Cognizant is a Fortune 500 IT services giant that has been Snowflake’s Global Data Cloud Services Implementation Partner of the Year 2025. With vast experience in cloud data modernization, Cognizant helps enterprises migrate legacy data warehouses to Snowflake and implement advanced analytics solutions at scale. The company leverages its deep pool of certified Snowflake experts and proprietary frameworks (such as Cognizant’s “Data Estate Migration” toolkit) to accelerate deployments while ensuring data governance and security. Cognizant’s global presence and industry-specific expertise (spanning finance, healthcare, manufacturing, and more) make it a go-to partner for large-scale Snowflake projects. Clients commend Cognizant for its ability to drive AI-ready transformations on Snowflake, delivering not just technical implementation but also strategic guidance for maximizing data value. Cognizant: company snapshot Revenues in 2024: US$ 20 billion Number of employees: 350,000+ Website: www.cognizant.com Headquarters: Teaneck, New Jersey, USA Main services / focus: IT consulting and digital transformation, cloud data warehouse modernization, Snowflake migrations, AI and analytics solutions, industry-specific data strategy 3. Accenture Accenture is one of the world’s largest consulting and technology firms, and an Elite Snowflake partner known for delivering enterprise-scale data solutions. Accenture’s Snowflake practice specializes in end-to-end cloud data transformation – from initial strategy and architecture design to migration, implementation, and managed services. The company has developed accelerators and industry templates that reduce the time-to-value for Snowflake projects. With a global workforce and expertise across all major industries, Accenture brings unparalleled scale and resources to Snowflake implementations. Notably, Accenture has been recognized by Snowflake for its innovative work in data cloud projects (including specialized solutions for marketing and advertising analytics). Clients choose Accenture for its comprehensive approach: blending Snowflake’s technology with Accenture’s strengths in change management, analytics, and AI integration to ensure that the data platform drives business outcomes. Accenture: company snapshot Revenues in 2024: US$ 64 billion Number of employees: 700,000+ Website: www.accenture.com Headquarters: Dublin, Ireland (global) Main services / focus: Global IT consulting, cloud strategy and migration, data analytics & AI solutions, large-scale Snowflake implementations, industry-specific digital solutions 4. Deloitte Deloitte’s consulting arm is highly regarded for its data and analytics expertise, making it a top Snowflake implementation partner for enterprises. As a Big Four firm, Deloitte offers a unique combination of strategic advisory and technical delivery. Deloitte helps organizations modernize their data architectures with Snowflake while also addressing business process impacts, regulatory compliance, and change management. The firm has extensive experience deploying Snowflake in sectors like finance, retail, and the public sector, often integrating Snowflake with BI tools and advanced analytics (including machine learning models). Deloitte’s global network ensures access to Snowflake-certified professionals and industry specialists in every region. Clients working with Deloitte benefit from its structured methodologies (like the “Insight Driven Organization” framework) which align Snowflake projects with broader business objectives. In short, Deloitte is chosen for its ability to deliver Snowflake solutions that are technically robust and aligned to enterprise strategy. Deloitte: company snapshot Revenues in 2024: US$ 65 billion Number of employees: 415,000+ Website: www.deloitte.com Headquarters: London, UK (global) Main services / focus: Professional services and consulting, data analytics and AI advisory, Snowflake data platform implementations, enterprise cloud transformation, governance and compliance 5. Wipro Wipro is a leading global IT service provider from India and an Elite Snowflake partner known for its strong execution capabilities. Wipro has established a Snowflake Center of Excellence and has reportedly helped over 100 clients migrate to and optimize Snowflake across various industries. The company’s Snowflake services span data strategy consulting, migration from legacy systems (like Teradata or on-prem databases) to Snowflake, and building data pipelines and analytics solutions on the Snowflake Data Cloud. Wipro leverages automation and proprietary tools to accelerate cloud data warehouse deployments while ensuring cost-efficiency and quality. They also focus on upskilling client teams for long-term success with the new platform. With large global delivery centers and experience in sectors ranging from banking to consumer goods, Wipro brings both scale and depth to Snowflake projects. Clients value Wipro’s flexibility and technical expertise, particularly in handling complex, large-volume data scenarios on Snowflake. Wipro: company snapshot Revenues in 2024: US$ 11 billion Number of employees: 250,000+ Website: www.wipro.com Headquarters: Bangalore, India Main services / focus: IT consulting and outsourcing, cloud data warehouse migrations, Snowflake implementation & support, data engineering and analytics, industry-focused digital solutions 6. Slalom Slalom is a modern consulting firm that has made a name for itself in cloud and data solutions, including Snowflake implementations. Recognized as Snowflake’s Global Data Cloud Services AI Partner of the Year 2025, Slalom excels at helping clients leverage Snowflake for advanced analytics and AI initiatives. The company operates in 12 countries with an agile, people-first approach to consulting. Slalom’s Snowflake offerings include migrating data to Snowflake, designing scalable data architectures, developing real-time analytics dashboards, and embedding machine learning workflows into the Snowflake environment. They are particularly known for accelerating the use of Snowflake to generate business insights. For example, Slalom helps clients enable marketing analytics, automate data workflows, and modernize BI platforms using Snowflake. Clients choose Slalom for its collaborative style and deep technical skillset; Slalom’s teams often work closely on-site with clients, ensuring knowledge transfer and tailored solutions. In Snowflake projects, Slalom stands out for bringing innovative ideas (like integrating Snowflake with predictive analytics and AI) while keeping focus on delivering measurable business