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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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Limitations of AI in Legal Software: Risks of Incorrect Advice, Defective Court Filings and Missed Deadlines
An authentic case number supporting a fabricated proposition is one of the most dangerous forms of legal hallucination. In June 2026, Poland’s Supreme Administrative Court described a submission in which counsel cited three judgments that genuinely existed. The problem was that they concerned different legal issues and did not contain the propositions attributed to them. The submission appeared credible until someone checked the original sources. This example illustrates the practical limitations of AI in legal software. An error may affect legal arguments, the assessment of evidence, deadline calculations, advice given to a client or the content of a court filing. The lawyer who approves the advice, opinion or filing remains responsible for its final content. Any potential liability for damages is assessed by reference to the applicable professional standard of care, the scope of the engagement and the circumstances of the individual case. In this article, you will learn about: the most common AI errors in legal work, hallucinations involving legislation, judgments and case numbers, the procedural consequences of using incorrect AI-generated content in a court filing, a lawyer’s liability for advice prepared with the assistance of AI, principles for the safe implementation of AI software in a law firm. For a broader discussion of compliance, confidentiality, providers and risk classification, see AI for lawyers in Europe and the UK: key risks and limitations. Here, we focus on what may go wrong in a specific case governed by Polish law and how to design a process that helps identify errors before legal advice or a court filing is sent. An authentic case number and a fabricated proposition: lessons from the Supreme Administrative Court’s order in case I FZ 104/26 In its order of 23 June 2026 in case I FZ 104/26, the Supreme Administrative Court considered an appeal against an order refusing to stay the enforcement of a tax decision. The appellant was required to substantiate the conditions set out in Article 61 § 3 of the Law on Proceedings before Administrative Courts, particularly the risk of substantial damage or consequences that would be difficult to reverse. Counsel cited three judgments together with propositions purportedly drawn from them. The Court found that the judgments concerned different issues and that their written reasons did not contain the propositions attributed to them. The dates given for those judgments were also incorrect. The Supreme Administrative Court identified another issue: the submission was general in nature and did not contain specific data or documents that would have allowed the Court to assess the party’s financial position. The appeal was dismissed. The Court criticised counsel’s uncritical use of AI and emphasised that a client is entitled to expect professional service. The decision illustrates three practical consequences of a defective workflow: a legal argument may lose the support of the source on which it purports to rely, a submission may omit facts and evidence that are material to the outcome, the court may question the reliability of counsel’s work. A proper verification of a judgment includes checking the court, date, case number and type of decision, followed by reading the full written reasons. The lawyer should assess the factual context, legal basis, significance of the cited passage, available information about the decision’s finality and subsequent case law concerning the same issue. Seven AI limitations that may affect the outcome of a case 1. A genuine source may be cited in support of a fabricated proposition A model may provide a genuine case number, a provision of legislation or the title of a publication and then attribute content to that source which it does not contain. This type of error is more dangerous than an entirely fabricated case number because it may remain undetected during a cursory review. The system should take the user directly to the relevant passage in the source. Verification by a lawyer remains necessary. A Stanford RegLab study of US legal research tools combining database searches with generated answers found hallucinations in 17-33% of responses. The study covered products and questions from the US market, so it does not measure the quality of Polish systems. Connecting a model to a legal database reduces the risk. Further safeguards are required to verify the accuracy of its output. 2. The model may apply an outdated or inapplicable provision An answer may sound plausible while relying on a provision that has been amended, has not yet entered into force or applies in another jurisdiction. In a cross-border matter, the model may confuse the applicable law, jurisdiction and procedural rules. An AI system for lawyers should identify the applicable legal system and the date as of which the law has been verified for each conclusion. The user should be able to see the version of the legislation, the date on which an amendment entered into force and the relevant official source. A label stating that the information is “up to date” has limited value unless the interface also indicates when and on what basis its currency was verified. 3. The model analyses only the case materials made available to it The case file may be incomplete, an attachment may not have been read correctly, or a material fact may be contained in a table, scan or message that was not included in the analysis. The model formulates its answer on the basis of the materials it has received and has no knowledge of documents that were not submitted for analysis. As a result, a confident answer may be based on an incomplete picture of the case. Before starting the analysis, the system should display a list of the files used, any processing errors and the scope of any omitted material. In matters requiring findings of fact, it is useful to provide a separate summary of assertions, supporting evidence and missing materials. This allows the lawyer to identify conclusions reached without access to a key document. 4. Calculating procedural deadlines requires clearly defined rules The calculation of a deadline depends on factors including the type of proceedings, the method of service, the date of the relevant event, transitional provisions, public holidays and exceptions applicable to the specific procedural step. A language model may overlook one of these conditions or misread a date from a scanned document. Procedural deadlines should be calculated using a controlled, rules-based mechanism with clearly stated input data. A model may extract dates from documents and suggest their legal significance. The final calculation should identify the legal basis, show how the deadline was calculated and be approved by the lawyer handling the case. Deadlines whose expiry may produce procedural consequences require an independent calculation and a second verification. 5. Factual assertions in an AI-assisted court filing require appropriate evidence According to press reports discussing the judgment of the Regional Court in Wrocław of 27 November 2025 in case X GC 455/25, the claimant used ChatGPT when preparing the statement of claim. The claim was dismissed because the formal conditions of the request for proposals had not been satisfied. The Court also addressed the AI-generated content and the evidential value of the materials submitted. Printouts containing generated arguments, legal analysis and an assessment of the prospects of success did not constitute evidence of facts material to the determination of the case. They could be treated as part of the claimant’s legal argument. A legal AI system should clearly distinguish between factual assertions, legal grounds and evidence. A missing contract, item of correspondence, proof of service or financial document remains an evidential gap regardless of the quality of the generated reasoning. 6. The model may reinforce the client’s assessment of the case Clients often describe a dispute from one perspective and expect confirmation of their own assessment. A model may adopt the client’s assumptions and overlook the other party’s arguments. This creates a risk that the client will make a decision without understanding the weaknesses of the case. The analysis should consider the opposing party’s perspective, including missing facts, potential counterarguments, procedural obstacles, alternative legal characterisations and the level of uncertainty. In client-facing communications, users should be able to refer a question to a lawyer, particularly when it concerns a deadline, a legal claim, criminal liability, termination of employment or a significant financial decision. 7. A convincing style may conceal errors and uncertainty A model generates an answer word by word and may maintain the same professional tone when presenting both correct and incorrect conclusions. Assessing reliability on the basis of style is therefore unsafe. A confidence score expressed as a percentage may also create a false impression of precision if it has not been calibrated for the specific use case. A well-designed system identifies its sources, missing information, conditions that could change the answer and situations in which it cannot provide a definitive conclusion. It should also be able to decline to answer and refer the matter to a lawyer. Can a client claim that AI caused them to lose the case? A client may raise such an allegation. The assessment of liability will depend on how the legal service was performed and on the circumstances of the individual case. In a contractual relationship, the primary rules are those set out in the Polish Civil Code. Under Article 355 § 2 of the Civil Code, the professional nature of the debtor’s activity must be taken into account when assessing due care. Article 471 of the Civil Code sets out the rules governing liability for non-performance or improper performance of an obligation. An adverse outcome does not in itself establish that a legal services agreement was performed improperly. The assessment covers compliance with the applicable professional standard of care, the correct determination of the factual and legal position, and the appropriateness of the actions taken. In a contractual liability dispute, the client should demonstrate: the existence and terms of the obligation, non-performance or improper performance of the obligation, the loss suffered, an adequate causal link between the breach and the loss. Article 471 of the Civil Code establishes a presumption that non-performance or improper performance of an obligation results from circumstances for which the debtor is responsible. Whether the debtor can avoid liability must be assessed in light of the circumstances of the individual case. The assessment may be affected by how AI was used, particularly whether the sources were verified, the complete case file was considered, the current law was applied and control over the final content of the advice was retained. A system failure or an error attributable to the provider will be assessed together with the choice of tool, the contractual terms, the scope of testing performed and the lawyer’s method of verifying the output. Examples include: missing a deadline because the date of service was determined incorrectly, advising against pursuing a legal remedy on the basis of an outdated provision, filing a submission containing a quotation that does not appear in the cited judgment, recommending a settlement on the basis of an incomplete case file or an incorrect calculation of the financial consequences, a chatbot giving the client a definitive answer without referring the matter to a lawyer. These examples are illustrative. Any assessment of liability requires an examination of the scope of the engagement, the applicable standard of professional care, the loss suffered and the causal link. The conditions for disciplinary liability may be assessed separately. The Polish Law on the Bar and the Polish Act on Attorneys-at-Law also require advocates and attorneys-at-law to hold professional liability insurance. Compulsory professional liability insurance does not necessarily cover every loss connected with the use of AI. The insurer’s liability depends on the terms of the policy and the circumstances of the individual event. What procedural consequences may result from an incorrect AI-assisted court filing? The consequences depend on the type of defect and the applicable procedural rules. An error resulting from the use of AI is assessed in the same way as any other error in a court filing. Its significance depends on how it affects compliance with formal requirements, proof of the relevant facts, the legal and factual basis of the relief sought, and compliance with applicable deadlines. Depending on the type of defect, the consequences may include: the return of a filing if its formal defects have not been remedied, the rejection of a statement of claim, appeal or other means of challenge where the conditions