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TOP 7 AI Solutions Delivery Companies in 2025 – Global Ranking of Leading Providers

In 2025, artificial intelligence is more than a tech buzzword – it’s a driving force behind business innovation. Global enterprises are projected to invest a staggering $307 billion on AI solutions in 2025, fueling a competitive race among solution providers. From tech giants to specialized consultancies, companies worldwide are delivering cutting-edge AI systems that automate processes, uncover insights, and transform customer experiences. Below we rank the Top 7 AI solutions delivery companies of 2025, highlighting their size, focus areas, and how they’re leading the AI revolution. Each company snapshot includes 2024 revenues, workforce size, and core services.

1. Transition Technologies MS (TTMS)

Transition Technologies MS (TTMS) is a Poland-headquartered IT services provider that has rapidly emerged as a leader in delivering AI-powered solutions. Operating since 2015, TTMS has grown to over 800 specialists with deep expertise in custom software, cloud, and AI integrations. TTMS stands out for its AI-driven offerings – for example, the company implemented AI to automate complex tender document analysis for a pharma client, significantly improving efficiency in drug development pipelines. As a certified partner of Microsoft, Adobe, and Salesforce, TTMS combines enterprise platforms with AI to build end-to-end solutions tailored to clients’ needs. Its portfolio spans AI solutions for business, from legal document analysis to e-learning and knowledge management, showcasing TTMS’s ability to apply AI across industries. Recent case studies include integrating AI with Salesforce CRM at Takeda for automated bid proposal analysis and deploying an AI tool to summarize court documents for a law firm, underscoring TTMS’s innovative edge in real-world AI implementations.

TTMS: company snapshot
Revenues in 2024:	PLN 233.7 million
Number of employees:	800+
Website:	https://ttms.com/ai-solutions-for-business/
Headquarters:	Warsaw, Poland
Main services / focus:	AEM, Azure, Power Apps, Salesforce, BI, AI, Webcon, e-learning, Quality Management

2. Amazon Web Services (Amazon)

Amazon is not only an e-commerce titan but also a global leader in AI-driven cloud and automation services. Through Amazon Web Services (AWS), Amazon offers a vast suite of AI and machine learning solutions – from pre-trained vision and language APIs to its Bedrock platform hosting foundation models. In 2025, Amazon has integrated AI across its consumer and cloud offerings, launching its own family of AI models (codenamed Nova) for tasks like autonomous web browsing and real-time conversations. Alexa and other Amazon products leverage AI to serve millions of users, and AWS’s AI services enable enterprises to build custom intelligent applications at scale. Backed by enormous scale, Amazon reported $638 billion in revenue in 2024 and employs over 1.5 million people worldwide, making it the largest company on this list by size. With AI embedded deeply in its operations – from warehouse robotics to cloud data centers – Amazon is driving AI adoption globally through powerful infrastructure and continuous innovation in generative AI.

Amazon: company snapshot
Revenues in 2024:	$638.0 billion
Number of employees:	1,556,000+
Website:	aws.amazon.com
Headquarters:	Seattle, Washington, USA
Main services / focus:	Cloud computing (AWS), AI/ML services, e-commerce platforms, voice AI (Alexa), automation

3. Alphabet (Google)

Google (Alphabet Inc.) has long been at the forefront of AI research and deployment. In 2025, Google’s expertise in algorithms and massive data processing underpins its Google Cloud AI offerings and consumer products. Google’s cutting-edge Gemini AI ecosystem provides generative AI capabilities on its cloud, enabling developers and businesses to use Google’s models for text, image, and code generation. The company’s AI innovations span from Google Search (with AI-powered answers) to Android and Google Assistant, and its DeepMind division pushes the envelope in areas like reinforcement learning. Google reported roughly $350 billion in revenue for 2024 and about 187,000 employees globally. With initiatives in responsible AI and an array of tools (like Vertex AI, TensorFlow, and generative models), Google helps enterprises integrate AI into products and operations. Whether through Google Cloud’s AI platform or open-source frameworks, Google’s focus is on “AI for everyone” – delivering powerful AI services to both technical and non-technical audiences.

Google (Alphabet): company snapshot
Revenues in 2024:	$350 billion
Number of employees:	187,000+
Website:	cloud.google.com
Headquarters:	Mountain View, California, USA
Main services / focus:	Search & ads, Cloud AI services, generative AI (Gemini, Bard), enterprise apps (Google Workspace), DeepMind research

4. Microsoft

Microsoft has positioned itself as an enterprise leader in AI, infusing AI across its product ecosystem. In partnership with OpenAI, Microsoft has integrated GPT-4 and other advanced models into Azure (its cloud platform) and flagship products like Microsoft 365 (introducing AI “Copilot” features in Office apps). The company’s strategy focuses on democratizing AI to boost productivity – for example, empowering users with AI assistants in coding (GitHub Copilot) and writing (Word and Outlook suggestions). Microsoft’s heavy investment in AI infrastructure and supercomputing (including building some of the world’s most powerful AI training clusters for OpenAI) underscores its commitment. In 2024, Microsoft’s revenue topped $245 billion, and it employs about 228,000 people worldwide. Key AI offerings include Azure AI services (cognitive APIs, Azure OpenAI Service), Power Platform AI (low-code AI integration), and industry solutions in healthcare, finance, and retail. With its cloud footprint and software legacy, Microsoft provides robust AI platforms for enterprises, making AI accessible through the tools businesses already use.

Microsoft: company snapshot
Revenues in 2024:	$245 billion
Number of employees:	228,000+
Website:	azure.microsoft.com
Headquarters:	Redmond, Washington, USA
Main services / focus:	Cloud (Azure) and AI services, enterprise software (Microsoft 365, Dynamics), AI-assisted developer tools, OpenAI partnership

5. Accenture

Accenture is a global professional services firm renowned for helping businesses implement emerging technologies, and AI is a centerpiece of its offerings. With a workforce of 774,000+ professionals worldwide and revenues around $65 billion in 2024, Accenture has the scale and expertise to deliver AI solutions across all industries – from finance and healthcare to retail and manufacturing. Accenture’s dedicated Applied Intelligence practice offers end-to-end AI services: strategy consulting, data engineering, custom model development, and system integration. The firm has developed industry-tailored AI platforms (for example, its ai.RETAIL platform that uses AI for real-time merchandising and predictive analytics in retail) and invested heavily in AI talent and acquisitions. Accenture distinguishes itself by integrating AI with business process knowledge – using automation, analytics, and AI to reinvent clients’ operations at scale. As organizations navigate generative AI and automation, Accenture provides guidance on responsible AI adoption and even retrains its own employees in AI skills to meet demand. Headquartered in Dublin, Ireland, with offices in over 120 countries, Accenture leverages its global reach to roll out AI innovations and best practices for enterprises worldwide.

Accenture: company snapshot
Revenues in 2024:	~$65 billion
Number of employees:	774,000+
Website:	accenture.com
Headquarters:	Dublin, Ireland
Main services / focus:	AI consulting & integration, analytics, cloud services, digital transformation, industry-specific AI solutions

6. IBM

IBM has been a pioneer in AI since the early days – from chess-playing computers to today’s enterprise AI solutions. In 2025, IBM’s AI portfolio is headlined by the Watson platform and the new watsonx AI development studio, which offer businesses tools for building AI models, automating workflows, and deploying conversational AI. IBM, headquartered in Armonk, New York, generated about $62.7 billion in 2024 revenue and has approximately 270,000 employees globally. Known as “Big Blue,” IBM focuses on AI for hybrid cloud and enterprise automation – helping clients integrate AI into everything from customer service (chatbots) to IT operations (AIOps) and risk management. Its research heritage (IBM Research) and accumulation of patents ensure a steady infusion of advanced AI techniques into products. IBM’s strengths lie in conversational AI, machine learning, and AI-powered automation, often targeting industry-specific needs (like AI in healthcare diagnostics or financial fraud detection). With decades of trust from large enterprises, IBM often serves as a strategic AI partner that can handle sensitive data and complex integration, bolstered by its investments in AI ethics and partnerships with academia. From mainframes to modern AI, IBM continues to reinvent its offerings to stay at the cutting edge of intelligent technology.

IBM: company snapshot
Revenues in 2024:	$62.8 billion
Number of employees:	270,000+
Website:	ibm.com
Headquarters:	Armonk, New York, USA
Main services / focus:	Enterprise AI (Watson/watsonx), hybrid cloud, AI-powered consulting, IT automation, data analytics

7. Tata Consultancy Services (TCS)

Tata Consultancy Services (TCS) is one of the world’s largest IT services and consulting companies, known for its vast global delivery network and expertise in digital transformation. Part of India’s Tata Group, TCS had $29-30 billion in revenue in 2024 and a massive talent pool of over 600,000 employees. TCS offers a broad spectrum of services with a growing emphasis on AI, analytics, and automation solutions. The company works with clients worldwide to develop AI applications such as predictive maintenance systems for manufacturing, AI-driven customer personalization in retail, and intelligent process automation in banking. Leveraging its scale, TCS has built frameworks and accelerators (like TCS AI Workbench and Ignio, its cognitive automation software) to speed up AI adoption for enterprises. Headquartered in Mumbai, India, and operating in 46+ countries, TCS combines deep domain knowledge with tech expertise. Its focus on AI and machine learning is part of a broader strategy to help businesses become “cognitive enterprises” – using AI to enhance decision-making, optimize operations, and create new value. With strong execution capabilities and R&D (TCS Research labs), TCS is a go-to partner for many Fortune 500 firms embarking on AI-led transformations.

TCS: company snapshot
Revenues in 2024:	$30 billion
Number of employees:	600,000+
Website:	tcs.com
Headquarters:	Mumbai, India
Main services / focus:	IT consulting & services, AI & automation solutions, enterprise software development, business process outsourcing, analytics

Why Choose TTMS for AI Solutions?

When it comes to implementing AI initiatives, TTMS (Transition Technologies MS) offers the agility and innovation of a focused specialist backed by a track record of success. TTMS combines deep technical expertise with personalized service, making it an ideal partner for organizations looking to harness AI effectively. Unlike industry giants that might take a one-size-fits-all approach, TTMS delivers bespoke AI solutions tailored to each client’s unique needs – ensuring faster deployment and closer alignment with business goals. The company’s experience across diverse sectors (from legal to pharma) and its roster of skilled AI engineers enable TTMS to tackle projects of any complexity. As a testament to its capabilities, here are a few TTMS AI success stories that demonstrate how TTMS drives tangible results:

AI Implementation for Court Document Analysis at a Law Firm: TTMS developed an AI solution for a legal client (Sawaryn & Partners) that automates the analysis of court documents and transcripts, massively reducing manual workload. By leveraging Azure OpenAI services, the system can generate summaries of case files and hearing recordings, enabling lawyers to find key information in seconds. This project improved the law firm’s efficiency and data security, as large volumes of sensitive documents are processed internally with AI – speeding up case preparations while maintaining confidentiality.
AI-Driven SEO Meta Optimization: For Stäubli, a global industrial manufacturer, TTMS implemented an AI solution to optimize SEO metadata across thousands of product pages. Integrated with Adobe Experience Manager, the system uses ChatGPT to automatically generate SEO-friendly page titles and meta descriptions based on page content. Content authors can then review and fine-tune these AI-suggested titles. This approach saved significant time for Stäubli’s team and boosted the website’s search visibility by ensuring consistent, keyword-optimized metadata on every page.
Enhancing Helpdesk Training with AI: TTMS created an AI-powered e-learning platform to train a client’s new helpdesk employees in responding to support tickets. The solution presents trainees with simulated customer inquiries and uses AI to provide real-time feedback on their draft responses. By interacting with the AI tutor, new hires quickly learn to write replies that adhere to company guidelines and improve their English communication skills. This resulted in faster onboarding, more consistent customer service, and higher confidence among support staff in handling tickets.
Salesforce Integration with an AI Tool: TTMS built a custom AI integration for Takeda Pharmaceuticals, embedding AI into the company’s Salesforce CRM system to streamline the complex process of managing drug tender offers. The solution automatically analyzes incoming requests for proposals (RFPs) – extracting key requirements, deadlines, and criteria – and provides preliminary bid assessments to assist decision-makers. By combining Salesforce data with AI-driven analysis, Takeda’s team can respond to tenders more quickly and accurately. This innovation saved the company substantial time and improved the quality of its bids in a highly competitive, regulated industry.

Beyond these projects, TTMS has developed a suite of proprietary AI tools that demonstrate its forward-thinking approach. These in-house solutions address common business challenges with specialized AI applications:

AI4Legal: A legal-tech toolset that uses AI to assist with contract drafting, review, and risk analysis, allowing law firms and legal departments to automate document analysis and ensure compliance.
AML Track: An AI-powered AML system designed to detect suspicious activities and support financial compliance, helping institutions identify fraud and meet regulatory requirements with precision and speed.
AI4Localisation: Intelligent localization services that leverage AI to translate and adapt content across languages while preserving cultural nuance and tone consistency, streamlining global marketing and documentation.
AI-Based Knowledge Management System: A smart knowledge base platform that organizes corporate information and FAQs, using AI to enable faster information retrieval and smarter search through company data silos.
AI E-Learning: A tool for creating AI-driven training modules that adapt to learners’ needs, allowing organizations to build interactive e-learning content at scale with personalized learning paths.
AI4Content: An AI solution for documents that can automatically extract, validate, and summarize information from large volumes of text (such as forms, reports, or contracts), drastically reducing manual data entry and review time.

Choosing TTMS means partnering with a provider that stays on the cutting edge of AI trends while maintaining a client-centric approach. Whether you need to implement a machine learning model, integrate AI into enterprise software, or develop a custom intelligent tool, TTMS has the experience, proprietary technology, and dedication to ensure your AI project succeeds. Harness the power of AI for your business with TTMS – your trusted AI solutions delivery partner. Contact us!

