Home •Blog
TTMS Blog
TTMS experts about the IT world, the latest technologies and the solutions we implement.
Sort by topics
Artificial Intelligence in Education: How AI Supports Learning and Training in the Workplace
Corporate L&D departments face a major challenge: how to develop effective training faster while maintaining high course quality. Artificial intelligence in education and training can significantly accelerate this process by generating course drafts from source documents. AI prepares the first draft, while subject matter experts review and refine the generated materials. This can reduce course development time from weeks to hours. How generative AI in education transforms corporate training E-learning courses have often been developed as one-off projects. Teams gathered materials, wrote a script and built the module, but the content could soon become outdated after publication. Artificial intelligence in learning supports a more continuous approach: an AI tool prepares the first draft, and a subject matter expert reviews, updates and approves the content. For L&D departments, this means a shorter path from identifying a business need to delivering a training programme and fewer hours spent on repetitive course development tasks. These two contexts must be distinguished. In schools and universities, AI supports the teacher and the student (as discussed in our article on AI in higher education). In corporate learning, AI helps reduce the time needed to develop employee training and supports consistent quality across different languages and cultural contexts. Organisations planning to use AI more widely usually start by defining their objectives and assessing the data available to them. If you want to learn more, visit our page on AI solutions for business. AI in education: benefits for course development time and costs Much of the cost of developing an e-learning course comes from gathering expert input, structuring the content and creating exercises. Artificial intelligence in training can shorten this stage by using source materials in DOC, PDF or PPT formats to generate a structured first draft of the course, complete with exercises and versions in different languages. AI4E-learning is an AI-powered tool for creating e-learning courses from source documents. It generates a structured draft that a subject matter expert can review and refine before exporting the approved course as a SCORM-compliant package for use in an existing LMS. Every element remains editable, giving the expert control over the course’s scope, factual accuracy and final content. This can significantly shorten course development time and help L&D teams deliver training sooner. With less time spent on repetitive production tasks, experts can focus on reviewing factual accuracy, refining the content and keeping it up to date. We have gathered practical examples of how to do this in our material on building effective e-learning with AI. AI-powered learning: personalised and adaptive learning paths The same content does not work equally well for every employee. Someone new to a role may need the basics, while a more experienced colleague may be ready for advanced topics. AI can help adapt the pace of learning, level of difficulty and feedback to each employee’s existing knowledge and progress. The AI Center team at the University of Szczecin identifies personalised learning as one of the main benefits of AI in education. In a corporate setting, this can help employees develop the required skills and knowledge more quickly, while reducing the risk of training being too basic or too demanding for some team members. Personalisation also requires careful design. Making learning easier does not necessarily help employees retain what they have learnt, as we discuss in the next section. Our company blog article on e-learning trends explores how corporate learning is evolving. What science says: AI, cognitive load and knowledge retention Faster course development and personalised learning are key benefits of AI. But does easier access to ready-made answers actually help people learn and retain information? Research suggests that this is not always the case. A study by a team of researchers from Carnegie Mellon University and Microsoft Research, led by Hao-Ping Lee (CHI 2025 conference), involved 319 knowledge workers. It turned out that individuals who trusted AI-generated answers more reported less engagement in critical thinking. Similar correlations were observed by Michael Gerlich, a professor at the SBS Swiss Business School, who surveyed 666 people in 2025. More frequent use of AI was associated with a greater tendency to offload cognitive effort onto technology. Both studies indicate a correlation, but they do not prove that AI causes a decline in these skills. Even more interesting conclusions come from an experiment by Bastani and colleagues, published in PNAS in 2025. Students using GPT-4 achieved better results during exercises, but when their access to AI was removed, their performance was worse than the control group. The problem was significantly reduced when the tool guided students through tasks using hints instead of providing ready-made solutions. What does this mean for companies designing training? AI should help employees understand the material, but it should not do the thinking for them. That is why it is worth using knowledge checks, practical exercises and tasks that require recalling previously learned information. This approach supports long-term retention and skill development. We also apply this principle in AI4E-learning, combining AI-generated content with exercises and expert review to create courses that engage learners and help them apply what they have learnt at work. AI is changing education: the role of subject matter experts In AI4E-learning, subject matter experts remain responsible for the quality of the final course. AI generates the initial draft and automates repetitive tasks, while an expert checks factual accuracy, adapts the content to the organisation’s context and approves the final version. This is the human-in-the-loop approach in practice. Control over data is equally important. In this configuration, AI4E-learning uses Azure OpenAI. Data processing and access controls depend on the client’s infrastructure and configuration settings. Responsible use of AI in training therefore requires appropriate technical safeguards alongside expert review of the generated content. This division of roles is visible in specific tasks: Task What AI does What humans decide Course structure proposes the layout of modules and learning objectives approves the scope and sequence Content and exercises generates the first draft from source materials verifies factual accuracy Language versions creates translations and variants approves terminology and context Data operates within the client tenant decides on access and data scope AI applications in learning: a practical example A training project for one of our clients’ helpdesk teams shows how AI can support corporate learning in practice. A traditional presentation was replaced with an interactive course in which employees completed exercises and received feedback tailored to their mistakes. The result was faster, more consistent onboarding for new employees and fewer questions directed to experienced colleagues. The course was developed using the company’s existing materials, and a subject matter expert reviewed each section before publication. We deliver projects like this as part of our e-learning services. AI in online learning: a corporate training case study Using AI in corporate training involves several risks, including inaccurate AI-generated content, inadequate protection of personal data and barriers to accessing training. These risks can be reduced through expert review, appropriate data security measures and regular monitoring of tool performance. Legal requirements must also be considered. The EU AI Act (Regulation 2024/1689) classifies certain AI systems used in education and vocational training as high-risk, including those used to evaluate learning outcomes or determine access to education. The requirements that apply to an AI tool in corporate e-learning depend on its intended purpose and use. Before implementation, we