value. Slalom: company snapshot Revenues in 2024: US$ 3 billion Number of employees: 13,000+ Website: www.slalom.com Headquarters: Seattle, Washington, USA Main services / focus: Business and technology consulting, cloud & data strategy, Snowflake migrations and data platform builds, AI and analytics solutions, customer-centric digital innovation 7. phData phData is a boutique data services company that focuses exclusively on data engineering, analytics, and machine learning solutions – with Snowflake at the core of many of its projects. As a testament to its expertise, phData has been awarded Snowflake Partner of the Year multiple times (including Snowflake’s 2025 Partner of the Year for the Americas). phData offers end-to-end Snowflake services: data strategy advisory, Snowflake platform setup, pipeline development, and managed services to optimize performance and cost. They also develop custom solutions on Snowflake, such as AI/ML applications and industry-specific analytics accelerators. With a team of Snowflake-certified engineers and a company culture of thought leadership (phData is known for publishing technical content on Snowflake best practices), they bring deep know-how to any Snowflake implementation. Clients often turn to phData for their combination of agility and expertise – the company is large enough to handle complex projects, yet specialized enough to provide personalized attention. If you need a partner that lives and breathes Snowflake and data analytics, phData is a top choice. phData: company snapshot Revenues in 2024: US$ 130 million (est.) Number of employees: 600+ Website: www.phdata.io Headquarters: Minneapolis, Minnesota, USA Main services / focus: Data engineering and cloud data platforms, Snowflake consulting & implementation, AI/ML solutions on Snowflake, data strategy and managed services 8. Kipi.ai Kipi.ai is a specialized Snowflake partner that has gained global recognition for innovation. In fact, Kipi.ai was named Snowflake’s Global Innovation Partner of the Year 2025, highlighting its creative approaches to implementing Snowflake solutions. As part of the WNS group, Kipi.ai blends the agility of a focused data startup with the resources of a larger enterprise. The company boasts one of the world’s largest pools of Snowflake-certified talent (hundreds of SnowPro certifications) and focuses on AI-driven data modernization. Kipi.ai helps organizations migrate data to Snowflake and then layer advanced analytics and AI applications on top. From marketing analytics to IoT data processing, they build solutions that exploit Snowflake’s performance and scalability. Kipi.ai also emphasizes accelerators – pre-built solution frameworks for common use cases, which can jumpstart projects. With headquarters in Houston and a global delivery model, Kipi.ai serves clients around the world, particularly those looking to push the envelope of what’s possible with Snowflake and AI. Companies seeking an innovative Snowflake implementation partner often find Kipi.ai at the forefront. Kipi.ai: company snapshot Revenues in 2024: Not disclosed Number of employees: 400+ Snowflake experts Website: www.kipi.ai Headquarters: Houston, Texas, USA Main services / focus: Snowflake-focused data solutions, AI-powered analytics applications, data platform modernization, Snowflake training and competency development 9. InterWorks InterWorks is a data consulting firm acclaimed for its business intelligence and analytics services, including Snowflake implementations. With roots in the United States, InterWorks has grown internationally but maintains a focus on client empowerment. In Snowflake projects, InterWorks not only handles the technical deployment (data modeling, loading pipelines, integrating BI tools like Tableau or Power BI) but also provides extensive training and workshops. Their philosophy is to enable clients to be self-sufficient with their new Snowflake environment. InterWorks has helped organizations of all sizes to migrate to Snowflake and optimize their analytics workflows, often achieving quick wins in performance and report reliability. They are known for a personal touch – working closely with client teams and tailoring solutions to specific needs rather than a one-size-fits-all approach. InterWorks also frequently collaborates with Snowflake on community events and knowledge sharing, which reflects its standing in the Snowflake ecosystem. For companies that want a partner to guide and educate them through a Snowflake journey, InterWorks is an excellent contender. InterWorks: company snapshot Revenues in 2024: US$ 50 million (est.) Number of employees: 300+ Website: www.interworks.com Headquarters: Stillwater, Oklahoma, USA Main services / focus: Business intelligence consulting, Snowflake data warehouse deployment, data visualization and reporting (Tableau, Power BI integration), analytics training and enablement 10. NTT Data NTT Data is a global IT services powerhouse (part of Japan’s NTT Group) and a prominent Snowflake implementation partner for large enterprises. With decades of experience in data management, NTT Data has a strong capability in handling complex, multi-terabyte migrations to Snowflake from legacy systems. The company often serves clients in finance, telecommunications, and public sector where security and reliability requirements are stringent. NTT Data’s approach to Snowflake projects typically involves thorough assessments and roadmap planning, ensuring minimal disruption during migration and integration. They also bring specialized expertise via acquisitions – for example, NTT Data acquired Hashmap, a boutique Snowflake consultancy, to bolster its Snowflake talent and tools. As a result, NTT Data clients benefit from both the customized solutions of a niche player and the scale/resources of a global firm. NTT Data provides end-to-end services including data architecture design, ETL/ELT development for Snowflake, performance tuning, and 24/7 managed support post-implementation. Enterprises seeking a reliable, full-service partner to make Snowflake the cornerstone of their data strategy often turn to NTT Data. NTT Data: company snapshot Revenues in 2024: US$ 30 billion Number of employees: 190,000+ Website: www.nttdata.com Headquarters: Tokyo, Japan Main services / focus: Global IT services and consulting, large-scale data warehouse migration to Snowflake, cloud infrastructure & integration, data analytics and business intelligence solutions, ongoing managed services Ready to Leverage Snowflake? Partner with the #1 Expert Choosing the right partner is crucial to the success of