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ChatGPT for financial services: what does combining GPT with professional data sources offer?
On 10 September 2026, OpenAI announced ChatGPT for Financial Services, a solution combining GPT-6 Astra with professional financial data sources and tools for preparing analyses. The product was developed in collaboration with Morgan Stanley and Evercore. It is designed for financial institutions, with an initial focus on investment banking and equity research. It allows analyst teams to find data, perform calculations and prepare client materials in one place. This could reduce the time spent gathering information and transferring it between tools. In this article, you will learn: what data and features ChatGPT for Financial Services offers, what preparing a company analysis with GPT could look like, why metric calculations and data sources need to be checked, which stages require an analyst’s review, how to assess whether implementation is worthwhile for your company. How does ChatGPT for Financial Services support analysts? Materials published by OpenAI and its data providers describe several specific use cases: Comparing companies. Daloopa, a provider of company financial data, makes selected data and metrics available for comparing business performance. Source references help analysts verify where the figures come from. Finding companies that meet specific criteria. Daloopa also describes searching for companies by business activity or geographical region. The resulting list can provide a starting point for further market analysis. Preparing client materials. OpenAI describes creating valuation models, research notes and presentations using company templates for Excel, Word and PowerPoint. ChatGPT for Financial Services provides access to selected data from Daloopa, PitchBook, LSEG News and Crunchbase. OpenAI is also developing integrations that will allow institutions to use data covered by their existing subscriptions. These include S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody’s. From data to company analysis: five steps in the workflow Let’s walk through preparing a comparison of two industrial companies ahead of a client meeting. The analyst needs to assess profitability, explain material differences and prepare a short note with a results table. Using fictional data, we will show what to check during a pilot, from selecting information to approving the final material. 1. Defining the question and scope of the comparison First, we establish which period to compare: the last full year, a six-month period or the trailing twelve months. We also check whether the figures cover the entire corporate group or an individual company, which currency they use and how each metric was calculated. In our example, we use consolidated data for both groups for the same calendar year. Amounts are stated in millions of euros. Before comparing results, we need to check the start and end dates of the reporting periods. One company’s financial year may end in December, while another’s ends in March. The documentation for the US SEC’s EDGAR database also highlights these differences. The analyst must then decide how to account for the mismatch and whether additional data is needed. The agreed approach should be recorded in the instructions for the AI and included with the completed analysis. This gives the model clear guidance and helps the reviewer understand which data was compared and why. 2. Gathering data and identifying its sources For each important figure, record the company and period it relates to, the units used and how it was calculated. A reference to the specific table or explanatory note in the report is also needed. Keeping the source document and its retrieval date makes it easier to review or update the analysis later. According to OpenAI’s description, ChatGPT for Financial Services lets users locate specific tables and document passages, highlighting the information used in the analysis. In our example, we compare EBITDA, or earnings before interest, taxes, depreciation and amortisation. The reviewer should be able to trace a reported value back to the company’s report and check how it was calculated. This also allows them to confirm that the figure covers the correct period and scope of operations. 3. Aligning definitions before comparing margins Companies may report adjusted EBITDA that excludes selected costs. These adjustments increase the value of the metric. Before comparing profitability, it is therefore necessary to check which adjustments have been applied. The US SEC also highlights differences in how individual companies calculate financial measures. Let’s look at two fictional companies. We assume that both calculate EBITDA before adjustments using the same principles. Company A then adds back EUR 4.59 million in costs that it excludes when calculating adjusted EBITDA. As a result, the metric rises from EUR 27.57 million to EUR 32.16 million. Company B has no such costs, so its figure remains unchanged. Illustrative example. Consolidated data for the same calendar year. Amounts are stated in EUR million. Item Company A Company B Revenue 229.73 183.78 EBITDA before adjustment 27.57 23.89 Costs excluded when calculating adjusted EBITDA 4.59 0.00 Adjusted EBITDA 32.16 23.89 EBITDA margin before adjustment 12% 13% Adjusted EBITDA margin 14% 13% After the adjustment, Company A’s margin is 14%, exceeding Company B’s margin of 13%. Before the adjustment, Company B has the higher margin: 13% compared with 12%. In this example, the treatment of costs determines which company has the higher EBITDA margin. The analyst should therefore check which costs make up the EUR 4.59 million adjustment and whether they also occurred in previous years. This helps them assess whether excluding these costs is justified for the analysis being prepared. They can also present both scenarios and explain the difference to the client. AI can help gather data and recalculate margins, while the expert assesses whether the adjustment is justified and how it affects the conclusions. 4. Verifying calculations in the spreadsheet In our example, simply divide EBITDA by revenue: 27.57 ÷ 229.73 gives a margin of approximately 12%, while 32.16 ÷ 229.73 gives approximately 14%. The displayed amounts are rounded; the spreadsheet should retain full precision for its calculations. More complex analyses may require currency conversion, alignment of reporting periods or the preparation of several forecast scenarios. The reviewer should be able to trace each of these steps. It is therefore worth asking AI to create a spreadsheet in which source data, assumptions and formulas are clearly separated. The analyst can then check the calculations and see how changing a single value affects the result. To test the spreadsheet, you can halve the adjustment, reducing it from approximately EUR 4.59 million to EUR 2.30 million. Company A’s adjusted EBITDA should then be approximately EUR 29.86 million, with a corresponding margin of 13%. These amounts are rounded for presentation; the spreadsheet should calculate the change using unrounded values. After making this change, check the comparison table and the commentary on the results as well. Both companies would now have the same margin, so the conclusion that Company A has a higher margin would need updating. This is a simple way to assess whether the calculations and accompanying text remain consistent. 5. Preparing client materials and reviewing conclusions The completed analysis can be presented in the company’s preferred format. According to OpenAI’s description, an administrator can share Excel, Word and PowerPoint templates with the team for the tool to use when creating documents and presentations. In our example, the client should receive a results table and a short explanation of how the cost adjustment affects the margin comparison. The expert reviewing the material checks whether the conclusions match the calculations and answer the client’s question. If anything needs clarification, they can request a further explanation or another version of the analysis. Time measurements should also include reviewing the material and making corrections before approval. How can you protect data and preserve a record of the analysis? When preparing a client analysis, the team may use public reports, paid databases and confidential documents. It is necessary to establish who can access this information, where it will be stored and who can receive the finished material. According to the ChatGPT Work security documentation, business data is encrypted and is not used to train models by default. Data retention periods, processing locations and the scope of recorded activity depend on the settings and connected services. Before implementation, check which user and tool actions are logged and which records can be exported. During the pilot, keep the source documents, successive versions of the spreadsheet and the final material, together with a record of who approved it and when. Then check whether this documentation allows you to reproduce the calculations and trace the approval of the analysis. Separately, verify whether system logs allow user and tool activity to be traced to the extent required by the company. How can you assess whether implementation is worthwhile? Start by choosing a task the team performs regularly, such as updating a company comparison after quarterly results are published. Before testing, measure how long this analysis takes using the existing method and define its quality requirements. These findings will provide a baseline for comparison with AI-assisted work. The time needed to review and correct the analysis must be added to the preparation time. OpenAI highlights this in its guidance on assessing the business value of AI, also recommending that implementation and ongoing usage costs be included. In practice, it is worth comparing: Metric What does it tell us? Time from starting the task to approving the analysis Does the client receive the finished material sooner? Include waiting time between stages. Total time spent by the analyst and reviewer Does the team spend fewer hours on preparation, review and corrections? Number of errors affecting the results or conclusions Does the analysis meet the same quality requirements as the existing approach? Accuracy and completeness of source references Can the origins of key figures and information be verified? Time needed to update the analysis How efficiently can new data be incorporated and the calculations and conclusions that depend on it be updated? Cost per approved analysis What is the cost of the finished material, including team time, the tool, data and the share of implementation and maintenance costs allocated to that analysis? The test should cover several tasks of varying difficulty. Define the assessment criteria before it begins. Someone performing the same analysis for a second time already knows the data and some of the answers, which may shorten the time needed. It is therefore worth using comparable tasks and varying the order in which participants work with AI and with the existing method. If AI saves time, check how the team used it. They may have prepared more analyses, responded to clients sooner or reduced overtime. The implementation assessment should show separately how the time saved was used and whether company spending decreased, and by how much. When is it worth starting a pilot? Consider a pilot if the team regularly gathers data from multiple sources and updates similar analyses. Choose a task that takes analysts a significant amount of time, such as comparing data from company reports. Assign a person to lead the pilot and experts to review the results. If the team only occasionally analyses a few annual reports, check whether tools already approved for use within the company are sufficient. Where data retrieval and calculations are already automated, identify a specific task that the new tool could improve. OpenAI makes the product available to financial institutions that meet its access requirements and directs interested companies to its sales team. Pricing, detailed terms and availability for a particular institution in Poland must be confirmed with the provider. Availability information. The implementation decision should be based on the pilot results: the quality of the analyses, the time needed to prepare and review them, and the total cost of the work. The test will also show whether the tool provides access to the data the team needs. Want to explore where AI could improve analysts’ work in your organisation? Talk to the TTMS team about choosing a task for a pilot, connecting the necessary data sources and assessing the results. How does ChatGPT for Financial Services differ from analysing reports in ChatGPT? ChatGPT for Financial Services provides access to selected professional financial data directly within the tool. It also supports references to specific tables and document passages, as well as the preparation of materials using company templates. When assessing its suitability for a team, check whether the available sources cover the companies, periods and metrics the team needs. Does ChatGPT for Financial Services require separate financial data subscriptions? Selected datasets are included in the product. These cover some of the information supplied by the providers named by OpenAI. The company is also developing integrations intended to let institutions use data covered by their existing subscriptions. Before purchasing, confirm which data is included in the offering and which requires additional access rights. Can ChatGPT for Financial Services be used to analyse companies listed on the Warsaw Stock Exchange? This depends on the availability of data for individual companies. Check whether the tool provides their financial statements, relevant metrics and historical data. The launch announcement alone does not confirm full coverage of the Warsaw Stock Exchange. The best way to assess the product’s suitability is to test it on several companies the team regularly analyses. What should you do if data from ChatGPT differs from the figures in a company’s report? Start by comparing the sources, reporting periods, units and definitions of the metrics. A discrepancy may arise, for example, from using standalone rather than consolidated financial data, or from including EBITDA adjustments. Also check whether the company has published an updated report. The analyst should explain the discrepancy and document which value they used and why.