therefore assess what the AI system will be used for, what data it will process and which decisions require human oversight. This helps us identify applicable regulatory obligations and plan appropriate security measures and content review procedures. We discuss these ethical considerations in more detail in our company blog article on AI ethics in education. The future of AI in education: making corporate learning effective Artificial intelligence can significantly streamline corporate training development, particularly when used in tools designed specifically for e-learning. AI4E-learning, for example, helps structure courses, automate repetitive tasks and create exercises. A subject matter expert remains responsible for factual accuracy and the quality of the final course. Using general-purpose language models in training requires careful content review and oversight of the learning process. AI can generate inaccurate information, and access to ready-made answers does not necessarily help employees retain knowledge or develop skills. A well-designed training programme should therefore encourage employees to solve problems independently and apply what they have learnt in practical situations. Where to start? Choose one repetitive training process, define the rules for expert content verification and check whether employees can independently apply the acquired knowledge. If you are planning to use AI in corporate training, contact our specialists. During a free consultation, we will discuss your needs and the possibilities of implementing AI4E-learning in your organisation. TTMS Malaysia is a Registered Training Provider with HRD Corp, so Malaysian employers can fund TTMS training from their HRD Corp levy. FAQ – Frequently asked questions How is AI changing education and corporate training? AI can be used to create courses faster, personalise learning paths, generate exercises and feedback, and translate content into multiple languages. The final result is always verified by an expert or trainer. Will AI replace trainers or teachers? No. AI supports the trainer by taking over repetitive work and personalising learning, while decisions regarding scope, quality and context remain in human hands. How does generative AI in education speed up course creation? Tools such as AI4E-learning generate a structured course draft from source materials. A subject matter expert reviews and refines the content before the approved course is exported as a SCORM-compliant package. This can significantly reduce development time, while expert review remains essential to ensuring quality. What are the risks of artificial intelligence in learning? The main risks include inaccurate AI-generated content, inadequate protection of personal data and barriers to accessing training. Certain AI systems used in education may also be subject to high-risk requirements under the EU AI Act, depending on their intended purpose and use. These risks can be reduced through expert review, appropriate controls over data access and processing, and assessment of applicable legal requirements. Does using AI impair knowledge retention? The studies discussed above suggest that relying on AI-generated answers may reduce engagement in critical thinking and affect performance when AI support is removed. The findings vary by study design and context. Training should therefore include tasks that require learners to recall information and solve problems independently, alongside expert review of the course content.
Read
Case study: How we improved a RAG chatbot for exam candidates
How can you help customers find a specific answer in hundreds of pages of documentation? An education company we work with planned to give its customers access to a chatbot that could answer questions about exam procedures. Before making the tool more widely available, the quality of its answers needed to improve. The TTMS team refined how the chatbot used the documents, increasing answer accuracy from around 70% to around 98% in an internal test. We prepared this case study using an internal presentation and project information shared by Jakub Kraśniewski, Senior AI Developer at TTMS. About the client: an education company supporting exam candidates Our client operates in the education sector and supports people preparing for a certification exam. Candidates need information at different stages, including registration, exam procedures, receiving results and appealing a grade. The company provided this information in PDF documents totalling several hundred pages, mainly in English. Finding the right answer therefore required customers to search through extensive materials themselves. The chatbot was intended to help them handle exam-related matters and reduce the support team’s workload. The challenge: improve answers before releasing the chatbot to customers Candidates needed to be able to describe their situation in their own words and receive clear guidance within around 4–5 seconds. Answers also needed to be complete. If a procedure applied only under certain conditions, the chatbot needed to explain them. TTMS was responsible for improving information retrieval in the existing solution, which had been built using CrewAI, a framework for developing AI agent-based systems. The chatbot used RAG (Retrieval-Augmented Generation) to generate answers from retrieved passages in the client’s documentation. Our analysis showed that the text chunks supplied to the model were too large, making them harder to match to a question. Text extraction from multi-column PDFs caused another problem: content from adjacent columns was being mixed together. Improving the chatbot therefore required examining what information the model received and whether it preserved the meaning of the source document. Solution and implementation: answers with the necessary context We changed how the documentation was divided into chunks, preserving the connections between sentences, paragraphs and pages. When the system found a sentence relevant to a question, it could also retrieve the surrounding paragraph or the entire page. This gave the model the information needed to explain a rule along with the conditions under which it applied. This approach allowed us to use the client’s existing materials and retain classic RAG. According to Jakub, the additional storage and processing costs were acceptable for this volume of documentation. We evaluated the changes using an internal set of questions covering the full scope of the materials. We also included real user questions collected anonymously during an earlier prototype launch. This helped us assess answers to questions phrased in the way customers actually asked them. We explain the technical approach in our article “RAG for chatbots using CrewAI – notes from a TTMS Tech Talk”. Results: around 98% answer accuracy in an internal test According to the data shared by Jakub Kraśniewski, answer accuracy increased from around 70% to around 98%, an improvement of approximately 28 percentage points. The solution also maintained a fast response time. This result applies to the internal test set prepared for the project. It confirmed the improvement in answer quality and helped prepare the tool for the next stage: release to a subset of customers. The actual reduction in the support team’s workload had not yet been assessed. What comes next? Evaluating the chatbot in everyday candidate support After completing internal testing, the company planned to make the chatbot available to a subset of customers. This stage will help establish whether candidates receive enough assistance to resolve their enquiries and need to contact a support adviser less often. The assessment will draw on chatbot usage data and support enquiries. Let’s talk about a chatbot for your company Would you like to make company documentation easier for customers or employees to use? Or do you already have a chatbot whose answers need improvement? Talk to the TTMS team. We will review your materials and users’ needs to recommend a solution that fits your business. Where should you start with a customer service chatbot project? Start by reviewing support enquiries to identify the questions customers ask repeatedly. Issues for which the company has clear, documented answers are good candidates for the