your Snowflake data cloud journey. TTMS, ranked #1 in our list, offers a unique blend of technical expertise, innovation, and industry-specific knowledge. Whether you need to migrate terabytes of data, implement real-time analytics, or integrate AI insights into your business, TTMS has the tools and experience to make it happen smoothly. As one of the top Snowflake partners, TTMS delivers top Snowflake consulting services that help enterprises unlock measurable value from their data. Don’t settle for less when you can work with the best. Get in touch with TTMS today and let us transform your data strategy with Snowflake. Your organization’s future in the cloud starts with a single step, and the experts at TTMS are ready to guide you all the way. For more details about our Snowflake consulting services and how we can support your data transformation, contact us today. FAQ How to choose a Snowflake implementation partner? When selecting a Snowflake partner, focus on their level of certification (Elite or Select), proven experience with large-scale data migrations, and ability to integrate Snowflake with your existing systems. A top partner should also offer end-to-end consulting services – from architecture design and security setup to analytics optimization. Look for companies that combine technical expertise with an understanding of your business domain to ensure the Snowflake platform truly drives value. Why work with top Snowflake partners instead of building in-house expertise? Partnering with top Snowflake consulting companies allows you to accelerate deployment and avoid costly implementation mistakes. These partners already have trained engineers, ready-to-use frameworks, and industry-specific templates. This ensures faster time-to-value, optimized performance, and best-practice security. Working with certified experts also reduces long-term maintenance costs while keeping your data cloud future-proof. How much do Snowflake consulting services typically cost in 2025? The cost of Snowflake consulting services in 2025 varies depending on project scope, data volume, and customization level. For small and medium projects, prices usually start from $30,000–$80,000, while enterprise-level implementations can exceed $250,000. The key is to view it as an investment – top Snowflake partners deliver scalable, efficient, and compliant data solutions that quickly pay off through improved analytics and decision-making.
Read
ChatGPT Pulse: How Proactive AI Briefings Accelerate Enterprise Digital Transformation
ChatGPT Pulse: Proactive AI Briefings Accelerating Enterprise Digital Transformation OpenAI’s ChatGPT Pulse is a new feature that delivers daily personalized AI briefings – a significant innovation that shifts AI from a reactive tool to a proactive digital assistant. Instead of waiting for user queries, Pulse works autonomously in the background to research and present a curated morning digest of relevant insights for each user. OpenAI even calls it their first “fully proactive, autonomous AI service,” heralding “the dawn of an AI paradigm” where virtual agents don’t just wait for instructions – they act ahead of the user by synthesizing data and surfacing critical updates while decision-makers sleep. For innovation managers and executives, this represents more than just a convenient feed – it marks a strategic evolution in how information flows and decisions are supported. By moving from on-demand Q&A to continual, tailored insight delivery, Pulse enables earlier trend detection and timely decision support. One analysis notes that with AI-driven practices, “decision cycles shrink from weeks to hours” and “insights become proactive rather than reactive,” leading to more agile, evidence-based management. In short, AI is no longer confined to answering questions after the fact; it’s now an active partner in helping leaders get ahead of fast-moving developments. 1. How ChatGPT Pulse Works: Personalized Daily AI Research and Briefings Personalized daily research: ChatGPT Pulse conducts asynchronous research on the user’s behalf every night. It synthesizes information from your past chats, saved notes (Memory), and feedback to learn what topics matter to you, then delivers a focused set of updates the next morning. These updates appear as *topical visual cards* in the ChatGPT mobile app which you can quickly scan or tap to explore in depth. Each card highlights a key insight or suggestion – for example, a follow-up on a project you discussed, a news nugget in your industry, or an idea related to your personal goals. Integrations and context: To make suggestions smarter, Pulse can connect to your authorized apps like Google Calendar and Gmail (if you choose to opt in). With calendar access, it might remind you of an upcoming meeting and even draft a sample agenda or talking points for it. With email access, it could surface a timely email thread that needs attention or summarize a lengthy report that arrived overnight. All such integrations are off by default and under user control, reflecting a privacy-first design. OpenAI also filters Pulse’s outputs through safety checks to avoid any content that violates policies, ensuring your daily briefing stays professional and on-point. User curation: Pulse is not a one-size-fits-all feed – you actively curate it. You can tell ChatGPT directly what you’d like to see more (or less) of in your briefings. Tapping a “Curate” button lets you request specific coverage (e.g. “Focus on fintech news tomorrow” or “Give me a Friday roundup of internal project updates”). You can also give quick thumbs-up or thumbs-down feedback on each card, teaching the AI which updates are useful. Over time, this feedback loop makes your briefings increasingly personalized. Not interested in a particular topic? Pulse will learn to skip it. Want more of something? A thumbs-up will encourage similar content. In essence, users steer Pulse’s research agenda, and the AI adapts to provide more relevant daily knowledge. Brief, actionable format: Each morning’s Pulse typically consists of a handful of brief cards (OpenAI suggests about 5-10) rather than an endless feed. This design is intentional – the goal is to give you the day’s most pertinent information quickly, not to trap you in scrolling. After presenting the cards, ChatGPT explicitly signals when the briefing is done (e.g. “That’s all for today”). You can then dive deeper by asking follow-up questions on a card or saving it to a chat thread, which folds it into your ongoing ChatGPT conversation history for further exploration. Otherwise, Pulse’s cards expire the