FAQ

What is an “AI solutions delivery” company?

An AI solutions delivery company is a service provider that designs, develops, and implements artificial intelligence systems for clients. These companies typically have expertise in technologies like machine learning, data analytics, natural language processing, and automation. They work with businesses to identify opportunities where AI can add value (such as automating a process or gaining insights from data) and then build custom AI-powered applications or integrate third-party AI tools. In essence, an AI solutions provider takes cutting-edge AI research and applies it to real-world business challenges – delivering tangible solutions like predictive models, chatbots, computer vision systems, or intelligent workflow automations.

How do I choose the best AI solutions provider for my business?

Selecting the right AI partner involves evaluating a few key factors. First, consider the company’s experience and domain expertise – do they have a track record of projects in your industry or addressing similar problems? Review their case studies and client testimonials for evidence of successful outcomes. Second, assess their technical capabilities: a good provider should have skilled data scientists, engineers, and consultants who understand both cutting-edge AI techniques and how to deploy them at scale. It’s also wise to look at their partnerships (for instance, are they partners with major cloud AI platforms like AWS, Google Cloud, or Azure?) as this can expand the solutions they offer. Finally, ensure their approach aligns with your needs – the best providers will take time to understand your business objectives and customize an AI solution (rather than forcing a one-size-fits-all product). Comparing proposals and conducting pilot projects can further help in choosing a provider that delivers both expertise and a comfortable working relationship.

What AI services does TTMS provide?

Transition Technologies MS (TTMS) offers a broad range of AI services, tailored to help organizations deploy AI effectively. TTMS can engage end-to-end in your AI project: from initial consulting and strategy (identifying use cases and assessing data readiness) to solution development and integration. Concretely, TTMS builds custom AI applications (for example, predictive analytics models, NLP solutions for document analysis, or computer vision systems) and also integrates AI into existing platforms like CRM systems or content management systems. The company provides data engineering and preparation, ensuring your data is ready for AI modeling, and employs machine learning techniques to create intelligent features (like recommendation engines or anomaly detectors) for your software. Additionally, TTMS offers specialized solutions such as AI-driven automation of business processes, AI in cybersecurity (fraud detection, AML systems), AI for content generation/optimization (as seen in their SEO meta optimization case), and much more. With its team of AI experts, TTMS essentially can take any complex manual process or decision-making workflow and find a way to enhance it with artificial intelligence.

Why are companies like Amazon, Google, and IBM leaders in AI solutions?

Tech giants such as Amazon, Google, Microsoft, IBM, etc., have risen to prominence in AI for several reasons. Firstly, they have invested heavily in research and development – these companies employ leading AI scientists and have contributed fundamental advancements (for instance, Google’s deep learning research via DeepMind or OpenAI partnership with Microsoft). This R&D prowess means they often have cutting-edge AI technology (like Google’s state-of-the-art language models or IBM’s Watson platform) ready to deploy. Secondly, they possess massive computing infrastructure and data. AI development, especially training large models, requires huge computational resources and large datasets – something these tech giants have in abundance through their cloud divisions and user bases. Thirdly, they have integrated AI into a broad array of services and made them accessible: Amazon’s AWS offers AI building blocks for developers, Google Cloud does similarly, and Microsoft embeds AI features into tools that businesses already use. Lastly, their global scale and enterprise experience give them credibility; they have proven solutions in many domains (from Amazon’s AI-driven logistics to IBM’s enterprise AI consulting) which showcases reliability. In summary, these companies lead in AI solutions because they combine innovation, infrastructure, and industry know-how to deliver AI capabilities worldwide.

Can smaller companies like TTMS compete with global IT giants in AI?

Yes, smaller specialized firms like TTMS can absolutely compete and often provide unique advantages over the mega-corporations. While they may not match the sheer size or brand recognition of a Google or IBM, companies like TTMS are typically more agile and focused. They can adapt quickly to the latest AI developments and often tailor their services more closely to individual client needs (large firms might push more standardized solutions or have more bureaucracy). TTMS, for instance, zeroes in on client-specific AI solutions – meaning they will develop a custom model or tool specifically for your problem, rather than a generic platform. Additionally, specialized providers tend to offer more personalized attention; clients work directly with senior engineers or AI experts, ensuring in-depth understanding of the project. There’s also the fact that AI talent is distributed – smaller companies often attract top experts who prefer a focused environment. That said, big players do bring strengths like vast resources and pre-built platforms, but smaller AI firms compete by being innovative, customer-centric, and flexible on cost and project scope. In practice, many enterprises employ a mix: using big cloud AI services under the guidance of a nimble partner like TTMS to get the best of both worlds.

TTMS blog – the world through the eyes of IT experts

The Cyber Resilience Act in the energy sector – obligations, risks, and how to prepare for 2025?

The EU’s Cyber Resilience Act (CRA) marks a turning point in the way digital products are secured across Europe. By 2027, all software will need to comply with CRA requirements, and as early as next year, companies will face mandatory cybersecurity incident reporting. This issue is particularly critical for the energy sector, where outdated and poorly secured systems are still in use. A lack of proper safeguards can lead to severe consequences – not only financial but also operational and social. CRA applies to all software in the EU starting in 2027. For the energy sector, this means obligations such as SBOM, secure-by-design, and incident reporting. TTMS supports companies in preparing for and implementing CRA requirements. Ignoring the regulation may result in fines, market exclusion, and exposure to real cyberattacks. 1. Why is the energy sector especially vulnerable? The energy sector is the backbone of modern society – the economy, public administration, and daily life all depend on its stability. As critical infrastructure, electricity supply must be uninterrupted. Any disruption can cause serious social and economic fallout – from halting transport and communications to crippling hospitals or emergency services. Yet, this infrastructure relies on complex control systems such as SCADA, RTU, EMS, or HMI. Many of them were designed in an era when cybersecurity was not a top design priority. Built primarily for performance and reliability, they are often ill-equipped to withstand today’s digital threats. The challenge intensifies with the convergence of OT and IT systems. More elements of physical infrastructure are now connected to corporate networks, increasing the attack surface and complicating risk management. Cybercriminals no longer need physical access to a power plant or substation – a single vulnerability in a remote-control system may be enough. Adding to the risk is technological legacy. Many organisations still rely on outdated operating systems and applications deeply embedded in technological processes. These cannot be easily updated or replaced, making them an easy target for cyberattacks. 1.1 The threat is not theoretical – real incidents prove it. In 2017, a cyberattack targeted the German company Netcom BW, a telecommunications network operator owned by EnBW, one of Germany’s largest energy providers. The attacker was a Russian national and a member of Berserk Bear, a group linked to Russia’s FSB intelligence service. The goal was to infiltrate communication infrastructure used not only by Netcom BW but also by energy system operators. While the companies assured that the core energy infrastructure remained intact, the attack exposed vulnerabilities in the supply chain and the dependencies between IT systems and critical energy assets. This is a warning that cannot be ignored. Incidents like this highlight that cybersecurity cannot stop at the boundaries of a power plant or transmission grid – it must extend to technology suppliers, communication systems, and all interconnected digital components. This is precisely why the implementation of the EU’s Cyber Resilience Act is not only a legal requirement but also a strategic step towards building a resilient energy sector for the future. 2. CRA – What Does It Mean for Energy Companies and How Can TTMS Help? The new EU regulation introduced by the Cyber Resilience Act (CRA) imposes binding cybersecurity obligations on software providers across the energy sector. For many organisations, this means reorganising development processes, implementing new tools, and ensuring both formal and technical compliance. This is where Transition Technologies MS steps in, offering both advisory and technological support. 2.1 Mandatory SBOMs (Software Bill of Materials) CRA requires every company delivering software to maintain a complete list of components, libraries, and dependencies used in their product. How TTMS helps: We implement tools that automate the creation and updating of SBOMs in popular formats (e.g. SPDX, CycloneDX), integrating them with CI/CD pipelines. We also support risk analysis of open-source components and help establish dependency management policies. 2.2 Secure-by-Design Development CRA enforces the obligation to embed security into products from the very first design stage. How TTMS helps: We provide threat modelling workshops, application architecture security audits, and the implementation of secure DevSecOps practices. Our support also includes penetration testing and code reviews at every stage of the product lifecycle. 2.3 Vulnerability Management The regulation requires organisations to detect, classify, and patch vulnerabilities quickly – not only in their own code but also in third-party components. How TTMS helps: We build and integrate vulnerability management processes – from static scanning (SAST) and dynamic testing (DAST) to real-time vulnerability monitoring systems. We help implement procedures aligned with best practices (e.g. CVSS, CVD). 2.4 Incident Reporting Every major security incident must be reported to ENISA or the local CSIRT within 24 hours. How TTMS helps: We create incident response plans (IRPs), implement detection and automated reporting systems, and train IT and OT teams in CRA-compliant procedures. TTMS can also act as an external cyber emergency response partner. 2.5 EU Declaration of Conformity Software providers must deliver a formal document confirming compliance with CRA requirements – this is not only a declaration but also a legal responsibility. How TTMS helps: We support companies in creating and maintaining CRA-required documentation, including declarations of conformity, security policies, and technical support plans. We provide pre-implementation audits and assistance in preparing for regulatory inspections. 2.6 Additional Support and Parallel Development Implementing CRA requirements does not have to mean halting other development projects. At TTMS, we provide additional resources in a staff augmentation model, enabling organisations to continue software development in parallel with the process of adapting applications to new regulations. This way, energy companies can maintain their pace of innovation while effectively meeting legal requirements. Moreover, we offer comprehensive cybersecurity testing support across three key areas: Infrastructure audits and penetration testing Application audits and penetration testing Source code audits All these services are delivered by TTMS in cooperation with Transition Technologies Software (TTSW), ensuring complete security both at the system and application level. Why Work with TTMS? Proven experience in the energy sector – deep knowledge of SCADA, EMS, DMS, and OT/IT environments. Dedicated Quality and Cybersecurity experts – supporting organisations throughout the entire CRA compliance cycle. Ready-to-use solutions and tools – from SBOM management to incident response and risk analysis. Security-as-a-Service – flexible support models tailored to client needs. 3. Ignoring CRA Could Cost More Than You Think Non-compliance with the Cyber Resilience Act is not just a formal issue – it is a real risk to business continuity and market presence in the EU. CRA foresees severe financial penalties – up to €15 million or 2.5% of global annual turnover – for failing to meet software security requirements. In addition, non-compliant products may be completely excluded from the EU market, which for many companies – especially those in critical infrastructure – could mean the loss of key contracts. Neglecting security also increases the risk of real cyberattacks that may paralyse systems, leak sensitive data, and cause massive financial and reputational losses. A notable example is the ransomware attack on the Norwegian company Norsk Hydro in March 2019. The global aluminium producer and energy provider had its IT systems worldwide shut down, forcing plants to switch to manual operations. The direct and indirect costs exceeded $70 million, and the company struggled for weeks to restore operations and rebuild market trust. Although this case dates back a few years, the number of similar attacks has been rising steadily amid Europe’s ongoing hybrid warfare. In 2025, Poland reported two major cybersecurity incidents in public institutions – one involving a personal data breach caused by an email system intrusion, and another targeting industrial control systems. Cases like these show that failing to act proactively on cybersecurity can cost far more than investing in CRA compliance. It is not only a legal obligation but also a condition for maintaining competitiveness and business resilience in the digital era. 4. Cyber Resilience Act – Consequences of Non-Compliance and Real Risks of Cyberattacks Failure to comply with CRA can result in: Financial penalties of up to €15 million or 2.5% of global annual turnover Exclusion from the EU market Increased risk of cyberattacks leading to system paralysis and massive financial losses 4.1 When Should You Start Acting? The Clock Is Ticking The Cyber Resilience Act was adopted in October 2024. While full compliance will not be required until December 2027, one of the key obligations – reporting security incidents within 24 hours – will already apply from September 2026. This means that companies – especially those in critical infrastructure sectors such as energy – have less than a year to prepare procedures, train teams, implement the right tools, and test their systems. Implementing CRA is not about a single document – it requires a comprehensive change in how software is developed and maintained, covering security, documentation, vulnerability management, and formal compliance. Leaving compliance until the last minute is a recipe for errors, system gaps, and costly consequences. Organisations that start preparing now will gain not only a time advantage but also a strategic one, demonstrating to partners and customers that they take cybersecurity seriously – before being forced to. This is precisely where Transition Technologies MS (TTMS) can make the difference. Our expert teams support organisations at every stage of CRA readiness – from analysing current processes and conducting security audits, to implementing SBOM and vulnerability management tools, developing incident reporting procedures, and preparing formal compliance documentation. TTMS does more than advise – we implement real technical solutions, deliver training, and provide ongoing support as part of a long-term partnership. If your organisation operates in the energy sector, do not delay CRA compliance – the consequences of inaction can be severe both operationally and financially. Talk to one of our cybersecurity experts and discover how TTMS can help you navigate this process smoothly and effectively. Visit ttms.pl/energy to learn more about the software and solutions we build for energy companies. Looking for a quick summary? Check out our FAQ section, where we have gathered the most important questions and answers from this article. When does the Cyber Resilience Act (CRA) come into force and what is the timeline? The Cyber Resilience Act was officially adopted in October 2024. Full compliance with its provisions will be mandatory from December 2027. However, from September 2026, companies will already be required to report security incidents within 24 hours. This leaves limited time for organisations to analyse, prepare, and implement the necessary processes – especially in the energy sector, where action must be both fast and methodical. Which products and systems in the energy sector are covered by CRA? The regulation applies to all “products with digital elements,” meaning both physical devices and software that can connect to a network. In practice, this includes critical energy management and control systems such as SCADA, RTU, EMS, DMS, and HMI – the backbone of digital energy infrastructure. If your software operates in this environment, CRA directly affects your organisation. What specific obligations does CRA impose on energy companies? Energy companies must introduce Software Bills of Materials (SBOMs), design systems with a secure-by-design approach, manage and patch vulnerabilities quickly, report major incidents to relevant institutions within strict deadlines, and prepare an EU Declaration of Conformity for their products. These are not mere formalities – they have a tangible impact on the security and resilience of entire energy systems. What are the risks for companies that ignore CRA requirements? Non-compliance may result in fines of up to €15 million or 2.5% of a company’s global annual turnover – whichever is higher. In addition, non-compliant products may be removed from the EU market entirely. Beyond financial penalties, ignoring CRA also exposes companies to real cyber risks, such as ransomware attacks. The Norsk Hydro case showed how a single incident can cause operational paralysis, data loss, and reputational damage with long-term consequences. Does every company have to report incidents, even if there was no service disruption? Yes. CRA requires reporting of any major security incident or actively exploited vulnerability within 24 hours of detection. A follow-up report must then be submitted within 72 hours, and a final summary within 14 days. This applies not only to incidents that cause outages but also to those that could potentially affect product or user security. The aim is to ensure early transparency and rapid mitigation across the entire EU market.