initial scope. Next, define who the chatbot should help and what users should be able to accomplish through a conversation. This makes it easier to set the scope of a pilot and decide how to assess its results. Involve customer service staff early, as they understand common misunderstandings and situations that require an individual decision. A scope prepared this way gives the implementation team a concrete starting point. Can you improve an existing chatbot without rebuilding it from scratch? In many cases, individual components of an existing solution can be improved, although the extent of the changes requires an initial assessment. The team should review examples of incorrect answers and identify where the problem occurs. The cause may lie in document preparation, information retrieval or how the model uses the retrieved content. The limitations of the current platform and access to its configuration also matter. Before deciding to rebuild, it is worth comparing the effects of selected changes using the same set of questions. This helps establish whether further development of the existing tool is justified. What determines the cost of implementing a chatbot that uses company documents? The cost depends partly on the condition of the source materials, the integrations required and the tool’s operating requirements. Documents with complex layouts or inconsistent content may need more preparation. Connecting the chatbot to company systems is a separate task, particularly when access to information depends on user permissions. The budget should also cover testing, support after launch and knowledge base updates. Ongoing costs depend on conversation volumes, the models selected and how questions are processed. An estimate should therefore distinguish between the cost of building the solution and the expected cost of maintaining it. How do you plan a customer chatbot pilot? A pilot should cover a clearly defined range of enquiries and a group of users whose questions will help assess the tool’s usefulness. Before launch, agree on the criteria for expanding access, including expected answer quality, acceptable response times and how errors will be handled. Users must be able to report a problem and contact support. During the pilot, the team should review conversations in line with the organisation’s data protection rules and collect customer feedback. It is important to check whether answers help users resolve their enquiries, including when questions are imprecise. The results help determine what needs to improve before the chatbot reaches a wider audience. How can you assess whether a chatbot actually reduces customer support workload? Use data from before the chatbot’s launch as a baseline: enquiry volumes for selected topics, handling times and how often customers make repeat contact. After implementation, compare the same measures while accounting for seasonality and changes in customer numbers. A user who does not contact an adviser after speaking to the chatbot has not necessarily resolved their issue, so customer feedback also matters. Support may receive fewer straightforward questions, leaving complex cases to account for a larger share of its work. In that situation, the average handling time per enquiry can rise even as the team’s overall workload falls. Considering these measures together provides a more reliable assessment of the business impact.
Read
GPT-6.1 Sol for business: Greater capabilities, lower costs
On 29 September 2026, at DevDay, OpenAI introduced GPT-6.1 Sol, just one week after the launch of GPT-6 Sol. The company aimed to bring Sol closer to GPT-6 Astra’s capabilities in coding, document analysis and application use, while keeping the model’s existing base prices. OpenAI kept GPT-6 Sol’s pricing: USD 2 per million input tokens and USD 10 per million output tokens, one fifth of Astra’s standard rates. OpenAI also links the update to the development of AI agents, programs that complete tasks step by step using available tools. Analysing documentation, preparing a code change and checking that it works may require multiple calls to the model. The cost of this work grows with the number of operations. GPT-6.1 Sol is intended to let application developers tackle more demanding tasks within the same budget. 1. What is GPT-6.1 Sol, and what has changed since GPT-6 Sol? GPT-6.1 Sol builds on GPT-6 Sol and is designed for complex tasks involving code, documents and applications. OpenAI’s documentation also lists support for tools, including search and code execution. The model can be used in an AI agent connected to a company’s data sources and tools. An agent could read a customer enquiry, find the relevant specification, compare requirements and draft a response. The company must define which data the agent can access, which actions it can perform independently and which require an employee’s approval. Base prices for input and output tokens remain the same as for GPT-6 Sol. The rate for reusing cached data, however, falls from USD 0.20 to USD 0.10 per million tokens. Companies using GPT-6 Sol should test both versions on the same tasks and compare the quality of their results, the number of retries and token usage. 2. What do benchmark results tell us about business applications? OpenAI publishes benchmark results covering document analysis and multistep tasks. When comparing results, check the reasoning level used. A higher level allows the model to spend more computation on analysing a task, which may increase token usage. Benchmark Area assessed Result reported by OpenAI GDP.pdf Answering questions based on complex PDF documents. Results close to Astra’s at approximately one fifth of the cost per task. AutomationBench 1.0.6 Multistep tasks in business applications. A lead of 4.8 percentage points over GPT-6 Sol at the medium reasoning level. OSWorld 2.0 Using desktop applications. A score 2.1 percentage points below Astra’s. The cost per task is approximately one seventh of Astra’s. Both models were tested at the maximum reasoning level. Each benchmark assesses different model capabilities, so their results should be considered separately. GDP.pdf, developed by Surge AI, tests whether a model can analyse workplace documents and answer questions about their contents. The model must also read tables, diagrams and footnotes correctly and connect information from different parts of a document. The result therefore shows how it handles materials similar to those reviewed by procurement departments or technical teams. Zapier’s AutomationBench assesses task execution in simulated applications. It checks whether the model actually made the required changes, such as updating the correct record and completing every requested action. This matters for businesses: a model’s claim that it has completed a task should be supported by data saved in the system. OSWorld tests computer use, including actions performed through application interfaces. The result cited by OpenAI includes credit for partial task completion. A high score may therefore mean that the model completed most steps correctly but missed the final one, such as submitting a form. When assessing a model’s suitability for automation, companies should also check how often it completes the entire task. These benchmark results help companies choose use cases for a pilot. Testing the model on their own documents and in the applications they use will show whether it meets their requirements and how much time is needed to check and correct its work. 3. How much does GPT-6.1 Sol cost, and how should you read the pricing table? API usage, which allows an application to access the model, is billed in tokens: units of processed text that often correspond to parts of words. Input includes instructions and supplied materials. Billable output tokens also include reasoning tokens, which are not visible in the response. Rates in USD per million tokens in Standard mode, for requests containing up to 272,000 input tokens: Model Input Cache read Cache write Output GPT-6 Sol 2.00 0.20 2.50 10.00 GPT-6.1 Sol 2.00 0.10 2.50 10.00 GPT-6 Astra 10.00 1.00 12.50 50.00 This comparison covers standard processing. Faster processing modes and requests with more input data have separate rates, and processing in a selected region may incur a