next day, keeping the cycle fresh. The result is a concise, focused briefing that respects your time, delivering value in minutes and then letting you get on with your day. 2. ChatGPT Pulse for Digital Transformation: Turning Data Into Actionable Intelligence From a digital transformation perspective, ChatGPT Pulse represents a powerful tool for driving smarter, faster decision-making across the enterprise. By automating the gathering and distribution of insights, Pulse shortens the path from data to decision. Routine informational tasks that might have taken analysts days or weeks – compiling market trends, monitoring KPIs, scanning news – can now be distilled into a morning briefing. Organizations that adopt such AI tools often find that decision cycles shrink dramatically, enabling a more responsive and agile operating model. Indeed, when companies successfully implement AI in their processes, “decision cycles shrink from weeks to hours” and teams can refocus on strategy over tedious data prep. In practical terms, this means leaders can respond to opportunities or threats faster than competitors who rely on traditional, slower information workflows. Enterprise surveys are already showing the impact of AI on digital transformation efforts. According to McKinsey, nearly two-thirds of organizations have launched AI-driven transformation initiatives – almost double the adoption rate of the year before – and those using generative AI report tangible benefits like cost reductions and new revenue growth in the business units deploying the tech. This underscores that proactive AI systems are not just hype; they are delivering material business value. With Pulse proactively delivering tailored intel each day, companies can foster a more data-driven culture where employees at all levels start their morning armed with relevant knowledge. Over time, this ubiquitous access to insights can enhance everything from operational efficiency to customer experience, as decisions become more informed and immediate. Another crucial benefit is continuous learning and innovation. In a fast-evolving digital landscape, employees need to constantly update their knowledge. Pulse effectively builds micro-learning into the workday. For instance, if someone was researching a new technology or market trend via ChatGPT, Pulse will follow up with fresh developments on that topic the next day. This turns casual inquiries into an ongoing learning curriculum, steadily deepening professionals’ expertise. Instead of formal training sessions or passive newsletter reading, employees get a personalized trickle of relevant updates that keep them current. Such AI-augmented learning supports digital transformation by upskilling the workforce in real time. It also helps break down information silos – the insights aren’t locked in one department’s report, they’re proactively pushed to each interested individual. Finally, by shifting AI into a proactive role, enterprises unlock new strategic opportunities. Rather than reacting to data after the fact, leaders can anticipate trends and make bold moves earlier. One famous example: an AI analytics platform at Procter & Gamble spotted an emerging spike in demand for hand sanitizer 8 days before sales surged during the pandemic, allowing the company to ramp up production and capture an estimated $200+ million in additional sales. That kind of foresight is invaluable. With ChatGPT Pulse, even smaller firms could gain a bit of that “early radar,” catching inflection points or market shifts sooner. In essence, proactive AI briefings help companies transition from being merely data-driven to truly insight-driven – using information not just to monitor the business, but to constantly and preemptively improve it. 3. How to Try ChatGPT Pulse ChatGPT Pulse is currently available in preview for ChatGPT Plus and Pro subscribers using the mobile app (iOS or Android). To check if you have access, open the ChatGPT app and look for the new Pulse section or the option “Enable daily briefings.” Once activated, Pulse will automatically prepare a personalized morning digest based on your recent chats, saved notes, and feedback. To get started, make sure you have the latest version of the app and that the Memory feature is turned on in your settings. You can further personalize Pulse by choosing your preferred topics (e.g., AI, finance, marketing) and by allowing optional integrations with Google Calendar or Gmail for meeting summaries and reminders. If you’re part of a Team or Enterprise plan, Pulse is expected to roll out there later this year as part of OpenAI’s business roadmap. 4. ChatGPT Pulse in Compliance and Regulated Sectors: Boosting AML and GDPR Readiness Highly regulated industries stand to benefit immensely from Pulse’s ability to stay ahead of changes. Compliance teams in finance, healthcare, legal, and other regulated sectors are inundated with evolving regulations and risks. ChatGPT Pulse can function as a vigilant compliance assistant, proactively monitoring relevant sources and alerting professionals to what they need to know each day. For example, in the financial sector, an AML (Anti-Money Laundering) officer could configure Pulse to track updates from regulators and news on financial crimes. Each morning, they might receive a distilled summary of any new sanction lists, AML directives, or notable enforcement actions around the world. Instead of digging through bulletins or relying on quarterly training, the compliance officer gets a daily heads-up on critical changes, reducing the chance of missing something important. Beyond external news, Pulse could integrate with internal compliance systems to highlight red flags. Imagine an investment firm’s compliance department that connects Pulse to its transaction monitoring software: the AI might brief the team on any unusual transaction patterns that cropped up overnight, or summarize the status of pending compliance reviews. This early warning system allows faster intervention. In fact, specialized providers like TTMS are already deploying AI-driven compliance automation. TTMS’s AML Track platform, for instance, uses AI to automatically handle key anti-money laundering processes – from customer due diligence and real-time transaction screening to compiling audit-ready reports – keeping businesses “compliant by default” with the latest regulations. This kind of always-on diligence is exactly what Pulse can bring to a wider range of compliance activities, by summarizing and directing attention to the highest-priority issues every day. The result is not only improved regulatory