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Automation Best Practices in Software Testing for 2026
Software release cycles keep shrinking, and testing teams are expected to keep pace without sacrificing reliability. Automation has become a core part of modern software delivery, but writing more scripts does not automatically lead to faster feedback, stronger coverage, or more dependable releases. Teams getting real value from automation treat it as an engineering discipline integrated with the wider QA and software delivery process. This article covers the practices that make automation sustainable in 2026, including test prioritization, methodology and tool selection, maintainable script design, CI/CD integration, continuous maintenance, and code ownership. 1. Why Test Automation Best Practices Matter More in 2026 Applications increasingly depend on connected services, frequently changing interfaces, and shorter delivery cycles. Automated testing may cover web applications, mobile products, APIs, integrations, and business-critical user journeys, making the way automation is planned, implemented, and maintained as important as the number of tests in the suite. Without clear standards, automation can become another source of delivery friction. Unstable tests slow down CI/CD pipelines, outdated scripts lose alignment with changing requirements, and duplicated coverage increases execution and maintenance effort without producing better evidence. As discussed in our guide to AI end-to-end testing, sustainable automation requires clear ownership, traceability, regular maintenance, and a deliberate connection between requirements, test intent, execution, and results. 2. A Decision Framework for What to Automate in Software Testing Not every test belongs in an automated suite. Teams need a repeatable way to identify scenarios where automation will provide lasting value rather than create additional maintenance work. 2.1 Scoring Test Cases by Frequency, Stability, and Cost A practical scoring model evaluates each candidate across several dimensions: how often the test runs, how stable the underlying feature is, how important the workflow is to the business, and how much effort the test will require to automate and maintain. Strong candidates are usually repeatable scenarios with clear expected results, stable preconditions, reliable test data, and a meaningful impact on release confidence. A frequently executed test may be valuable, but frequency alone is not enough. Teams should also consider whether the scenario can be executed consistently and whether its expected outcome can be verified without subjective interpretation. 2.2 Keep Judgment-Based Testing Manual Exploratory testing, first-impression usability reviews, visual assessments, and scenarios that depend heavily on human interpretation are usually better handled manually. Automating these activities can remove the flexibility and observation that make them valuable. Rarity alone should not exclude a test from automation. An unusual scenario may still deserve automated coverage when a failure could interrupt a critical process, compromise data, or create significant operational risk. The decision should reflect business impact as well as execution frequency. 2.3 Prioritize Business-Critical User Paths After unsuitable candidates have been removed, rank the remaining tests by business impact and regression risk. Authentication, checkout, account access, approvals, and core transaction flows are common priorities because failures can prevent users from completing essential tasks. Starting with these workflows allows the automation suite to protect the parts of the application that matter most. Lower-impact scenarios can be added later when their expected value justifies the development and maintenance effort. 3. Match the Automation Approach to the Application and Team Once teams have identified the right candidates for automation, they need to choose an approach that fits the application, delivery model, and available skills. The decision should balance execution speed, maintainability, technical control, accessibility for QA, and the effort required to integrate automation with the existing development workflow. 3.1 Use the Test Pyramid to Balance Feedback and Coverage The test automation pyramid remains a useful starting point. Fast unit tests usually form the broadest layer, integration and service-level tests verify interactions between components, and a smaller set of end-to-end tests validates complete user journeys. The exact proportions should reflect the application architecture and its risks. A system built around multiple services may need stronger integration coverage, while a business application may require more end-to-end validation of critical workflows. The goal is to detect problems at the lowest practical level while retaining enough end-to-end coverage to confirm that essential user journeys work as expected. 3.2 Choose Between Code-Based, Codeless, and Hybrid Automation Code-based automation provides direct control over test architecture, integrations, reusable components, and repository conventions. It is often appropriate when a team has strong engineering skills, complex testing requirements, or an established automation framework. Codeless and low-code approaches reduce the amount of scripting required to define common test scenarios. They can make automation more accessible to manual testers and domain specialists, although teams should still evaluate how the platform handles complex logic, maintenance, version control, and code ownership. A hybrid approach combines accessible test definition with standard code-based automation. QA professionals can define and review test intent, while automation engineers maintain technical standards and review the resulting code. This model can reduce handovers without limiting the team to a proprietary execution format. 3.3 Evaluate Technical Fit and Long-Term Ownership The right methodology depends on what the team is testing and who will maintain the automation. Complex backend behavior and service integrations may require direct technical control, while repeatable web user journeys may be suitable for higher-level automation. Teams should also consider where generated automation will run, whether the output can be reviewed in the existing repository, how results return to the QA workflow, and whether the chosen approach supports the organization’s deployment and security requirements. Tool popularity matters less than alignment with the team’s application, skills, governance model, and long-term maintenance responsibilities. 4. Design Test Automation for Maintainability Script and framework design have a major influence on long-term reliability, regardless of the selected tool. Maintainable automation separates reusable technical components from test intent, follows consistent repository conventions, and makes failures easier to understand and repair. 4.1 Using Stable Locators and Resilient Element Selection Locators based on visual position, generated identifiers, or deeply nested DOM paths can break when the interface changes. Where possible, teams should use selectors based on stable and meaningful attributes, such as dedicated test identifiers, accessible roles, labels, or other elements that reflect how users interact with the application. A locator should be both stable and specific enough to identify the intended element. The goal is not to eliminate maintenance entirely, but to reduce unnecessary failures caused by implementation details that are unrelated to the behavior being tested. 4.2 Apply Reusable Design Patterns Patterns such as the Page Object Model can separate interactions with the application from the business logic being verified. When an interface element changes, the team can update the relevant reusable component instead of modifying every test that uses it. The appropriate structure may also include component objects, shared fixtures, helper functions, reusable actions, and project-specific abstractions. Teams should select patterns that match the application architecture and apply them consistently across the repository. 4.3 Define Clear Assertions Every automated scenario should include an explicit and observable expected result. A test that performs a sequence of actions without verifying the outcome may pass even when the underlying business process is not working correctly. Assertions should confirm the intended behavior rather than incidental implementation details. Clear expected results also make test cases easier to review, automate, diagnose, and trace back to the original requirement. 4.4 Manage Test Data as a Dedicated Discipline Test data should have clear ownership and a repeatable setup process. Data creation, seeding, reuse, protection, and cleanup should be considered when the test is designed rather than added after the script has already been implemented. Teams should also avoid hidden dependencies on data left behind by earlier executions. Predictable test data makes failures easier to reproduce and reduces false results caused by stale, incomplete, or conflicting records. 4.5 Keep Tests Independent and Isolated Automated tests should not depend on the order in which other tests are executed. Each test should begin from a known state and establish the preconditions required for its own scenario. Isolation can be implemented through fixtures, controlled data setup, separate browser contexts, API-based preparation, environment resets, or cleanup procedures. When one test fails, that failure should not create misleading results elsewhere in the suite. 4.6 Follow Repository Conventions Automation code should follow the same structural and quality standards as the rest of the project. Consistent naming, fixtures, helpers, hooks, error handling, formatting, and review rules make generated and manually written tests easier to understand and maintain. Repository alignment becomes particularly important when automation code is generated rather than hand-written. Generated automation should fit the existing framework rather than introduce a parallel structure that the engineering team must maintain separately. 5. Integrate the Right Tests at the Right CI/CD Stage Automated tests provide the greatest operational value when they are integrated with the delivery pipeline and return timely, actionable results to the people responsible for the change. The goal is not to run every test after every update, but to apply the right level of validation at each stage. 5.1 Match Test Scope to the Pipeline Stage A staged pipeline balances feedback speed with test depth. Fast unit tests and static checks can validate individual changes early, while integration tests and a relevant regression subset can provide broader evidence during pull or merge request review. More extensive regression testing can run after changes are merged, before deployment, on a schedule, or when the risk of a release justifies wider coverage. Smoke tests serve a different purpose. They verify that a deployed application is available and that its most critical user paths remain operational. They should complement, rather than replace, deeper regression testing. The exact structure and runtime expectations should reflect the application architecture, infrastructure, release cadence, and business risk. Teams should define their own quality gates based on the type of change and the evidence required before it can move forward. 5.2 Select Tests Based on Change and Risk Running the full suite for every code change can create unnecessary delays as automation grows. A more sustainable approach selects tests based on the components affected by the change, related requirements, business-critical workflows, previous execution results, and known areas of risk. Test selection should remain transparent. Teams need to understand why a test was included or excluded and should be able to expand the scope when a change has wider implications than the initial analysis suggests. This staged, risk-based structure is also the foundation that AI-driven test selection and prioritization build on. If you’re looking at how AI fits into this part of the pipeline specifically, see our guide, AI in Software Test Automation: 2026 Guide. 5.3 Return Actionable Results to the Workflow Test execution should produce more than a pass or fail status. Results should identify the affected scenario, provide enough evidence to investigate a failure, and remain linked to the relevant requirement, test case, code change, and execution record. Returning results to the pull or merge request helps engineering teams review automation alongside the application change. Returning them to the QA layer and requirement source gives QA teams traceability from test intent through code to execution. This closes the feedback loop and supports informed release decisions. 6. Treat Test Maintenance as a Continuous Engineering Process Flaky tests can quickly undermine confidence in an automation suite. When the same test passes and fails without a relevant application change, teams may begin treating failures as noise. Rerunning the test can unblock a pipeline temporarily, but it does not resolve the underlying problem. 6.1 Diagnose the Root Cause of Flaky Tests Flakiness can originate in the test code, application, test data, execution environment, or an external dependency. Common causes include fixed delays, missing synchronization, unstable locators, shared state between tests, conflicting test data, asynchronous application behavior, network variability, and resource constraints in the execution environment. The corrective action should match the source of the problem. Timing failures may require condition-based waits rather than longer fixed delays. Shared-state failures call for stronger isolation and controlled data setup. Selector failures may require more stable locators or reusable application abstractions. Failures caused by external dependencies may require controlled test environments, mocks, or other ways to reduce unnecessary variability. Teams should capture enough information to distinguish a product defect from a test defect or an environment failure. Execution traces, logs, screenshots, videos, network activity, environment details, and the affected test step can make intermittent failures easier to reproduce and diagnose. 6.2 Review and Prune the Test Suite Regularly Automated tests should be reviewed throughout their lifecycle. Product changes can make tests obsolete, duplicate existing coverage, or reduce their business value. Keeping every test indefinitely increases execution time and maintenance effort without necessarily improving release confidence. A defined maintenance cadence should include reviewing flaky tests, retiring obsolete scenarios, consolidating duplicate coverage, and reassessing tests whose business impact or technical stability has changed. The frequency should reflect the size of the suite, release cadence, application risk, and volume of product changes. Each test should have a clear outcome after review. It may be repaired, rewritten, moved to a more appropriate test layer, temporarily isolated with an assigned owner, or removed when it no longer provides useful evidence. 6.3 Track Execution Health and Maintenance Effort Pass rate alone does not show whether an automation suite is healthy. Teams should also monitor recurring failures, rerun frequency, execution duration, maintenance effort, obsolete tests, duplicated coverage, and the time required to identify and repair a failure. These signals help distinguish a growing automation suite from a sustainable one. Adding tests increases coverage only when the team can understand the results, maintain the assets, and trust the evidence produced by each execution. Maintenance decisions should also remain traceable. Teams need visibility into what changed, why a test was updated, how the change was reviewed, and whether the revised test still validates the original requirement. We will discuss this lifecycle-based approach in our article: Best QA Practices in Software Testing. 7. Treat Automation Code as Production Code Automation code should follow the same engineering standards as application code. Tests need clear ownership, version control, consistent repository conventions, review rules, and quality gates. Without these practices, scripts become difficult to understand, update, and trust as the application evolves. Ownership should cover both test intent and technical implementation. QA professionals can define the business scenario, expected result, and required coverage, while automation engineers or developers verify that the resulting code follows project conventions and can be maintained within the existing framework. Whether automation is written manually or generated with the help of AI tooling, it should enter the repository through a standard pull or merge request. Reviewers should be able to inspect what the test validates, how it interacts with the application, which reusable components it uses, and whether it introduces unstable dependencies or duplicated coverage. Generated tests should not remain inside a proprietary execution environment when code ownership and portability matter to the organization. Keeping automation in the client’s repository makes changes visible, reviewable, and subject to the same governance as other project assets. Teams should also define who responds when a test becomes unstable or outdated. Clear ownership prevents failed tests from remaining unresolved because QA, development, and automation teams each assume that another group is responsible. 8. Where AI Fits Into This Process AI is increasingly used alongside these practices — drafting test cases from requirements, verifying that a proposed path actually works before code is generated, and flagging tests that are likely to become unstable. Getting this right is less about the automation practices covered above and more about strategy, tooling, and governance, so we cover it separately in depth in AI in Software Test Automation: 2026 Guide. 9. Common Test Automation Mistakes to Avoid Even a technically sound automation program can lose value when teams make poor decisions about scope, ownership, and maintenance. Common mistakes include: Automating every available scenario instead of prioritizing repeatable, stable, and business-critical tests. Treating reruns as a permanent solution to flaky tests rather than investigating their root causes. Allowing obsolete and duplicated tests to remain in the suite without regular review. Selecting a platform based on feature count without evaluating integration, maintainability, deployment, code ownership, and team fit. Generating automation without first verifying that the test scenario works against the actual application. Accepting generated tests without checking whether they reflect the original requirement and intended business behavior. Keeping automation in a proprietary environment when the organization requires reviewable, portable code in its own repository. Managing test scripts outside standard version control, code review, and CI/CD quality gates. Leaving responsibility for test intent, code quality, and ongoing maintenance undefined. Avoiding these mistakes requires more than adding tools or scripts. Teams need a controlled workflow that connects requirements, test design, execution evidence, automation code, review, and maintenance. 10. How Qatana Applies These Test Automation Best Practices Qatana brings these practices together in an agentic test automation platform. It turns Jira or GitLab requirements into automation-ready test cases, verifies the proposed user path through Playwright execution, and generates standard Playwright code delivered through a pull-ready merge request. QA retains control of test intent, while engineering reviews the generated automation through the existing merge request process. Execution results remain linked to the requirement, test case, and code, providing traceability across the workflow. Qatana runs entirely on-premise and supports the organization’s selected LLM, keeping project context, generated code, and test evidence within the customer’s environment. Book a demo to see how Qatana turns requirements into execution-validated Playwright automation. 11. Frequently Asked Questions How much of my test suite should be automated? There is no universal target. Prioritize stable, repeatable, and business-critical scenarios, while keeping exploratory and judgment-based testing manual. What is the difference between a test automation strategy and a framework? A strategy defines what to automate, why, and how success will be measured. A framework is the technical structure used to build, execute, and maintain automated tests. How do I know if my automation ROI is positive? Compare the time and effort saved through automation with implementation, execution, and maintenance costs. If maintenance consistently outweighs the benefits, review the selected tests and automation approach. What’s the most common reason automation programs stall after an initial pilot? Usually a lack of ongoing ownership rather than a tooling problem. Teams automate an initial batch of tests successfully, but without a defined maintenance cadence, clear code ownership, and a repository review process, the suite accumulates flaky and obsolete tests faster than anyone repairs them, and confidence in the results erodes.