surcharge. Caching can reduce the cost of subsequent requests that use the same instructions or documents. Savings depend on how much data can be reused. The total cost of a task includes token usage, caching charges and the tools used. 4. Example: how much could you save on 1,000 tasks per month? Let’s work through an example: a company completes 1,000 tasks per month, each using 20,000 input tokens and 3,000 billable output tokens, including reasoning. We assume identical usage for both models. To keep the calculation clear, we exclude cache reads and writes, tool fees and surcharges. Token usage must be measured in an actual deployment. Monthly cost component GPT-6.1 Sol GPT-6 Astra 20 million input tokens USD 40 USD 200 3 million output tokens USD 30 USD 150 Total token cost USD 70 USD 350 Token savings amount to USD 280 per month. Now let’s add another assumption: an hour of work by the person checking the results costs the company USD 30. If checking Sol’s results took an average of one extra minute per task, monthly labour costs would rise by USD 500. In this example, using Sol would cost the company USD 220 more per month overall than using Astra. If checking Sol’s results takes the same amount of time as checking Astra’s, or less, lower token spending may reduce the total cost of completing a task. The pilot should compare result quality, model costs and the time spent checking outputs. To calculate the cost per successfully completed task, divide the total process costs by the number of tasks completed according to the agreed requirements. The calculation should also include failed attempts, corrections, exception handling and maintenance. Data preparation and system integration costs should be spread over the planned period of use. 5. Which GPT-6.1 Sol use cases are worth testing in your company? A pilot could cover comparing documents and proposals, preparing information for customer service and developing code fixes. 5.1 Comparing technical documents and proposals A procurement department receives proposals containing specifications, parameter tables and service terms. The system could prepare a comparison against an agreed set of requirements. For each value, it should identify the source document and the location where it found the information, and flag any missing data. The test set should include two versions of the same specification, parameters expressed in different units and a qualification stated in a footnote. The reviewer can then check whether the system selected the current version and interpreted the qualifications correctly. The total time needed to prepare and check the comparison should be compared with the time needed to complete the task without AI. 5.2 Preparing customer service information across multiple systems After receiving a sales enquiry, an agent could find the customer’s account history, check the status of previous support tickets and prepare an entry in a customer relationship management (CRM) system. This requires connections to the relevant applications and access to the necessary data. The first stage of deployment could end with proposed changes being presented to an employee. Once approved, the system saves them and checks the result. Tests should include records for different customers with similar names to check whether the agent selects the correct one. The same task should also be assigned again to check whether the agent recognises the existing entry and avoids creating a duplicate. 5.3 Diagnosing bugs and preparing software changes An IT team can compare the models when analysing tickets, reproducing bugs and preparing fixes with tests. Previously resolved tickets, where the cause of the bug and the required software behaviour are known, make useful test cases. The assessment should cover the correctness of the change, test results and the time needed for a developer’s review. It should also check whether the model limited its modifications to the necessary scope. Too many code changes can make the review longer and make it harder to identify which changes fix the reported bug. 6. How can you compare GPT-6.1 Sol with Astra in your own process? OpenAI’s model selection guidance recommends comparing Sol and Astra on the same task. The comparison should cover result quality, completion time and task cost under conditions close to the intended use. Choose a task with a clear completion criterion. This could be comparing all required proposal parameters or preparing a code change that passes specified tests. Build a set of examples from day-to-day work. Include typical cases, incomplete data and known exceptions. Keep the materials used to refine instructions separate from those used to evaluate the final configuration. Ensure comparable conditions. Give the models the same materials, tools and permissions. Record reasoning settings and limits. You can then adjust each model’s settings to meet the same quality requirements. Calculate the full cost and check the result. Measure correctness, retries, system processing time and human review time. Also record cases that require manual completion. Expand use once the agreed conditions are met. Define which results can be used immediately, which require approval and when a case should be referred to a specialist. OpenAI recommends testing AI systems on tasks that reflect day-to-day work and having their results assessed by people with relevant expertise. Tests should be repeated after changes to the model, instructions or data sources. You can also test a division of work between the models. Sol would handle cases where it meets the required quality standard, while Astra would handle selected, more difficult cases. Define in advance when a task should be passed to Astra, for example when Sol misinterprets documents or produces an incorrect response. If required data is missing, it should be supplied before another analysis. The cost of that additional analysis must be included in the calculation. The final division of work will depend on the test results. 7. Let’s discuss automation in your company At TTMS, we help select AI solutions for specific processes and integrate them with business systems. If you are considering GPT-6.1 Sol, contact us. Together, we can identify tasks worth including in a pilot and determine how to assess result quality and the full cost of completing them. Can GPT-6.1 Sol be connected to a company knowledge base? Yes. An application can provide the model with information retrieved from approved company materials. This can be done using retrieval mechanisms described in OpenAI’s documentation. The approach in which a system first retrieves relevant source passages and then uses them to prepare an answer is known as retrieval-augmented generation (RAG). Implementation requires keeping documents up to date, identifying versions and respecting user access permissions. Providing sources helps users check the answers. Performance should also be assessed on questions for which the knowledge base does not contain enough information. Does switching from GPT-6 Sol to GPT-6.1 Sol require changes to the application? The scope of changes depends on how the application is integrated. The team should check the available interfaces, reasoning settings, tool support and response formats. For example, the GPT-6.1 Sol model documentation states that tool use requires the Responses API and that the none reasoning setting is not supported. This may affect applications built for the previous version. After adjusting the configuration, the team should test it against a saved set of tasks, including error handling. It is worth retaining the option to revert to the previous configuration if the new one does not meet requirements. What is GPT-6.1 Sol? GPT-6.1 Sol is an OpenAI model designed for tasks involving code, documents and business applications. It builds on GPT-6 Sol while keeping the same base prices for input and output tokens. It also supports tools, allowing developers to use it in AI agents that retrieve information and perform actions across connected systems. For businesses, its appeal lies in the balance between capability and cost. Its suitability for a particular process should be assessed through tests that measure result quality, token usage and the time needed for human review.