Deepfake Detection Breakthrough: Universal Detector Achieves 98% Accuracy

Deepfake Detection Breakthrough: Universal Detector Achieves 98% Accuracy Imagine waking up to a viral video of your company’s CEO making outrageous claims – except it never happened. This nightmare scenario is becoming all too real as deepfakes (AI-generated fake videos or audio) grow more convincing. In response, researchers have unveiled a new universal deepfake detector that can spot synthetic videos with an unprecedented 98% accuracy. The development couldn’t be more timely, as businesses seek ways to protect their brand reputation and trust in an era when seeing is no longer believing. A powerful new AI tool can analyze videos and detect subtle signs of manipulation, helping companies distinguish real footage from deepfakes. The latest “universal” detector boasts cross-platform capabilities, flagging both fake videos and AI-generated audio with remarkable precision. It marks a significant advance in the fight against AI-driven disinformation. What is the 98% Accurate Universal Deepfake Detector and How Does It Work? The newly announced deepfake detector is an AI-driven system designed to identify fake video and audio content across virtually any platform. Developed by a team of researchers (notably at UC San Diego in August 2025), it represents a major leap forward in deepfake detection technology. Unlike earlier tools that were limited to specific deepfake formats, this “universal” detector works on both AI-generated speech and manipulated video footage. In other words, it can catch a lip-synced synthetic video of an executive and an impersonated voice recording with the same solution. Under the hood, the detector uses advanced machine learning techniques to sniff out the subtle “fingerprints” that generative AI leaves on fake content. When an image or video is created by AI rather than a real camera, there are tiny irregularities at the pixel level and in motion patterns that human eyes can’t easily see. The detector’s neural network has been trained to recognize these anomalies at the sub-pixel scale. For example, real videos have natural color correlations and noise characteristics from camera sensors, whereas AI-generated frames might have telltale inconsistencies in texture or lighting. By focusing on these hidden markers, the system can discern AI fakery without relying on obvious errors. Critically, this new detector doesn’t just focus on faces or one part of the frame – it scans the entire scene (backgrounds, movements, audio waveform, etc.) for anything that “doesn’t fit.” Earlier deepfake detectors often zeroed in on facial glitches (like unnatural eye blinking or odd skin textures) and could fail if no face was visible. In contrast, the universal model analyzes multiple regions per frame and across consecutive frames, catching subtle spatial and temporal inconsistencies that older methods missed. It’s a transformer-based AI model that essentially learns what real vs. fake looks like in a broad sense, instead of using one narrow trick. This breadth is what makes it universal – as one researcher put it, “It’s one model that handles all these scenarios… that’s what makes it universal”. Training Data and Testing: Building a Better Fake-Spotter Achieving 98% accuracy required feeding the detector a huge diet of both real and fake media. The researchers trained the system on an extensive range of AI-generated videos produced by different generator programs – from deepfake face-swaps to fully AI-created clips. For instance, they used samples from tools like Stable Diffusion’s video generator, Video-Crafter, and CogVideo to teach the AI what various fake “fingerprints” look like. By learning from many techniques, the model doesn’t get fooled by just one type of deepfake. Impressively, the team reported that the detector can even adapt to new deepfake methods after seeing only a few examples. This means if a brand-new AI video generator comes out next month, the detector could learn its telltale signs without needing a complete retraining. The results of testing this system have been record-breaking. In evaluations, the detector correctly flagged AI-generated videos about 98.3% of the time. This is a significant jump in accuracy compared to prior detection tools, which often struggled to get above the low 90s. In fact, the researchers benchmarked their model against eight different existing deepfake detection systems, and the new model outperformed all of them (the others ranged around 93% accuracy or lower). Such a high true-positive rate is a major milestone in the arms race against deepfakes. It suggests the AI can spot almost all fake content thrown at it, across a wide variety of sources. Of course, “98% accuracy” isn’t 100%, and that remaining 2% error rate does matter. With millions of videos uploaded online daily, even a small false-negative rate means some deepfakes will slip through, and a false-positive rate could flag some real videos incorrectly. Nonetheless, this detector’s performance is currently best-in-class. It gives organizations a fighting chance to catch malicious fakes that would have passed undetected just a year or two ago. As deepfake generation gets more advanced, detection had to step up – and this tool shows it’s possible to significantly close the gap. How Is This Detector Different from Past Deepfake Detection Methods? Previous deepfake detection methods were often specialized and easier to evade. One key difference is the new detector’s broad scope. Earlier detectors typically focused on specific artifacts – for example, one system might look for unnatural facial movements, while another analyzed lighting mismatches on a person’s face. These worked for certain deepfakes but failed for others. Many classic detectors also treated video simply as a series of individual images, trying to spot signs of Photoshop-style edits frame by frame. That approach falls apart when dealing with fully AI-generated video, which doesn’t have obvious cut-and-paste traces between frames. By contrast, the 98% accurate detector looks at the bigger picture (pun intended): it examines patterns over time and across the whole frame, not just isolated stills. Another major advancement is the detector’s ability to handle various formats and even modalities. Past solutions usually targeted one type of media at a time – for instance, a tool might detect face-swap video deepfakes but do nothing about an AI-cloned voice in an audio clip. The new universal detector can tackle both video and audio in one system, which is a game-changer. So if a deepfake involves a fake voice over a real video, or vice versa, older detectors might miss it, whereas this one catches the deception in either stream. Additionally, the architecture of this detector is more sophisticated. It employs a constrained neural network that homes in on anomalies in data distributions rather than searching for a predefined list of errors. Think of older methods like using a checklist (“Are the eyes blinking normally? Is the heartbeat visible on the neck?”) – effective until the deepfake creators fix those specific issues. The new method is more like an all-purpose lie detector for media; it learns the underlying differences between real and fake content, which are harder for forgers to eliminate. Also, unlike many legacy detectors that heavily relied on seeing a human face, this model doesn’t care if the content has people, objects, or scenery. For example, if someone fabricated a video of an empty office with fake background details, previous detectors might not notice anything since no face is present. The universal detector would still scrutinize the textures, shadows, and motion in the scene for unnatural signs. This makes it resilient against a broader array of deepfake styles. In summary, what sets this new detector apart is its universality and robustness. It’s essentially a single system that covers many bases: face swaps, entirely synthetic videos, fake voices, and more. Earlier generations of detectors were more narrow – they solved part of the problem. This one combines lessons from all those earlier efforts into a comprehensive tool. That breadth is vital because deepfake threats are evolving too. By solving the cross-platform compatibility issues that plagued older systems, the detector can maintain high accuracy even as deepfake techniques diversify. It’s the difference between a patchwork of local smoke detectors and a building-wide fire alarm system. Why This Matters for Brand Safety and Reputational Risk For businesses, deepfakes aren’t just an IT problem – they’re a serious brand safety and reputation risk. We live in a time where a single doctored video can go viral and wreak havoc on a company’s credibility. Imagine a fake video showing your CEO making unethical remarks or a bogus announcement of a product recall; such a hoax could send stock prices tumbling and customers fleeing before the truth gets out. Unfortunately, these scenarios have moved from hypothetical to real. Corporate targets are already in the crosshairs of deepfake fraudsters. In 2019, for example, criminals used an AI voice clone to impersonate a CEO and convinced an employee to wire $243,000 to a fraudulent account. By 2024, a multinational firm in Hong Kong was duped by an even more elaborate deepfake – a video call with a fake “CEO” and colleagues – resulting in a $25 million loss. The number of deepfake attacks against companies has surged, with AI-generated voices and videos duping financial firms out of millions and putting corporate security teams on high alert. Beyond direct financial theft, deepfakes pose a huge reputational threat. Brands spend years building trust, which a single viral deepfake can undermine in minutes. There have been cases of fake videos of political leaders and CEOs circulating online – even if debunked eventually, the damage in the interim can be significant. Consumers might question, “Was that real?” about any shocking video involving your brand. This uncertainty erodes the baseline of trust that businesses rely on. That’s why a detection tool with very high accuracy matters: it gives companies a fighting chance to identify and respond to fraudulent media quickly, before rumors and misinformation take on a life of their own. From a brand safety perspective, having a nearly foolproof deepfake detector is like having an early-warning radar for your reputation. It can help verify the authenticity of any suspicious video or audio featuring your executives, products, or partners. For example, if a doctored video of your CEO appears on social media, the detector could flag it within moments, allowing your team to alert the platform and your audience that it’s fake. Consider how valuable that is – it could be the difference between a contained incident and a full-blown PR crisis. In industries like finance, news media, and consumer goods, where public confidence is paramount, such rapid detection is a lifeline. As one industry report noted, this kind of tool is a “lifeline for companies concerned about brand reputation, misinformation, and digital trust”. It’s becoming essential for any organization that could be a victim of synthetic content abuse. Deepfakes have also introduced new vectors for fraud and misinformation that traditional security measures weren’t prepared for. Fake audio messages of a CEO asking an employee to transfer money, or a deepfake video of a company spokesperson giving false information about a merger, can bypass many people’s intuitions because we are wired to trust what we see and hear. Brand