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ChatGPT, an Integrated LLM, an SLM or Automation? How to Choose the Right AI for Your Business Process
In 2025, 20% of enterprises in the European Union used artificial intelligence, compared with 13.5% a year earlier. In Poland, the figure was 8.4%. The most common application was analysing written language, used by 11.8% of the companies surveyed. The authors of a study published in 2026 in Organization Science described the uneven boundaries of AI capabilities as a “jagged technological frontier”: tasks of similar difficulty for humans can pose very different challenges for a model. The next step requires answering a more specific question: which form of AI fits a particular task? In an experiment involving 758 consultants, participants using GPT-4 completed 12.2% more tasks and finished them 25.1% faster on average when the tasks fell within the model’s capabilities. For a complex task beyond those capabilities, the probability of reaching the correct solution fell by 19 percentage points. The authors of the study, published in 2026 in Organization Science, called this uneven boundary of AI capabilities the “jagged technological frontier”. Effectiveness therefore depends on matching the technology to the task, data, risk and approach to verifying results. One process may be best served by an enterprise LLM assistant. Another may require an application connected to a CRM, a knowledge base and an access control system. A third may benefit from a small model running locally. Operations governed by explicit rules can be handled by code, rules engines or robotic process automation (RPA). Traditional machine learning is suitable for tasks such as forecasting and data classification. 1. LLMs and SLMs in Business: Choosing the Model, Integrations and Where Data Is Processed Terms such as “LLM”, “deployed LLM” and “closed SLM” combine several layers of technology. In a business context, it helps to separate four decisions: Way of working: does an employee interact with a ready-made assistant, or does the process start automatically? Scope of integration: does the solution work with materials supplied by the user, or does it retrieve data and perform actions in company systems? Model type: does the task require the broad capabilities of a large language model, or would a specialised SLM be sufficient? Processing location: does the model run as a cloud service, in a dedicated environment, in a private cloud, on premises or directly on a device? SLM stands for Small Language Model, which typically requires fewer computing resources. The term “closed system” needs clarification: it may refer to restricted access, an isolated environment or data processing within the organisation’s own infrastructure. A large language model can run in a private environment, while an SLM can be available through a public API. Model size alone does not determine how data is secured. 2. Six Ways to Use AI in Business Processes Approach How It Works Best Fit Key Metric Enterprise LLM assistant An employee assigns a task and checks the result in an approved environment, such as ChatGPT Business or Enterprise Analysis, drafting, summarising, developing alternatives and ad hoc work Time saved per task after accounting for review and corrections Integrated LLM or RAG application The model uses company sources, rules, permissions and integrations Repeatable processes involving documents, knowledge and data from business systems Cost per successfully resolved case AI agent The solution plans its next steps, selects tools and pursues a goal within a defined scope Multi-step processes involving exceptions and a dynamic sequence of actions Percentage of tasks completed correctly SLM A smaller model handles a narrow range of tasks in the cloud, on a company server, at the edge or on a device High task volumes, a fixed subject area, short response times and offline operation Quality compared with an LLM within a specified cost budget and p95 response time limit Private or on-premises deployment An LLM or SLM runs in a controlled environment, private cloud, company network or on a device Requirements relating to data residency, business continuity, connectivity, infrastructure or security policies Compliance with requirements, quality, availability and total cost of ownership (TCO) Rules, RPA or traditional ML The process is defined through code, conditions, a predictive model or a state machine Calculations, transactions, fixed workflows and unambiguous decisions Accuracy, repeatability and completeness of the audit trail In a mature deployment, these approaches often work together. The language model interprets a message, rules check the conditions, an application retrieves data, a person approves the action, and the transactional system records the result. 3. Which Tasks Are Suitable for ChatGPT or Another LLM Assistant? A ready-made LLM assistant supports tasks where an employee is responsible for checking and using the result. The user initiates the work, provides context, evaluates the response and decides how to use it. The output takes the form of a draft, recommendation, analysis or working document. Examples include: preparing a first draft of a report, message, presentation or article, summarising documents and correspondence, comparing several materials supplied by the user, developing questions, scenarios and alternative solutions, translating content for subsequent review, exploring data and explaining findings, organising meeting notes, drafting a procedure or action plan. This approach works well in processes where every response undergoes human review, the result can easily be corrected or withdrawn, and the task does not require automatic writes to a critical system. Wide variation in the source material and the need for language-related work further increase the usefulness of an LLM. Security depends on the approved product and its configuration. OpenAI states that data from ChatGPT Business, ChatGPT Enterprise and the API is not used to train models by default. The API data controls documentation also describes separate retention policies, including standard abuse monitoring logs and a Zero Data Retention option for eligible customers. Before using the solution, an organisation should review data classification, contractual terms, processing region, retention, administrator permissions and policies for connected applications. For more examples, see our overview of 15 ChatGPT integrations with business applications. 3.1 How Can You Measure Time Savings and the Quality of Work with an LLM? Relying solely on employee surveys can overstate the benefits. In a METR experiment, experienced developers took 19% longer to complete the tasks studied when using AI tools, yet afterwards still estimated that AI had made their work 20% faster. The study involved 16 participants and 246 real tasks in repositories they knew well, so its findings apply to that specific setting. The methodological lesson has broader relevance: actual task duration and quality need to be measured before deployment. For an LLM assistant, useful metrics include median task completion time, the proportion of outputs accepted without changes, average correction time, quality assessed against consistent criteria, frequency of use and the number of cases in which users return to their previous way of working. 4. When Should You Integrate an LLM with Company Systems, and When Should You Deploy an AI Agent? Integration becomes justified when the value of a process depends on current company data, a repeatable workflow and coordination across several systems. The model then receives controlled access to documents, a knowledge base, CRM, ERP, a ticketing system or email. The application verifies the user’s identity, controls which data is shared, defines the response format, checks results, logs actions and routes selected operations for approval. A typical integrated AI application consists of five layers: Input: a message, document, form, system event or record. Context: data retrieved in line with access permissions, often using RAG. Model: an LLM or SLM selected for the specific stage. Validation: rules, completeness checks, source verification and risk classification. Output: a response for a person, a draft record or an approved action in a system. Use cases requiring this architecture include finding answers in an internal knowledge base, reviewing contracts against a company’s risk checklist, preparing quotations using CRM data and a price list, classifying support tickets, onboarding employees, checking procurement documents and drafting responses based on customer history. RAG retrieves up-to-date passages from approved sources and adds them to the context used to generate a response. An AI agent represents a further level of integration. It receives a goal, selects tools and plans a sequence of actions. According to the current Google Cloud guidance on agentic architecture, agents are suited to open-ended, multi-step problems that require external data and a degree of autonomy. An application with a predefined sequence of steps is usually sufficient for a single summary or translation. We explore the role of an advanced model as a reasoning layer connected to tools, data and permissions in our article GPT-5.5 for Business: A New Era of AI Agents. The scope of an agent’s autonomous actions can be expanded when test results confirm the required effectiveness and safety. Its permissions should be limited to the functions needed for the process, read access should be separated from write access, and actions with significant consequences for the company or customer should require approval. OWASP identifies excessive functionality, excessive permissions and excessive autonomy as the three main causes of Excessive Agency risk. The principle of least privilege limits the consequences of misinterpretation, fabricated information or an attack that feeds malicious instructions to the model (prompt injection). 5. Which Business Processes Are Suitable for a Small Language Model (SLM)? A Small Language Model uses fewer parameters and computing resources than a large language model. According to Microsoft Azure, this can support faster responses, lower infrastructure requirements and data processing close to where the data originates (edge computing), for example on industrial devices. A specialised SLM can be suitable for tasks with a fixed subject area, predictable inputs and a clearly defined output. An SLM is worth testing when a process meets several of the following conditions: a fixed set of categories, intents, fields or response types, a high and predictable volume of requests, a requirement for a short response time measured as p95, the threshold within which 95% of responses are completed, limited hardware resources, a need to operate offline or directly on a device, access to data from the relevant business domain and a set of reference answers, the ability to escalate difficult cases to a larger model or a person. Examples include classifying support tickets into 30 fixed categories, identifying a user’s intent, extracting field values from a single document type, generating short responses within a tightly defined subject area or analysing messages locally on an industrial device. The choice depends on testing with company data. An SLM should meet the required targets for quality, response time and cost per correctly handled case. For example, a company might require the smaller model to retain at least 98% of the reference LLM’s quality score, reduce the cost per correct result by at least 20% and stay within the p95 response time limit. These are illustrative decision thresholds that the process owner sets before the pilot. 5.1 When Should You Deploy an LLM or SLM On Premises or in a Private Cloud? A private or on-premises deployment may be driven by requirements relating to data sovereignty, security policies, business continuity, network latency or operation without internet access. Such a deployment can use an SLM or a larger model. An enterprise application can also use a managed API with encryption, retention controls, an appropriate processing region and contractual provisions governing data handling. A cascading architecture can be the most effective approach. Microsoft describes a hybrid model in which an SLM handles routine queries and passes more complex cases to an LLM. In a business setting, it is worth adding a third route to this cascade: referring the case to an employee when the result is uncertain, the risk is high or required data is missing. 6. Which Processes Should You Automate with Rules, RPA or Machine Learning? Processes governed by fixed rules require a clearly defined sequence of actions and conditions for carrying them out. AWS documentation on orchestration distinguishes between rule-based workflows, where successive states and transitions are explicitly defined, and agentic orchestration, where a model interprets the goal and dynamically selects tools. Both layers can operate within a single application. Process Recommended Mechanism Role of the Language Model Calculating tax, pay or a discount Code and a rules engine Explaining the result or interpreting the user’s query Executing a payment, refund or limit change A transactional process with access controls Identifying intent and preparing data for approval Granting or revoking permissions Identity and access management (IAM), role-based rules and approvals Handling a request expressed in natural language Checking that all required fields are complete A schema validator Extracting fields from an unstructured document Predicting customer churn or forecasting demand Traditional machine learning Explaining contributing factors and drafting communications Interpreting a free-form message An LLM or SLM Classifying intent and passing data to a controlled workflow In a financial process, an LLM can read a message, identify the request and prepare a proposal. Rules check the balance, limits, customer status and required approvals. The transactional system executes the operation once the conditions are met. Each layer performs a task for which clear criteria for correctness can be defined. 