Read
Nearshoring to Poland: The 2026 Guide
Nearshoring to Poland means working with a software development or IT delivery partner located in a nearby European country rather than a distant offshore location. For many organizations, Poland offers a combination of technical expertise, close collaboration, shared working hours, and a business environment aligned with European regulations. As a result, it has become a popular destination for companies looking to scale delivery teams while maintaining effective communication, quality, and operational oversight.
Read
GPT-6 Cyber: What OpenAI’s Cybersecurity Model Means for Business
OpenAI has introduced GPT-6 Cyber, an AI model designed for cybersecurity work. For companies managing customer portals, payment services, and internal platforms, potential benefits include faster vulnerability investigations, more frequent security reviews, and better support for engineers preparing and testing fixes. Its practical value will depend on whether it helps teams confirm vulnerabilities and test fixes while reducing the time specialists spend on those tasks. What Is GPT-6 Cyber? GPT-6 Cyber is positioned as OpenAI’s specialized model for cybersecurity work. Before its introduction, BusinessToday reported that selected customers were testing an alpha version through Daybreak Red. According to Investing.com, citing Fortune, OpenAI is also developing a separate product to help customers deploy GPT-6 Cyber securely, automate security tasks, and patch vulnerabilities. The product would give OpenAI greater oversight of how its models are used. For businesses, this raises practical questions about how the service would fit into existing security operations: which tasks it could carry out, which actions would require approval, and what information OpenAI would receive about its use. These details would influence both implementation costs and the suitability of the service for handling sensitive company systems. There is already a documented foundation for this approach. OpenAI describes GPT-5.6 Cyber as a model for approved users conducting advanced vulnerability research and security testing. It also offers Codex Security, an application security agent that helps teams investigate vulnerabilities and prepare fixes. These products show how OpenAI already supports security work; GPT-6 Cyber needs its own assessment of capabilities and performance. For a buyer, three parts of an AI security solution deserve separate attention: Part of the solution What it determines What the business should verify Model The analysis and reasoning available for a task Accuracy on relevant security cases, limitations, and cost Access program Who can use particular capabilities and under which conditions Eligibility, approval requirements, and permitted activities Application and integrations The data the system can inspect and the actions it can execute Supported tools, permissions, review controls, and audit records Before purchasing access, check how the solution will connect to the company’s code repositories, monitoring tools, and process for approving changes. Why Cybersecurity AI Matters to Business Leaders Security work competes for engineering capacity. Investigating a suspicious code change, checking a supplier’s advisory, and validating a patch all draw on people who also maintain applications and deliver new features. AI creates an opportunity to move some of the research and preparation into a repeatable workflow, allowing specialists to spend more time on judgment, validation, and decisions about production systems. Faster security work can also help companies keep their services running. A retailer may need to secure a checkout integration without disrupting sales. A manufacturer may need to understand the consequences of updating software connected to operational processes. A financial services company may need clear evidence of what was investigated and how a problem was resolved. In each case, useful automation must fit the business process surrounding the software. OpenAI’s broader investment shows the importance it assigns to this market. On September 3, 2026, the company announced a $1 billion commitment covering subsidized Daybreak access, training, technical support, and partnerships for organizations protecting essential services. Four Business Uses to Evaluate for GPT-6 Cyber The following scenarios are our analysis of where a cybersecurity model could create value. They are proposed evaluation areas whose suitability for GPT-6 Cyber requires testing. Each depends on the capabilities, tools, and permissions available in the eventual deployment. 1. Investigating Vulnerabilities in Business Applications A useful pilot could focus on a small set of applications with a clear business owner. The model would be given approved code and architecture information, then assessed on whether it helps explain suspected weaknesses and identify the evidence needed to confirm them. For example, a team might investigate whether a customer portal consistently enforces access permissions across related services. The business benefit would come from reducing the time needed to reach a defensible conclusion. A helpful result should identify affected components, explain the conditions under which the issue matters, and make it easier for an engineer to reproduce the problem in an authorized test environment. 2. Preparing and Testing Security Fixes Once a vulnerability is confirmed, the next task is to prepare a change that addresses it. A security AI workflow could help draft a patch, identify related code paths, and propose tests for both the security issue and normal application behavior. Existing Codex Security documentation already describes workflows for reviewing changes, validating findings, and preparing fixes, making this a concrete area for evaluating future model improvements. For the business, success means less engineering effort per validated fix while maintaining release quality. Engineers should still review the change and run appropriate tests before it reaches production. 3. Prioritizing Work Using Business Context Using an up-to-date list of systems, their owners, and supporting documentation, an AI system could help connect technical findings with business consequences. This could help the team decide which vulnerabilities to fix first and who should handle them. 4. Helping Teams Investigate Security Incidents Another use to test is building an incident timeline from security alerts and system logs, with internal procedures guiding the investigation. An evaluation could test whether the model links its statements to evidence, distinguishes observations from hypotheses, and identifies missing information. This scenario would require suitable integrations; it should not be assumed to be a built-in GPT-6 Cyber feature. A useful output could help the next analyst continue an investigation or help a service owner understand the affected business process. Decisions such as disabling accounts, blocking traffic, or isolating systems need explicitly assigned authority because they can interrupt legitimate activity. What Could This Look Like in a Customer Portal? Consider a company preparing a new version of a customer portal connected to its order management system. A security review flags a possible weakness in how the portal checks access to order details. In a controlled pilot, an AI system would receive the relevant code, a description of expected permissions, and test accounts containing fictional customer data. The team would assess whether it can help trace the affected logic, produce a clear explanation, and propose a test that demonstrates the problem. If the finding is confirmed, engineers could evaluate its suggested correction and additional regression tests, which check that existing functions still work after a change. The final result should be a reviewed change with evidence: what was wrong, what was modified, which tests passed, and who approved the release. This would require access to the relevant code, a test environment, and a