impersonation through deepfakes can mislead customers – for instance, a fake video “announcement” could trick people into a scam investment or phishing scheme using the company’s good name. The 98% accuracy detector, deployed properly, acts as a safeguard against these malicious uses. It won’t stop deepfakes from being made (just as security cameras don’t stop crimes by themselves), but it significantly boosts the chance of catching a fake in time to mitigate the harm. Incorporating Deepfake Detection into Business AI and Cybersecurity Strategies Given the stakes, businesses should proactively integrate deepfake detection tools into their overall security and risk management framework. A detector is not just a novelty for the IT department; it’s quickly becoming as vital as spam filters or antivirus software in the corporate world. Here are some strategic steps and considerations for companies looking to defend against deepfake threats: Employee Education and Policies: Train staff at all levels to be aware of deepfake scams and to verify sensitive communications. For example, employees should be skeptical of any urgent voice message or video that seems even slightly off. They must double-check unusual requests (especially involving money or confidential data) through secondary channels (like calling back a known number). Make it company policy that no major action is taken based on electronic communications alone without verification. Strengthen Verification Processes: Build robust verification protocols for financial transactions and executive communications. This might include multi-factor authentication for approvals, code words for confirming identity, or mandatory pause-and-verify steps for any request that seems odd. An incident in 2019 already highlighted that recognizing a voice is no longer enough to confirm someone’s identity – so treat video and audio with the same caution as you would a suspicious email. Deploy AI-Powered Detection Tools: Incorporate deepfake detection technology into your cybersecurity arsenal. Specialized software or services can analyze incoming content (emails with video attachments, voicemail recordings, social media videos about your brand) and flag possible fakes. Advanced AI detection systems can catch subtle inconsistencies in audio and video that humans would miss. Many tech and security firms are now offering detection as a service, and some social media platforms are building it into their moderation processes. Use these tools to automatically screen content – like an “anti-virus” for deepfakes – so you get alerts in real time. Regular Drills and Preparedness: Update your incident response plan to include deepfake scenarios. Conduct simulations (like a fake “CEO video” emergency drill) to test how your team would react. Just as companies run phishing simulations, run a deepfake drill to ensure your communications, PR, and security teams know the protocol if a fake video surfaces. This might involve quickly assembling a crisis team, notifying platform providers to take down the content, and issuing public statements. Practicing these steps can greatly reduce reaction time under real pressure. Monitor and Respond in Real Time: Assign personnel or use services to continuously monitor for mentions of your brand and key executives online. If a deepfake targeting your company does appear, swift action is crucial. The faster you identify it’s fake (with the help of detection AI) and respond publicly, the better you can contain false narratives. Have a clear response playbook: who assesses the content, who contacts legal and law enforcement if needed, and who communicates to the public. Being prepared can turn a potential nightmare into a managed incident. Integrating these measures ensures that your deepfake defense is both technical and human. No single tool is a silver bullet – even a 98% accurate detector works best in concert with good practices. Companies that have embraced these strategies treat deepfake risk as a when-not-if issue. They are actively “baking deepfake detection into their security and compliance practices,” as analysts advise. By doing so, businesses not only protect themselves from fraud and reputational damage but also bolster stakeholder confidence. In a world where AI can imitate anyone, a robust verification and detection strategy becomes a cornerstone of digital trust. Looking ahead, we can expect deepfake detectors to be increasingly common in enterprise security stacks. Just as spam filters and anti-malware became standard, content authentication and deepfake scanning will likely become routine. Forward-thinking companies are already exploring partnerships with AI firms to integrate detection APIs into their video conferencing and email systems. The investment in these tools is far cheaper than the cost of a major deepfake debacle. With threats evolving, businesses must stay one step ahead – and this 98% accuracy detector is a promising tool to help them do exactly that. Protect Your Business with TTMS AI Solutions At Transition Technologies MS (TTMS), we help organizations strengthen their defenses against digital threats by integrating cutting-edge AI tools into cybersecurity strategies. From advanced document analysis to knowledge management and e-learning systems, our AI-driven solutions are designed to ensure trust, compliance, and resilience in the digital age. Partner with TTMS to safeguard your brand reputation and prepare for the next generation of challenges in deepfake detection and beyond. FAQ How can you tell if a video is a deepfake without specialized tools? Even without an AI detector, there are some red flags that a video might be a deepfake. Look closely at the person’s face and movements – often, early deepfakes had unnatural eye blinking or facial expressions that seem “off.” Check for inconsistencies in lighting and shadows; sometimes the subject’s face lighting won’t perfectly match the scene. Audio can be a giveaway too: mismatched lip-sync or robotic-sounding voices might indicate manipulation. Pause on individual frames if possible – distorted or blurry details around the edges of faces (especially between transitions) can signal something is amiss. While these clues can help, sophisticated deepfakes today are much harder to spot with the naked eye, which is why tools and detectors are increasingly important. Are there laws or regulations addressing deepfakes that companies should know about? Regulation of deepfakes is starting to catch up as the technology’s impact grows. Different jurisdictions have begun introducing laws to deter malicious use of deepfakes. For example, China implemented regulations requiring that AI-generated media (deepfakes) be clearly labeled, and it bans the creation of deepfakes that could mislead the public or harm someone’s reputation. In the European Union, the upcoming AI Act treats manipulative AI content as high-risk and will likely enforce transparency obligations – meaning companies may need to disclose AI-generated content and could face penalties for harmful deepfake misuse. In the United States, there isn’t a blanket federal deepfake law yet, but some states have acted: Virginia was one of the first, criminalizing certain deepfake pornography and impersonations, and California and Texas have laws against deepfakes in elections. Additionally, existing laws on fraud, defamation, and identity theft can apply to deepfake scenarios (for instance, using a deepfake to commit fraud is still fraud). For businesses, this regulatory landscape means two things: you should refrain from unethical uses of deepfakes in your operations and marketing (to avoid legal trouble and backlash), and you should stay informed about emerging laws that protect victims of deepfakes – such laws might aid your company if you ever need to take legal action against parties making malicious fakes. It’s wise to consult legal experts on how deepfake-related regulations in your region could affect your compliance and response strategies. Can deepfake creators still fool a 98% accurate detector? It’s difficult but not impossible. A 98% accurate detector is extremely good, but determined adversaries are always looking for ways to evade detection. Researchers have shown that by adding specially crafted “noise” or artifacts (called adversarial examples) into a deepfake, they can sometimes trick detection models. It’s an AI cat-and-mouse game: as detectors improve, deepfake techniques adjust to become more sneaky. That said, fooling a top-tier detector requires a lot of expertise and effort – the average deepfake circulating online right now is unlikely to be that expertly concealed. The new universal detector raises the bar significantly, meaning most fakes out there will be caught. But we can expect deepfake creators to try developing countermeasures, so ongoing research and updated models will be needed. In short, 98% accurate doesn’t mean invincible, but it makes successful deepfake attacks much rarer. What should a company do if a deepfake of its CEO or brand goes public? Facing a deepfake attack on your company requires swift and careful action. First, internally verify the content – use detection tools (like the 98% accuracy detector) to confirm it’s fake, and gather any evidence of how it was created if possible. Activate your crisis response team immediately; this typically involves corporate communications, IT security, legal counsel, and executive leadership. Contact the platform where the video or audio is circulating and report it as fraudulent content – many social networks and websites have policies against deepfakes, especially those causing harm, and will remove them when alerted. Simultaneously, prepare a public statement or press release for your stakeholders. Be transparent and assertive: inform everyone that the video/audio is a fake and that malicious actors are attempting to mislead the public. If the deepfake could have legal ramifications (for example, stock manipulation or defamation), involve law enforcement or regulators as needed. Going forward, conduct a post-incident analysis to improve your response plan. By reacting quickly and communicating clearly, a company can often turn the tide and prevent lasting damage from a deepfake incident. Are deepfake detection tools available for businesses to use? Yes – while some cutting-edge detectors are still in the research phase, there are already tools on the market that businesses can leverage. A number of cybersecurity companies and AI startups offer deepfake detection services (often integrated into broader threat intelligence platforms). For instance, some provide APIs or software that can scan videos and audio for signs of manipulation. Big tech firms are also investing in this area; platforms like Facebook and YouTube have developed internal deepfake detection to police their content, and Microsoft released a deepfake detection tool (Video Authenticator) a few years ago. Moreover, open-source projects and academic labs have published deepfake detection models that savvy companies can experiment with. The new 98% accuracy “universal” detector itself may become commercially or publicly available after further development – if so, it could be deployed by businesses much like antivirus software. It’s worth noting that effective use of these tools also requires human oversight. Businesses should assign trained staff or partner with vendors to implement the detectors correctly and interpret the alerts. In summary, while no off-the-shelf solution is perfect, a variety of deepfake detection options do exist and are maturing rapidly.