7. How Do You Match AI to a Business Process? Four Assessment Criteria An initial assessment can be carried out during a short workshop. The process owner evaluates the process across four areas, assigning a score from 0 to 3 in each. The individual scores help define requirements for the model, integrations, safeguards and infrastructure. Dimension 0 1 2 3 Complexity of content interpretation Fixed fields and rules A fixed set of categories Interpreting context Combining information and reasoning across multiple sources Integration and autonomy No access to systems Reading from a single source Reading from multiple sources or preparing data to be written to a system Transactions and dynamic tool selection Impact of an error Easily reversible Limited operational cost Significant financial, legal or reputational impact Critical or irreversible consequences, or an impact on rights and safety Infrastructure and data requirements A managed cloud meets the requirements A specific region or retention policy is required A private network or strict latency limit Operation offline, in an air-gapped environment, at the edge or directly on a device The scores can be interpreted as follows: Content interpretation complexity of 0-1 with stable rules: code, workflows, RPA or traditional ML. Complexity of 2-3, integration of 0-1 and error impact of 0-1: an enterprise LLM assistant with user review. Complexity of 2-3 and integration of 2-3: an integrated LLM application, RAG or an agent. Complexity of 1-2, a narrow domain and infrastructure and data requirements of 2-3: an SLM as a candidate for comparative testing. Error impact of 2-3: approval by an authorised person, safeguards based on predefined rules and a complete activity log, regardless of model type. The table helps identify solutions for a pilot. The final decision follows a comparison of their performance on the same set of real cases. 8. ChatGPT, Integrated LLMs, SLMs and Automation: Business Use Cases Example Process Recommended Architecture Key Performance Indicator Human Oversight Drafting marketing content An enterprise LLM assistant Median time saved and percentage of outputs accepted Approval of every publication Summarising a meeting and listing action items An enterprise assistant with access to an approved source Completeness of action items and number of corrections Verification of task owners and deadlines Answering questions about internal procedures An integrated LLM with RAG and source citations Percentage of answers grounded in sources and accuracy of citations Escalation when no source is available Assigning support tickets to predefined queues An SLM or classifier, with an LLM for exceptions Macro-F1 and the proportion of priority tickets correctly identified Review of uncertain cases Reviewing contracts against a company’s risk checklist An integrated LLM with RAG, rules and logging Rates of detected and missed risky clauses Decision by a lawyer Extracting fields from a single invoice type OCR, traditional ML or an SLM, combined with rule-based validation Field-level accuracy and cost per document processed Review of exceptions Preparing a quotation using CRM data and a price list An integrated LLM, RAG and price retrieval governed by predefined rules Preparation time and percentage of quotations requiring commercial corrections Approval of pricing and terms Checking refund eligibility Process rules Compliance with policy and time to decision Handling exceptions Executing a refund A transactional workflow with authorisation 100% accounting accuracy and a complete audit trail Depends on the amount and risk Analysing machine messages locally An SLM or specialised model running at the edge p95 response time, alarm detection rate and availability without an internet connection Escalation of critical alarms Revoking access for a departing employee IAM and a deterministic workflow Completeness of access revocation Approval in line with policy Handling a customer case across multiple steps An AI agent with a restricted set of tools Task success rate, correctness of tool use and percentage of cases referred to an employee Approval checkpoints for high-impact actions 9. How Do You Measure the Results of an AI Deployment? Quality, Time and Cost Metrics Measurement starts with the existing process. The Generative AI at Work study, involving 5,179 customer support employees, found an average increase of 14% in the number of issues resolved per hour. Less experienced employees saw the greatest improvement. Productivity defined this way has a clear numerator, denominator and comparison group. An enterprise pilot requires similar precision. Area Metric How to Measure It Scale Case volume Number of cases per month, seasonality and peak demand periods Time Case handling time Mean, median and p90 before and after deployment Quality Task success rate Percentage of cases meeting all criteria for correct task completion Usefulness Percentage of outputs accepted without substantive changes Proportion of outputs accepted without changes to their substance Oversight Percentage of AI decisions changed by an employee Proportion of decisions or proposals modified by an employee Risk Critical error rate Number of critical errors per 1,000 or 10,000 cases Classification Precision, recall and F1 score Measured separately for each important class, particularly rare events RAG Consistency of responses with sources Percentage of claims supported by a cited, up-to-date source Agent Correctness of tool selection and supplied parameters Whether the correct tool was selected and valid parameters were supplied Automation Percentage of cases handled entirely automatically Proportion of cases completed without manual intervention Performance Time from task initiation to the final result p50 and p95 of end-to-end task completion time Economics Cost per successfully completed task Total cost divided by the number of correct outcomes Stability Changes in system quality over time Variation in quality by time period, language, category and user type Google Cloud identifies cost per successfully completed task as a key metric for AI agents operating in real business processes. A model that costs USD 0.10 per run and achieves a 50% success rate incurs a model invocation cost alone of USD 0.20 per correct result. Human review, retries, integrations, monitoring and the cost of errors must also be included. 10. How Do You Calculate the ROI of an LLM or SLM Deployment? The full cost of the solution should include development and integration, the model or API, infrastructure, monitoring, updates, human review, corrections and expected losses resulting from errors. Cost per successfully completed task: C_success = (development cost allocated to the period + model/API + infrastructure + monitoring + human review + corrections + expected losses from errors) / number of successfully completed tasks Annual benefit: Benefit = value of working time saved + avoided correction and error costs + additional margin + avoided SLA penalties ROI: ROI = (Benefit – TCO of the AI solution) / TCO of the AI solution × 100% Suppose a team classifies 20,000 support tickets per month. Each ticket takes an average of 4 minutes, and the fully loaded hourly labour cost is PLN 120. The monthly cost of manual classification is approximately PLN 160,000. During the pilot, 88% of the system’s outputs are accepted without correction. Reviewing each output takes an average of 45 seconds, correcting the remaining cases takes 3 minutes each, and the monthly costs of the model, infrastructure, maintenance and amortised implementation total PLN 51,000. reviewing all cases: approximately PLN 30,000, correcting 12% of cases: approximately PLN 14,400, model, infrastructure, maintenance and implementation: PLN 51,000, total process cost after deployment: approximately PLN 95,400, monthly cost reduction: approximately PLN 64,600, or 40.4%. This example illustrates the calculation method using assumed values. A financial assessment of an actual deployment must account for changes in the cost of errors, seasonality, downtime, exception handling costs and the pace of employee adoption. For a revenue-generating process, the calculation should also include changes in margin, conversion rate or customer retention. 11. How Do You Run a Measurable LLM or SLM Pilot? Establish a baseline. Measure volume, time, quality, errors, escalations and the cost of the current approach over at least one full business cycle. Prepare a test dataset. Include typical tasks, difficult and rare situations, boundary cases and deliberate attempts to mislead the system. Google Cloud recommends a custom dataset that reflects the full range of intended uses. Set thresholds before testing. Document the minimum quality, maximum cost, acceptable latency, critical error limit and escalation rules. Compare several approaches. Include the current process, a capable LLM, a smaller model and a hybrid architecture. Run the system in shadow mode. AI generates outputs alongside the existing process. Decisions and actions continue under the established rules. This helps identify errors before AI is allowed to handle live operations. Deploy the system within a limited scope. Start with proposals and approvals. Expand automated actions based on the results. Monitor performance quality. Repeat testing whenever the model, AI instructions, tools, data sources, rules or types of input material change. 11.1 How Many Test Cases Do You Need to Evaluate an LLM or SLM? For a metric expressed as a proportion, a conservative sample size at a 95% confidence level and a margin of error of +/-5 percentage points is approximately 385 independent cases. A margin of +/-3 percentage points requires approximately 1,068 cases. The representative sample should be supplemented with a separate set of critical and edge cases. For rare errors, the so-called rule of three is useful. If no critical errors occur in 300 tests, the approximate upper bound on their true probability at a 95% confidence level is still around 1%. Zero errors in 3,000 tests reduces that bound to approximately 0.1%. High-risk processes therefore require much larger datasets and tests targeting specific threats. 11.2 When Should You Complete an AI Pilot and Move into Production? Example Criteria Process Example Criteria for Moving into Production Support ticket classification Macro-F1 of at least 0.90; recall for priority tickets of at least 0.99; override rate no higher than 8%; p95 response time no longer than 2 seconds Internal knowledge base Acceptance rate of at least 85%; citation accuracy of at least 98%; no unsupported claims in the critical test set; p95 response time no longer than 8 seconds Draft quotation Median preparation time reduced by at least 30%; at least 75% of drafts accepted with minor changes; 100% of prices retrieved from an authorised source; human approval for every quotation These values illustrate how to define acceptance criteria. The process owner sets the thresholds according to the cost of errors, required quality and the organisation’s risk tolerance. 12. How Do You Match Human Oversight to AI Risk? NIST defines risk as a combination of the likelihood of an event and the scale of its consequences. This principle translates general concerns about AI into a measurable model: error frequency, the value of funds or resources at risk, the ability to reverse an action, the time needed to detect an error and the cost of correcting it. In the consolidated text of the EU AI Act, requirements for high-risk systems include continuous risk management, appropriate levels of accuracy, robustness and cybersecurity, and effective human oversight. Oversight measures should be proportionate to the risk, degree of autonomy and context of use. Testing should use predefined metrics and thresholds appropriate to the intended purpose. In business practice, this means assigning a specific person responsibility for approvals, monitoring and intervention. The interface should display sources, actions taken and the level of uncertainty, and the user must be able to stop the process. The risk of serious consequences from an error justifies restricting model permissions, adding further checks and extending shadow-mode testing. The regulatory classification should be assessed separately for the intended use and the organisation’s role. 13. How Can You Combine LLMs, SLMs and Rules in a Single Business Process? Combining several technologies allows each stage of a process to be handled appropriately. For example, an SLM identifies the topic of a customer’s request, while an LLM drafts a response using the contact history and documents retrieved through RAG. If the case involves a refund, the system checks conditions and limits against established rules, then routes the operation for execution or approval by an authorised employee. This approach combines automation with oversight of decisions that have financial consequences. At TTMS, we can help you assess where a similar solution would deliver the greatest benefit. We will start with the tasks that take up the most time: repetitive activities, searching for information or correcting errors. During a consultation, we will examine your workflows, available data and existing systems. Based on this assessment, we will recommend the technology and pilot scope, then work with you to define the expected outcomes and how to measure quality, time and costs. Ready to choose your first process to improve? Book a consultation with us about implementing AI. Sources Eurostat, 20% of EU enterprises use AI technologies, 11 December 2025. Fabrizio Dell’Acqua et al., Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality, Organization Science. Erik Brynjolfsson, Danielle Li, Lindsey Raymond, Generative AI at Work, NBER Working Paper 31161, 2023. Joel Becker, Nate Rush, Beth Barnes, David Rein, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, METR, 10 July 2025. Microsoft Azure, What Are Small Language Models (SLMs)? Microsoft Azure, Boost processing performance by combining AI models, 8 January 2025. OpenAI, Enterprise privacy at OpenAI. Google Cloud, The KPIs that actually matter for production AI agents, 26 February 2026. NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1, July 2024. European Union, Regulation (EU) 2024/1689, consolidated text of 27 July 2026. OWASP GenAI Security Project, LLM06:2025 Excessive Agency, 2025. FAQ Do You Need to Run Your Own AI Model to Work with Company Data? Running your own model is one of several options. Companies can use an approved business environment, a managed API, a private cloud, dedicated infrastructure or a model running locally. The choice depends on data classification, processing location, retention, encryption, user identities, industry requirements and the agreement with the provider. For example, OpenAI states that business customer data is not used for training by default and offers additional retention controls for eligible API customers. Organisations should document the data flow for their specific