process for assigning confirmed issues to engineers. How to Measure the Business Value of GPT-6 Cyber A pilot should compare the AI-assisted workflow with the team’s current process using comparable cases. Include confirmed vulnerabilities, issues previously dismissed as false alarms, and cases where the evidence is incomplete. Record analyst review time as well as model execution time. An answer produced quickly can still require substantial investigation. The following measures provide a practical basis for a decision: Measure What to record Why it matters Time to assess a suspected vulnerability Elapsed time and analyst effort needed to confirm or dismiss an issue Shows whether the workflow accelerates investigation Finding quality Confirmed findings, false alarms, and missed issues in a reference test set Reveals whether apparent productivity comes with additional errors Time to a tested and approved fix Time from confirmation to a tested, approved correction Connects AI assistance to remediation Engineering effort Hours spent reviewing, correcting, testing, and documenting outputs Makes supervision costs visible Total cost per resolved case Model use, tools, test environments, integration, and staff time Supports a realistic decision about scaling For example, if a pilot saves analyst time but generates extra work for developers, both effects belong in the assessment. If it uncovers more valid vulnerabilities, the organization also needs capacity to fix them. Access, Pricing, and Deployment Questions for Buyers OpenAI’s existing Daybreak documentation requires appropriate organization approval and project access. It distinguishes access to a program from access to a particular model. For GPT-6 Cyber, organizations should verify the applicable requirements directly, including whether access is limited to an evaluation or supports the planned production use. Commercial evaluation should cover pricing, usage limits, supported integrations, and the handling of company data. Teams should establish what source code, logs, and configuration information would be processed; where processing occurs; how long data is retained; and which contractual controls apply. Availability through a particular cloud provider or within an existing subscription should be confirmed for the specific offering. A pilot budget should include implementation and review costs alongside any model usage fees. How to Keep AI Security Work Under Control A business should define the scope of an AI security workflow as carefully as it defines the scope of an external security assessment. Specify the systems it may inspect, the information it may access, and the actions it may take. Separate permission to analyze a problem from permission to change a live service. OpenAI’s published Daybreak guidance recommends isolated environments, monitoring of agent actions, and enforced limits on authorized activity. For an initial business pilot, that supports a controlled test environment, narrowly scoped credentials, review of proposed changes, and records that allow the team to understand what happened. The operating process also needs an owner. Someone must review unresolved findings, decide when additional evidence is required, and stop a workflow that behaves unexpectedly. Training should cover how to challenge an AI-generated conclusion and how to recognize when the system lacks enough information to proceed. Where Businesses Should Start Start with one application and one recurring security task, such as investigating suspected vulnerabilities or testing fixes. Assign someone to review the results and agree how you will measure accuracy, time saved, and the effort required from engineers. A focused pilot can help establish whether the approach is useful enough to expand. Considering AI for your company’s security processes? Talk to TTMS about the task you want to improve, the systems involved, and your data requirements. Together, we can explore a suitable approach and define what a useful pilot should demonstrate. Discuss your AI use case with TTMS How is GPT-6 Cyber different from using a general-purpose AI model for cybersecurity? GPT-6 Cyber is described in published reports as a model focused on cybersecurity, with early testing through OpenAI’s Daybreak Red program. General-purpose models can also assist with security tasks, such as explaining code, reviewing documentation, and analyzing supplied findings. The question for a business is whether the specialist model produces more accurate, useful results on the tasks its team actually performs. Its name alone does not establish that advantage. A meaningful comparison should give both models equivalent information, tools, and time, then have security specialists assess the results. Published evaluations can help inform that comparison, but results from another model should not be attributed to GPT-6 Cyber. Can GPT-6 Cyber replace a cybersecurity team or an external security provider? A company should retain qualified people responsible for assessing risks, validating findings, and approving changes. A model’s analysis depends on the information and tools available to it, which may leave gaps in its understanding of the company’s systems. Security specialists also consider operational priorities, investigate ambiguous evidence, and coordinate the response when something goes wrong. AI assistance may reduce the effort needed for particular tasks, but that needs to be demonstrated in practice. For a business using an external security provider, a useful discussion is how the provider validates AI-generated work and whether any time savings improve the service. Responsibility for protecting systems and resolving problems should remain clearly assigned. Is GPT-6 Cyber useful for a company that mainly uses software from external vendors? Its usefulness would depend on what the company controls and what information it can access. A business using standard cloud applications may have limited visibility into the vendor’s underlying code. Its own responsibilities may include account permissions, configuration, integrations, and custom extensions. Those areas could provide relevant tasks for AI-assisted analysis, subject to the model’s verified capabilities and the available integrations. Before testing, establish which systems the company is authorized to assess and what requires the vendor’s involvement. Findings affecting the vendor’s product should be reported through its security process so that they can be investigated and addressed. Does an AI security review that finds no vulnerabilities mean an application is secure? No. A review can miss vulnerabilities because of incomplete information, limited test coverage, or errors in the analysis. A finding-free report should therefore describe what was examined, which tests were performed, and what remained outside the scope. For example, reviewing selected source files does not establish that the application’s production configuration and access permissions were also checked. The result should be considered alongside other security evidence, including testing and specialist review appropriate to the application’s risk. Record unresolved questions and limitations so that a clean report does not create unjustified confidence. How should a company assess a supplier offering “GPT-6 Cyber-powered” services? Ask the supplier to explain which model it uses, how it accesses that model, and which parts of the service rely on it. Request a demonstration using a representative, authorized task and ask to see the evidence supporting the findings. Establish who reviews the output, who implements corrections, and what happens when the analysis is wrong or incomplete. The service description should also explain how company data is handled and which actions require approval. If the supplier changes the underlying model, ask how it checks that the service still meets the agreed requirements. Evaluate the offer against clear deliverables, such as validated findings and tested fixes, with named people responsible for the work.