specified in the applicable procedural rules are met, the court disregarding an application for evidence or the evidence itself, a finding that a material fact has not been proven, the court declining to accept an argument based on a source that does not support the proposition attributed to it, the dismissal of an application, appeal or claim because the required conditions have not been established, an order requiring the party to pay the costs of the proceedings or the imposition of a procedural sanction where provided for by the applicable rules. In case I FZ 104/26, the Supreme Administrative Court dismissed the appeal because the appellant had failed to substantiate the conditions for staying the enforcement of the decision under Article 61 § 3 of the Law on Proceedings before Administrative Courts. The general nature of the arguments and the absence of supporting documents were relevant to this assessment. The incorrect references to case law were an additional factor in the Court’s critical assessment of how the submission had been prepared. Any claim for damages against counsel is considered in separate proceedings. Disciplinary liability is assessed by the competent bodies of the relevant professional organisation. What follows from professional rules and the AI Act in 2026? On 15 June 2026, the Polish Bar Council announced the adoption of a resolution amending the Code of Ethics for Advocates and Dignity of the Profession by adding § 23e. According to the information published by the Polish Bar Council, technological tools should serve an auxiliary function. Their use must respect professional secrecy, the advocate’s independence and the advocate’s personal role in handling the case. The output produced by such a tool must be independently assessed and verified by the advocate. For attorneys-at-law, relevant points of reference include the recommendations on the use of AI published by the Polish National Bar Council of Attorneys-at-Law. These practical guidelines address professional responsibility, confidentiality, output verification and human oversight. At EU level, Article 4 of the AI Act has applied since 2 February 2025. It requires providers and deployers of AI systems to take measures supporting the development of AI literacy among their personnel. The measures selected should take account of the personnel’s technical knowledge, experience, education and training, as well as the context in which the system is used and the people in relation to whom it is intended to be used. The European Commission explains that the appropriate way to fulfil this obligation depends on the organisation’s role and the risks associated with the specific use of AI. Since 2 August 2026, the competent authorities have been responsible for supervising compliance with this obligation. The classification of an AI system depends on its intended purpose. High-risk systems may include solutions intended to be used by, or on behalf of, a judicial authority to assist that authority in researching and interpreting facts and law and in applying the law to a specific set of facts. The assessment covers the system’s actual function, the intended purpose specified by the provider and the way in which the deployer uses it. Law firm tools used to search documents, draft text or prepare summaries require an individual classification assessment. The fact that a system is used by a law firm or legal department does not in itself place it in the high-risk category. Following the amendments adopted in 2026, the obligations concerning systems listed in Annex III are due to apply from 2 December 2027. Depending on the system’s function, the type of data involved and the way in which it is used, the GDPR, rules protecting professional secrecy, the applicable procedural rules, and civil and disciplinary liability rules may also apply. How should an AI system for lawyers be designed to reduce risk? A properly designed implementation should make it easier to identify errors, limit their effect on the matter being handled and document completion of the required review. Risk Control built into the product or process Review record Fabricated proposition or quotation A link to the full source and the specific passage, a contextual preview, and mandatory approval before export Court, date, case number, type of decision, source, document version and approving reviewer Outdated law Jurisdiction and date metadata, version control for legislation, and notifications of amendments Date as of which the law was verified and the version of the provision used Incomplete case file A list of analysed files, OCR error notifications and an inventory of missing data Document inventory and file processing report Incorrect deadline calculation A mechanism based on defined rules, clearly stated input data and a second human review Legal basis, input data, calculation method and approving reviewer Overly definitive advice Questions about missing facts, escalation criteria and the ability to decline to provide an answer Reason for escalation and details of the follow-up action taken Disclosure of information protected by professional secrecy Case-level permissions, controls over access by the provider and its subprocessors, a defined processing location, data retention and deletion rules, and exclusion of client data from model training Access logs, provider configuration, retention period, information about subprocessors and incident records Changes in quality following a system update Testing on representative matters before deployment and after any change of model Test results, model version and the decision approving the new version for use The scope of documentation should be proportionate to the risk. Audit logs, including the history of prompts and outputs, may contain information protected by professional secrecy. The organisation should define which data is recorded, who is authorised to access it, how long it is retained, how it is deleted and how the logs are secured. The full history of interactions with the system need not be retained where a narrower set of information is sufficient to demonstrate that the required review was performed. Natalia Lener-Bobek, a partner at Sawaryn i Partnerzy law firm, returns to the example discussed earlier of a court filing containing incorrect references to case law. She highlights decisions made before work with AI begins: the choice of tool and the terms of the contract with its provider. “This example illustrates user error. The risk arises earlier, when the tool itself is selected. Law firms check whether the model provides good answers and overlook the contract governing their use of it. The record of prompts and outputs discussed in this article exists only if the provider has undertaken to maintain it and make it available on request. This is a contractual commitment, not a feature that can be taken for granted. Before implementation, I check three things with my clients: whether the contract specifies who is liable for incorrect system output, whether the provider guarantees access to query logs for long enough to defend against a potential claim, and whether the processing terms permit information protected by professional secrecy to be entered into the tool at all. Without answers to these three questions, the staff training obligation under Article 4 of the AI Act has no practical foundation.” Natalia Lener-Bobek Partner at Sawaryn i Partnerzy law firm A safe allocation of tasks between AI and the lawyer Tasks should be assigned according to the potential harm and how easily an error can be detected. Task Role of AI Required review Summarising a long document Preparing a working summary with references to the relevant pages Reviewing the passages material to the decision Comparing versions of a contract Identifying and organising changes Assessment of their legal significance by a lawyer Case law research and analysis Identifying potentially relevant judgments and extracting the relevant passages Reading the full judgment and assessing its factual and legal context Court filing Preparing a draft structure, editing the text and checking consistency Full verification of the facts, evidence, relief sought, legal grounds and attachments Procedural deadline Extracting dates and identifying potentially applicable calculation rules Determining the event that starts the time limit, the legal basis, the calculation method and the consequences of missing the deadline Substantive-law time limit Organising dates and identifying provisions requiring analysis Determining the nature of the time limit, when it begins and ends, and the consequences of its expiry Limitation period Organising events that may affect the running of the limitation period Assessing when the period begins, whether it has been suspended or interrupted, and when it expires Final advice to the client Preparing working materials and alternative analyses The lawyer’s personal assessment, approval and communication of the advice This approach is consistent with the practical direction set out by the CCBE in its guide for lawyers: lawyers remain responsible for their work, advice and representations, and generative AI output must be reviewed before it is used. A safe AI implementation in a law firm begins with process analysis The first step is to identify where AI-generated output may affect advice given to a client, a court filing, the assessment of a document or the calculation of a deadline. This provides the basis for defining the appropriate data sources, access permissions, verification rules and the people responsible for approving the output. If you are planning to use AI to analyse case files, work with documents or prepare contracts, explore the AI4Legal solution. We help law firms and legal departments design tools tailored to their workflows, security requirements and the scope of lawyers’ professional responsibilities. Sources Supreme Administrative Court, order of 23 June 2026, I FZ 104/26. Law on Proceedings before Administrative Courts, consolidated text, Journal of Laws of 2026, item 143. Polish Civil Code, consolidated text. Polish Law on the Bar, consolidated text. Polish Act on Attorneys-at-Law, consolidated text. Polish Bar Council, amendments to the professional ethics rules concerning AI, 15 June 2026. Polish National Bar Council of Attorneys-at-Law, recommendations on the use of AI. CCBE, Guide on the Use of Generative AI for Lawyers, 2 October 2025. European Commission, AI Act regulatory framework and guidance on AI literacy. Regulation (EU) 2026/1744 amending the timeline for the application of certain provisions of the AI Act. Stanford RegLab, Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools. Dziennik Gazeta Prawna, discussion of the judgment of the Regional Court in Wrocław in case X GC 455/25, 7 January 2026. Dziennik Gazeta Prawna, interview concerning the grounds for dismissing the claim in case X GC 455/25. Law and sources current as of 1 September 2026. This material is provided for informational purposes and does not constitute legal advice. FAQ: limitations of AI in legal software Can a client claim damages if incorrect legal advice was produced using AI? Such a claim may be possible. Liability will depend on whether the legal service was performed with due professional care, whether the client suffered loss and whether there is a causal link between the breach and the loss. The assessment may also cover the lawyer’s professional standard of care, including how the AI-generated output was reviewed and verified. Will a court reject a filing solely because it was prepared using AI? There is no general rule under Polish law requiring a court to reject a filing for this reason. The court assesses compliance with formal requirements, applicable deadlines, legal arguments and evidence under the relevant procedural rules. Incorrect citations, insufficient evidence or a defective claim may, however, lead to the procedural consequences ordinarily associated with those deficiencies. Can AI manage procedural deadlines on its own? For critical deadlines, AI should operate as part of a broader workflow that includes controlled rules and human approval. A safer process uses verified calculation rules, clearly stated input dates, the relevant legal basis, human approval and an independent reminder. How can you verify whether a judgment actually supports a proposition generated by AI? Open the full judgment in an official or reliable legal database, locate the cited passage, read it in context and verify the date, judicial panel, type of ruling and applicable law. A correct case number and court name do not establish that the judgment supports the proposition attributed to it. Must every AI prompt and response be retained in the case file? There is no single general requirement to retain the full history of every interaction with an AI system. The appropriate scope of documentation should reflect the level of risk, the law firm’s internal policies, professional secrecy, data protection requirements and audit needs. In higher-stakes matters, it is advisable to retain the sources used, the system version, the scope of human review and the identity of the person who approved the output.