configuration, as the model’s name does not describe the full security architecture. Can RAG Replace Fine-Tuning a Model on Company Data? RAG and fine-tuning address different needs. RAG retrieves current information from a controlled source when generating a response, making it well suited to knowledge bases, procedures, documentation and frequently updated content. Fine-tuning uses examples to adapt a model’s behaviour to a particular style, format or specialised task. AWS documentation comparing RAG and fine-tuning recommends starting with RAG for a question-answering system based on your own documents, particularly when up-to-date information and source references matter. Both approaches can work together when a process requires current knowledge and consistent behaviour within a specific domain. Can a Single Process Use Both an LLM and an SLM? Yes. A routing component can direct routine, clearly identified cases to an SLM and complex cases to a larger LLM. A third route passes cases to a person when the system detects missing data, low confidence or a high level of risk. Another option is to divide the process by function: an SLM classifies the document, an LLM prepares an explanation, code calculates values, and a workflow records the approved decision. This setup helps control costs and response times while retaining access to more advanced capabilities for difficult cases. How Many Examples Do You Need for an LLM or SLM Pilot? The number depends on the required measurement precision and how rare the errors are. When measuring the proportion of successful responses, a sample of approximately 385 independent cases provides an approximate margin of error of +/-5 percentage points at a 95% confidence level under conservative assumptions. A margin of +/-3 percentage points requires approximately 1,068 cases. The random sample should reflect the actual distribution of cases across languages, channels and user types, in proportion to their volumes. A separate test set should cover critical, edge and rare cases, along with attempts to manipulate the system. How Often Should You Retest an Application Built on a Language Model? A full evaluation should be run whenever the model, prompt version, tool, permissions, data source, business rules or input format changes. Production use also requires continuous monitoring of key performance indicators and regular regression testing. The frequency depends on the level of risk and how quickly the process changes. An application handling marketing content may follow a different schedule from a system supporting financial decisions. A practical approach is to run automated tests after every technical change, review trends monthly and conduct a business evaluation quarterly, with shorter cycles for high-risk applications.
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How to Prepare Data for Power BI Copilot and Build a Semantic Model for AI
Copilot can speed up data analysis, visual creation and work with semantic models. It does not replace well-managed data sources, correct relationships or agreed metric definitions. If a model contains several similar sales measures, technical column names and ambiguous relationships, the AI assistant inherits the same problems that already affect report users. The difference is that a natural-language answer may sound convincing even when it relies on the wrong metric. To prepare data for Power BI Copilot, organisations need to improve model quality, provide business context and define how the data may be used. Enabling Copilot alone will not make an inconsistent model unambiguous. The model needs clear names, validated measures, a controlled data scope, appropriate permissions and a test set based on questions that users actually ask. This guide explains how to prepare Power BI data and semantic models for Copilot, how to use Prep data for AI and how to assess readiness before making the solution available to employees. It focuses on implementation and ongoing quality control. The broader capabilities of the assistant are covered in a separate TTMS article about AI and Copilot in Power BI. 1. Power BI model readiness for AI An AI-ready model allows users to ask questions in business language without knowing the technical names of tables and columns. This does not mean that Copilot knows the organisation or can discover every internal rule on its own. It can use the information provided through the model, its metadata, the AI feature configuration and the report context. The quality of this layer determines whether a question about sales is mapped to the official net revenue measure, order value or another similarly named field. Assess model readiness across five areas: the quality and freshness of source data; the accuracy and simplicity of the semantic model; unambiguous business concepts, names and measures; data security, permissions and ownership; a repeatable process for testing Copilot answers. If one of these areas is weak, refining prompts will have limited value. A user may phrase a question more precisely but still cannot know which of three margin measures is official or why one table uses the order date while another uses the invoice date. 2. Why the semantic model determines answer quality The semantic model sits between source data and the report user. It contains tables, columns, measures, relationships, formats, hierarchies and security rules. For Copilot, it is the main source of information about how data is organised and how it should be interpreted. The assistant may use the model schema, relationships, object properties, data types, formats and selected metadata. It should not be expected to infer definitions that carry a specific meaning within the organisation. If an active customer is defined as a customer who purchased something in the past 90 days, that definition should be implemented in the model and used by the official measure. Leaving several plausible interpretations increases the risk that Copilot will select the wrong one. 2.1 An example of an ambiguous model Suppose a model contains measures called Sales, Total Sales, Net Sales and Sales Adjusted. An analyst familiar with the project may understand the differences. A business user and Copilot see four plausible answers to the question, “What were sales last quarter?” The fix is not simply to add an instruction telling Copilot to choose one measure. First, the organisation should agree the official definition, give it a clear name, describe the calculation, hide technical fields and remove unused objects. An AI instruction can then clarify the context, but it should not compensate for disorder in the model. 3. Technical requirements before work begins Before redesigning the model, confirm that the environment meets Microsoft’s current requirements. Copilot availability depends on tenant settings, permissions, the workspace and the assigned capacity. Requirements can vary between Power BI Desktop, the Power BI service and individual Copilot experiences. At the time of writing, Microsoft’s documentation for Copilot in Power BI identifies requirements that include enabling the relevant tenant setting and using supported paid Fabric capacity or Power BI Premium capacity. Model authors also need the appropriate permissions for the workspace and semantic model. Prep data for AI is currently a preview feature. Before implementing it, account for current limitations, including the requirement to enable Power BI Q&A and the connection types supported in Power BI Desktop. Area What to check Why it matters Capacity Whether the workspace uses supported paid Fabric or Power BI Premium capacity Without the required capacity, some Copilot experiences may be unavailable Tenant settings Whether the administrator has enabled Copilot and Azure OpenAI based features for the relevant groups Access should be granted deliberately and in line with organisational policy Permissions Whether the author can edit the model and publish to the target workspace Model configuration requires permissions appropriate to the environment Connection type Whether the connection is supported by the feature used in Desktop or the service Support differs between tools and may change Power BI Q&A Whether Q&A is enabled for the model This is currently one of the requirements for Prep data for AI Licensing and feature requirements should not be copied into an internal procedure and treated as permanent. Microsoft continues to develop Copilot and change the availability of individual experiences. Check the latest documentation and the organisation’s tenant settings before implementation. 4. How to prepare data for Power BI Copilot Data needs to be reliable before it reaches the model. Copilot will not repair missing records, incorrect customer mappings or inconsistent currency codes. It can, however, use faulty data to generate an answer that hides the problem behind a plausible explanation. 4.1 Source data quality Start with data profiling and quality controls. Check completeness, uniqueness, consistency, freshness and compliance with business rules. These controls should be repeatable, not limited to the period before the first publication. In practice, this includes: identifying missing keys and orphaned records; checking for duplicates in dimension tables; aligning time zones, calendars, currencies and units; confirming that status values have the same meaning across systems; assigning data ownership and a process for resolving quality issues; monitoring data freshness and failed refreshes. Every official metric should have an identified source, owner, refresh frequency and calculation rule. This gives the organisation a reference value against which to test a Copilot answer, rather than judging the answer only by whether it sounds reasonable. 4.2 Names that match business language and stable definitions Technical names such as fct_sales_hdr, cust_id and rev_net_adj make the model harder for people to use and make user intent more difficult to interpret. The semantic layer should use names that match the language of the organisation, such as Sales, Customer, Net Revenue and Sales Region. Readable names alone are not enough. Margin remains ambiguous if the organisation uses margin amount, margin percentage, planned margin and adjusted margin. Each measure needs a precise name and definition. If one measure is official, the model should reflect that status through naming, its description, display folders and restricted visibility for supporting fields. 4.3 Correct data types and formats The data type tells the model whether a value is a date, number, text or category. The format controls how the value is presented, for example as currency, a percentage or a decimal. An incorrect type can prevent valid grouping and calculations, while an ambiguous format can cause users to misinterpret the result. Data categories should also be assigned where relevant, including for addresses, cities and geographic codes. The model needs to distinguish clearly between order, invoice, shipping and payment dates. A marked date table and explicit measures reduce the number of accidental interpretations. 5. A semantic model that Copilot can understand Microsoft’s tutorial on preparing a semantic model for AI recommends practices that include star schema design, clear naming and reduced complexity. This does not require every model to look the same. The goal is to make relationships, table roles and measure definitions unambiguous. 5.1 Star schema In a star schema, fact tables store events or numeric values and dimension tables provide context such as customer, product, time and region. This structure helps users and AI systems distinguish what is being measured from the dimensions used to break down the result. A complex snowflake model, numerous helper tables and several possible filter paths may be technically justified, but they make interpretation more difficult. If the physical model cannot be simplified, use the AI data schema to narrow the part exposed to Copilot. 5.2 Relationships and filter direction Relationships should reflect the actual data logic. Review their cardinality, active status and filter direction. Bidirectional relationships and multiple alternative paths can produce unexpected results, particularly when a question does not specify enough context. Document inactive relationships and special rules used by selected measures. If analysing sales by shipping date requires different logic from analysing by order date, users need to know how to phrase the question and the model should provide clearly differentiated measures. 5.3 Explicit DAX measures Explicit DAX measures place approved business logic in one location. They provide a safer basis for answers than ad hoc aggregation of numeric columns. Each measure should have a clear name, correct format, useful description and a business owner. Before making a model available to Copilot, check: whether official KPIs are implemented as measures; whether similar or duplicate names remain in the model; whether the result format matches the business meaning; whether measures work correctly across filters and aggregation levels; whether technical fields and helper measures are hidden from users; whether results have been reconciled with reference reports. 6. Prep data for AI in Power BI Prep data for AI is a collection of tools that saves configuration at semantic model level rather than on an individual report. This matters because one model can support multiple reports. A change to the AI schema or instructions may therefore affect more than one use case. Microsoft describes four elements used to prepare a model for natural-language interaction: the AI data schema, verified answers, AI instructions and descriptions. These elements do not work in exactly the same way across every Copilot experience. Test the configuration in the same experiences that users will access. Mechanism Purpose When it is particularly useful What it does not replace AI data schema Defines the subset of tables, columns and measures made available to Copilot When a model is large or contains technical fields and similar measures Data cleansing and correct relationships Verified answers Connect approved visuals to trigger phrases For frequent or ambiguous questions that need a consistent interpretation Source data testing and access controls AI instructions Provide rules, definitions and business context When the organisation uses terms whose meaning cannot be inferred from field names Official measures and an unambiguous model Descriptions Document the meaning of tables, columns and measures When an object’s name does not fully explain its use Instructions that cover rules across the domain 6.1 AI data schema The AI data schema limits the part of the model that Copilot considers when answering questions about data. Do not expose the entire schema by default. Technical tables, key fields, unused measures and objects created only to support a report increase the number of possible interpretations. A well-designed AI schema should include official dimensions, approved measures and the fields required for common analyses. Review the scope with business process owners. A schema that is too narrow will prevent valid questions from being answered, while one that is too broad may increase ambiguity. 