AI in E-Learning: How to Track and Prove Training Effectiveness

Imagine an organization where every employee knows exactly how to grow their skills, and training is no longer seen as a cost but as an investment that drives the entire business forward. Today, this vision is possible thanks to AI-powered tools. These solutions make it easier than ever to connect corporate strategy with everyday learning and development needs. In this article, you’ll discover how AI can help diagnose skill gaps, design tailored development programs, and act as a strategic advisor to the board by clearly demonstrating how training impacts business results – from cost reduction to increased innovation. 1. AI as a Breakthrough in Measuring Training Effectiveness 1.1 Why Course Completion Rates Are No Longer Enough Just a few years ago, the success of training programs was measured by simple metrics: how many employees completed a course and how they rated it in a survey. At first glance, those tables full of “checked-off” results gave leaders a sense of control. But today, that picture is far too flat. Boards are no longer satisfied with completion clicks. They want proof that training drives real change – higher revenues, lower costs, faster onboarding, or greater readiness to embrace innovation. The e-learning function cannot operate in isolation from the company’s strategy – its effectiveness depends on close collaboration with the board. This is what shifts training from being a “nice-to-have” to a strategic growth tool. When priorities are set together, development programs focus on the skills that truly matter – entering new markets, supporting digital transformation, or boosting innovation. This collaboration also enables faster responses to business needs and provides stronger budget justification by showing ROI in hard numbers. Even more, integrating learning data with analytics tools makes it possible to report measurable outcomes – from reducing operational errors to increasing sales – positioning training as a genuine investment in the company’s future. 1.2 How AI and Power BI Enable Real-Time Reporting Artificial intelligence opens a new chapter. AI tools now automate course creation and, when connected with e-learning platforms, enable reporting almost in real time. This is exactly how AI4E-learning works – a dedicated solution that automates and streamlines the entire course creation process, from analyzing source materials to generating ready-to-use e-learning modules. With AI4E-learning, training that once took weeks can now be created in hours or days. What’s more, it immediately delivers performance data – such as completion rates, time spent on tasks, and areas needing further improvement. When integrated with platforms like Power BI, AI4E-learning allows CLOs to present data through clear dashboards and link training activity with any business KPI. By synchronizing information from LMS, CRM, and HR systems, organizations gain a full picture of how development programs impact company performance. And because AI4E-learning accelerates course design, it also helps organizations quickly adapt to shifting business priorities. 2. The Strategic Role of the CLO in AI-Enhanced Learning 2.1 The CLO as a Transformation Leader The Chief Learning Officer is no longer simply responsible for delivering training. Today, the CLO is a transformation leader who leverages AI to monitor, predict, and optimize the impact of development initiatives. The example of L’Oréal illustrates how this role is evolving. Nicolas Pauthier implements a learning strategy built on cohort-based learning and precise skills mapping. As CLO, he doesn’t just organize training – he advises the board strategically. His focus is on creating experiences that emotionally engage employees, motivating them to learn, while also reporting the business value of training programs – from increased sales to cost reductions. This shows that an effective CLO bridges the gap between people development and strategic business goals – and AI-driven analytics are invaluable in achieving this. 2.2 Linking Training to Business Priorities When training is directly tied to company priorities, employee development stops being a cost and becomes an investment that truly drives business growth. That’s when learning starts working toward strategic goals – and the results are visible in practice. Imagine a company entering a new market. Without preparation, this could mean months of chaos and costly mistakes. But with prior training on local regulations, customer service, or language skills, employees are ready from day one, making expansion faster and safer. The same applies to cost reduction: when production teams complete safety training on new procedures, workplace accidents and downtime decrease, delivering immediate savings. In digital transformation, training also bridges the gap between investing in new technologies and actually using them. A company that equips employees with AI and automation skills will see a faster return on investment than one that expects staff to “figure it out themselves.” Similarly, strategically developed skills – such as customer service excellence or agile methodologies – are hard to replicate and become a unique competitive asset. And finally, there’s the human factor. Employees who see that training is not “for show” but genuinely helps them in their daily work and supports organizational goals feel a stronger sense of purpose. This boosts motivation, increases engagement, and ultimately reduces turnover and recruitment costs. 3. Key Business Metrics Measured Through E-Learning E-learning opens entirely new possibilities for measuring effectiveness, allowing organizations to track indicators that were practically impossible to capture in traditional training. Learning Management Systems (LMS) record every step of the learning journey – from logins and activity on the platform to test results. When combined with analytics tools and artificial intelligence, this data goes far beyond completion rates. It becomes a valuable source of insight into skill development and its impact on overall business performance. So, what do learning leaders in large organizations measure today? 3.1 Revenue Growth Prediction – Linking Training to Sales This metric predicts how specific training programs can directly influence company revenue growth. AI-powered tools analyze data from LMS platforms and sales systems to identify correlations between employee training participation and business results. For example: after a product training, the sales team may achieve a higher conversion rate or increase average deal size. AI not only identifies these relationships retroactively but can also forecast how much revenue will grow if a given group of employees completes the course. This measurement helps set training priorities – highlighting which programs have the greatest impact on sales and business growth. It also enables companies to predict which skills will be most critical for financial performance in the near future. 3.2 Cost Reduction Analysis – Fewer Errors and Downtime Another measurable benefit of AI-driven e-learning is cost savings. This analysis shows to what extent training helps reduce both operational and strategic costs. In practice, this could mean fewer production errors after quality training, fewer customer complaints following service courses, or reduced downtime thanks to better-prepared technical teams. AI compares LMS data with inputs from operational, financial, and HR systems to clearly demonstrate where training has lowered costs. This approach allows CLOs to speak the board’s language: instead of reporting how many employees completed a course, they can show that customer complaints dropped by 15% – translating into hundreds of thousands of dollars saved annually. Training thus becomes a tangible element of cost optimization and organizational efficiency. 3.3 Time-to-Competency – Faster Path to Full Productivity Time-to-Competency measures how long it takes an employee to reach full productivity after training. Traditionally, this was difficult to capture – organizations often didn’t know exactly when a new hire became fully effective. With e-learning, especially AI-enhanced tools, this process is measurable. LMS platforms track how quickly employees absorb knowledge, complete assignments, and pass assessments. AI then compares these results with job performance data – such as projects delivered, customers handled, or sales closed. CLOs can therefore precisely determine how long it takes to move from training to peak performance. Shortening Time-to-Competency brings measurable benefits: faster onboarding, less disruption in operations, and reduced costs of adaptation. 3.4 Sentiment Analysis – The Learner’s Voice as a Data Source With natural language processing (NLP), organizations can analyze comments, surveys, ratings, and even communication patterns to understand learners’ satisfaction and engagement levels. Traditional training relied on simple surveys like “Rate the course from 1 to 5.” Sentiment analysis goes much further – capturing nuances and distinguishing between polite ratings and genuine enthusiasm (or frustration). AI can, for example, reveal that employees respond positively to interactive modules and practical exercises but react negatively to long, monotonous video content. This measurement is extremely valuable, not only for improving training programs but also for linking learner satisfaction to broader metrics – such as talent retention and organizational culture. In effect, sentiment analysis provides a window into how training influences workplace climate, employee motivation, and the team’s readiness for future growth. 3.5 Innovation Readiness Score – Preparing for Innovation This metric answers a crucial question: are our employees ready to adopt and co-create innovation, or do they still need additional support? AI evaluates not only e-learning course data but also the pace of acquiring new skills, engagement in project tasks, and openness to new technologies. This helps determine the extent to which a team is prepared for the implementation of AI tools, new sales processes, or digital production solutions. The metric is highly practical because it reflects not only current skill levels but also the organization’s innovation potential. A high score signals that the company can confidently invest in new technologies or business models, while a low score highlights the need to strengthen training programs and foster a culture that embraces change. 4. From AI Data to Strategic Insights for the Board 4.1 Reports that Speak the Language of Business Data gathered from AI tools only gains real value when translated into insights that executives can act upon. Raw statistics – such as logins, course completions, or average learning time – don’t reveal whether training investments truly support business growth. Only well-prepared reports allow CLOs to highlight clear connections: faster onboarding of new hires, reduced operational costs, or increased sales following product training. In this way, training becomes part of strategic discussions, not just an operational activity of the L&D department, and executives receive concrete proof that people development drives both financial results and competitiveness. In practice, one of the most effective ways to report training outcomes to the board is through interactive dashboards. With tools like Power BI, organizations can build visualizations that clearly show how learning initiatives impact business performance. For example, a dashboard might display course completion rates alongside sales results, making it easy to see how product training improves sales team effectiveness. Another visualization could compare the number of errors or operational downtimes before and after training, providing evidence of cost savings. Equally valuable for executives is tracking Time-to-Competency – the average time it takes new employees to reach full productivity. For companies focused on innovation, a dedicated panel displaying the Innovation Readiness Score adds another dimension, showing the organization’s readiness to adopt new technologies and business models. Dashboards like these help structure complex data and enable more informed business decisions based on facts, figures, and forecasts. 4.2 Predictive Analytics as a Driver of Smarter Planning Predictive analytics is more than just a buzzword – it’s a powerful tool that is changing the way business decisions are made. Its strength lies in the ability to forecast the future based on data, rather than only analyzing the past. In the context of e-learning, this means CLOs and L&D teams don’t have to wait until skill gaps emerge – they can proactively design development programs in the areas where demand will grow in one, two, or three years. For example, if a company is introducing process automation in customer service, predictive analytics will show that the demand will shift away from routine operational skills – soon to be handled by AI – and toward soft skills such as problem-solving, abstract thinking, relationship building, and empathy. These are precisely the qualities that artificial intelligence has yet to master, and they are becoming increasingly valuable in modern organizations. As AI automates repetitive tasks, the focus of human work moves to more complex and creative areas. For employees, this means developing new capabilities – analyzing data instead of manually entering it, designing solutions rather than just following instructions, or engaging in conversations with clients in challenging, emotional situations where empathy and emotional intelligence are crucial. For CLOs, this represents both a challenge and an opportunity: well-designed training programs can prepare the organization for a future where competitive advantage is defined not by the quantity of work done, but by its quality and adaptability. In other words, predictive analytics powered by AI helps not only forecast which skills will be needed in the future but also build development programs around the capabilities that AI will not replace anytime soon – abstract thinking, creativity, empathy, and decision-making under uncertainty. In the e-learning context, predictive analytics provides CLOs and L&D teams with the ability to: Forecast skill demand – anticipate which competencies will be critical in 2–3 years due to expansion plans or the introduction of new technologies. Identify skill gaps before they become problems – AI can highlight which departments will need additional training to meet future challenges. Predict the business impact of training – estimate outcomes such as increased sales after launching a targeted development program. Optimize training investments – identify which programs deliver the highest ROI and which have only a marginal impact. 5. AI-Based Measurement Challenges – and How to Overcome Them 5.1 System integration One of the biggest challenges in implementing AI-driven solutions is the lack of integration between systems. The key to overcoming this lies in having a technology partner who not only understands integration but also the business context and the specifics of different organizational areas. This is exactly how TTMS operates – combining expertise in AI implementation with practical knowledge in HR, sales, and e-learning. Our developers work hand in hand with domain experts, ensuring that solutions address real business needs. This approach is particularly valuable for companies without specialized in-house teams. By partnering with TTMS, they gain immediate access to proven practices from large organizations, regardless of their own resource scale. 5.2 Data security and compliance Adhering to data security standards and ensuring ethical data use are fundamental in today’s unstable geopolitical climate. Cyberattacks are increasing every year, and data leaks are no longer a movie plotline but a real and serious threat to businesses. That’s why it is essential to implement modern cybersecurity measures and ensure full compliance with regulations such as the AI Act and ISO standards. Collaborating with a partner who can embed cybersecurity into every stage of software implementation is the safest path forward. 5.3 New analytical competencies for L&D teams To fully unlock the potential of AI, L&D teams need to strengthen their ability to interpret data and apply it in a business context. Modern e-learning programs collect and integrate large volumes of information from LMS platforms, which requires developing new analytical skills, including: Data literacy – the ability to read, interpret, and draw conclusions from reports and dashboards. Learning analytics – identifying participation trends, measuring engagement, and evaluating training effectiveness. Data storytelling – translating raw numbers into clear narratives for managers and executives (e.g., ROI of training, impact on business KPIs). Predictive analytics – using AI models and statistics to forecast training needs, knowledge gaps, and future competency demands. Data governance and compliance – understanding legal frameworks (e.g., GDPR, AI Act) and applying ethical, secure data management practices. Connecting HR and business data – integrating learning metrics with workforce turnover, performance, and team outcomes. Experimentation and A/B testing – designing and analyzing training format experiments to optimize L&D programs. Fortunately, many of these areas can already be supported by AI-powered tools. AI can: Automate data analysis – process large data sets quickly and uncover hidden patterns. Generate predictions – anticipate which employees may struggle to complete courses or which competencies will be in shortage in the future. Deliver actionable insights – e.g., “sales teams learn faster with video content than with e-books.” Personalize learning experiences – adapt training to individual learner profiles and preferences. Support data storytelling – automatically create summaries that make training results more accessible to decision-makers. 6. Strategic Recommendations for CLOs and Executive Boards 6.1 Designing AI-Ready KPIs Designing KPIs with AI-powered tools in mind should begin as early as the program development stage. Clearly defining business goals and performance indicators allows organizations to measure training effectiveness with precision later on. Modern e-learning platforms provide data that significantly enrich analysis – from tracking participant engagement in detail (e.g., where learners pause during video modules or which quizzes they find most challenging) to assessing learning speed and preferred learning styles (visual vs. text-based), as well as measuring knowledge transfer into practice by integrating training outcomes with corporate systems. As a result, KPIs can be designed to capture real training effectiveness, not just user activity. Examples include developmental indicators such as tracking skill progression over time or predictive KPIs that use AI algorithms to forecast whether an employee will reach the required knowledge level within a defined timeframe. When building KPIs, it is important to avoid focusing solely on quantitative data – for instance, the number of LMS logins does not reflect training effectiveness. A dynamic approach is essential: KPIs should be reviewed and adjusted during training programs. Equally important is combining data from multiple systems – LMS, CRM, and HRIS – to provide a holistic view of training impact on the organization. In practice, AI-powered e-learning KPIs can be divided into several categories: Cost-efficiency KPIs – measuring training ROI, e.g., cost per employee vs. performance improvement or reduced onboarding time. Adaptive KPIs – focusing on organizational readiness for market changes, such as reskilling and upskilling speed or time to adopt new tools and processes. Business KPIs – directly tied to company results, such as increased sales after training or improved customer service quality. Strategic KPIs – measuring competitive positioning, e.g., response time to industry shifts or the percentage of critical competencies covered by AI-driven learning paths. 6.2 Quarterly Reporting Cycles Quarterly reporting provides the optimal balance between strategic and practical perspectives for executive boards. A three-month cycle is long enough to capture the real effects of both training and business initiatives, yet short enough to allow for timely adjustments when results diverge from the intended strategy. Quarterly reports avoid the information overload often caused by monthly reporting, focusing instead on what matters most to executives: trends, patterns, and the impact of initiatives on business goals. This reporting rhythm also aligns naturally with corporate budgeting and financial cycles, making it easier to compare learning KPIs with operational and financial outcomes. In the training context, quarterly summaries offer an additional advantage – they allow enough time to gather reliable data, observe how knowledge is applied in practice, and analyze results through AI-powered tools. Regular quarterly reporting also strengthens organizational accountability and transparency by creating a consistent rhythm in which every initiative is not only launched but also evaluated and continuously improved based on actionable insights. 7. Conclusion – AI as a Lever for Strategic Growth Artificial intelligence not only streamlines the course creation process but also empowers Chief Learning Officers (CLOs) to report training effectiveness in a way that is accurate, predictive, and aligned with executive expectations. Transition Technologies MS (TTMS) supports learning leaders in measuring the impact of development initiatives by delivering solutions that combine data analytics, AI tools, and seamless integration with enterprise systems. With deep expertise in designing and implementing digital platforms, TTMS enables organizations not just to capture learner activity but to translate it into concrete business metrics. By integrating e-learning platforms with CRM, HRIS, and ERP systems, TTMS helps link training outcomes directly to measurable results such as revenue growth, improved customer service quality, or faster onboarding of new employees. The company also provides support in creating dedicated dashboards and quarterly reports that clearly present the effectiveness of L&D initiatives and the ROI of workforce development to executive boards. As a result, e-learning teams gain tools that not only simplify performance monitoring but also demonstrate the strategic value of training for the entire organization. And if managing e-learning courses and organizational knowledge feels like a challenge, make sure to visit our page – LMS Administration Services | TTMS. Explore our dedicated tool for rapid online course creation – AI4E-learning. Check out our full range of AI solutions for business.

ChatGPT 5 Modes: Auto, Fast (Instant), Thinking, Pro – Which Mode to Use and Why?