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Global Employee Training: 2026 Strategies That Work
A sales rep in Manila may need to learn the same product update as an engineer in Warsaw or a compliance officer in Toronto. Yet they work in different languages, time zones, and regulatory environments. For companies running global employee training programs in 2026, this makes a single standardized training deck increasingly impractical. Global training therefore requires a balance between consistency and local relevance. Core knowledge, processes, and brand standards may stay the same across markets, while other parts of the training need to reflect local regulations, language, culture, or job-specific requirements. The challenge is not simply to translate the same course into multiple languages. It is to decide what should remain standardized, what needs to be adapted, and where full localization is necessary. The right approach can make training easier to scale, more relevant for employees, and more consistent across regions. 1. What Global Employee Training Looks Like in 2026 Global employee training in 2026 is increasingly designed around a shared core with room for local adaptation. Companies may standardize product knowledge, internal processes, brand guidelines, or compliance principles, while adjusting language, examples, legal references, and delivery formats for individual markets. Digital learning platforms make this approach easier to manage across regions. Employees in São Paulo and Seoul can complete the same core course while receiving content adapted to their language, role, or local requirements. This helps organizations maintain consistency without if every audience should receive exactly the same version of the training. Artificial intelligence and data analytics have added another layer of sophistication. Training systems now track how individual employees learn, where they struggle, and what content keeps them engaged, then adjust the experience accordingly. Personalization has become the baseline expectation for global employee training and development, not some nice-to-have extra. 1.1 Key Differences from Traditional, Single-Region Training Traditional training is often designed for one language, regulatory environment, and organizational context. Global employee training has to account for several of these at the same time. A compliance or safety course, for example, may need more than a direct translation. Legal terminology, procedures, examples, and even the way instructions are presented can differ between countries. Live training also requires additional planning when employees are spread across time zones. As a result, global training programs are usually built around a combination of standardized and localized content. The key is deciding which elements need to remain consistent across the organization and which should be adapted for a particular market or audience. 1.2 Why This Matters Now: Distributed Teams, AI, and Skills Gaps Distributed teams are the norm rather than the exception, and that alone forces companies to rethink how they train people. Add rapidly evolving AI tools and widening skills gaps across industries, and the pressure to modernize training becomes hard to ignore. Companies that fail to adapt risk losing talent to competitors offering more relevant, more accessible learning experiences. Those that invest in scalable solutions for global employee training are better positioned to keep pace with both technology shifts and workforce expectations. 2. The Business Case: Benefits of Global Employee Training and Development Global employee training supports much more than compliance. It helps companies build the skills they need across different markets, introduce new processes more consistently, reduce operational risk, and give employees a clearer understanding of what is expected of them. Its value is especially visible in organizations that operate across several countries, where differences in skills, regulations, language, and local working practices can quickly create gaps between teams. 2.1 Closing Skills Gaps Across Markets Skills gaps rarely look the same from one region to the next. A well-designed global training program identifies where those gaps exist and builds targeted content to close them, so every market has the competencies it needs to hit business goals. This matters especially as new technologies and processes roll out faster than ever, leaving less room for regional teams to fall behind. 2.2 Boosting Engagement, Confidence, and Retention Worldwide Employees who feel equipped to do their jobs well tend to stick around longer. Strong training programs give people the confidence to take on new responsibilities, and that confidence translates into higher engagement and better retention across every office, not just headquarters. 2.3 Strengthening Compliance and Reducing Regional Risk Regulations differ from country to country, and getting them wrong can be costly. A structured global training approach makes sure compliance training reflects local laws while still aligning with company-wide standards, which keeps costly missteps in any one market to a minimum. 2.4 Building a Consistent Culture Across Borders Culture can fracture quickly across a distributed workforce if there’s no shared thread connecting offices. Training is one of the most effective tools for reinforcing company values and expectations everywhere the business operates. It gives teams a sense of belonging to the same organization, even when they’ve never met face to face. 3. Common Challenges in Training a Global, Distributed Workforce Running training across several countries introduces challenges that are less visible in a single-market program. Language, time zones, local regulations, infrastructure, and differences in learning culture all affect how training should be designed and delivered. The difficulty is usually not creating one course. It is maintaining a program that works across different environments without making it unnecessarily complex or expensive. 3.1 Language and Cultural Diversity Language barriers can quietly undermine even the best-designed course. Beyond translation, cultural context shapes how people read examples, humor, feedback, all of it, and training that ignores this risks losing its audience before the message ever lands. Automated translation tools help with speed, but they still miss idiom and tone often enough that human review remains necessary before content goes live in a new market. 3.2 Time Zone and Logistical Barriers Coordinating live sessions across a dozen time zones is nearly impossible without leaving someone out. That’s pushing companies toward asynchronous, self-paced formats that let employees engage with material on their own schedule instead of forcing everyone into the same time slot. The trade-off: self-paced courses without any live touchpoint or accountability structure tend to see weaker completion rates than blended formats, which is worth weighing before going fully asynchronous. 3.3 Balancing Global Consistency with Local Relevance Lean too hard on standardization and training feels disconnected from local realities. Lean too hard on localization and the company loses a consistent message, plus the cost and coordination burden can outweigh the benefit for smaller or less regulated markets. Striking that balance is one of the harder judgment calls in designing any global employee training and development strategy. 3.4 Technology and Infrastructure Disparities Not every office has the same bandwidth, devices, or digital literacy. Training platforms need to work reliably across varying levels of technological infrastructure, or entire regions risk being left with a worse learning experience than others. 3.5 Measuring Impact Across Multiple Regions Data collected in one market doesn’t always translate cleanly to another. Comparing outcomes across regions requires consistent metrics and reporting tools, otherwise it becomes difficult to know whether the program is working everywhere it’s deployed. 4. FourCore Strategies for Structuring Global Training Programs Companies generally choose from four broad approaches when structuring global training, each with its own trade-offs between simplicity and personalization. Strategy 1: Fully Standardized Training for All Topics This approach delivers identical content everywhere. It’s the easiest to build and maintain, but it risks missing the cultural and regulatory details that matter in specific markets. Strategy 2: Standardized Approach, Customized by Topic Here, some topics stay uniform across the company while others get adapted per region. This gives more flexibility than a fully standardized model without the resource demands of full localization. Strategy 3: Shared Objectives with Region-Specific Content Under this strategy, every region works toward the same learning objectives but builds content that fits local context. It’s a middle ground that keeps the company aligned while respecting regional differences. Strategy 4: Fully Localized Objectives and Content per Region This is the most tailored approach, with both objectives and content built specifically for each market. It delivers the most relevant experience but demands significant time, budget, and coordination, and it’s often overkilled for smaller regional offices or lightly regulated topics where a shared, lightly adapted version works just as well. Choosing the Right Strategy for Your Organization The right strategy depends on company size, industry regulation, and how much variation exists between regional teams. Organizations with tighter budgets often start with a standardized core and layer in customization as they scale, while larger multinationals with complex regulatory needs may need full localization from day one. 5. Building Blocks of an Effective Global Training and Development Program A global training strategy needs to translate into a program that employees can actually use across different countries, roles, and working environments. That means deciding what people need to learn, which content should be shared globally, where local adaptation is necessary, and how employees will access the training. 5.1 Types of Training to Include: Technical, Compliance, Leadership, and Soft Skills Most global training programs cover several different areas. These may include role-specific technical skills, compliance and safety training, leadership development, product knowledge, and soft skills such as communication or teamwork. They do not all require the same approach. Product or process training can often use a common global core, while compliance content may need significant changes to reflect local regulations. Leadership and communication training may also need different examples or scenarios depending on the cultural and organizational context. 5.2 Localization and Multilingual Content Delivery Localization goes beyond swapping words from one language to another. It means adjusting examples, tone, and even visual design so the material feels natural to the audience. Multilingual delivery has become a baseline expectation for any employee training platform for global companies serving a diverse workforce. 5.3 Blended and Self-Paced Learning Models for Different Time Zones Combining live sessions with self-paced modules gives employees flexibility to learn when it suits them, without losing the benefits of interactive discussion when schedules do align. This blended model has become one of the most practical answers to the time zone problem. 5.4 Peer Learning and Regional Mentorship Networks Some knowledge is easier to develop through interaction with colleagues than through a course alone. Regional mentors, subject-matter experts, and peer groups can help employees apply what they have learned to their actual work. They can also answer questions that are specific to a particular market, customer group, or local process. This is especially useful after formal training has finished, when employees start applying new knowledge in day-to-day situations. 6. Using AI and Technology to Scale Training for Skills Development Technology makes it possible to deliver and manage training across large, distributed teams. AI can support this process by helping organizations personalize learning, adapt content, translate materials, and analyze training data. Human review is still important, especially when content involves compliance, safety, culture, or sensitive terminology. 6.1 AI-Driven Personalization and Adaptive Learning Paths AI can adjust a learning path in real time based on how an individual employee is progressing. It gives someone more practice where they’re struggling and pushes them faster through material they’ve already nailed down. This kind of personalization would be nearly impossible to manage manually across a large, distributed workforce. 6.2 Automated Translation and Localization Tools Automated translation tools speed up the process of adapting content for multiple markets, cutting both cost and turnaround time. Paired with human review for cultural accuracy, these tools make multilingual delivery far more manageable than it used to be, though relying on machine translation alone still creates a real risk of tone-deaf or awkward phrasing in markets with limited review. 6.3 Learning Analytics for Real-Time Performance Insights Learning analytics help L&D teams understand how employees are progressing across courses and regions. They can show completion rates, assessment results, engagement with individual modules, or areas where learners repeatedly encounter difficulties. This data can be used to improve existing courses, identify skills gaps, and decide where additional training or support is needed. It also gives global training teams a more consistent way to compare results across markets. TTMS supports organizations in building and maintaining this type of learning environment through its AI Solutions and E-Learning administration services. Depending on the scale and complexity of the program, this may include AI-assisted content creation and adaptation, multilingual course management, hosting, reporting, and ongoing updates. The level of technology should match the actual training needs. A large international program may benefit from automation and advanced analytics, while a smaller rollout can often be managed effectively with a simpler platform and a well-defined review process. 