compliance but also significant time savings and risk reduction (since the AI can reduce human error in sifting through data). Data privacy and GDPR compliance are also crucial considerations. Pulse’s personalized briefings inherently rely on user data – which in an enterprise scenario could include emails, calendar entries, and chat history, some of which might be sensitive. OpenAI has built safeguards into the product (for example, integrations are opt-in and can be toggled off at any time), and all content passes through safety filters. However, companies will need to ensure that using Pulse aligns with data protection laws like GDPR. That means evaluating what data is fed into the model and enabling features like ChatGPT’s data anonymization and retention controls. As one analysis put it, ChatGPT has measures to prioritize privacy, but “full GDPR compliance involves actions from both developers and users”. In practice, organizations should avoid pumping highly confidential or personal data through Pulse, or at least obtain proper consent and use data-handling best practices (encryption, anonymization, access controls) when they do. With the right governance, the payoff is that even heavily regulated firms can leverage Pulse as a compliance ally – for example, a pharmaceutical company could get daily briefings on changes in FDA or EMA guidelines, or a privacy officer could be alerted to new rulings from data protection authorities. Pulse shifts compliance from a reactive, error-prone process to a proactive, continuous monitoring function, all while allowing humans to concentrate on complex judgment calls. 5. ChatGPT Pulse Business Use Cases Across Departments Because ChatGPT Pulse learns an individual user’s context and goals, it can be applied creatively in virtually every department. Here are some of the high-impact use cases across different business functions: 5.1 ChatGPT Pulse for Marketing and Sales: Smarter Insights, Faster Results Marketing teams thrive on timely information and trend awareness – Pulse can give them a decisive edge. Consider a marketing team preparing for a major seasonal campaign. They’re normally juggling Google Trends, customer feedback, and competitor announcements to decide their approach. With Pulse, much of this groundwork can be automated into the morning briefing. For example, Pulse could surface: Which influencers or topics are trending in the industry this week (to guide partnerships or content themes). Quick summaries of any competitor product launches or major marketing moves that were revealed in the last day or two. Suggestions for content angles tied to current events or cultural moments, so the team can ride the wave of what people are talking about. This doesn’t replace the marketing team’s own research and creativity, but it knocks out the “where do we start?” moment by filtering the noise and highlighting actionable intel. Instead of spending the morning sifting through articles and social media, the team can immediately discuss strategy using Pulse’s pointers – saving time and reducing stress. In sales, a similar advantage applies: a salesperson could get a daily card with a heads-up that one of their target clients was mentioned in the news, or an alert that a relevant market indicator (say, an interest rate change) moved overnight. By arming sales and marketing personnel with early insights, Pulse helps them personalize their pitches and campaigns to what’s happening right now, which usually translates into better engagement and conversion rates. 5.2 ChatGPT Pulse for Human Resources: Enhancing Employee Experience With Proactive AI HR is another arena where proactive information can make a big difference – both for efficiency and for culture. HR teams often strive to improve employee engagement and retention by paying attention to the “little things” that matter to people. ChatGPT Pulse can act like a smart HR aide that remembers those little things. For instance, each morning it could deliver a card highlighting which employees have birthdays or work anniversaries coming up that day or week, so managers can acknowledge them (especially useful in large organizations where it’s easy to forget dates). It could also share industry insights on HR trends – e.g. a brief on the latest research around employee well-being or talent retention strategies – giving HR leaders fresh ideas to consider. Another card might even suggest a thoughtful conversation starter for an upcoming one-on-one meeting a manager has, based on what’s been going on with that team member (perhaps drawn from recent pulse survey comments or project successes). The value of these applications is not just in automating tasks, but in amplifying the human touch in HR. By keeping track of personal details and relevant insights, Pulse lets managers and HR professionals focus more on the quality of their interactions rather than the logistics. As one expert noted, when an AI keeps track of the details, leaders can devote their energy to “showing up” fully in those conversations and coaching moments. Additionally, from a compliance angle, HR could use Pulse to stay on top of labor law updates or compliance deadlines (for example, reminding that GDPR training refreshers are due for certain staff, linking to the relevant modules). All told, Pulse helps HR move faster on administrative to-dos while fostering a more personalized employee experience. 5.3 ChatGPT Pulse for IT and Operations: Always-On Monitoring and Predictive Efficiency IT departments can leverage ChatGPT Pulse to maintain better situational awareness of systems and projects, without having to manually check multiple dashboards each morning. An IT operations manager might receive a Pulse briefing card summarizing overnight system health: for example, “All servers operational, except Server X had two restart events at 3:00 AM – auto-recovered” or “No critical alerts from last night’s security scan; 5 low-priority vulnerabilities flagged.” Instead of arriving and combing through logs, the manager knows at a glance where to focus. Another card could highlight any emerging cybersecurity threats relevant to the business – perhaps news of a software vulnerability that popped up on tech forums, which Pulse caught via its web browsing or connected feeds. This gives the IT team a head start in patching or mitigation, potentially before an official advisory is widely circulated. Pulse can also assist with IT project management by reminding teams of upcoming deployment dates or summarizing updates. For example, if yesterday a developer discussed a blocker in a