6.2 Verified answers Verified answers connect an approved visual to defined trigger phrases. They are useful when users frequently ask about the same indicator or use several terms with a similar meaning. Consider a question about sales by area. In one organisation, area may mean a geographic region; in another, it may refer to a product group. A verified answer can direct the relevant wording to a visual that has already been checked. The underlying measure, filters and permissions still need to be tested. For each verified answer, record an owner, the supported questions, the metric source and the date of the latest review. A change to the indicator definition or report structure should trigger another validation. 6.3 AI instructions AI instructions give Copilot context that the model structure alone cannot express easily. They can explain organisational terminology, preferred measures, rules for interpreting periods and relationships between concepts. For example, an instruction may state that an active customer is one who purchased in the past 90 days and that peak season covers June to August. It should use the exact names of model objects and short, testable rules. Instructions are not a security control and do not guarantee that every rule will be followed. Microsoft notes that the language model treats them as guidance. Important financial and operational definitions should still be implemented through measures, relationships and data governance processes. 6.4 Descriptions for model objects Descriptions should explain an object’s meaning, intended use and relevant limitations. Instead of sales value, specify that the measure represents net revenue after discounts and returns, in the reporting currency and by invoice date. According to Microsoft’s current documentation, descriptions do not affect every Copilot capability in the same way. They are used in selected search and DAX query scenarios. They are still worth maintaining because they improve model documentation and prepare the model for further development of AI features. 7. Business context for Copilot A model can be technically correct and still fail to reflect the language used in the business. The sales team may use the term active customer, finance may refer to recognised revenue and operations may discuss a closed order. Each concept needs a definition and an owner. A business glossary is a useful starting point. It should include: the term and its accepted synonyms; a definition approved by the business owner; the corresponding table, column or measure in Power BI; the applicable time, currency, scope and aggregation rules; exceptions and situations in which the metric should not be used; the person responsible for approving changes. The glossary should not exist only outside Power BI. Transfer its most important information into names, descriptions, measures, AI instructions and verified answers. Otherwise, users and Copilot will continue to work with incomplete context. 8. Data security and governance Copilot makes it easier to ask questions, but the core access principle remains the same: users should only have access to the data required for their role. Review workspace and model permissions, row-level security roles, object-level security, Microsoft Entra groups and the way reports and models are shared. The control design should also reflect Microsoft’s guidance on the privacy, security and responsible use of Copilot in Microsoft Fabric. Write permissions require particular attention. In Power BI, the enforcement of row-level security depends in part on the user’s role and permissions for the model. Testing should use accounts that represent real user roles, not only an author or administrator account. Check whether object names, descriptions and report metadata reveal information that a user should not see. Microsoft’s documentation on using Copilot with semantic models states that, in Power BI Desktop, metadata from the current report page may in some situations be used as grounding data and may contain data values. A security assessment should therefore cover both the records and the descriptive layer of the model. Governance should define: who may prepare a model for AI use; who approves definitions and verified answers; which models may be marked as Approved for Copilot; how often regression tests are run; how incorrect answers are reported and analysed; when a model change requires renewed approval. 9. Testing Copilot answers A test should involve more than one sample question. Build a set of scenarios that reflects the language and needs of users. For each question, define the expected measure, filter scope, source of the reference value and acceptable presentation. Test type Example What to assess Basic question What were net sales last month Selection of the measure, period and value format Synonym Show turnover by region Whether the user’s language maps correctly to the official concept Ambiguous question Show the result for each area Whether Copilot asks for clarification or selects the approved interpretation Complex filters Sales to manufacturing customers in Poland last quarter Accuracy of all filters and relationships Permissions The same question asked by users from different regions Whether each user sees only the permitted data scope Change resilience Repeating tests after a measure or relationship changes Whether the update has degraded previously correct answers Assessment should cover the numeric result and how Copilot arrived at the answer. Where available, diagnostic information about how Copilot created an answer can help with investigation. The explanation of the mechanism is not proof that the result is correct. The approved metric and controlled data set remain the reference point. Copilot outputs are nondeterministic. The same prompt and grounding data can therefore produce different results. In some experiences, however, asking the same question within 24 hours while the model remains unchanged may return a cached answer. Testing is not intended to prove that every answer will always be identical. It should show that the model directs Copilot to the right data, common questions receive correct answers and users understand the known risks and limitations. 10. Common mistakes when preparing Power BI for AI 10.1 Enabling Copilot before cleaning up the model The team focuses on licensing and settings but does not review names, relationships and measures. Copilot is enabled on a model that analysts already found difficult to use. The result is ambiguous answers and a rapid loss of user trust. 10.2 Exposing the entire schema Every table and field is included in the AI data schema, including technical keys, helper measures and unused objects. More elements do not necessarily provide better context. In a large model, they can make it harder to select the correct field. 10.3 Treating AI instructions as a substitute for modelling The instructions attempt to explain dozens of exceptions that should be implemented in measures and model rules. Such a document is difficult to test and maintain. The more important the rule, the stronger the case for enforcing it in the model or data process rather than describing it only in natural language. 10.4 No ownership of metrics Analysts create verified answers without formal confirmation of which indicator definition is authoritative. When results differ, no one knows who can approve a change or which value should be used as the reference. 10.5 Testing only by model authors Model authors know the names and data structure, so they ask questions that fit the design. Business users rely on abbreviations, synonyms and incomplete terms. Tests need to include real questions collected from intended users. 10.6 No regression testing after changes A change to a measure definition, relationship, field name or report can affect earlier scenarios. Without regression testing, the organisation cannot know whether the model remains ready for Copilot. 11. Power BI Copilot readiness checklist [ ] We have identified owners for data, models and key metrics. [ ] Source data is subject to repeatable quality and freshness checks. [ ] Official KPIs have unambiguous definitions and explicit DAX measures. [ ] Table, column and measure names match the language used by the business. [ ] Technical, unused and supporting fields are hidden or removed from the AI scope. [ ] Data types, formats, categories and the date table are configured correctly. [ ] Relationships use the correct cardinality and do not create ambiguous filter paths. [ ] We have defined an AI data schema that includes only the required objects. [ ] Common and ambiguous questions have verified answers where appropriate. [ ] AI instructions explain terminology and rules that cannot be inferred from the model structure. [ ] Tables, columns and measures have useful descriptions. [ ] We have checked tenant settings, capacity, licensing and current feature limitations. [ ] We have tested RLS roles, permissions and model access with test user accounts. [ ] The test set covers basic questions, synonyms, ambiguity and complex filters. [ ] Copilot results are compared with approved reports or reference values. [ ] We have defined a process for reporting incorrect answers and revalidating the model. [ ] If the organisation uses the Approved for Copilot setting, which is currently in preview, the model is marked only after testing is complete. 12. Preparing the organisation to use Copilot Even a well-prepared model will not help if users do not know how to interpret the answers. Implementation should include short training, sample questions, an explanation of the data scope and rules for validating results. Make clear which scenarios provide decision support and which require review by an analyst or process owner. A pilot based on one model with a clearly defined scope is a practical starting point. It allows the team to collect user questions, assess ambiguity and build a test set before extending the feature to other areas. The pilot should have completion criteria, such as correct handling of priority scenarios, approval from metric owners and no critical permission issues. After launch, monitor usage, capacity cost, user reports and changes to Microsoft’s documentation. AI readiness requires ongoing monitoring and another round of testing after material changes. 13. Why TTMS Preparing Power BI for Copilot requires skills in data integration, semantic modelling, Power BI, Microsoft Fabric, security and user adoption. Focusing only on the Copilot interface does not address problems in data sources, transformations and business definitions. An engagement with TTMS can cover an assessment of existing model readiness, improvements to the data layer, changes to relationships and measures, and preparation of the test approach. Prep data for AI configuration should follow confirmation of the environment requirements and agreement on the scope of work. The engagement may focus on one pilot model or a programme spanning multiple domains and teams. A practical engagement may include: An inventory of data sources, models, reports and user groups. An assessment of data quality, model architecture and the risk of ambiguous answers. Agreement on official metrics and a glossary of business concepts. Semantic model optimisation and configuration of the AI data schema, verified answers and AI instructions. Functional, regression, security and performance testing. Preparation of governance rules, documentation and user materials. Model maintenance and renewed validation after changes to data or Microsoft features. The intended result is a model whose structure is clear to analysts and business users, with Copilot answers that can be assessed against approved definitions. The aim is to reduce ambiguity and establish a quality-control process. Even a well-prepared model cannot guarantee error-free generative AI output. 14. Discuss Power BI AI readiness If your organisation uses Power BI and plans to make Copilot available, start with a review of the data and semantic models. TTMS can help define the pilot scope, identify gaps and prepare a roadmap from data sources through to user testing. Contact TTMS to discuss preparing your Power BI and Microsoft Fabric environment for the secure use of AI capabilities. 15. FAQ Who should own a Power BI semantic model prepared for Copilot? A semantic model prepared for Copilot should have clearly assigned owners for data quality, KPI definitions and model maintenance. Ownership helps ensure that business terms, measures and AI configurations remain accurate as data sources, processes and reporting requirements evolve. How large should the AI data schema be? The AI data schema should include only the tables, columns and measures needed for common business questions. Exposing too many technical objects can increase ambiguity, while an overly restrictive schema may prevent Copilot from answering valid questions. Can Power BI Copilot use business terminology that does not exist in the data model? Copilot can better understand business terminology when organisations provide context through measure names, descriptions, AI instructions and verified answers. However, important business concepts should still be represented directly in the semantic model whenever possible. When should a Power BI model be revalidated for Copilot? A model should be revalidated after significant changes to data sources, KPI definitions, relationships, security settings, AI configurations or Microsoft Copilot features. Regular regression testing helps confirm that previously validated scenarios still return correct results. Is preparing a model for Copilot a one-time project? No. Copilot readiness should be treated as an ongoing governance process rather than a one-time implementation task. As data, business definitions and AI capabilities change, organisations need to review model quality, test key scenarios and update documentation on a regular basis.