Read
AI in Software Test Automation – 2026 Guide
AI in software test automation is moving beyond isolated experiments and becoming part of broader QA and engineering workflows. Teams are using it to support test design, verify scenarios, generate automation code, analyze execution results, and reduce repetitive maintenance work. The value, however, depends on how these capabilities are introduced and governed. This guide explains where AI can support the test automation lifecycle, what organizations should evaluate before adopting a platform, and how to maintain control over test intent, generated code, execution evidence, and ongoing optimization. 1. AI in Software Test Automation: Why It Matters in 2026 As applications and delivery processes become more complex, test automation must cover more than the execution of predefined scripts. Teams also need to keep tests aligned with changing requirements, maintain automation assets, interpret execution results, and decide which scenarios require attention. AI can support these activities by connecting requirements, test design, execution evidence, code generation, and maintenance signals. The value comes from reducing repetitive work while preserving clear ownership, review, and traceability across the test automation lifecycle. For organizations adopting AI in software test automation, the priority in 2026 should be building a controlled workflow rather than adding isolated AI features. Generated outputs need reliable project context, execution-based verification, defined approval gates, and integration with existing repositories and CI/CD processes. 2. What Is AI in Software Test Automation? AI in software test automation refers to the use of capabilities such as natural language processing, contextual analysis, pattern recognition, and AI agents to support the creation, execution, verification, and maintenance of automated tests. Depending on the platform, AI can support different stages of the test automation lifecycle, from interpreting requirements to generating and maintaining automated tests. 2.1 How AI-Powered Testing Differs from Traditional Test Automation Traditional test automation executes predefined scripts according to explicitly programmed steps, conditions, and assertions. This provides predictability and technical control, but creating and maintaining the scripts can require significant engineering effort as requirements, interfaces, test data, and application behavior change. AI-powered testing adds a contextual layer to this process. It can interpret requirements, use project knowledge to prepare test scenarios, support execution-based verification, generate code, and analyze feedback from test runs. Depending on the implementation, AI may also identify unstable tests, suggest updates, or highlight gaps in coverage. The difference is not that AI removes scripts or human involvement. AI-assisted workflows can reduce the manual effort between a requirement and working automation, while QA remains responsible for test intent and engineering retains control over the code entering the repository. 3. Where AI Supports the Test Automation Lifecycle AI in test automation is not a single capability. Understanding its role at each stage of the testing lifecycle helps teams evaluate platforms more accurately and set realistic expectations for adoption. Understanding these capabilities separately helps teams evaluate platforms more accurately and decide where AI can provide useful support without removing human review and technical control. 3.1 Requirements and Test Design AI can translate requirements, acceptance criteria, tickets, and existing QA knowledge into structured test scenarios. This reduces the manual effort involved in turning business requirements into detailed test cases and can help teams identify missing preconditions, expected results, or alternative paths. The quality of the output depends on the available project context. Generated test cases should therefore remain reviewable and should clearly distinguish confirmed information from gaps that require input from QA or business stakeholders. 3.2 Execution-Based Verification A generated test scenario may appear correct while still being impossible to execute against the actual application. Execution-based verification allows an AI agent to replay the proposed steps, observe application behavior, identify relevant elements, and confirm whether the expected result can be reached. This creates technical evidence before automation code is generated. QA still needs to confirm that the verified path represents the intended business scenario. 3.3 Automation Code Generation AI can use test intent and execution evidence to generate automation code. The output should follow the project’s existing conventions, use its reusable components, and remain readable for the engineering team. Generated code should be tested and delivered through the organization’s normal version control and review process. This keeps automation visible, reviewable, and maintainable instead of isolating it inside a proprietary environment. 3.4 AI-Assisted Test Maintenance and Optimization AI can analyze execution history, recurring failures, unstable assertions, selector changes, and overlapping coverage to identify tests that may require attention. It can then recommend repairs, consolidation, or removal. Significant updates should remain visible and subject to review. An automatically modified test may continue to pass while no longer validating the original requirement, so maintenance decisions need clear ownership and approval. 3.5 Reporting, Traceability, and Review AI can help organize execution evidence and connect results with the relevant requirement, test case, code change, and review decision. This gives QA and engineering teams a clearer view of what was tested, how the automation was created, and why it changed. For enterprise use, teams should be able to inspect AI inputs, generated outputs, execution results, and reviewer decisions rather than relying on an opaque automation process. 4. Where AI Creates Business Value in Test Automation AI creates business value in test automation by shortening the path from a requirement to reviewable automation. It can reduce the manual work involved in interpreting tickets, drafting test scenarios, translating test intent into code, and analyzing execution results. This allows QA professionals to contribute their domain knowledge more directly to automation, while engineering teams focus on technical review and repository standards instead of building every test from scratch. The result can be faster automation delivery without creating a separate handover for every scenario. Greater traceability also helps teams understand how test intent becomes automation and whether later changes still reflect the original requirement. The value depends on implementation quality. AI-generated outputs still require reliable project context, execution-based verification, clear ownership, and review before they enter the test suite. Organizations should measure success through reduced manual effort, shorter time from requirement to reviewed automation, acceptance of generated code, lower maintenance effort, and more dependable execution results. 5. How to Evaluate and Integrate an AI Test Automation Platform Choosing an AI testing platform requires more than comparing feature lists. The platform should fit the team’s existing requirements workflow, automation frameworks, repositories, CI/CD processes, security policies, and technical skills. Decision-makers should begin with the workflow they want to support. A platform designed only to generate test cases may not solve the wider challenge of turning requirements into executable, maintainable automation. Teams should evaluate how the tool handles test design, execution-based verification, code generation, review, reporting, and ongoing maintenance. 