chat, Pulse might follow up with suggestions or resources to resolve it, or simply remind the project lead that the issue needs attention today. In IT support functions, a morning Pulse might list how many helpdesk tickets came in after hours and which ones are high priority, so the support lead can allocate resources immediately. Essentially, Pulse brings the “lights-out” operations concept to information work – routine monitoring and triage happen automatically at night. OpenAI’s push into this area (even developing “lights-out” AI data centers to handle overnight info work) signals that much of IT’s grunt work can be offloaded to AI. That frees up technical staff to concentrate on planning and solving complex problems rather than constantly firefighting. Over time, this proactive ops model could improve system reliability and incident response, since the AI never sleeps on the job. 5.4 ChatGPT Pulse for Leadership and Strategy: Executive Intelligence at a Glance For executive leaders and strategy teams, ChatGPT Pulse serves as a virtual analyst that keeps a finger on the organization’s pulse as well as the external environment. Each morning, C-level executives could receive a tailored briefing that spans both macro and micro levels of their business. This might include a digest of key industry news (e.g. economic indicators, competitor headlines, regulatory changes) alongside internal insights like yesterday’s sales figures or a highlight from an operational report. In fact, Pulse is explicitly designed with busy professionals in mind – executives can get a summary of top industry developments plus relevant meeting reminders in one go. For instance, a CEO’s Pulse might show: “1) Stock markets reacted to X event – expect potential impact on our sector, 2) Competitor A announced a new product launch, 3) Reminder: 10:00 AM strategy review meeting with draft agenda attached.” By consolidating external intelligence and internal priorities, Pulse ensures leaders start the day informed without having to skim dozens of emails or news sites. At the strategic level, this could fundamentally improve knowledge flow in the upper echelons of the company. Instead of information trickling up through multiple layers (with delays and filters), the AI delivers a snapshot directly to the decision-maker, which can then be immediately shared or acted on. It’s easy to see how this aids quick, well-informed decisions – whether it’s seizing an opportunity or convening a team to address a risk. Even specialized domain experts on the team benefit, as they can set Pulse to provide daily knowledge refreshers in their field (for example, a Chief Data Scientist might get a daily card on notable AI research breakthroughs relevant to the business). In a way, Pulse can function like a digital chief of staff for each leader, quietly monitoring both “the micro and the macro” context so that nothing important slips through the cracks. The human executive remains in charge, but they’re augmented by an always-on assistant scanning the horizon and whispering timely intelligence in their ear. This bodes well for strategic agility – companies can identify inflection points or nascent trends and discuss them in leadership meetings days or weeks earlier than they otherwise would, potentially leaping ahead of competitors who are still catching up on yesterday’s news. 6. ChatGPT Pulse and the Future of Knowledge Flow and Automation The introduction of proactive AI agents like ChatGPT Pulse has deep implications for how knowledge flows through an organization and how much of it can be automated. Traditionally, gathering the information needed for decisions has been a manual, effort-intensive process – reports written, meetings held, emails sent, all to push relevant knowledge to the right people. Pulse flips this dynamic by automating the dissemination of knowledge. It seeks out the information and delivers it to stakeholders without being asked, effectively acting as an autonomous knowledge curator. This means that important insights are less likely to languish in silos or get stuck in someone’s inbox; instead, they’re routinely surfaced to those who can act on them. Companies that harness this will likely see faster alignment across teams, since everyone’s briefed on the latest developments in their sphere each day. Over time, such transparency and responsiveness can become a competitive advantage in itself. One analysis describes this shift as moving from reactive info consumption to “proactive, tailored insights” – a change that could automate much of the daily planning and update process, “freeing teams from routine prep work and enabling deeper strategic focus”. In practical terms, meetings might become more forward-looking because attendees come in already aware of yesterday’s results and today’s news (courtesy of Pulse). Middle managers might spend less time compiling status decks for senior leadership, because the AI has been quietly updating the leadership with key metrics all along. In fact, organizations should evaluate how embedding a push-style AI assistant into internal communication channels could “boost decision speed and simplify knowledge management”. Instead of waiting for a weekly report, an executive might ask, “What did Pulse show this morning?” and make a decision by 9 AM. The latency between data generation and decision-making compresses dramatically, which can make the organization more nimble. Another strategic implication is the increasing automation of knowledge work. We’ve seen automation in physical tasks and transaction processing; now we’re seeing it in researching, summarizing, and advising – activities typically done by analysts or knowledge workers. Pulse is an early example of an “ambient” or always-on agent that works in the background to advance your goals. This heralds a future where AI doesn’t just assist when asked, but continuously works alongside humans. As a result, the role of employees may shift to more high-level judgment and creativity, with AI handling the rote informational tasks. Executives and workers alike will need to adjust to this new partnership: it requires trust in the AI (to let it run with certain tasks) and new skills in guiding and overseeing AI outputs (since an AI briefing is now part of one’s daily toolkit). Notably, OpenAI itself views Pulse as “the first step toward a new paradigm for interacting with AI”. By combining conversation, memory, and app integrations, ChatGPT is moving from simply answering questions to a proactive assistant that works on your behalf. This signals a broader technological