Unlocking ChatGPT 5 Modes: How Auto, Fast, Thinking, and Pro Really Work Most of us use ChatGPT on autopilot – we type a question and wait for the AI to answer, without ever wondering if there are different modes to choose from. Yet these modes do exist, though they’re a bit tucked away in the interface and less visible than they once were. You can find them in the model picker, usually under options like Auto, Fast, Thinking, or Pro, and they each change how the AI works. But is it really worth exploring them? And how do they impact speed, accuracy, and even cost? That’s exactly what we’ll uncover in this article. ChatGPT 5 introduces several modes of operation – Auto, Fast (sometimes called Instant), Thinking, and Pro – as well as access to older model versions. If you’re wondering what each of these modes does, when to switch between them (if at all), and how they differ in speed, quality, and cost, this comprehensive guide will clarify everything. We’ll also discuss which modes are best suited for everyday users versus business or professional users. Each mode in GPT-5 is designed for a different balance of speed and reasoning depth. Below, we answer the key questions about these modes in an SEO-friendly Q&A format, so you can quickly find the information you need. 1. What are the new modes in ChatGPT 5 and why do they exist? ChatGPT 5 (GPT-5) has transformed the old model selection into a unified system with four mode options: Auto, Fast, Thinking, and Pro. These modes exist to let the AI adjust how much “thinking” (computational effort and reasoning time) it should use for a given query: Auto Mode: This is the default unified mode. GPT-5 automatically decides whether to respond quickly or engage deeper reasoning based on your question’s complexity. Fast Mode: A mode for instant answers – GPT-5 responds very quickly with minimal extra reasoning. (This is essentially GPT-5’s standard mode for everyday queries.) Thinking Mode: A deep reasoning mode – GPT-5 will take longer to formulate an answer, performing more analysis and step-by-step reasoning for complex tasks. Pro Mode: A “research-grade” mode – the most advanced and thorough option. GPT-5 will use maximum computing power (even running parts of the task in parallel) to produce the most accurate and detailed answer possible. These modes were introduced because GPT-5 is capable of dynamically adjusting its reasoning. In previous versions like GPT-4, users had to manually pick between different models (e.g. standard vs. advanced reasoning models). Now GPT-5 consolidates that into one system with modes, making it easier to get the right balance of speed vs. depth without constantly switching models. The Auto mode in particular means most users can just ask questions normally and let ChatGPT decide if a quick answer will do or if it should “think longer” for a better result. 2. How does ChatGPT 5’s Auto mode work? The Auto mode is the intelligent default that makes GPT-5 decide on the fly how much reasoning is needed. When you have GPT-5 set to Auto, it will typically answer straightforward questions using the Fast approach for speed. If you ask a more complex or multi-step question, the system can automatically invoke the Thinking mode behind the scenes to give a more carefully reasoned answer. In practice, Auto mode means you don’t have to manually select a model for most situations. GPT-5’s internal “router” analyzes your prompt and chooses the appropriate strategy: For a simple prompt (like “Summarize this paragraph” or “What’s the capital of France?”), GPT-5 will likely respond almost immediately (using the Fast response mode). For a complex prompt (like “Analyze this financial report and give insights” or a tricky coding/debugging question), GPT-5 may “think” for a bit longer before answering. You might notice a brief indication that it’s reasoning more deeply. This is GPT-5 automatically switching into its Thinking mode to ensure it works through the problem. Auto mode is ideal for most users because it delivers the best of both worlds: quick answers when possible, and more thorough answers when necessary. You can always override it by manually picking Fast or Thinking, but Auto means less guesswork – the AI itself decides how long to think. If you ever explicitly want it to take its time, you can even tell GPT-5 in your prompt to “think carefully about this,” which encourages the system to engage deeper reasoning. Tip: When GPT-5 Auto decides to think longer, the interface will indicate it. You usually have an option to “Get a quick answer” if you don’t want to wait for the full reasoning. This allows you to interrupt the deep thinking and force a faster (but potentially less detailed) reply, giving you control even in Auto mode. 3. What is the Fast (Instant) mode in GPT-5 used for? The Fast mode (labeled “Fast – instant answers” in the ChatGPT model picker) is designed for speedy responses. In Fast mode, GPT-5 will generate an answer as quickly as possible without dedicating extra time to extensive reasoning. Essentially, this is GPT-5’s standard mode for everyday tasks that don’t require heavy analysis. When to use Fast mode: Simple or routine queries: If you’re asking something straightforward (factual questions, brief explanations, casual conversation), Fast mode will give you an answer within a few seconds. Brainstorming and creative prompts: Need a quick list of ideas or a first draft of a tweet/blog? Fast mode is usually sufficient and time-efficient. General coding help: For small coding questions or debugging minor errors, Fast mode can provide answers quickly. GPT-5’s base capability is already high, so for many coding tasks you might not need the extra reasoning. Everyday business tasks: Writing an email, summarizing a document, responding to a common customer query – Fast mode handles these with speed and improved accuracy (GPT-5 is noted to have fewer random mistakes than GPT-4 did, even in its fast responses). In Fast mode, GPT-5 is still quite powerful and more reliable than older GPT-4 models for common tasks. It’s also cost-efficient (lower compute usage means fewer tokens consumed, which matters if you have usage limits or are paying per token via the API). The trade-off is that it might not catch extremely subtle details or perform multi-step reasoning as well as the Thinking mode would. However, for the vast majority of prompts that are not highly complex, Fast mode’s answers are both quick and accurate. This is why Fast (or “Standard”) mode serves as the backbone for day-to-day interactions with ChatGPT 5. 4. When should you use the GPT-5 Thinking mode? GPT-5’s Thinking mode is meant for situations where you need extra accuracy, depth, or complex problem-solving. When you manually switch to Thinking mode, ChatGPT will deliberately take more time (and tokens) to work through your query step by step, almost like an expert “thinking out loud” internally before giving you a result. You should use Thinking mode for tasks where a quick off-the-cuff answer might not be good enough. Use GPT-5 Thinking mode when: The problem is complex or multi-step: If you ask a tough math word problem, a complex programming challenge, or an analytical question (e.g. “What are the implications of this scientific study’s results?”), Thinking mode will yield a more structured and correct solution. It’s designed to handle advanced reasoning tasks like these with higher accuracy. Precision matters: For example, drafting a legal clause, analyzing financial data for trends, or writing a medical report summary. In such cases, mistakes can be costly, so you want the AI to be as careful as possible. Thinking mode reduces the chance of errors and hallucinations even further by allocating more computation to verify facts and logic. Technical or detailed writing: If you need longer, well-thought-out content – such as an in-depth explanation of a concept, thorough documentation, or a step-by-step guide – the Thinking mode can produce a more comprehensive answer. It’s like giving the model extra time to gather its thoughts and double-check itself before responding. Coding complex projects: For debugging a large codebase, solving a tricky algorithm, or generating non-trivial code (like a full module or a complex function), Thinking mode performs significantly better. It’s been observed to greatly improve coding accuracy and can handle more elaborate tasks like multi-language code coordination or intricate logic that Fast mode might get wrong. Trade-offs: In Thinking mode, responses are slower. You might wait somewhere on the order of 10-30 seconds (depending on the complexity of your request) for an answer, instead of the usual 2-5 seconds in Fast mode. It also uses more tokens and computing resources, meaning it’s more expensive to run. If you’re on ChatGPT Plus, there are even usage limits for how many Thinking-mode messages you can send per week (because each such response is heavy on the system). However, those downsides are often justified when the question is important enough. The mode can deliver dramatically improved accuracy – for example, internal OpenAI benchmarks showed huge jumps in performance (several-fold improvements on certain expert tasks) when GPT-5 is allowed to think longer. In summary, switch to Thinking mode for high-stakes or highly complex prompts where you want the best possible answer and you’re willing to wait a bit longer for it. For everyday quick queries, it’s not necessary – the default fast responses will do. Many Plus users might use Thinking mode sparingly for those tough questions, while relying on Auto/Fast for everything else. 5. What does GPT-5 Pro mode offer, and who really needs it? GPT-5 Pro mode is the most advanced and resource-intensive mode available in ChatGPT 5. It’s often described as “research-grade intelligence.” This mode is only available to users on the highest-tier plans (ChatGPT Pro or ChatGPT Business plans) and is intended for enterprise-level or critical tasks that demand maximum accuracy and thoroughness. Here’s what Pro mode offers and who benefits from it: Maximum accuracy through parallel reasoning: GPT-5 Pro doesn’t just think longer; it also can think more broadly. Under the hood, Pro mode can run multiple reasoning threads in parallel (imagine consulting an entire panel of AI experts simultaneously) and then synthesize the best answer. This leads to even more refined responses with fewer mistakes. In testing, GPT-5 Pro set new records on difficult academic and professional benchmarks, outperforming the standard Thinking mode in many cases. Use cases for Pro: This mode shines in high-stakes, mission-critical scenarios: Scientific research and healthcare: e.g. analyzing complex biomedical data, discovering drug candidates, or interpreting medical imaging results (where absolute precision is vital). Finance and legal: e.g. risk modeling, auditing complex financial portfolios, generating or reviewing legal contracts with extreme accuracy – tasks where an error could cost a lot of money or have legal implications. Large-scale enterprise analytics: e.g. processing lengthy confidential reports, performing deep market analysis, or powering a virtual assistant that needs to reliably handle very complex queries from users. AI development: If you’re a developer building AI-driven applications (like agents that plan and act autonomously), GPT-5 Pro provides the most consistent reasoning depth and reliability for those advanced applications. Who needs Pro: Generally, businesses and professionals with intensive needs. For a casual user or even most power-users, the standard GPT-5 (and occasional Thinking mode) is usually enough. Pro mode is targeted at enterprise users, research institutions, or AI enthusiasts who require that extra edge in performance – and are willing to pay a premium for it. Drawbacks of Pro mode: The word “Pro” implies it’s not for everyone. First, it’s expensive – both in terms of subscription cost and computational cost. As of 2025, ChatGPT Pro subscriptions run at a much higher price (around $200 per month) compared to the standard Plus plan, and that buys you the privilege of using this powerful mode without the normal usage caps. Also, each Pro mode response consumes a lot of compute (and tokens), so from an API or cost perspective it’s the priciest option (roughly double the token cost of Thinking mode, and ~10 times the cost of a quick response). Second, speed: Pro mode is the slowest to respond. Because it’s doing so much work under the hood, you might wait 20-40 seconds or more for a single answer. In interactive chat, that can feel lengthy. Lastly, Pro mode currently has a couple of limitations in features (for instance, certain ChatGPT tools like image generation or the canvas feature may not be enabled with GPT-5 Pro, due to its specialized nature). Bottom line: GPT-5 Pro is a potent tool if you truly need the highest level of AI reasoning and are in an environment where accuracy outweighs all other concerns (and cost is justified by the value of the results). It’s likely overkill for everyday needs. Most users, even many developers, won’t need Pro mode regularly. It’s more for organizations or individuals tackling problems where that extra 5-10% improvement in quality is worth the extra expense and time. 6. How do the modes differ in speed and answer quality? Each mode in ChatGPT 5 strikes a different balance between speed and the depth/quality of the answer: Fast mode is the quickest: It typically responds within a couple of seconds for a prompt. The answers are high-quality for normal questions (much better than older GPT-3.5 or even GPT-4 in many cases), but Fast mode will not always catch very subtle nuances or deeply reason through complicated instructions. Think of Fast mode answers as “good enough and very fast” for general purposes. Thinking mode is slower but more thorough: When GPT-5 Thinking is engaged, response times slow down (often 10-30 seconds depending on complexity). The quality of the answers, however, is more robust and detailed. GPT-5 Thinking will handle multi-step reasoning tasks significantly better. For example, if a Fast mode answer might occasionally miscalculate or simplify a complex answer, the Thinking mode is far more likely to get it correct and provide justification or step-by-step details in its response. In terms of quality, you can expect far fewer factual errors or “hallucinations” in Thinking mode responses, since the AI took extra time to verify and cross-check its answer internally. Pro mode is the most meticulous (and slowest): GPT-5 Pro will take even more time than Thinking mode for a response, as it uses maximum compute. It might explore several potential solutions internally before finalizing an answer, which maximizes the quality and correctness. The answers from Pro mode are usually the most detailed, well-structured, and accurate. You might notice they contain deeper insights or handle edge cases that the other modes might miss. The trade-off is that Pro mode responses can easily take half a minute or more, and you wouldn’t use it unless you truly need that level of depth. In summary: Speed: Fast > Thinking > Pro (Fast is fastest, Pro is slowest). Answer depth/quality: Pro > Thinking > Fast (Pro gives the most advanced answers, Fast gives concise answers). Everyday effectiveness: For most simple queries, all modes will do fine; you won’t necessarily notice a quality difference on an easy question. The differences become apparent on challenging tasks. Fast mode might give a decent but not perfect answer, Thinking mode will give a correct and well-explained answer, and Pro mode will give an exceptionally detailed answer with minimal chance of error. It’s also worth noting that GPT-5’s base quality (even in Fast mode) is a leap over previous generations. Many users find that even quick answers from GPT-5 are more accurate and nuanced than what GPT-4 produced. So speed doesn’t degrade quality as much as you might think for typical questions – it mainly matters when the question is particularly difficult. 