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GPT-6 Astra in Microsoft 365 Copilot: Access, Tasks and Cowork Costs
Does your company use Copilot, and would you like to try GPT-6 Astra? OpenAI’s model is also available in Copilot Cowork. This means you can try it when working with documents, email and calendars in Microsoft’s environment. Access to Astra depends on your organisation’s licences and settings, while tasks performed in Cowork are billed based on credit usage. What does your administrator need to enable? Which tasks can you delegate to Astra in Cowork, and how are they handled in ChatGPT Work? Below, we explain access requirements, differences in working with files and billing rules. For guidance on choosing an assistant for your organisation, see our comparison of Microsoft Copilot and ChatGPT for business. 1. What Does GPT-6 Astra Bring to Copilot Cowork? Microsoft lists GPT-6 Astra among the models available in Copilot Cowork. Users select a model from the list enabled by their organisation. The default Auto setting lets Cowork choose a model for the task; a label next to the response shows which model was used. Selecting Astra applies to work within Cowork. The availability of a particular model in other Copilot features needs to be checked separately. GPT-6 Astra is another model you can assign tasks to in Cowork. Cowork itself provides the tools for finding information, creating files and taking action in Microsoft 365. Your choice of model may affect how information is analysed, the level of detail in the response and the time taken to complete the task. When evaluating Astra, check whether it handles an existing task better: whether it brings together findings more accurately, accounts for exceptions and produces a result that requires fewer revisions. Work IQ gives Cowork access to the context of your organisation’s work. When preparing a project summary, the information needed may be spread across documents, correspondence and meeting materials. Cowork can search for the organisational resources required for the task. Before trying it, check that the employee’s account has access to the relevant materials and that they include the latest decisions and updates. This determines which information Astra will use to produce its result. We discuss the model’s test results and examples of its use in our article GPT-6 Astra: Impressive Achievements and New Possibilities for Business. 2. How Can You Access Astra in Copilot Cowork? For business users, Microsoft describes Cowork as a service that requires a Microsoft 365 Copilot licence and usage-based billing for task execution. An administrator then needs to configure employee access. There are two separate settings to configure: Access to Cowork. The employee must belong to a group covered by a spending policy that includes Cowork. The administrator configures this in the Microsoft 365 admin centre under Copilot, Cost Management, Configuration. This is where they specify the users, budget and billing method. Access to models provided by OpenAI. In the Copilot settings, the administrator specifies which users can use OpenAI as a Microsoft subprocessor. Once you have access, open Cowork and select Astra from the model list. For your first task, check the model label next to the response. If an employee can see Cowork but cannot find Astra, the administrator should check the model provider settings. To make Cowork available to a specific team, the administrator must grant access to the relevant user group. A low credit limit restricts spending while still allowing employees covered by it to get started. 3. Astra in Cowork and ChatGPT Work: Differences in Task Execution Preparing a report involves finding up-to-date data, processing it and saving the result somewhere the team can access. At each stage, the tools available to the model matter. The comparison below shows how the two environments work with the materials needed for a task. Working with Materials in Copilot Cowork and ChatGPT Work Task Component Copilot Cowork ChatGPT Work Finding materials Searches Microsoft 365 resources accessible to the user, including email and files. Plugins can provide access to additional sources. Uses files provided for the task and information retrieved through enabled apps and authorised accounts. Working on documents Creates and modifies documents, spreadsheets and presentations. Output files are saved to the workspace in OneDrive or SharePoint. Creates and edits files. Transferring them to another system depends on the operations supported by the connection to that system. Files stored on the computer A file can be uploaded to the session. Cowork does not edit files directly on the user’s drive. Work in a supported desktop app can use local files once the appropriate access has been granted. Using an application through a browser The local Edge browser uses the employee’s existing sign-in. The feature must be enabled by an administrator. Access depends on the browser tool selected and the permissions granted. A cloud task requires separate authorisation to access company resources. When working through a browser, you need to consider where the task is running. Cowork supports the local browser when its web version is open in Edge. This feature is currently unavailable in the Copilot desktop app and on mobile devices. Cowork and Edge must also use the same work account. If the computer goes to sleep, actions requiring the local browser may be paused. In ChatGPT Work, the model can use shared files and applications on the computer during a local task. A task launched in the cloud runs in a separate environment. If the required materials are stored only on the employee’s drive or are accessible through a company VPN, they need to be made available to that environment through a supported method. As it works, Cowork displays the successive stages of the task. You can interrupt the session, clarify your instructions or provide missing information. Before taking significant actions, such as sending a message or scheduling a meeting, Cowork asks for approval. The additional confirmations it requests also depend on permissions granted earlier. For your first trial, choose a task for which you can clearly identify both the source materials and where the result should be saved. You can find examples of responsibilities in sales, HR, finance and other departments in our overview of 10 practical uses of Microsoft Copilot in an organisation. 4. How Much Does It Cost to Use Astra in Cowork and ChatGPT Work? Your budget needs to cover both the subscription and the use of tools to carry out tasks. For a company that already has the appropriate licences, enabling Cowork primarily means budgeting for usage charges. Below are the public prices for selected business plans. Subscription prices. Charges for task execution are explained below. Plan Monthly Price per User Terms Microsoft 365 Copilot Business EUR 18.20; currently EUR 15.60 under a promotional offer Billed annually, excluding tax. A separate qualifying Microsoft 365 licence is required. Available for up to 300 users. Microsoft 365 Copilot for enterprise EUR 26 Billed annually, excluding tax. A separate qualifying Microsoft 365 licence is required. ChatGPT Business USD 20 when billed annually or USD 25 when billed monthly A minimum of two users. Public pricing in USD; the final amount depends on factors including taxes and the market where the subscription is purchased. ChatGPT Enterprise Custom pricing Usage limits and billing are defined in the agreement. The Copilot Business promotion applies to the first year with an annual commitment and runs from 1 July to 31 December 2026. 4.1 How Are Cowork Tasks Billed? Cowork charges for factors including model usage, context retrieval, tool calls and runtime. Usage is converted into Copilot Credits; under the published pay-as-you-go pricing, one credit costs USD 0.01. One thousand credits therefore cost USD 10. The cost of an individual task depends on the number of credits consumed. The selected reasoning level also affects usage. Cowork offers Light, Medium, High, Extra High and Max settings. A higher level may increase task duration and credit consumption. For recurring work, check whether increasing this setting improves the result enough to justify the cost. 4.2 What Does Credit Usage Mean in ChatGPT Work? ChatGPT Work follows the usage limits and billing rules of the relevant plan. Under agreements based on a shared credit pool, tasks reduce the available balance. Credits already paid for under the agreement are covered by that payment. Additional charges may arise once those credits run out, if the agreement and settings allow work to continue. When comparing costs, use the same set of tasks and output requirements. Record usage, the number of retries and the extent of any revisions needed. Calculating the cost per successfully completed task shows how much you pay for a result your team can use. First, convert each service’s credit usage into a monetary amount using its own pricing. 5. What Data Protection Rules Apply to Astra in Cowork? In Copilot Cowork, Astra is provided by OpenAI as a Microsoft subprocessor. According to the documentation, this use of the model is governed by Microsoft’s terms and Data Protection Addendum, subject to specified exclusions. These services fall within the EU Data Boundary, with documented exceptions. Microsoft currently excludes them from its commitments to process data in a specific country. This detail matters to organisations that require processing exclusively in Poland, for example. When enabling Astra, the administrator should therefore consider the model provider’s policies and access to the materials used in the task. In ChatGPT Work, whether the task runs locally or in the cloud also matters. During a local task, file excerpts, screenshots and tool outputs may be sent to OpenAI. Company AI policies should account for this method of sharing information as well. 6. Which Task Should You Start with When Trying Astra? Start with a responsibility that regularly involves an employee gathering information and preparing material for other people. This workflow lets you assess both Astra’s analysis and the tools available in Cowork or Work. A weekly project summary is one example. A sample prompt for your own trial: Using the project folder [link] and correspondence about this project from the past seven days, prepare a report for the manager. List revised deadlines, pending decisions and the people responsible for next steps. Provide a source and date for each finding. If the materials contain conflicting information, show the discrepancy and explain what you need to resolve it. Save the report as a DOCX file using the attached template in the folder [link]. Draft a message to [recipients] with a link to the report. Leave sending it subject to my approval. Check whether the report reflects the latest decisions and updates, provides sources and dates, identifies conflicting information and assigns responsibilities correctly. Also assess whether it follows the template and whether the file has been saved in a folder accessible to its recipients. After a successful trial, you can consider running the task regularly. Cowork supports scheduled tasks and tasks triggered by events such as an email or a Teams post. By default, event-triggered tasks prepare actions for approval. We discuss how to design the entire process in our guide to business process automation with Copilot. 7. Prepare Your First Astra Tasks with TTMS Through our AI consulting services, we help you determine which data a task requires, which tools need to be made available and how to assess the result. We also analyse the required licences and usage billing arrangements. We combine consulting with AI solution design and the integration of business systems. TTMS was the first company in Poland to obtain accredited ISO/IEC 42001 certification for its artificial intelligence management system. The TÜV Nord Poland audit covered AI design and usage policies, including risk management and project documentation. Tell us which task you would like to delegate to Astra and which applications your team uses. Talk to TTMS about AI consulting for your business. GPT-6 Astra in Copilot Cowork: Frequently Asked Questions Does selecting Astra in Cowork change the model across all Copilot applications? The selection applies to work within Cowork. Microsoft describes a separate model selection option for this environment. To find out which model powers a particular feature in Word, Excel or Teams, check that feature’s documentation. When reviewing a Cowork task, you can see which model was used by checking the label next to the response. Will the same model give an identical response in Copilot and ChatGPT? The result may differ. The model works with the information provided by each product and uses its tools, instructions and reasoning settings. When comparing results, check which materials the model received and which actions it could perform. Only then can you meaningfully assess the differences in the outputs. Why can I see Cowork but cannot select GPT-6 Astra? Access to Cowork and access to OpenAI models are controlled by separate settings. Your administrator should confirm that your account is allowed to use models provided by OpenAI as a Microsoft subprocessor. The model list displayed in Cowork reflects the access granted by your organisation. Does a Microsoft 365 Copilot subscription cover all Cowork tasks? Cowork tasks incur additional usage-based charges. Copilot Credit consumption depends on factors including the model, information retrieval and tools used. Administrators can set spending policies for users and groups. Your budget should account for both the subscription and expected Cowork usage. Can Astra in Cowork edit a document saved on my computer? You can upload a document to a Cowork session. According to the current FAQ, the service does not open or edit files directly on your local drive. Cowork works with the materials you provide and files available in OneDrive and SharePoint. Support for the Edge browser is a separate feature. Will the same Astra model produce the same result in Cowork and ChatGPT Work? The result also depends on the available data, instructions, tools and reasoning settings. A task performed using the same model may therefore proceed differently in the two environments. Comparing results using the same materials will reveal differences in the completeness of the output and the actions performed.