5.1 Key Criteria for Evaluating an AI Test Automation Tool When evaluating an AI test automation tool, consider: Requirement integration: Can the platform use tickets, acceptance criteria, and existing QA knowledge as test context? Execution model: Does it verify proposed scenarios against the application before generating automation? Code ownership: Does the output remain accessible as standard code in the organization’s repository? Review and governance: Can teams inspect AI inputs, generated outputs, execution evidence, and reviewer decisions? Framework compatibility: Does the platform support the automation technologies already used by the organization? Repository alignment: Can generated code follow existing fixtures, helpers, hooks, naming rules, and quality standards? Deployment and data control: Can the platform operate within the organization’s required infrastructure and AI governance model? Maintainability: Does it use execution feedback to identify unstable, outdated, or redundant automation? Team fit: Can QA define and review test intent while engineering retains control over code quality? These criteria help distinguish a complete AI test automation workflow from an isolated generation feature. 5.2 Fitting AI Testing into Your CI/CD Pipeline An AI testing platform should work with the organization’s existing version control and CI/CD processes. Generated automation should enter the repository through the normal pull or merge request workflow, where it can be reviewed, executed, and approved before being merged. Execution results should return to the relevant code change, test case, and requirement source. This creates traceability between test intent, automation code, execution evidence, and release decisions. Integration may require repository access, execution environments, credentials, pipeline configuration, and approval gates. These requirements should be assessed during implementation rather than assuming the platform can be added without changes to the existing delivery architecture. 6. How to Adopt AI Test Automation with the Right Controls A practical AI test automation strategy should begin with one clearly defined workflow rather than an attempt to automate the entire testing process. Choose a repeatable, business-critical user journey, document the current effort and maintenance burden, and define what successful adoption should improve. Start with requirements and test cases that have clear preconditions, actions, and expected results. AI output depends on the quality of this context, so incomplete requirements and inconsistent testing conventions should be addressed before the workflow is scaled. Define ownership and approval gates from the beginning. QA should remain responsible for test intent and business relevance, while automation engineers or developers review generated code, repository alignment, and technical quality. AI-generated automation should not enter the test suite without execution-based verification and the required human approval. Implementation should include the technical and governance work required to connect the platform with repositories, execution environments, CI/CD, models, and reporting processes. Finally, track practical outcomes such as the time from requirement to reviewed automation, the number of generated tests accepted with limited changes, maintenance effort, recurring failures, and user adoption. Expand to additional workflows only after the initial process is stable, reviewable, and understood by the teams responsible for maintaining it. This shift changes how QA, automation, and engineering teams collaborate, a topic explored further in our guide to how AI is changing the work of developers, testers, and analysts. 7. Challenges and Limitations of AI in Test Automation AI in test automation depends heavily on the quality of the requirements, project context, test data, and repository conventions available to the platform. Incomplete or inconsistent inputs can lead to scenarios that are technically executable but do not reflect the intended business behavior. Execution-based verification can confirm that a proposed path works against the application, but it cannot independently determine whether the path represents the correct test intent. QA still needs to review the scenario, expected result, and alignment with the original requirement. Generated automation may also require adjustment to match project-specific fixtures, helpers, authentication methods, data setup, and coding standards. Teams should expect an implementation and review process rather than treating AI test automation as a plug-and-play capability. Security and governance create additional requirements. Organizations also need clear controls over deployment, model usage, data access, generated code, and approval of AI-supported changes, especially in business-critical or regulated workflows. The strongest implementations use AI to reduce repetitive work while keeping responsibility for test intent, code quality, and release decisions clearly assigned. For a broader view of risk-based testing, traceability, and quality governance, see our QA best practices guide. 8. How Qatana Brings AI Test Automation into One Controlled Workflow Qatana brings test design, execution-based verification, code generation, review, and maintenance into one agentic test automation workflow. It starts with requirements from Jira or GitLab, turns them into automation-ready test cases, and verifies the proposed user path through Playwright execution before generating code. The resulting standard Playwright code is delivered through a pull-ready merge request. QA retains control of test intent, while engineering reviews the technical implementation through the organization’s existing version control process. Execution results remain linked to the requirement, test case, code, and review history, providing traceability across the workflow. Qatana also uses execution results, CI/CD signals, and reviewer feedback to support test maintenance and optimization. The platform runs entirely on-premise and supports the organization’s selected LLM, keeping project context, generated code, and execution evidence within the customer’s controlled environment. Book a demo to see how Qatana turns requirements into execution-validated Playwright automation. 9. Frequently Asked Questions About AI in Software Test Automation Can AI fully replace manual testers? No. AI can support test design, execution, code generation, and maintenance, but testers still define business intent, review outputs, and perform exploratory testing that requires human judgment. How does AI improve self-healing test automation? AI can detect changes affecting automated tests and suggest or generate updates based on execution evidence. Significant changes should remain visible and require review to ensure the test still validates the intended behavior. Is AI-driven testing suitable for regulated industries? Yes, provided the platform supports appropriate governance, traceability, access control, and data protection. Organizations should also evaluate deployment options, model control, audit evidence, and internal compliance requirements. What skills do QA teams need to work with AI testing tools? QA teams need strong test design, domain knowledge, and the ability to review AI-generated scenarios and execution evidence. Familiarity with automation workflows, repositories, and AI limitations is also valuable.
ReadThe world’s largest corporations have trusted us
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.
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.
Ready to take your business to the next level?
Let’s talk about how TTMS can help.
Michał Trojanowski
Managing Director TTMS Software UK Ltd.