7. Do different GPT-5 modes use more tokens or cost more to use? Yes, the modes do differ in terms of token usage and cost, though it might not be obvious at first glance. The general rule is: the more thinking a mode does, the more tokens and cost it will incur. Here’s how it breaks down: Fast mode (Standard GPT-5): This mode is the most token-efficient. It generates answers quickly without a lot of internal computation, so it tends to use only the tokens needed for the answer itself. If you’re using the ChatGPT subscription, there’s no direct “cost” per message beyond your subscription, but Fast mode also consumes your message quota more slowly (because each answer is concise and doesn’t involve hidden extra tokens). If you were using the API, Fast mode’s underlying model has the lowest price per 1000 tokens (OpenAI has indicated something on the order of $0.002 per 1K tokens for GPT-5 Standard, which is even a bit cheaper than GPT-4 was). Thinking mode: This mode is resource-intensive, meaning it will use more tokens internally to reason through the problem. When GPT-5 “thinks,” it might be effectively doing multi-step reasoning which uses up extra tokens behind the scenes (these don’t all show up in the answer, but they count towards computation). The cost per token for this mode is higher (roughly 5× the cost of standard mode on the API side). In ChatGPT Plus, using Thinking mode too often is limited – for instance, Plus users can only initiate a certain number of Thinking-mode messages per week (because each one is expensive to run on the server). So effectively, each Thinking response “costs” much more in terms of your usage allowance. In practical terms, expect that a deep Thinking answer might consume significantly more of your message limits than a quick answer would. Pro mode: Pro mode is the most expensive per use. It not only carries a higher token cost (approximately double that of Thinking mode per token, or about 10× the base cost of Fast mode), but it often produces longer answers and does a lot of work internally. This is why Pro mode is reserved for the highest-paying tier – it would be infeasible to offer unlimited Pro responses at a low price point. If you have a Pro subscription or enterprise access, you effectively have no hard limit on GPT-5 usage, but your cost is the hefty monthly fee instead. If you were using an API equivalent, Pro mode would be quite costly per 1000 tokens. The benefit is that because Pro is so accurate, in theory you might save money by not having to repeat queries or fix mistakes – but you’d only worry about that if you’re using GPT-5 for high-value tasks. In terms of token usage in answers, deeper modes often yield longer, more detailed replies (especially if the task warrants it). That means more output tokens. Also, they reduce the chance you’ll need to ask follow-up questions or clarifications (which themselves would consume more tokens), which is another way they can be “cost-effective” despite higher per-message cost. But if you’re on the free plan or Plus, the main thing to know is that the heavy modes will hit your usage limits faster: Free users only get a very limited number of GPT-5 messages and just 1 Thinking-mode use per day on free tier. This is because Thinking uses a lot of resources. Plus users get more (currently around 160 messages per 3 hours for GPT-5, and up to 3,000 Thinking messages per week maximum). If a Plus user sticks to Fast/Auto primarily, they can get a lot of answers within those caps; if they use Thinking for every query, they’ll hit weekly limits much sooner. Pro/Business users have “unlimited” use, but that comes at the high subscription cost. So, in conclusion, each mode does “cost” differently: Fast mode is cheapest and most token-efficient, Thinking mode costs several times more per question, and Pro is premium priced. If you’re concerned about token usage (say, for API billing or hitting message caps), use the heavier modes only when needed. Otherwise, the Auto mode will handle it for you, using extra tokens only when it determines the value of a better answer is worth the cost. 8. Should you manually switch modes or let ChatGPT decide automatically? For most users, letting GPT-5 Auto mode handle it is the simplest and often the best approach. The auto-switching system was built to spare you from micromanaging the model’s behavior. By default, GPT-5 will not waste time “overthinking” an easy question, and similarly it won’t give you a shallow answer to a really complex prompt – it will adjust as needed. That said, there are scenarios where manually choosing a mode makes sense: When you know you need a deep analysis: If you’re about to ask something very complex and you want to ensure the highest accuracy (and you have access to Thinking mode), you might manually switch to Thinking mode before asking. This guarantees GPT-5 spends maximum effort, rather than waiting to see if it might decide to do so. For example, a data scientist preparing a detailed report might directly use Thinking mode for each query to get thorough answers. When you’re in a hurry for a simple answer: If GPT-5 (Auto) starts “Thinking…” but you actually just want a quick answer or a brainstorm, you can click “Get a quick answer” or simply switch to Fast mode for that question. Sometimes the AI might be overly cautious and begin deep reasoning when you didn’t need it – in those cases, forcing Fast mode will save you time. When conserving usage: If you’re on a limited plan and near your cap, you might stick to Fast mode to maximize the number of questions you can ask, since Thinking mode would burn through your quota faster. Conversely, if you have plenty of headroom and need a top-notch answer, you can use Thinking mode more liberally. Using Pro mode deliberately: If you’re one of the users with Pro access, you’ll likely switch to Pro mode only for the most critical queries. It doesn’t make sense to use Pro for every single chat message due to the slower speed – better to reserve it for when you have a genuinely high-value question that justifies it. In short, Auto mode is usually sufficient and is the recommended default for both casual and many professional interactions. You only need to manually switch modes in special cases: either to force extra rigor or to force extra speed. Think of manual mode switching as an override for the AI’s decisions. The system’s pretty good at picking the right mode on its own, but you remain in control if you disagree with its choice. 9. Are older models like GPT-4 still available in ChatGPT 5? Yes, older models are still accessible in the ChatGPT interface under a “Legacy models” section – but you may not need to use them often. With the rollout of GPT-5: GPT-4 (often labeled GPT-4o or other variants) is available to paid users as a legacy option. If you have a Plus, Business, or Pro account, you can find GPT-4 in the model picker under legacy models. This is mainly provided for compatibility or specific use cases where someone might want to compare answers or use an older model on prior conversations. Additionally, OpenAI has allowed access to some intermediate models (like GPT-4.1, GPT-4.5, or older 3.5 models often labeled as o3, o4-mini, etc.) for certain subscription tiers, but these are hidden unless you enable “Show additional models” in your settings. Plus users, for example, can see a few of those, while Pro users can see slightly more (like GPT-4.5). By default, if you don’t specifically switch to an older model, all your chats will use GPT-5 (Auto mode). And if you open an old chat that was originally with GPT-4, the system may automatically load it with the GPT-5 equivalent to continue the conversation. So OpenAI has tried to transition seamlessly such that GPT-5 handles most things going forward. Do you need the older models? For the majority of cases, no. GPT-5’s Standard/Fast mode is intended to replace GPT-4 for everyday use, and it’s better at almost everything. There might be a rare instance where an older model had a particular style or a specific capability you want to replicate – then you could switch to it. But generally, GPT-5’s intelligence and the Auto mode’s adaptability mean you won’t often have to manually use GPT-4 or others. In fact, some of the older GPT-4 variants might be slower or have lower context length compared to GPT-5, so unless you have a compatibility reason, it’s best to let GPT-5 take over. One thing to note: if you exceed certain usage limits with GPT-5 (especially on the free tier), ChatGPT will automatically fall back to a “GPT-5 mini” or even GPT-3.5 temporarily until your limit resets. This is done behind the scenes to ensure free users always get some service. In the UI, it might not clearly say it switched, but the quality might differ. Paid users won’t experience this fallback except when they intentionally use legacy models. In summary, older models are there if you need them, but GPT-5’s modes are now the main focus and cover almost all use cases that older models did – typically with better results. 10. Which GPT-5 mode is best for business users versus general users? The choice of mode can depend on who you are and what you’re trying to accomplish. Let’s break it down for individual (general) users and business users or professionals: General Users / Individuals: If you’re an everyday user (for personal projects, learning, or casual use), you’ll likely be perfectly satisfied with the default GPT-5 Auto mode, using Fast responses most of the time and occasionally letting it dip into Thinking mode when you ask a harder question. A ChatGPT Plus subscription might be worthwhile if you use it very frequently, since it gives you more GPT-5 usage and access to manual Thinking mode when you need it. However, you probably do not need GPT-5 Pro mode. The Pro tier is expensive and geared toward unlimited heavy use, which average users don’t usually require. In short, general users should stick with the standard GPT-5 (Auto/Fast) for speed and ease, and use Thinking mode for those few cases where you want a deep dive answer. This will keep your costs low (or your Plus subscription fully sufficient) while still giving you excellent results. Business Users / Professionals: For business purposes, the stakes and scale often increase. If you run a business integrating ChatGPT, or you’re using it in a professional setting (for instance, to assist with your work in finance, law, engineering, customer service, etc.), you need to consider accuracy and reliability carefully: Small Business or Plus for Professionals: Many professional users will find that a Plus account with GPT-5’s Thinking mode available is enough. You can manually invoke Thinking mode for those complex tasks like data analysis or report generation, ensuring high quality when needed, while keeping most interactions quick and efficient in standard mode. This approach is cost-effective and likely sufficient unless your domain is extremely sensitive. Enterprises or High-Stakes Use: If you’re an enterprise user or your work involves critical decision-making (say, a medical AI tool, or a financial firm doing big analyses), GPT-5 Pro might be worth the investment. Businesses benefit from Pro mode’s extra accuracy and from the unlimited usage it offers. There’s no worry about hitting message caps, which is important if you have many employees or customers interacting with the system. Moreover, the larger context window on the Pro plan (GPT-5 Pro supports dramatically bigger inputs, up to 128K tokens context for Fast and ~196K for Thinking, according to OpenAI) allows analysis of very large documents or datasets in one go – a huge plus for enterprise use cases. Cost-Benefit: Businesses should weigh the cost of the Pro subscription (or Business plan) against the value of the improved outputs. If a single mistake avoided by Pro mode could save your company thousands of dollars, then using Pro mode is justified. On the other hand, if your use of AI is more routine (like answering common customer questions or writing marketing content), the standard GPT-5 might already be more than capable, and a Plus plan at a fraction of the cost will do the job. In summary, for general users: stick with Auto/Fast, use Thinking sparingly, and you likely don’t need Pro. For business users: start with GPT-5’s standard and Thinking modes; if you find their limits (in accuracy or usage caps) hindering your mission-critical tasks, then consider upgrading to Pro mode. GPT-5 Pro is predominantly aimed at businesses, research labs, and power users who truly need that unparalleled performance and can justify the expense. Everyone else will find GPT-5’s default modes already a significant upgrade that addresses both casual and moderately complex needs effectively. 11. Final Thoughts: Getting the Most Out of ChatGPT 5’s Modes ChatGPT 5’s new modes – Auto, Fast, Thinking, and Pro – give you a flexible toolkit to get the exact type of answer you need, when you need it. For most people, letting Auto mode handle things is easiest, ensuring you get fast responses for simple questions and deeper analysis for tough ones without manual effort. The system is designed to optimize speed and intelligence automatically. However, it’s great that you have the freedom to choose: if you ever feel a response needs to be more immediate or more thorough, you can toggle to the corresponding mode. Keep an eye on how each mode performs for your use case: Use Fast mode for quick, on-the-fly Q&A and save precious time. Invoke Thinking mode for those problems where you’d rather wait a few extra seconds and be confident in the answer’s accuracy and detail. Reserve Pro mode for the rare instances where only the best will do (and if your resources allow for it). Remember, all GPT-5 modes leverage the same underlying advancements that make this model more capable than its predecessors: improved factual accuracy, better following of instructions, and more context capacity. Whether you’re a curious individual user or a business deploying AI at scale, understanding these modes will help you harness GPT-5 effectively while managing speed, quality, and cost according to your needs. Happy chatting with GPT-5! 12. Want More Than Chat Modes? Discover Bespoke AI Services from TTMS ChatGPT is powerful, but sometimes you need more than a mode toggle – you need custom AI solutions built for your business. That’s where TTMS comes in. We offer tailored services that go beyond what any off-the-shelf mode can do: AI Solutions for Business – end-to-end AI integration to automate workflows and unlock operational efficiency. (See https://ttms.com/ai-solutions-for-business/) Anti-Money Laundering Software Solutions – AI-powered AML systems that help meet regulatory compliance with precision and speed. (See https://ttms.com/anti-money-laundry-software-solutions/) AI4Legal – legal-tech tools using AI to support contract drafting, review, and risk analysis. (See https://ttms.com/ai4legal/) AI Document Analysis Tool – extract, validate, and summarize information from documents automatically and reliably. (See https://ttms.com/ai-document-analysis-tool/) AI-E-Learning Authoring Tool – build intelligent training and learning modules that adapt and scale. (See https://ttms.com/ai-e-learning-authoring-tool/) AI-Based Knowledge Management System – structure and retrieve organizational knowledge in smarter, faster ways. (See https://ttms.com/ai-based-knowledge-management-system/) AI Content Localization Services – localize content across languages and cultures, using AI to maintain nuance and consistency. (See https://ttms.com/ai-content-localization-services/) If your goals include saving time, reducing costs, and having AI work for you rather than just alongside you, let’s talk. TTMS crafts AI tools not just for “general mode” but for your exact use case – so you get speed when you need speed, and depth when you need rigor. Does switching between ChatGPT modes change the creativity of answers? Yes, the choice of mode can influence how creative or structured the output feels. In Fast mode, responses are more direct and efficient, which is useful for brainstorming short lists of ideas or generating quick drafts. Thinking mode, on the other hand, allows ChatGPT to explore more options and refine its reasoning, which often leads to more original or nuanced results in storytelling, marketing, or creative writing. Pro mode takes this even further, producing well-polished, highly detailed content, but it comes with longer wait times and higher costs. Which ChatGPT mode is most reliable for coding? For simple coding tasks such as generating small functions, fixing syntax errors, or writing snippets, Fast mode usually performs well and delivers answers quickly. However, when working on complex projects that involve debugging large codebases, designing algorithms, or ensuring higher reliability, Thinking mode is a better choice. Pro mode is reserved for scenarios where absolute precision matters, such as enterprise-level software or mission-critical applications. In short: use Fast for convenience, Thinking for accuracy, and Pro only when failure isn’t an option. Do ChatGPT modes affect memory or context length? The modes themselves don’t directly change the memory of your conversation or the context size. All GPT-5 modes share the same underlying architecture, but the subscription tier determines the maximum context length available. For example, Pro plans unlock significantly larger context windows, which makes it possible to analyze or generate content across hundreds of pages of text. So while Fast, Thinking, and Pro modes behave differently in terms of reasoning depth, the real impact on memory and context length comes from the plan you are using rather than the mode itself. Can free users access all ChatGPT modes? No, free users have very limited access. Typically, the free tier allows only Fast (Auto) mode, with an occasional option to test Thinking mode under strict daily limits. Access to Pro mode is reserved exclusively for paid subscribers on the highest tier. Plus subscribers can use Auto and Thinking regularly, but only Business or Pro users have unrestricted access to the full range of modes. This limitation is due to the high computational costs associated with Thinking and Pro modes. Is there a risk in always using Pro mode? The main “risk” of using Pro mode is not about accuracy, but about practicality. Pro mode delivers the most thorough and precise results, but it is also the slowest and the most expensive option. If you rely on it for every single question, you may find that you’re spending more time and resources than necessary for simple tasks that Fast or Thinking could easily handle. For most users, Pro should be reserved for the toughest or most critical challenges. Otherwise, it’s more efficient to let Auto mode decide or to use Fast for everyday queries. Does ChatGPT switch modes automatically, or do I need to do it manually? ChatGPT 5 offers both options. In Auto mode, the system decides automatically whether a quick response is enough or if it should engage in deeper reasoning. That means you don’t need to worry about switching manually – the AI adjusts to the complexity of your query on its own. However, if you prefer full control, you can always manually select Fast, Thinking, or Pro in the model picker. In practice, Auto is recommended for everyday use, while manual switching makes sense if you explicitly want either maximum speed or maximum accuracy.