trajectory. We can expect future AI systems to research, plan, and even execute routine actions “so that progress happens even when you are not asking”. In enterprise settings, that could mean AI agents initiating workflows – imagine Pulse not only telling you that a software build failed overnight, but automatically creating a ticket for the dev team and scheduling a brief stand-up to address it. We are not far off from AI that takes on more of a project management or coordination role in the background, orchestrating small tasks to keep the machine running smoothly. As one report succinctly put it, this development is shifting AI “from a passive tool to an active system that can independently serve business needs”. For knowledge flow, it means information will increasingly find you (the right person) at the right time, rather than you having to hunt for information. For automation, it means more white-collar workflows can be handled end-to-end by intelligent agents, with humans providing direction and final approval. 7. The Future of ChatGPT Pulse in AI-Driven Decision Making Looking ahead, ChatGPT Pulse hints at a future where AI is deeply embedded in decision-making processes at all levels of the enterprise. The current version of Pulse is just the beginning – limited to daily research and suggestions – but OpenAI’s roadmap suggests it will grow more capable and connected. We can anticipate Pulse tying into a broader range of business applications: not just your calendar and email, but potentially your CRM, ERP, project management tools, data warehouses, and more. Imagine a future Pulse that, before your workday starts, has queried your sales database, your customer support ticket queue, and the latest market analytics, and then presents you with an integrated briefing: “Sales are 5% above target this week (driven by Product X in Region Y), two major clients have escalated issues that need personal attention, and a new competitor just entered our niche according to news reports.” This kind of multi-source synthesis would truly make AI an executive’s co-pilot in steering the business. We’re already seeing signs of this trajectory. Early adopters of AI agents in business are experimenting with systems that perform more complex, multi-step tasks autonomously. Enterprises are actively exploring use cases for agents that not only inform but act – for example, an AI that can proactively initiate workflows on behalf of users. ChatGPT Pulse could evolve in that direction. OpenAI leaders have spoken about the “real breakthrough” coming when AI understands your goals and helps you achieve them without waiting to be told. In the context of Pulse, that might mean it won’t just tell you about a trend – it might also draft a strategy memo about how your company could respond, or it might automatically schedule a brainstorming meeting with relevant team members if you give it a nudge of approval. The groundwork for this is being laid in the current design: Pulse already connects to calendars and emails, and OpenAI is exploring ways for it to deliver “relevant work at the right moments throughout the day” (say, a resource popping up precisely when you need it). It’s a short step from delivering a resource to executing an action, once trust and reliability in the AI are established. In terms of AI-driven decision making, the long-term potential is that Pulse becomes less of a separate feature and more of an integrated decision support system woven into daily operations. It could evolve into an enterprise-wide “knowledge nerve center” – one that not only briefs individuals but also detects patterns across the organization and raises flags or suggestions to the people best positioned to respond. For instance, if Pulse notices that multiple regional offices are asking the same question, it might alert corporate HQ about a possible knowledge gap or training need. If a certain KPI is dipping across several departments, Pulse might recommend a cross-functional meeting and supply the background material. Essentially, as it gains the ability to connect to more apps and ingest more realtime data, Pulse could function as an early warning and opportunity-detection system spanning the whole company. OpenAI’s own vision supports this direction: they envision AI that can plan and take actions based on your objectives, operating even when you’re offline. Pulse in its current form introduces that future in a contained way – “personalized research and timely updates” delivered regularly to keep you informed. But soon it will likely integrate with more of the tools we use at work, and with that will come a more complete picture of context. We may also see Pulse delivering nudges throughout the day (not just in the morning) – for example, a quick Pulse check before a big client call, or at 4 PM a Pulse card might remind a product manager that it’s been 90 days since Feature A was launched and suggest looking at the usage analytics. Over time, as these assistants become more deeply trusted, they might even execute decisions within pre-set boundaries. A mature Pulse might auto-adjust some marketing spend based on early campaign results or reorder stock from a supplier when inventory runs low – basically crossing into the territory of autonomous decision implementation. In summary, the future of Pulse points toward AI becoming a ubiquitous collaborator in the enterprise. It will accelerate and enhance human decision-making, not replace it. As OpenAI’s Applications CEO, Fidji Simo, remarked about this shift: moving from a chat interface to a proactive, steerable AI assistant working alongside you is how “AI will unlock more opportunities for more people”. One day, having an AI like Pulse might be as routine as having an email account – it will be the morning briefing, the research analyst, the project assistant, and the compliance checker all in one, quietly empowering employees to make better decisions every day. Organizations that embrace this shift early could see substantial gains in productivity, innovation, and responsiveness. Those that don’t may find themselves perpetually a step behind in the information race. Pulse today is daily briefings; Pulse tomorrow could be a central nervous system for the intelligent enterprise. FAQ How is ChatGPT Pulse different from regular ChatGPT or a news feed? Unlike the standard ChatGPT which only responds when you ask something, ChatGPT Pulse works proactively. It automatically researches and delivers a personalized briefing each day based on your interests and data (calendar, emails, past chats). In essence, regular