7. How to Implement a Global Training Strategy Rolling out a global training strategy starts with a clear assessment of organizational needs and a definition of what success should look like. From there, companies select the training methods and delivery formats that fit their workforce, whether that means blended learning, mobile-first content, or live regional workshops. Engaging local stakeholders early is essential, since they’re the ones who know which cultural or regulatory details need attention before content goes live. Technology plays a central role in execution. An employee training platform for global companies needs to handle content hosting, multilingual delivery, and progress tracking, ideally within a single system rather than a patchwork of tools. Continuous evaluation and feedback loops then let teams refine the program over time, rather than treating the initial rollout as a finished product. TTMS can also support the operational side of a global training rollout by automating processes around course assignment, approvals, reminders, and completion tracking. With Process Automation and Low-Code Power Apps, these workflows can be connected across departments and regional offices, reducing the need to manage them through separate spreadsheets or manual email exchanges. For organizations already using Microsoft 365 and Azure, training processes can also be integrated with the tools employees and administrators use every day. 8. Measuring ROI and Impact of Corporate Training Programs Globally Proving the value of a global training investment requires looking at more than completion rates. Organizations should track performance indicators tied to productivity, retention, and skill application on the job, alongside qualitative feedback that reveals how employees perceive the training’s usefulness. Comparing outcomes between trained and untrained groups offers one of the clearest ways to demonstrate tangible impact and gives leadership the evidence it needs to justify continued investment or adjust course where results fall short. Business Intelligence tools, such as Snowflake DWH and Power BI, can play a useful role here by consolidating training data from multiple regions into a single view. That makes it far easier to spot trends and report results across the organization instead of reviewing each market’s numbers in isolation. 9. Real-World Examples of Global Employee Training Done Right Successful global employee training starts with matching the learning format to the content, audience, and business context. Some topics can be delivered through standardized materials across regions, while others require a more tailored approach because of local regulations, safety requirements, language, or cultural differences. A good example comes from a global production and technology company that needed to standardize Health & Safety training for production and office employees across five locations worldwide. Previously, individual sites used different materials, which made it difficult to ensure that employees received the same information and that training completion was properly tracked. TTMS developed a single interactive e-learning course built around workplace scenarios and storytelling. Employees worked through situations that could lead to accidents and selected the appropriate response, receiving immediate feedback on their decisions. The course helped the company deliver the same core safety principles across a multicultural workforce while replacing part of its previously time-consuming classroom training. The platform also gave managers visibility into who had started or completed the training and automatically reminded employees about approaching deadlines. According to the case study, the organization subsequently recorded fewer accidents across its locations. This example shows an important principle of global employee training: not every subject should be handled in the same way. Compliance and safety content often needs more careful adaptation and stronger learner engagement, while other training can remain more standardized. Technology makes it easier to distribute and update learning across locations, but the format and level of localization should still reflect the needs of each audience. If you are planning to scale employee training across countries, languages, or business units, TTMS can help you choose the right mix of standardization, localization, technology, and content formats. Talk to our e-learning experts about your training needs and the best way to structure a global program. Frequently Asked Questions What is global employee training? Global employee training refers to the systematic development of skills and knowledge among employees across different regions and cultures, ensuring that training is relevant, accessible, and effective for a diverse workforce. How to manage global employee training? Managing global employee training involves understanding cultural differences, using technology to improve accessibility, and making sure training content is both standardized and localized to meet regional needs. How do companies handle language barriers in global training? Companies address language barriers by localizing training content, using multilingual support, and employing translation tools to ensure that all employees can understand and engage with the training material. What’s the difference between standardized and localized training? Standardized training provides a uniform approach across all regions, while localized training adapts content to fit the specific cultural and linguistic needs of different employee groups. How do you measure the success of a global training program? Success can be measured through various metrics, including employee performance improvements, retention rates, engagement levels, and feedback from participants regarding the training’s relevance and effectiveness. Building an effective global employee training program takes more than good intentions. It needs the right mix of strategy, localization, and technology, plus a partner who knows how to bring those pieces together. Companies exploring global employee training management software or looking to modernize their approach to workforce learning can turn to TTMS for guidance grounded in real IT implementation experience across AI, automation, and e-learning administration.
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What Can GPT DO in 2026 That It Couldn’t Do in 2025?
In 2025, ChatGPT could search the web, analyse documents, create great-looking visuals and help write code. Even then, the pace of development was staggering: over the course of a single year, OpenAI released five major GPT versions, from GPT-4.5 to GPT-5.2. Some of us, users of one of the world’s most popular language models, were already struggling to identify what else could be significantly improved. This article is for everyone else, especially those who still wanted: to delegate an entire task to ChatGPT to let it work freely across applications and on a computer to have it deliver a finished result without being guided through every stage of the process. These are precisely the missing elements that began to appear in 2026. Below, I have collected more than a dozen new ChatGPT capabilities, some of which I had not even heard of until recently, that best illustrate how much the way we work with this tool has changed. Not all the capabilities described below belong to the GPT-5.6 model itself. Some are part of ChatGPT Work, Codex, the desktop app or the Responses API, which provide the environments and tools through which the model completes its work. Readers interested in GPT’s evolution over previous years can also read The Evolution of AI: From GPT-1 to GPT-4o. Users could already choose models designed for more demanding reasoning tasks in 2025. GPT-5.6 provides greater control over this process: the Max level allocates more resources to analysis, while Pro mode allows the model to do additional work before presenting a single final answer. The two settings can be combined for tasks in which the importance of the decision justifies a longer wait and higher token usage, such as selecting a technology, assessing risk or comparing several business scenarios. I have written more about how to manage this “slider” and match GPT modes to specific tasks in the article GPT-5.6 from OpenAI: What Has Changed? Pricing, Capabilities and Business Applications. 1. ChatGPT Can Operate Your Computer for You Behind this slightly clickbait-style heading is a practical situation in which you want to check something that a website does not describe explicitly, meaning that a language model cannot simply read the answer from the page. Normally, you would need to open the website and navigate its interface yourself. With the Computer Use skill, ChatGPT can do this for you: launch a browser or application, click the appropriate controls, enter data and observe the results of each action. You define the scope of the task, approve access to the website or application and retain control over actions that require confirmation. The first capabilities of this kind appeared in 2025 with Operator and later ChatGPT agent. In 2026, they were extended to applications running on macOS and Windows computers, local browser sessions and broader use with ChatGPT Work and Codex. I recently tested this with a very simple example. I wanted to find out whether the free version of a particular VOD service interrupted a specific film with advertisements, something that would have taken me at least several minutes to check manually. I delegated the task to GPT, which, after receiving my permission, opened the website, started the film, scrubbed through the entire video and reassured me that it contained no advertising breaks. The whole process took a minute and a half and ran entirely in the background. Since August 2026, ChatGPT Work has also been able to continue a task on a supported website that requires the user to sign in. When it encounters a login screen, it hands control to the user through a secure form for entering a username, password and 2FA code. According to OpenAI, the model neither sees nor stores these details. Once the user has signed in, ChatGPT returns to the task, and the session can remain active for subsequent instructions. You can therefore ask ChatGPT to find an invoice in a customer portal, check a bill or compare plans that become visible only after signing in. More information is available in the official ChatGPT browser documentation. 2. ChatGPT Can Combine Information from Several Business Applications A client meeting begins in an hour, and you need to review the latest email arrangements, important Slack messages, the current proposal stored on the company drive and the status of the sales opportunity in your CRM. Gathering this information manually means opening one tab after another, reconstructing the project history and dealing with unnecessary stress before the meeting. ChatGPT can search approved sources, select the information related to the client and prepare a one-page brief covering the current situation, open issues, risks and suggested next steps. With the appropriate permissions, it can also save a note in the CRM or prepare tasks for approval. In 2025, ChatGPT could already search and combine information from connected applications. In 2026, ChatGPT Work can gather information from several sources, turn it into a finished deliverable and then perform actions for which it has received permission. Access to each application and the ability to save changes depend on the installed integrations, granted permissions and workspace settings. 3. You Can Create an Agent for a Specific Business Process Imagine an agent assigned to support your most important clients. A new request can automatically trigger its work. The agent collects previous correspondence, checks similar cases and finds the appropriate procedure in the company’s documentation. It then prepares a response and, if it has the necessary permissions, creates a task for the appropriate team. The message to the client remains pending until an employee approves it. In 2025, a similar assistant could be built as a custom GPT equipped with instructions, knowledge and integrations. Workspace Agents, introduced in 2026, expand this concept into a saved process that includes data sources, actions in applications, required approvals and the format of the finished deliverable. An agent can be shared with a team and started manually, according to a schedule or through a signal sent by a company system. During configuration, you define which applications and sources the agent can use, which actions it is allowed to perform and when it should stop and ask a person to make a decision. This allows the same process to follow a consistent set of rules every time a new request arrives. 