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GPT-6 Astra: Impressive Achievements, New Opportunities for Business
OpenAI unveiled GPT-6 Astra on 3 September 2026, and early research findings and user reports show why the release is generating so much excitement. The new model solved mathematical problems that earlier GPT models and competing models failed to solve in the same test. Users have already tested GPT-6 across a range of tasks, from rebuilding a sales workflow in a CRM system and creating a detailed steam locomotive model to analysing a novel spanning more than 500 pages. What exactly has it achieved, and how can businesses put these capabilities to use? 1. GPT-6 Astra’s early achievements are impressive Early tests and user reports show how Astra handles complex tasks. These include findings from a test conducted by Epoch AI and accounts from people who put the model to work on their own projects. 1.1 Solving two previously unsolved mathematical problems Epoch AI tasked five models with solving 68 Erdős problems, giving each the same time and budget limits. Only the pre-release version of Astra solved two of them and produced solutions in a form that passed automated mathematical verification. GPT-5.6 Sol, GPT-5.5, Claude Fable 5.1 and Claude Fable 5 did not solve any of the problems in this test. Astra had already recorded other mathematical achievements. Before its release, OpenAI presented ten further results involving problems in mathematics and theoretical computer science. We covered them in our article “Astra, the future GPT-6: a new model from OpenAI?”. 1.2 A 3D steam locomotive model with 3,295 editable components Tom Krcha, a designer and creator of the AI-assisted interface design tool Pencil, gave Astra an old drawing of a steam locomotive. The task was to recreate the machine in Blender, an application for building 3D models, animations and scenes. Within a few minutes, it produced a model containing 3,295 separate objects. The wheels, axles, boiler components and other parts can be selected, moved and modified independently. Astra had to interpret a flat drawing, reconstruct the machine’s three-dimensional structure and preserve the relationships between thousands of components. According to Krcha, it did this mainly by writing Python scripts that built the geometry piece by piece. The resulting model can serve as a starting point for an animation or a game project. 1.3 Rebuilding a sales workflow directly in a CRM system Claire Vo, creator of the ChatPRD platform, gave Astra access to her customer relationship management system, or CRM. Working through Codex, the model was tasked with changing how new sales leads were handled. It had to understand the existing workflow, find the relevant settings and rebuild the rules in a visual editor. After the changes, the system automatically routed leads to Claire or Zach, inserted a link to book a meeting with the appropriate person and sent the draft message to Slack for approval. The new rules would also apply to future leads. 1.4 Adding new capabilities to a Bluetooth speaker with Astra Claire Vo also used the model to experiment with a small Divoom speaker fitted with a colour pixel display. She wanted to show her own images and messages on it. According to her account, the device had no public API, an interface that would allow other software to control it. This meant working out how to communicate with the hardware. Astra built an application that displayed drawings made with a computer mouse on the speaker’s screen. It then created a tool for controlling the speaker. The model also looked up information about the latest podcast episode and sent scrolling text and an animated graphic to the display. Vo noted that earlier attempts with other models had only allowed her to display a simple greeting. This time, she received custom software for controlling the device that she could develop further to suit her ideas. 1.5 Checking plot consistency in a novel spanning more than 500 pages Jakub Szczęsny of Antyweb gave Astra an extensive draft of his own book. The model was asked to check the chronology of events, the logic of the plot and storylines introduced in one part of the text and developed many chapters later. Analysing a manuscript of this length requires tracking the characters’ stories, the sequence of events, their motivations and the consequences of earlier decisions at the same time. Astra mapped these connections and flagged passages that needed further work, consistently checking the entire text for the specified issues. Szczęsny particularly valued its ability to connect information scattered across hundreds of pages. A similar skill is useful when reviewing contracts, project documentation and reports, where details in one section affect how others should be interpreted. 1.6 An AI agent completes the entire game Portal A creator publishing as CozyBlaze connected Astra to Portal, a spatial puzzle game in which players create passages between distant locations and use the laws of physics to overcome obstacles. The model received screenshots and information about the player character’s position and the direction they were facing. It used this information to plan moves, execute them and check the results. After approximately 23 hours and 43 minutes, including waiting time and technical interruptions, it reached the end credits. The creator developed custom controls and settings to make precise movement easier, and the game paused while the model was processing its next actions. The creator also resumed the session following service availability issues, while the agent made the gameplay decisions. Completing the game required interpreting the situation on screen, spatial awareness and hours of planning moves and checking their effects. These examples help explain the enthusiasm around Astra. The model analyses a problem, carries out successive actions and uses the results to guide what it does next. For businesses, this opens up the possibility of assigning AI more complex tasks involving information and applications. Comparisons with GPT-5.6 show where performance has improved and what those gains could mean for businesses. 2. GPT-6 Astra in business: what does better performance mean for companies? Businesses using AI also bear the cost of checking responses, correcting errors and stepping in when the model cannot finish a task. Better performance from the next generation could therefore make more tasks cost-effective to delegate to AI. Astra’s results give businesses reason to reconsider how responsibilities are divided, how existing systems are used and how much time employees spend reviewing AI output. GPT-6 Astra vs GPT-5.6 Sol: test results and their business implications Skill tested GPT-6 Astra GPT-5.6 Sol Business implications Successfully completing a task across several applications, AutomationBench 41.4% 28.77% Reason to test whether AI can handle a larger part of a process independently. Locating the correct elements on screen, ScreenSpot-Pro 92.7% 76.9% Greater precision when AI interacts with business software. Detecting bugs that require analysing several files, the more challenging CodeRabbit subset 57.1% 47.6% Better performance when analysing complex dependencies in software. The results come from different tests and measure distinct skills. AutomationBench: Zapier’s leaderboard as of 9 September 2026, with both models set to Max. ScreenSpot-Pro: OpenAI’s comparison. CodeRabbit: the more challenging subset of code reviews. The business implications are interpretations of the results; any savings need to be assessed within the company’s own process. 2.1 A broader range of tasks to delegate The more stages a task involves, the more opportunities there are for mistakes. The model needs to find information, apply the right rules, perform actions in the correct order and use the same assumptions consistently throughout. These are the demands placed on models by AutomationBench, Zapier’s benchmark for business processes. On the leaderboard dated 9 September 2026, Astra successfully completed 41.4% of tasks, compared with 28.77% for GPT-5.6 Sol, with both models using their highest reasoning setting. A task counted as successful only when all required conditions were met. That amounts to roughly 13 more successfully completed tasks out of every 100. For businesses, this is an opportunity to test whether AI can complete more stages without employee assistance. It is worth reviewing tasks where an employee repeatedly prompts the model to take the next step, supplies more data or transfers the output to another application. Each intervention takes time and reduces the benefit of automation. Astra gives businesses reason to test which of these stages it can now handle together. The 41.4% result also shows how demanding these tasks remain. The level of autonomy should be determined by the model’s performance within the company’s own process. 2.2 Automating more tasks in existing business software Companies use many applications introduced at different stages of their development. AI’s access to these resources plays a significant role in how useful it can be. When a model can navigate an application’s interface effectively, businesses can consider using it for tasks that employees currently perform through forms, buttons and menus. In ScreenSpot-Pro, a test of locating elements in screenshots, Astra achieved 92.7% accuracy, compared with 76.9% for GPT-5.6 Sol. The test covers complex applications with densely packed controls. The result measures one specific skill needed to operate software. This improvement gives businesses reason to consider automating more tasks in the applications they already use. Automation planning should include tasks that currently require employees to navigate software manually. Better interface recognition could help AI carry them out when equipped with the appropriate tools and permissions. Cost-effectiveness will depend on the success of the entire operation, including reading data, making changes and checking the result. Accurately locating a button is one requirement for completing that operation successfully. 2.3 Detecting errors that require connecting information from several places Business tasks are often difficult because of the connections between pieces of information. A change to one agreed detail can affect subsequent decisions, documents and team activities. The person responsible for the overall task needs to identify these dependencies and assess their consequences. In CodeRabbit’s evaluation, Astra’s advantage was particularly clear when detecting bugs that required examining several code files. The model detected 57.1% of labelled bugs, compared with 47.6% for GPT-5.6 Sol. Across the full evaluation, the difference was smaller: 61.3% versus 59.0%. The greatest improvement was therefore seen in the more challenging cases. For those overseeing AI adoption, this offers a useful lesson: evaluate the new generation on tasks where the earlier model missed connections between pieces of information or required extensive corrections. With simple prompts, the difference between models may be small. Materials containing exceptions, interdependent conditions and information spread across several places can reveal more about Astra’s usefulness. CodeRabbit documents this improvement in software analysis. Establishing whether similar gains apply to documentation, reports or business agreements requires testing on the company’s own materials. This is a useful way to assess the model in organisations where employees spend considerable time working out how different pieces of information affect one another. 2.4 When does better AI performance translate into savings? Even a small improvement in quality can matter when a task is repeated hundreds of times a month. If employees spend less time correcting outputs and helping AI finish tasks, the team gains time back. The scale of that benefit depends on how often errors occur, how long they take to correct and the consequences of those mistakes. Astra’s results justify reassessing applications of AI that previously proved too unreliable or required too much supervision. A company can revisit a shelved idea and test it on a set of real cases, including more difficult ones. Three measures matter in this assessment: the proportion of tasks completed correctly, employee time spent on checks and corrections, and the total cost of handling each case. These indicators help establish whether the model’s better performance benefits the team as a whole. Astra’s progress could therefore make tasks that were previously too costly to automate economically viable. Tasks that have required constant human assistance are worth testing again, particularly when they are frequent, time-consuming and have a clearly defined outcome. How can your business put GPT-6 Astra to use? Our AI consulting services help you identify the tasks where improvements would deliver the greatest business benefit and plan the implementation. At TTMS, we work with clients to analyse the process, identify the data and integrations required, and agree on how to measure results. We combine consulting with designing AI solutions and integrating them with business systems. We were the first company in Poland to obtain accredited ISO/IEC 42001 certification for our artificial intelligence management system. For clients, this confirms that our practices for risk assessment, project documentation and AI oversight have been reviewed by independent auditors. Tell us about a task you would like to improve. Together, we will explore how AI could help and where to begin. Talk to TTMS about AI consulting for your business. GPT-6 Astra in business: frequently asked questions What does GPT-6 Astra change for businesses already using AI? GPT-6 Astra gives businesses reason to test whether AI can handle a larger share of a task with less employee assistance. Comparisons with GPT-5.6 Sol show improvements in completing tasks across several applications, locating on-screen elements and analysing dependencies in code. It is therefore worth revisiting processes where the team frequently corrects outputs or guides the model step by step. The benefit comes when better performance reduces the work needed to achieve a correct result. Can GPT-6 Astra work in business systems without building a new integration? In some cases, yes, provided its environment gives Astra tools to operate a browser or computer. The model can then interact with applications through forms, menus and buttons. It needs access to the system and the appropriate permissions. Whether this approach is suitable depends on the application and the task. For frequent, repetitive operations, it is worth comparing it with an API integration, which allows software systems to exchange data directly. Which tasks are worth trying with AI again if earlier automation attempts failed? Start with tasks where the earlier model lost track of the steps, confused interface elements or missed connections between pieces of information. These are areas where the tests discussed in this article show Astra’s progress. Use the same materials and assessment criteria for the new trial, including cases that previously proved difficult. If outdated data, conflicting instructions or a lack of application access caused the original failure, those issues also need to be resolved. How can you decide what GPT-6 Astra can do independently and what needs employee approval? The level of autonomy should depend on the consequences of a mistake, whether an action can be reversed and the results of tests using company data. Drafting a document or organising a copy of a dataset allows the output to be checked before use. Sending a proposal, changing commercial terms or deleting records may require prior approval. Process instructions should clearly define when AI can proceed, when it should request a decision and when it should stop because information is missing. How can you tell whether GPT-6 Astra saves your business money after checks and corrections are included? Compare the total cost of completing the same set of tasks with and without Astra. Include data preparation, tool use, reviewing outputs, making corrections and any cases that employees have to redo. A useful measure is the cost per successfully completed task that meets the required quality standard. Base the assessment on a representative set of tasks, including exceptions. This will help establish whether the savings hold up in day-to-day work.
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