ChatGPT is reactive – you pose questions or prompts to get answers. Pulse flips that model: it’s more like a smart morning newsletter tailored just for you. It filters through information and suggests what’s relevant without you having to hunt for it. Traditional news feeds or newsletters are one-size-fits-all and require you to do the filtering. Pulse, by contrast, curates content specifically to your needs and even learns from your feedback to get better. It’s as if you had a researcher on staff who knows your priorities and hands you a brief each morning, rather than you spending time pulling info from various sources. Can my whole team or company use ChatGPT Pulse, or is it only for individual users? Right now, ChatGPT Pulse is available as a preview for individual ChatGPT Pro subscribers (on the mobile app). It’s not yet deployed as an enterprise-wide solution that companies can centrally manage for all employees. Essentially, an individual user – say an executive or manager – can use Pulse through their own ChatGPT account. OpenAI has indicated they plan to roll it out to more users (ChatGPT Plus subscribers and eventually wider audiences) as it matures, but at this stage it’s not a standard offering bundled into ChatGPT Enterprise. That said, companies keen to experiment could have key team members trial it with Pro accounts to gauge its usefulness. In the future, we can expect that OpenAI or third parties will offer more enterprise-integrated versions of Pulse once issues like data privacy, admin controls, and scaling are addressed. For now, think of it as a personal productivity tool with tremendous business potential, but not something like an “enterprise Pulse server” you can deploy to everyone just yet. How does ChatGPT Pulse handle sensitive data and privacy? Is it GDPR-compliant? ChatGPT Pulse respects the same data handling policies as ChatGPT. It uses content from your chat history and any connected apps only to generate your briefings. Those integrations (like email or calendar) are completely optional – they’re off by default, and you have to give permission to use them. If you do connect them, the data is used to tailor your results but still processed under OpenAI’s privacy safeguards. OpenAI anonymizes and encrypts data to protect personal information, and they have a privacy policy detailing how user data is managed (which is important for GDPR compliance). However, “full GDPR compliance” isn’t just on OpenAI – it also depends on how users and organizations employ the tool. For instance, a company using Pulse should avoid inputting any personal data that isn’t allowed out of a secure environment. Practically, this means you wouldn’t have Pulse read highly confidential documents or sensitive customer data unless you’re sure it’s permitted. Users can also delete chat history or turn off memory in ChatGPT if they want past data wiped. In short, Pulse can be used in a privacy-conscious way (and OpenAI has built-in measures to facilitate that), but companies should do their due diligence – treating Pulse like any cloud service when it comes to compliance. With proper usage – and perhaps additional enterprise features in the future – Pulse can be part of a GDPR-compliant workflow, but it’s wise to consult your IT and legal teams about any sensitive use cases. Will AI daily briefings like Pulse replace human analysts or our existing reports/newsletters? ChatGPT Pulse is a powerful automation tool, but it’s not a wholesale replacement for human expertise. What it can replace (or greatly reduce) is the rote work of gathering and synthesizing information. For example, if your team puts out a daily media monitoring report or an internal newsletter, Pulse can automate a large chunk of that by pulling in the latest info. However, human analysts add value through context, interpretation, and judgment. Pulse gives you facts and preliminary insights; it doesn’t know your business strategy or the nuanced implications of a particular development. In many cases, the best use of Pulse is to complement human work – it frees your analysts from spending hours on basic research so they can focus on deeper analysis and advising leadership on decisions. Some companies might indeed streamline routine report workflows and let Pulse handle the first draft, but you’ll still want humans to validate and augment those briefings. Also, Pulse is individualized – each user gets a custom brief. It won’t automatically know what the whole team needs unless everyone configures it that way. So newsletters and broad reports might still continue for a shared company perspective. In summary, expect Pulse to automate the mundane 60-70% of info gathering. The remaining critical thinking and decision-making pieces remain with humans, who are now armed with Pulse’s output. It’s more “augmentation” than “replacement.” What are the limitations of ChatGPT Pulse today? Since ChatGPT Pulse is a new and evolving feature, there are a few limitations to keep in mind. First, it currently runs on a fixed schedule (once per day in the morning). It’s not a real-time alert system, so if something big happens in the afternoon, Pulse won’t tell you until the next day’s briefing. Second, its suggestions are only as good as the data it has and the guidance you give. Early users have found that sometimes Pulse might surface an irrelevant tip or something you already know – for example, a suggestion for a project you’ve finished, or an outdated news item. It takes a little training via feedback to refine what it shows you. Third, Pulse doesn’t have deep integration with every enterprise system yet. It works great with web information and connected apps like Calendar or Gmail, but it’s not natively plugged into, say, your internal databases or Slack (unless you copy info over or an integration is built). So it may miss internal happenings that weren’t in your ChatGPT history or connected sources. Additionally, like any AI, Pulse can occasionally get things wrong. It might summarize a topic imperfectly or miss a nuance that a human would catch. That means users should treat it as an assistant – helpful for a head start – but still verify critical facts. Finally, access is limited (Pro preview on mobile), which is a practical limitation if you prefer desktop or if not everyone on your team can use it yet. These limitations are likely to be addressed over time as OpenAI improves the feature. For now, being aware of them helps you use Pulse effectively – lean on it for convenience and speed, but keep humans in the loop for judgment calls and fact-checking.
Read