4. ChatGPT Can Learn a Process by Observing Your Work Some procedures are difficult to describe. You may know exactly where to click and what to change, yet writing down every step would take longer than completing the task again. A good example is a monthly report prepared in a legacy system: you select the appropriate filters, export the data, organise the spreadsheet and save the file in the correct folder. Record & Replay, introduced in 2026, allows you to complete the process once with recording enabled. ChatGPT or Codex observes the required actions and uses them to create a skill describing the workflow, its variable data and the method for checking the result. You can review it, make corrections, add your own rules and then use it when preparing the next report. This is particularly useful when process knowledge exists mainly in employees’ heads and a proper set of instructions has never been created. I personally cannot wait to try this feature while editing a video in DaVinci Resolve. I will show ChatGPT how I remove unsuccessful takes, organise the audio track and conceal visible cuts with a short zoom-in. I am curious to see how much of this repetitive work can be turned into a reusable skill for future recordings. 5. ChatGPT Can Help You Resume Interrupted Work Human memory is unreliable. It was probably Tuesday, after a conversation with your manager, when you made changes to an important spreadsheet. You cannot remember its name or location, or even whether it was open in the desktop version of Excel or in a browser. A standard file search is not much help. This is where Computer History can help. You can ask ChatGPT, for example: “Find the spreadsheet I edited after my conversation with Krzysztof.” Your activity history can connect the sequence of applications you opened, the actions you performed and the available context, and then identify the material you probably have in mind. If ChatGPT has access to the appropriate source, it can also open the file directly. You can ask what you were working on before a break, request a summary of the previous day’s work or identify activities that occur regularly and could be turned into a skill or automation. You choose which applications and websites are included in the history, and you can pause data collection at any time. Computer History records information including clicks, typed text, keyboard shortcuts, application changes and context provided by macOS. Temporary event data is deleted after no more than 48 hours, while local memories created from that data remain until the user deletes them. The feature is currently available in the ChatGPT app for macOS, requires Memories to be enabled and is turned off by default. In Business and Enterprise workspaces, an administrator must first grant access, after which each employee can decide whether to enable it. 6. ChatGPT Can Design, Test and Publish a Web Application How many good ideas have become stuck in the IT department’s backlog because something more urgent always came up? Suppose the administration team needs a simple tool for reporting faults, with a form, a list of cases, their statuses and a separate view for the person responsible for repairs. With Sites, you can describe the tool in your own words and provide sample data, company materials or a screenshot of a similar system. GPT-5.6 will prepare a working prototype, and you can ask it to simplify the form, add filters or adapt the design to your brand identity. ChatGPT or Codex can then test the application in a browser, fix any identified issues and publish the finished version at a specified address. Websites and simple applications were being created with GPT long before 2026. This year’s change concerns the entire process: GPT-5.6 is better at designing usable interfaces, while Sites provides a single place to launch a prototype, make changes, save subsequent versions and publish the finished application. The same process can be used to create calculators, dashboards and small internal tools. Before they are deployed in a real business environment, their security, permissions, data storage and regulatory compliance still need to be reviewed. 7. ChatGPT Can Work Directly in Excel and Google Sheets Consider a simple example. You open a spreadsheet containing campaign results and notice that some formulas are outdated, several cells are empty and the cost per lead has suddenly increased in two campaigns. Instead of checking everything one item at a time, you ask ChatGPT to analyse the data, complete the calculations, identify unusual results and prepare a chart for the monthly summary. ChatGPT performs this work directly in Excel or Google Sheets. You can see the changes it makes, review the formulas and immediately request another adjustment, such as changing the chart range, adding a comparison with the previous month or highlighting campaigns that exceed the target cost. In 2025, a similar analysis usually required uploading a spreadsheet to ChatGPT and then downloading the modified file. ChatGPT for Excel and Google Sheets, introduced in May 2026, allows you to work on the spreadsheet where the data is stored and where you will later use it. This makes the conversation with the model part of the document workflow instead of a separate stage outside it. 8. ChatGPT Can Create and Refine Content in Its Final Format The board meeting is one day away, and the information needed for the presentation is scattered across meeting minutes, a spreadsheet of results and several project documents. There is also a company template that must be followed. ChatGPT Work can collect these materials, organise them into a coherent story, prepare the slides, add charts and identify figures or conclusions that are not supported by the source data. You receive an editable PowerPoint or Google Slides presentation that can retain the structure, visual style and brand elements of the provided template. In 2025, GPT could already generate editable PowerPoint, Word and Excel files. In 2026, ChatGPT Work combined document creation with previews, targeted revisions, saved templates and final material checks. The entire process can therefore take place within one conversation, from gathering the sources to preparing subsequent versions of the document. 9. Codex Can Manage Long-Running Work as a Persistent Goal You have a large audit of a company website ahead of you. There are hundreds of pages to review, along with outdated information, broken links and differences between language versions. Finally, all the issues must be grouped and converted into a list of specific changes. Codex could already perform tasks in the background in 2025. Goal Mode, introduced in 2026, allows you to delegate much longer assignments and manage them across successive stages. You can monitor progress, change the direction of the work when necessary and access the active project from your phone. Several such assignments can run in parallel while you return to them whenever your input or decision is required. 10. You Can Talk to ChatGPT While It Works on Your Screen Voice messages have become part of everyday communication, while assistants such as Siri and Google Assistant have accustomed us to controlling technology by voice (“Hey Siri”, “OK Google”). We increasingly expect the same from the tools we use at work, especially when a dashboard, document or application is already open and pointing something out is easier than describing it in detail. Needless to say, OpenAI has kept pace with this trend. Imagine that, hypothetically, your screen displays a sales dashboard showing a clear drop in performance in one region. You only need to say: “Find out what happened.” ChatGPT can see the report, compare the relevant periods and, if it has access to the source data, investigate the cause and prepare a comment for the results presentation. Voice conversations and screen sharing were already available in 2025. In 2026, voice also became a way to direct work performed by ChatGPT Work. Instructions such as “compare these two periods”, “check the data in the application” or “improve this section” refer directly to the material visible on the screen. Part of the task can then continue in the background. 11. A Recurring Report Can Be Generated Without Repeating the Prompt Imagine that every Monday in your company begins with the preparation of a sales report. Each time, someone has to retrieve the latest results, compare them with the plan, identify the largest variances and turn them into a short summary for management. ChatGPT could already run simple tasks at a specified time in 2025. In 2026, an entire repeatable process can be placed on a schedule. Once configured, the task accesses the specified available sources, performs the analysis and leaves the completed report for your review. The same approach can be used to prepare a morning briefing, review new requests, summarise changes in documents or monitor competitors regularly. Each run has its own status and history, allowing you to review the result, refine the instructions and improve the process based on subsequent reports. 12. You Can Combine Company Skills and Tools in a Single Plugin Imagine that several teams regularly prepare materials for clients. They should always use current service descriptions, approved case studies, company templates and the same communication guidelines. They also need access to the content management system, the asset library and the tool in which documents are submitted for approval. An Agent Plugin allows these elements to be combined into a single package available in the company workspace. Once installed, ChatGPT can use the appropriate instructions, sources and permitted actions when preparing a presentation, proposal, service description or website content. When the company template or approval process changes, the plugin can be updated once instead of modifying the configuration of every agent separately. In 2025, a similar solution required custom GPTs, instructions and integrations to be connected and configured separately. Agent Plugins, introduced in 2026, allow them to be distributed as a single installable package. A plugin is not a separate agent and does not start work by itself. It extends ChatGPT, Codex or a Workspace Agent with the company knowledge, rules and tools needed to complete a task. 13. GPT-5.6 Can Divide a Task Among Several Agents You are considering entering a foreign market, which requires an analysis from several perspectives. You need to research the competition, compare prices, estimate costs, assess demand for the services offered and prepare an initial list of potential partners. GPT-5.6 can divide these areas among several subagents. Each subagent handles its own part of the analysis, and the work can proceed in parallel. The main agent collects the results, compares the findings and combines them into a single recommendation with sources and a list of issues requiring further verification. Multi-agent solutions were already being developed in 2025, but they required the system to be designed independently. In GPT-5.6, the mechanism for dividing work has been built into the Responses API and is currently available as a beta feature. It works best when a task can be divided into independent parts assessed according to shared criteria. Parallel work by several agents can reduce the time required for the analysis, although it usually increases token usage. 14. GPT-5.6 Can Combine Multiple Operations into a Single Program Consider a hypothetical scenario: a CRM migration is only a few weeks away, and before it begins, 800 records must be checked, missing information located and data requiring correction identified. GPT-5.6 can write a JavaScript program that retrieves successive records through an available tool, checks the required fields, compares the data against defined criteria and creates a list of missing information. Loops, conditions and result processing are handled within the runtime environment, so the model does not need to analyse each of the 800 records separately. Tool calling was already possible in 2025, but successive operations usually required the results to be sent back to the model or the necessary logic to be written within the application. Programmatic Tool Calling allows GPT-5.6 to prepare code that combines multiple predictable operations. This can reduce processing time and token usage. The company decides which tools and data the program can access and which actions require approval. The feature operates through the API, so it must be implemented within a company application or process. It is not an option available directly in the ChatGPT window. 