Top 10 Polish IT Providers for the Pharma Sector in 2025

Top 10 IT Companies in Poland Serving the Pharmaceutical Industry (2025 Ranking) The pharmaceutical industry relies on advanced IT solutions – from clinical data management and AI-driven drug discovery to secure patient portals and regulatory compliance systems. Poland’s tech sector hosts a range of providers experienced in delivering these solutions for pharma companies. Below is a ranking of the Top 10 Polish IT service providers for the pharma sector in 2025. These companies combine technical excellence with domain knowledge in life sciences, helping pharma organizations innovate while meeting strict regulations. Each entry includes key facts like 2024 revenue and workforce size, as well as main service areas. 1. Transition Technologies MS (TTMS) TTMS leads the pack as a Poland-headquartered IT partner with deep expertise in pharmaceutical projects. Operating since 2015, TTMS has grown rapidly by delivering scalable, high-quality software and managed IT services for regulated industries. The company’s 800+ specialists support global pharma clients in areas ranging from clinical trial management systems to validated cloud platforms. TTMS stands out for its AI-driven solutions – for example, implementing artificial intelligence to automate tender analysis and improve drug development pipelines. As a certified partner of Microsoft, Adobe, Salesforce, and more, TTMS offers end-to-end support, from quality management and computer system validation to custom application development. Its strong pharma portfolio (including case studies in AI for R&D and digital engagement) underscores TTMS’s ability to combine innovation with compliance. TTMS: company snapshot Revenues in 2024: PLN 233.7 million Number of employees: 800+ Website: https://ttms.com/pharma-software-development-services/ Headquarters: Warsaw, Poland Main services / focus: AEM, Azure, Power Apps, Salesforce, BI, AI, Webcon, e-learning, Quality Management 2. Sii Poland Sii Poland is the country’s largest IT outsourcing and engineering company, with a substantial track record in the pharma domain. Founded in 2006, Sii has over 7,700 professionals and offers broad expertise – from software development and testing to infrastructure management and business process outsourcing. Its teams have supported pharmaceutical clients by developing laboratory information systems, validating applications for FDA compliance, and providing IT specialists (e.g. data analysts, QA engineers) under flexible outsourcing models. With 16 offices across Poland and a reputation for quality delivery, Sii can execute large-scale pharma IT projects while ensuring GxP standards and data security are met. Sii Poland: company snapshot Revenues in 2024: PLN 2.13 billion Number of employees: 7700+ Website: www.sii.pl Headquarters: Warsaw, Poland Main services / focus: IT outsourcing, engineering, software development, BPO, testing, infrastructure services 3. Asseco Poland Asseco Poland is the largest Polish-owned IT group and a powerhouse in delivering technology to regulated sectors. With origins dating back to 1991, Asseco today operates in over 60 countries (33,000+ staff globally) and reported PLN 17.1 billion in 2024 revenue (group level). In the pharmaceutical field, Asseco leverages its experience in enterprise software to offer validated IT systems, data integration, and software outsourcing services. The company’s portfolio includes healthcare and life-sciences solutions – from hospital and laboratory systems to drug distribution platforms – ensuring interoperability and compliance with EU and FDA regulations. Asseco’s deep R&D capabilities and local presence (headquartered in Rzeszów with major offices across Poland) make it a trusted partner for pharma companies seeking long-term, reliable IT development and support. Asseco Poland: company snapshot Revenues in 2024: PLN 17.1 billion (group) Number of employees: 33,000+ (global) Website: pl.asseco.com Headquarters: Rzeszów, Poland Main services / focus: Proprietary software products, custom system development, IT outsourcing, digital government solutions, life sciences IT 4. Comarch Comarch, founded in 1993, is a leading Polish IT provider with a strong footprint in healthcare and industry. With 6,500+ employees and 20+ offices in Poland, Comarch blends product development with IT services. In the pharma and medtech sector, Comarch’s Healthcare division offers solutions like electronic health record platforms, remote patient monitoring, and telemedicine systems – all crucial for pharma companies engaged in clinical research or patient support programs. Comarch also provides custom software development, integration, and IT outsourcing services, tailoring its broad portfolio (ERP, CRM, business intelligence, IoT) to the needs of pharmaceutical clients. Known for robust R&D and secure infrastructure (including its own data centers), Comarch helps pharma firms improve operational efficiency and data-driven decision making. Comarch: company snapshot Revenues in 2024: PLN 1.916 billion Number of employees: 6500+ Website: www.comarch.com Headquarters: Kraków, Poland Main services / focus: Healthcare IT (EHR, telemedicine), ERP & CRM systems, custom software development, cloud services, IoT solutions 5. Euvic Euvic is a fast-growing Polish IT group that has become a major player through the federation of dozens of tech companies. With around 5,000 IT specialists and an estimated PLN 2 billion in annual revenue, Euvic delivers a wide spectrum of IT services. For pharmaceutical clients, Euvic’s team offers everything from custom application development and integration (e.g. R&D data platforms, CRM for pharma sales) to analytics and cloud infrastructure management. The group’s decentralized structure allows it to tap specialized skills (AI, data science, mobile, etc.) across its subsidiaries. This means a pharma company can find in Euvic a one-stop partner for digital transformation – whether implementing a secure patient mobile app, automating supply chain processes, or migrating legacy systems to the cloud. Euvic’s scale and flexible engagement models have made it a preferred IT vendor for several life sciences enterprises in Central Europe. Euvic: company snapshot Revenues in 2024: ~PLN 2 billion (est.) Number of employees: 5000+ Website: www.euvic.com Headquarters: Gliwice, Poland Main services / focus: Custom software & integration, cloud services, AI & data analytics, IT outsourcing, consulting 6. Billennium Billennium is a Poland-based IT services company known for its strong partnerships with global pharma and biotech clients. Established in 2003, Billennium has expanded worldwide (nearly 1,800 employees across Europe, Asia, and North America) and achieved record revenues of PLN 351 million in 2022 (with continued growth through 2024). In the pharmaceutical arena, Billennium provides teams and solutions for enterprise application development, cloud transformation, and AI implementations. The company has helped pharma organizations modernize core systems (for example, deploying Salesforce-based platforms for customer management), and it offers validated software development aligned with GMP/GAMP5 quality standards. With expertise in cloud (Microsoft Azure, AWS) and data analytics, Billennium ensures pharma clients can leverage emerging technologies while maintaining compliance. Its mix of expert IT staffing and managed services makes Billennium a flexible partner for both short-term projects and long-term digital initiatives in life sciences. Billennium: company snapshot Revenues in 2024: ~PLN 400 million (est.) Number of employees: 1800+ Website: www.billennium.com Headquarters: Warsaw, Poland Main services / focus: IT outsourcing & team leasing, cloud solutions (Microsoft, AWS), custom software development, AI & data, Salesforce solutions 7. Netguru Netguru is a prominent Polish software development and consultancy company, acclaimed for building cutting-edge digital products. Headquartered in Poznań and operating globally, Netguru has around 600+ experts in web and mobile development, UX/UI design, and strategy. While Netguru’s portfolio spans many industries, it has delivered innovative solutions in healthcare and pharma as well – such as patient-facing mobile apps, telehealth platforms, and internal tools for pharma sales teams. Netguru’s agile approach and focus on user-centric design help pharma clients create engaging applications (for patients, doctors, or field reps) that are also secure and compliant. With ~PLN 300 million in annual revenue (2022) and recognition as one of Europe’s fastest-growing companies, Netguru combines startup-like innovation with enterprise-level reliability. Pharma companies turn to Netguru to accelerate their digital transformation initiatives – whether it’s prototyping an AI-powered health app or scaling up an existing platform to global markets. Netguru: company snapshot Revenues in 2024: ~PLN 300 million (est.) Number of employees: 600+ Website: www.netguru.com Headquarters: Poznań, Poland Main services / focus: Custom software & app development, UX/UI design, digital product strategy, mobile and web solutions, innovation consulting 8. Lingaro Lingaro is a Polish-born data analytics powerhouse that has made its mark delivering business intelligence and data engineering solutions. Founded in Warsaw, Lingaro grew to over 1,300 employees and an estimated PLN 500 million in 2024 revenue by serving Fortune 500 clients. In pharma, where data-driven decisions are critical (from R&D analytics to supply chain optimization), Lingaro provides end-to-end services: data warehouse development, big data platform integration, advanced analytics, and AI/ML solutions. They have built analytics dashboards for pharmaceutical sales and marketing, implemented data lakes to consolidate research data, and ensured compliance with GDPR and HIPAA in data handling. Lingaro’s strength lies in merging technical prowess (across Azure, AWS, and Google Cloud) with a deep understanding of data governance. For pharma companies aiming to become more data-driven and insight-rich, Lingaro offers a proven track record in transforming raw data into actionable intelligence. Lingaro: company snapshot Revenues in 2024: ~PLN 500 million (est.) Number of employees: 1300+ Website: www.lingarogroup.com Headquarters: Warsaw, Poland Main services / focus: Data analytics & visualization, data engineering & warehousing, AI/ML solutions, cloud data platforms, analytics consulting 9. ITMAGINATION ITMAGINATION is a Warsaw-based IT consulting and software development firm known for accelerating innovation in enterprises. With around 400+ professionals, ITMAGINATION has served clients in banking, telecom, and also collaborated with pharmaceutical corporations on digital initiatives. The company offers custom development, data analytics, and cloud services – for example, building data platforms that unify clinical and operational data, or developing custom software to automate specific pharma workflows. ITMAGINATION’s expertise in Microsoft technologies (Azure cloud, Power BI, .NET) and agile delivery make it well-suited for pharma projects that require quick turnaround and strict quality control. In recent years, ITMAGINATION has also focused on AI solutions and machine learning, which can be applied to pharma use cases like predictive analytics for patient adherence or drug supply optimization. Now part of a larger global group (via acquisition by Virtusa in 2023), ITMAGINATION combines Polish tech talent with international reach, benefitting pharma clients with scalable delivery and domain know-how. ITMAGINATION: company snapshot Revenues in 2024: ~PLN 150 million (est.) Number of employees: 400+ Website: www.itmagination.com Headquarters: Warsaw, Poland Main services / focus: Custom software development, data & BI solutions, Azure cloud services, IT consulting, staff augmentation 10. Ardigen Ardigen is a specialist IT company at the intersection of biotechnology and software, making it a unique player in this list. Based in Kraków, Poland, Ardigen focuses on AI-driven drug discovery and bioinformatics solutions for pharma and biotech clients worldwide. Its team of around 150 bioinformatics engineers, data scientists, and software developers builds platforms that accelerate R&D – such as AI models for identifying drug candidates, machine learning tools for personalized medicine, and advanced software for analyzing genomic data. Ardigen’s deep domain expertise in areas like immunology and molecular biology sets it apart: it understands the science behind pharma, not just the code. For pharmaceutical companies looking to leverage artificial intelligence in research or to implement complex algorithms (while navigating compliance with new EU AI regulations and GMP standards), Ardigen is a go-to partner. The company’s rapid growth and cutting-edge projects (often in collaboration with top global pharma firms) highlight Poland’s contribution to innovation in life sciences IT. Ardigen: company snapshot Revenues in 2024: ~PLN 50 million (est.) Number of employees: 150+ Website: www.ardigen.com Headquarters: Kraków, Poland Main services / focus: AI/ML in drug discovery, bioinformatics, data science, precision medicine software, digital biotech solutions Why Choose Polish IT Companies for Pharma Polish IT companies have built a strong reputation for combining technical expertise with cost efficiency, making them attractive partners for global pharma organizations. The country offers a large pool of highly educated specialists who are experienced in working under strict EU and FDA regulations. Many Polish providers also invest heavily in R&D and AI, ensuring access to the latest innovations in data analytics, clinical platforms, and digital health. Their proximity to major European markets guarantees smooth communication and alignment with regulatory frameworks. This unique mix of skills, compliance, and innovation positions Poland as a reliable hub for pharma technology services. Key Factors When Selecting a Pharma IT Partner Selecting the right IT vendor for pharma requires careful consideration of both technical and regulatory capabilities. Beyond standard expertise in software development, providers must demonstrate experience with GxP, GMP, and GDPR compliance. It is also critical to assess their track record in delivering validated systems and managing sensitive patient or clinical data securely. Decision-makers should evaluate whether the partner offers scalable solutions, such as cloud and AI, that can adapt to future needs. Finally, strong communication, transparent project management, and industry references are essential to ensuring long-term success in pharma IT projects. Leverage TTMS for Pharma IT Success – Our Experience in Action Choosing the right technology partner is crucial for pharmaceutical companies to innovate safely and efficiently. Transition Technologies MS (TTMS) offers the full spectrum of IT services tailored to the pharma sector, backed by a rich portfolio of successful projects. We invite you to explore some of our impactful case studies – each demonstrating TTMS’s ability to solve complex pharma challenges with technology. Below are our latest case studies showing how we support global clients in transforming their business: Chronic Disease Management System – A digital therapeutics solution for diabetes care, integrating insulin pumps and glucose sensors to improve adherence. Business Analytics and Optimization – Data-driven insights enabling pharmaceutical organizations to optimize performance and enhance decision-making. Vendor Management System for Healthcare – Streamlining contractor and vendor processes in pharma to ensure compliance and efficiency. Patient Portal (PingOne + Adobe AEM) – A secure and high-performance patient platform with integrated single sign-on for safe access. Automated Workforce Management – Replacing spreadsheets with an integrated system to improve planning and save costs. Supply Chain Cost Management – Enhancing transparency and control over supply chain costs in the pharma industry. Customized Finance Management System – Building a tailor-made finance platform to meet the specific needs of a global enterprise. Reporting and Data Analysis Efficiency – Improving reporting speed and quality with advanced analytics tools. SAP CIAM Implementation for Healthcare – Delivering secure identity and access management for a healthcare provider. Each of these examples showcases TTMS’s commitment to quality, innovation, and understanding of pharma regulations. Whether you need to modernize legacy systems, harness AI for research, or ensure compliance across your IT landscape – our team is ready to help your pharmaceutical business thrive in the digital age. Contact us to discuss how we can support your goals with proven expertise and tailor-made solutions. How do IT vendors support regulatory inspections in the pharma sector? IT vendors experienced in pharma often build solutions with audit trails, automated reporting, and strict access control that make regulatory inspections smoother. They also provide documentation aligned with GMP and GAMP5 standards, which inspectors typically require. Some vendors offer validation packages that demonstrate compliance from day one. This not only reduces inspection risks but also saves valuable time during audits. Ultimately, an IT partner becomes part of the compliance ecosystem rather than just a technology supplier. Can Polish IT providers help reduce the time-to-market for new drugs? Yes, Polish IT providers frequently implement AI and automation to speed up processes like clinical trial management, data analysis, and patient recruitment. Faster and more reliable data handling allows pharma companies to make informed decisions more quickly. These efficiencies shorten the development timeline and can lead to earlier regulatory submissions. In some cases, innovative platforms built in Poland have cut months from the R&D cycle. This ability to accelerate time-to-market is one of the biggest advantages of working with a tech-savvy partner. What role does data security play in choosing a pharma IT partner? Data security is paramount in pharma because of the sensitivity of patient information and clinical data. A reliable vendor must follow strict cybersecurity protocols, encryption standards, and comply with GDPR and HIPAA. Many Polish providers invest in secure data centers and cloud platforms certified by global standards. They also implement monitoring and anomaly detection systems to prevent breaches. Companies that prioritize data security not only protect patient trust but also safeguard the company’s reputation. How do cultural and geographic factors influence collaboration with Polish IT firms? Poland’s central location in Europe ensures overlapping working hours with both Western Europe and North America, which improves communication. Cultural proximity and strong English proficiency make collaboration smoother than with many offshore destinations. Additionally, Polish teams often adopt agile methodologies that encourage transparency and regular feedback. This makes cooperation with global pharma firms efficient and predictable. Such cultural and geographic alignment is a hidden but powerful advantage when selecting a vendor. Are Polish IT providers active in emerging areas like digital therapeutics and AI in drug discovery? Absolutely, many Polish IT companies are pioneers in digital therapeutics, mobile health apps, and AI solutions tailored for drug discovery. They collaborate closely with research organizations and biotech startups, bringing innovation directly into pharma pipelines. For example, AI algorithms can help identify promising compounds or predict patient responses. Digital therapeutics developed by Polish teams support patient engagement and improve adherence to treatment. This forward-looking expertise ensures pharma companies are prepared for the future of medicine.

Wiktor Janicki

We hereby declare that Transition Technologies MS provides IT services on time, with high quality and in accordance with the signed agreement. We recommend TTMS as a trustworthy and reliable provider of Salesforce IT services.

Julien Guillot Schneider Electric

TTMS has really helped us thorough the years in the field of configuration and management of protection relays with the use of various technologies. I do confirm, that the services provided by TTMS are implemented in a timely manner, in accordance with the agreement and duly.

Let’s talk about how TTMS can help.

Monika Radomska

Sales Manager

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