15. ChatGPT Can Turn Data into an Interactive Visualisation Before a meeting, you receive a table containing the results of an employee survey. A standard chart shows the average scores, but it does not allow you to explore how responses differ between departments, locations and seniority groups. ChatGPT can prepare an interactive visualisation in which you select the criteria you are interested in, change the data range and observe how these choices affect the results. In 2025, GPT could already create charts and analyse data. Visualize, introduced in August 2026, makes it possible to build interactive diagrams, maps, timelines, simulations and information exploration tools directly within a conversation. The finished visualisation can be filtered, adjusted and used to investigate additional questions that arise during the analysis. GPT and ChatGPT Features in 2026: Availability by Plan and Device The table below summarises the availability of the features described in this article. Information accurate as of 31 August 2026. Feature Free / Go Plus / Pro Business / Enterprise / Edu Where is it available? Key limitations GPT-5.6 Yes, GPT-5.6 Luna is the default model Yes, with access to the Sol, Terra and Luna family Yes, depending on workspace settings Web, desktop app, Codex and iOS; also through the API Usage limits and the choice of model variant depend on the plan and environment. ChatGPT Work Yes, with lower usage limits Yes Yes, with additional administrator controls Web and desktop app; some capabilities are also available on mobile It shares usage limits with Codex. Tool availability depends on the plan, region and workspace settings. Computer Use Full availability has not been clearly confirmed Yes, in supported regions Yes, if permitted by workspace settings Desktop app on macOS and Windows Work running on a computer can be monitored remotely from iOS. Linux does not yet support this feature. Browser and website interaction Limited availability Yes Yes, although some capabilities may be disabled by an administrator Web, desktop app and partially on mobile Signing in to websites through the cloud browser is available on Plus and Pro, but is not currently available on Enterprise and Edu. Plugins, skills and application integrations Depends on the individual plugin Yes Yes, after administrator approval Web, desktop, iOS and Android; also Codex CLI Plugins do not work in the Codex IDE extension. The availability of a specific integration may depend on the plan and region. Workspace Agents No No Yes, on Business, Enterprise and Edu plans Company ChatGPT workspace An administrator must enable agents and grant permissions to create, publish and connect them to applications. Computer History No Pro only Yes, on Business and Enterprise ChatGPT desktop app on macOS only The feature is disabled by default, requires Memories and needs administrator approval in a company workspace. It is available in the EEA, Switzerland and the United Kingdom. Creating documents, spreadsheets and presentations Yes, with lower usage limits Yes Yes ChatGPT Work on the web and in the desktop app The scope of editing depends on the format, available plugins and permissions for the source files. ChatGPT for Excel and Google Sheets Availability has not been confirmed Yes Yes Directly within a supported spreadsheet The feature uses ChatGPT Work limits. It is not provided through API-key authentication alone. Sites No Yes Yes ChatGPT on the web and in the desktop app The feature remains in public beta. Limits depend on the plan, and full Sites management is not described as a mobile app capability. Long-running tasks and Goal Mode The available scope depends on plan limits Yes Yes ChatGPT Work on the web, the desktop app, Codex CLI and the IDE extension Tasks that work with local files require the application to remain running and the computer to be available. Voice control for tasks Not in the ChatGPT Work form described here Yes Yes Desktop app; on iOS through Remote after pairing with a computer Voice conversation limits depend on the plan. Tasks started by voice also use the Codex allowance. Scheduled tasks and automations Availability depends on the account Yes Yes, if enabled by an administrator Web and desktop app; event triggers are also available on iOS and Android Triggers can respond to supported events in Gmail, Slack and GitHub. Subagents and Ultra mode Full access has not been confirmed Yes, depending on the available model and reasoning level Yes ChatGPT Work, the desktop app and Codex CLI In most modes, the user must explicitly request delegation. Ultra can launch subagents automatically and consumes more of the usage allowance. Interactive visualisations Depends on whether the feature has reached the account Yes, on supported accounts Yes, if the workspace allows the plugin to be used Web; currently rolling out on desktop and mobile Visualisations are not rendered in Codex CLI or the IDE extension. Programmatic Tool Calling Not available as a ChatGPT interface feature Available through paid API usage Available through the API Responses API This feature is intended for developers building their own applications. It allows GPT-5.6 to coordinate tools through executed JavaScript code. Persisted Reasoning and Pro and Max modes Not available as separate settings for Free users Partially available in the interface; full configuration through the API Depends on workspace settings; full configuration through the API Responses API and selected ChatGPT and Codex surfaces GPT-5.6 can retain compatible reasoning elements between successive calls. Pro mode can improve quality at the cost of additional time and token usage. The availability of OpenAI features changes with subsequent updates. It may depend on the plan, region, operating system, application version, selected model and permissions granted by the administrator of a company workspace. How Do You Turn GPT’s Capabilities into a Solution That Works for Your Business? These fifteen examples are enough to illustrate the scale of the change. Companies still need to make the most important decisions: which processes should be delegated to GPT, which data it may access, which actions it is allowed to perform and when human approval is required. At TTMS, we begin with these questions. We analyse the process, its objective, the systems involved and the acceptable level of risk. We then select the appropriate solution, whether ChatGPT Work, Codex or an integration through the OpenAI API, design the permissions and connect GPT to the company’s applications. We test the finished solution using real cases and measure its quality and costs. Following the launch, we provide monitoring, maintenance and further development. We have delivered projects of this kind. For Stäubli, we integrated ChatGPT with Adobe Experience Manager, while for Takeda, we created an AI solution supporting document analysis in Salesforce. This experience is backed by more than 800 TTMS specialists and an approach to AI management and security confirmed by ISO/IEC 42001 and ISO/IEC 27001 certifications. Would you like to find out which process could deliver real value to your company with GPT? Discuss it with the TTMS experts. Are all new GPT features available to every user? Access to individual features may depend on the subscription plan, region, operating system, and workspace settings. Some capabilities are initially released to selected users or require administrator approval. In a business environment, availability also depends on user permissions and access to specific applications. Before planning a process, it is worth checking which features are supported in your organisation’s current setup. How can you identify a process that is suitable for GPT? A good candidate is a process that is performed regularly, uses digital data, and follows rules that can be clearly described. A practical starting point is a time-consuming task such as gathering information from several sources, preparing reports, comparing documents, or completing missing data. The process should also have a clearly defined outcome, making it possible to evaluate the quality of GPT’s work, the time saved, and the number of errors. Can GPT-5.6 work securely with company data? Yes, provided that access to the data is properly designed and controlled. The organisation should define which sources the model can use, which actions it is allowed to perform, and when human approval is required. Data storage policies, permission management, monitoring, and regulatory compliance also need to be considered. For processes involving confidential information or personal data, security should be built into the implementation from the beginning. How can a company measure the benefits of implementing GPT? The most reliable approach is to compare the process before and after implementation. Useful metrics include task completion time, operating costs, the number of errors, the percentage of cases requiring manual correction, and the quality of the final output. It is also worth checking whether employees actually use the solution and whether it makes their everyday tasks easier to complete. A pilot involving a limited number of cases can help measure these results before the implementation is expanded.
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Astra, the Future GPT-6? OpenAI’s New Model Explained
Solving mathematical problems that scientists had wrestled with for years – could there be a better demonstration of what a new AI model can do? OpenAI has typically previewed new versions of its large language models with benchmark results, meaning scores from standardised tests designed to measure a model’s capabilities. I have to admit that seeing GPT tackle genuine research problems makes a much stronger impression on me. What will you learn about OpenAI Astra? What Astra is and why it is being discussed as a potential GPT-6, 10 results in mathematics and theoretical computer science presented by OpenAI, How Astra analyses problems, tests hypotheses and changes its approach, The differences between a conversational model, an AI agent and a system capable of managing an entire project, What Astra could mean for science, business and the future of AI models, Critical responses to the model’s achievements, Cybersecurity risks associated with autonomous AI agents, Which important questions OpenAI has yet to answer. What is OpenAI Astra, and could it become GPT-6? OpenAI describes Astra, the prototype’s working name, as “our next major model”, although the company has disclosed very few details so far. Its task was to develop arguments independently, test hypotheses, recognise unproductive approaches and find new paths towards a solution. The results of its work can then undergo formal and independent verification. We do not know how Astra is built, how much information it can analyse at once or how it organises its work on a complex task, although we can speculate about the last of these. OpenAI has also not disclosed whether Astra is a single model, a team of collaborating AI agents or a more extensive system equipped with mechanisms for coordinating their work and retaining previous results. The prototype may be connected to a model previously described by OpenAI as capable of operating autonomously over very long periods. Such a system can make repeated attempts, analyse intermediate results and maintain its direction of work for many hours, potentially even days. According to media reports, Sam Altman has already presented Astra to US politicians and regulators. The term “GPT-6 Astra” should therefore be treated as media shorthand. Astra could eventually be released as GPT-6, another version of GPT-5 or a separate family of models. For now, all of these possibilities remain open. Why could Astra’s 10 results matter more than another benchmark record? OpenAI presented ten results concerning problems that had remained open for at least a decade and, in most cases, considerably longer. The problems come from eight fields: high-dimensional geometry, coding theory, group theory, operator algebras, computational complexity theory, quantum computing, lattice geometry and post-quantum cryptography, extremal combinatorics. In simple terms, the process worked as follows: GPT generated mathematical arguments. Once the results had been obtained, researchers worked with the model to develop them into scientific papers. The system then translated the arguments into Lean 4, allowing a computer to check every step of the proofs. For readers interested in the technical details, here are the relevant links: the complete collection of papers, the Lean formalisation repository and reconstructions of how the solutions were developed. Independent verification of all the claims by the scientific community is only beginning. Mathematicians can now review the papers, check the definitions, run the formalised proofs and look for potential gaps. I discuss this in more detail in one of the final sections. 10 new results from Astra in mathematics and theoretical computer science A quick warning: this section is about to become fairly technical. These subjects are new, abstract and extraordinarily difficult for me as well, so I have tried to explain each result in the simplest possible terms. Here is how GPT Astra approached the individual problems. 1. Sphere packing in high-dimensional spaces The sphere-packing problem asks how densely identical spheres can be arranged, much like coins on a table or balls in a box. Mathematicians also study this question in spaces with hundreds or thousands of dimensions because it has applications in areas such as information theory and data encoding. Astra used an established mathematical method to determine more precisely how densely spheres can be packed in spaces with a very large number of dimensions. According to the authors, this is the first improvement since 1978 to the value used in the formula describing how quickly the possible packing density decreases as the number of dimensions increases. The difference becomes more significant as the number of dimensions grows and enables a more precise estimate of the maximum packing density. Put simply, Astra’s calculations improve our understanding of how many spheres can fit inside such a “high-dimensional box”. 2. Binary and spherical codes: new bounds on the number of error-resistant codes A binary code is a set of sequences made up of zeros and ones. These sequences must differ from one another sufficiently for a system to detect and correct transmission errors. This can be compared to positioning transmitters at safe distances from one another so that their signals remain easy to distinguish. Astra determined more precisely how many codes can be placed sufficiently far apart for a system to continue distinguishing between them and correcting errors. This enables mathematicians to estimate more accurately how many codes with the required level of error resistance can fit within a given space. The model tested its initial idea on a simple example consisting of eight digits and discovered that it produced an incorrect result. It therefore abandoned that approach and reformulated the problem. This case demonstrates Astra’s ability to test its own assumptions and redesign its solution when the original direction proves unsuccessful. OpenAI’s published materials support four important conclusions. 3. The first explicit example of a non-sofic group A group is a mathematical way of describing symmetries and operations that can be performed in sequence, much like a set of moves used to rotate a Rubik’s Cube. Sofic groups can be approximated with arbitrary precision using simpler structures based on a finite number of elements. For decades, mathematicians wondered whether this property applied to every group. Astra identified a specific example of a group that cannot be approximated with arbitrary precision using simpler models composed of a finite number of elements. The result demonstrates that these simplified models cannot represent every mathematical group. The solution combined several distant areas of mathematics, demonstrating the model’s ability to bring together tools that had not previously formed an obvious path towards a proof. 4. Disproving Connes’ rigidity conjecture The von Neumann algebra associated with a group can be compared to its highly complex mathematical “fingerprint”. Connes’ conjecture proposed that, for a certain class of particularly rigid groups, this fingerprint uniquely identifies the group in question. Astra constructed infinitely many different groups with exactly the same mathematical “fingerprint”. In doing so, it disproved Connes’ conjecture and answered a later question posed by mathematician Sorin Popa. Astra used a mechanism resembling the carrying operation in binary addition. This made it possible to construct many different groups with the same mathematical “fingerprint”. Put simply, Astra demonstrated that a single mathematical “fingerprint” can belong to infinitely many different groups. 5. The matrix permanent: the minimum number of operations required for its computation The permanent of a matrix is calculated in a similar way to the determinant, except that all terms are added with a positive sign. This seemingly minor change makes the permanent one of the most important examples of a problem with extremely high computational complexity. Astra determined the minimum number of basic operations required to calculate the permanent. It proved that no solution within this class can be simplified below a certain level of complexity. This can be compared to determining the minimum number of components required to build any machine capable of performing a particular task. Such a proof must cover every possible construction that meets the specified conditions, which makes it exceptionally difficult to develop. The result provides a more precise lower bound on the number of operations needed to solve this problem. It also brings mathematicians closer to answering a fundamental question: which problems can be solved efficiently, and which will always require an enormous amount of computation? 6. Quantum games: why does the probability of a perfect win decrease so rapidly? Imagine a game in which two players answer a referee’s questions separately, while their shared goal is to complete every round successfully. In the classical version, each additional round rapidly reduces the probability of a perfect win, much like repeatedly tossing a coin reduces the chance of getting heads every time. In the quantum version, the players’ results can be correlated even when they do not communicate during the game. They can also analyse several rounds as a single combined problem. Astra proved that even such quantum correlations cannot prevent the probability of winning every repeated round from decreasing very rapidly. The problem had remained open since at least 2004. The key to the solution was a method for transforming quantum states without changing the probabilities of their possible outcomes. The result advances the theory of interactive proofs, quantum information theory and methods for increasing the reliability of protocols. 7. The Closest Vector Problem: even an approximate solution remains difficult A lattice can be imagined as a regular grid of points, similar to street intersections in a perfectly planned city, extending across many dimensions. The Closest Vector Problem (CVP) involves finding the point on this grid that lies closest to a selected location. It is highly relevant to geometry, coding theory and post-quantum cryptography. Astra connected CVP with the well-known 3SAT logic problem and demonstrated that finding even a solution that merely approximates the optimal one is extremely difficult. This difficulty increases with the number of dimensions in the lattice. The model represented the logical puzzle as a system of points and distances between them. This can be compared to encoding a complex logic puzzle in a spatial arrangement of points so that solving one problem also provides a solution to the other. The result deepens our understanding of the theoretical difficulty of lattice-based mathematical problems. Assessing the security of specific cryptographic algorithms requires a separate analysis of their variants, parameters and methods of data generation. 8. Proving Ehrhart’s conjecture on the volume of high-dimensional shapes A high-dimensional convex body can be imagined as a solid placed on a regular lattice of points, with its centre of gravity being the only lattice point located inside it. Ehrhart’s conjecture specified the maximum possible volume of such a body, and Astra proved it for any number of dimensions: vol(K) ≤ (n+1)n / n! The main difficulty was connecting the number of individual lattice points with the volume of the entire body. The first approach provided only part of the information required. Astra therefore reformulated the problem in the language of another branch of geometry and began searching for a solution using its tools. The model combined several advanced methods for describing the body’s shape, its boundaries and the distribution of points. Put simply, Astra translated the geometric puzzle into a different mathematical language in which it became possible to determine the exact volume bound. 9. Multicolour Ramsey numbers A complete graph can be imagined as a group of people in which every pair is connected by a line, with each line assigned one of k colours. Mathematicians ask how large such a network must become before it inevitably contains three people whose connecting lines are all the same colour. This minimum size is denoted by Rk(3). Astra developed new colouring methods which, when combined with previous results, established the growth rate of this number: Rk(3) = kΘ(k). The result does not provide an exact value for every number of colours, but it reveals the correct scale of growth. In doing so, it resolves Erdős Problem No. 183. Astra expanded the network in stages according to the same rule. This made it possible to construct increasingly large configurations without creating a triangle whose edges were all the same colour. 10. Two counterexamples in extremal graph theory An extremal number determines how many connections a network can contain before a specified forbidden configuration inevitably appears. Astra disproved two conjectures proposed by Erdős and his collaborators concerning how this value could be predicted. In the first case, it constructed a family of graphs in which forbidding each member individually still allowed approximately n4/3 edges, while applying all the restrictions simultaneously reduced the maximum number to O(n21/16). This shows that several forbidden structures can constrain a graph far more strongly together than when each is considered separately. In the second case, Astra found a network divided into two groups in which every small section contained few connections, while the complete construction could be considerably denser than the conjecture predicted: ex(n, H) ≥ cn3/2+ε. The two results resolve Erdős Problems No. 146 and 180. They also show that the simple structure of small sections of a network does not always allow us to predict how dense the entire construction can become. A critical perspective: how do experts assess Astra’s mathematical achievements? After the initial excitement, important reservations began to emerge. Mathematicians pointed out that at least two of Astra’s results rely heavily on earlier work, raising questions about their novelty. OpenAI has since changed the way it describes the experiment. It now increasingly refers to “making meaningful progress”, rather than solely to “solving longstanding problems”. Interestingly, a researcher affiliated with Anthropic reported that the Claude Fable model had reproduced solutions to five of the ten problems tackled by “GPT-6” within 24 hours, although these results have yet to be fully verified. This does not undermine Astra’s capabilities, but it makes it more difficult to determine whether we are witnessing a breakthrough driven by the exceptional abilities of one model or broader progress across AI models as a whole. Above all, there is still no reliable, independent and fair comparison conducted using the same problems, prompts, computational budgets and rules governing access to tools. What do the results reveal about how Astra works? OpenAI’s published materials support three important conclusions. 1. The model can abandon dead ends The published reconstructions show Astra trying different approaches, identifying obstacles, reformulating problems and returning to earlier stages of its work when necessary. This resembles genuine research more closely than an extended answer generated in a single pass. In the binary-codes problem, the first recurrence was rejected after the model found a small counterexample. When working on Ehrhart’s inequality, Astra spent considerable time developing an approach based on symmetrisation before reformulating the problem in terms of toric geometry. In the proof concerning quantum games, it recognised that the classical argument lost control after conditioning on rare events and began searching for a representation that preserved quantum probabilities. The published document does not reveal the model’s complete internal reasoning process. It is a narrative produced by a model that reviewed the original reasoning traces and the final papers. 2. GPT Astra combines discovery with automated verification Lean checks the correctness of a formal proof step by step. The repository contains separate files for all ten results, along with instructions for performing additional checks of the formalised proofs. Computer verification does not replace assessment by independent mathematicians. Researchers must still establish, among other things: whether the formal theorem corresponds precisely to the original problem, whether the definitions introduce any unintended simplifications, whether the result is genuinely new, how significant it is for the relevant field, whether the manuscript correctly connects the formalisation with the informal argument. A computer can confirm that a written proof is logically correct under the adopted definitions and assumptions. It does not automatically confirm that the authors formalised precisely the version of the problem that mathematicians intended to address. The research was published on 1 August 2026, so full independent verification by the mathematical community will take time. Thomas Bloom of the University of Manchester nevertheless described the results as “big news” and rated the significance of the presented constructions particularly highly. 3. The cost of Astra’s results and the importance of additional computing power OpenAI claims that, based on the API pricing for GPT-5.6 Sol, the tokens required to find all ten solutions would have cost approximately $2,000. This figure is, of course, neither the actual cost of developing Astra nor the full cost of the project. It does not include model training, infrastructure, researchers’ work, problem selection, validation or all the unsuccessful attempts. It is simply the cost of the tokens used to find the published solutions, calculated according to current API pricing. The average comes to approximately $200 per published result, but we do not know: the total number of problems presented to the model, the success rate, how the costs were distributed across the problems, how long the system operated, how many agents were involved, how many runs were conducted in parallel. Noam Brown, an OpenAI researcher involved in the work on Astra, acknowledged that the system had also been tested unsuccessfully on other major mathematical challenges, including the Millennium Prize Problems. He added that OpenAI had not allocated an especially large amount of computing power to each problem. The company therefore believes that Astra could achieve better results if given more time and resources to search for solutions. From GPT-5.6 to Astra: how AI is moving from answering questions to managing projects GPT-5.6 already includes several features that point towards the direction described above. The model can independently select tools, analyse the results it obtains and use them to plan its next actions. Ultra mode uses four agents by default, while OpenAI has also tested configurations involving sixteen agents. The company also offers a multi-agent mode in the Responses API in beta. Astra may develop this architecture towards much longer and more coherent periods of autonomous operation. The most important difference would be its ability to manage an entire project over many hours or days. The system would need to remember what it had already tried, which ideas it had rejected, what results it had obtained and how the individual tasks related to one another. From the user’s perspective, the change could be very tangible. Instead of guiding the model through a sequence of prompts, the user gives it an objective, a set of available tools, a defined scope of permissions, a budget and completion criteria. The user then returns to a finished result accompanied by a record of the attempts, tests and decisions made along the way. This progression can be presented as three successive units of work: A conversational model generates an answer. An agent completes a task using tools. A multi-agent system manages a project in which tasks are created and modified as the work progresses. Only the technical documentation will show whether Astra genuinely operates at the third level as a coherent system. The mathematical demonstration is, however, the first strong indication that this direction is becoming more than a promise. OpenAI, Google DeepMind and Anthropic: the race to develop long-horizon AI models Google DeepMind, Anthropic and OpenAI are developing AI systems capable of independently handling increasingly long and complex tasks. Aletheia, Google’s mathematical agent based on Gemini Deep Think, can generate solutions, verify their correctness and revisit them when it detects an error. When analysing 700 Erdős problems, it solved four questions that had previously remained open. Anthropic, meanwhile, is focusing on coordinating the work of multiple agents. According to the company, Claude Opus 4.8 can divide a large project into smaller parts and assign them to hundreds of subagents working in parallel. This allows it to carry out tasks such as migrations involving hundreds of thousands of lines of code. Claude Science, another environment being developed by the company, is intended to make it possible to trace and verify the successive stages of research work. All these projects point in the same direction: models are expected to work towards a single objective for longer, monitor their own results and revise earlier decisions. Astra stands out for producing results at the frontier of contemporary knowledge and for formally encoding some of its proofs, allowing their correctness to be checked by a computer. How could Astra change the AI model and agentic tool market? 1. Benchmarks may lose their role as the primary evidence of AI model quality Competition will increasingly focus on the final outcome: a new hypothesis, a discovered vulnerability, a completed system migration, a developed scientific model, a working application, a result that can be verified automatically. Astra was presented through its scientific results because conventional benchmarks do a poor job of communicating the difference between a model that answers a question and a system that manages an entire project. Benchmarks will remain necessary for comparing models under controlled conditions. Their market significance may, however, decline in favour of evaluations that measure project completeness, operational continuity and the quality of the final result. 2. The cost of a completed task may matter more than the price per token For business customers, the following factors will become increasingly important: the cost of completing the project, the time required to obtain the result, the probability of success, the number of human interventions, the cost of validation, the ability to resume work after an error. Approximately $2,000 for the tokens that produced ten published results is a powerful economic signal, even with all the reservations concerning how the results were selected. Perhaps future pricing models will include offers such as the “cost of a successfully completed migration”. This would, however, require transparent information about the number of failed attempts, the additional human work involved and the cost of verifying the result. 3. Astra could affect platforms that coordinate AI agents If models begin dividing work among agents, retaining a record of their progress and combining the resulting outputs themselves, some external platforms used to coordinate AI agents may lose part of their current advantage. The value of these platforms will primarily come from integration with company systems, access and cost controls, visibility into agent activity and the ability to audit the entire workflow afterwards. Dividing a task among several agents may become a standard model capability. The implementation layer will derive its value from embedding agents securely within organisational processes. 4. Keeping AI agent workflows under full control When a task lasts several days, the system must regularly save its progress and check whether subsequent actions are still leading towards the defined objective. OpenAI acknowledges that long-running work requires monitoring the model’s complete sequence of actions. Each individual step may appear safe even though, taken together, the steps lead to an unacceptable outcome. The company described a case in which a model attempted to bypass a safeguard by splitting a digital access key into separate parts and reassembling them only when the program was launched. Each step, considered in isolation, could appear relatively harmless, while the objective of the complete sequence was unacceptable. In another experiment, the model was instructed to deliver its results through Slack. Instructions found in the code it was analysing suggested publishing the proposed changes directly to GitHub instead. The model spent approximately an hour searching for a way around the test environment’s restrictions before finding a vulnerability and publishing the result there. Earlier models had abandoned the task under similar circumstances. The case shows that a longer operating horizon increases both the model’s effectiveness and the amount of time it can devote to searching for ways around safeguards. In response, OpenAI developed a system that analyses the model’s entire sequence of actions as its work progresses. The system can stop the session, notify the user and ask them to decide whether the work should continue. Further details are available in OpenAI’s report on the safety of long-horizon models. 5. Research fields in which Astra could accelerate progress The most immediate impact is likely to appear in fields with: precisely defined problems, extensive available literature, formal or automated verification tools, the ability to conduct computational experiments, unambiguous criteria for measuring progress. Mathematics is an ideal testing ground because a proof can be verified. Similar conditions exist in software development, chip design, some areas of chemical research, bioinformatics and cybersecurity. Economics, strategy, law, management and social research will remain much more challenging because correctness cannot be reduced to a machine-verifiable certificate. In these fields, a model may produce an impressively coherent project that is still based on flawed assumptions or a poorly defined objective. 6. Long-horizon models will require more computing power An important capability of a model will be the option to allocate more computing power and more attempts to particularly difficult problems. This will give an advantage to laboratories with: extensive computing resources, efficient communication between agents, effective context management, automated detection of dead ends, the ability to run multiple attempts and select the best result. The next stage of competition may concern more than model size. It may also depend on how effectively models use time and computing power when working on a specific task. The same model could operate as a relatively inexpensive assistant for everyday questions and as a costly research system when the user increases the budget for time, agents and parallel attempts. The section likely to age quickly: what do we still not know about Astra? OpenAI has not disclosed basic information about Astra, including its architecture, size, method of agent collaboration, memory mechanism or capabilities beyond mathematics. We also do not know its price, release date or whether OpenAI plans to make the model available through ChatGPT or the API. The published results do not demonstrate that Astra selected the problems independently, operated without supervision or can manage an entire research process. Nor do we know whether it can achieve similar results in other fields. There is therefore no basis for describing Astra as a system that matches human capabilities across a broad range of intellectual tasks. We also do not know the total number of failures. OpenAI published selected successes, while Noam Brown confirmed that the system had attempted to solve other major problems without success. Without knowing the total number of attempts, it is impossible to calculate Astra’s actual success rate or the expected cost of obtaining one valuable result. Why is Astra not yet an autonomous scientist? The published papers show a system solving problems selected and presented by humans. An autonomous scientist would also need to: select research directions independently, assess which questions are important, determine whether a result is genuinely new, design subsequent experiments, decide when sufficient evidence has been collected, place the result within the broader context of the field. Astra completed the most technically demanding part of this process: it developed new arguments and brought them to a form that could be formally verified. This is a major achievement, but it does not encompass the full scope of scientific work. Can Astra succeed beyond mathematics and controlled environments? The ten published papers demonstrate what Astra was able to achieve in a carefully selected environment. Mathematics offers clearly defined problems, extensive literature, precise language and formal verification tools. The real test will be whether this capability can be transferred to projects in which the objective changes as the work progresses, tools fail, data is incomplete and the correctness of the result requires human judgement. If Astra can maintain a coherent process over many hours or days, delegate subtasks, retain the results of previous attempts and return to a problem after detecting an error, the change will be more significant than another increase in benchmark scores. Models such as Astra demonstrate how rapidly the capabilities of artificial intelligence are advancing. In business, their value depends on selecting the right process, ensuring data quality, integrating AI with company systems and maintaining control over its operation. TTMS helps organisations design and implement solutions tailored to specific operational needs. Explore TTMS AI solutions for business and implementation examples. How autonomous was Astra when solving mathematical problems? OpenAI states that the mathematical arguments were generated by the system, while humans contributed to preparing the manuscripts, formalising the results and verifying their correctness. The papers list OpenAI as the author, and the company has not attributed individual proofs to specific employees. This creates an interesting precedent: the organisation assumes responsibility for the publications while crediting the model with producing the arguments. However, it remains unclear who selected the problems, prepared the prompts, initiated subsequent attempts and decided which results were suitable for publication. Without this information, it is difficult to determine Astra’s precise level of autonomy or distinguish the capabilities of the model itself from the work of the wider research team. Can artificial intelligence be the author of a scientific paper? Authorship involves responsibility for the research method, the evidence presented, the conclusions and any potential errors. An AI system cannot formally accept such responsibility, so researchers should remain the authors of scientific publications. The model’s contribution should be described clearly in the methodology, including how it was used and which elements of its work were verified by humans. How can researchers verify whether AI has made a genuinely new discovery? A correct result is not necessarily a new one. Researchers must compare it with the existing literature, previously unpublished work and known variants of the same problem. One particular challenge is determining whether the model developed a new solution or reproduced a relationship contained in its training data. Novelty should therefore be assessed separately from the correctness of the proof itself. Can a result produced by a closed AI model be reproduced? Reproducing an experiment is difficult when researchers do not know the model’s architecture, training data or exact settings. Recording the prompts, system version, tools used, intermediate results and human interventions can make the process more transparent. The final result should also be verifiable using a method independent of the model that generated it. Without this documentation, other scientists may be able to verify the result itself, but not the full process that led to it. Could AI agents increase the risk of errors and unreliable scientific publications? An AI agent can generate large numbers of convincing hypotheses, proofs and interpretations of data in a short time. This scale can accelerate research, but it can also spread flawed assumptions more quickly. Academic journals and research institutions will need clear rules for disclosing the use of AI, preserving a record of the research process and independently verifying the most important results. The transparency of the process will become as important as the quality of the final publication. How should a research team prepare to work with AI agents? A good starting point is to select tasks with results that can be verified unambiguously. The team should determine which data and tools the agent can access, which actions require human approval and who is responsible for accepting the final result. It should also establish procedures for recording each stage of the work, reporting errors and stopping an experiment when necessary. This preparation allows researchers to benefit from the speed of AI while maintaining control over the quality of the research.
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AI Avatars in E-Learning: Boost Engagement in 2026
Online learning has amotivationproblem.Courses get built, learners enroll, and thena significant portionquietlystopshowing up. The content may be excellent, but without a human presence to guide and engage, it canfeel like readinga manual alone.AI avatars in e-learning are changing the learning experience by making online training feel more engaging, interactive, and easier to remember than traditional course formats. 1. Why AI Avatars Are Changing the Way People Learn Online AI avatars work because they make online training feel less like clicking through slides and more like being guided by a real instructor. A face, voice, and consistent on-screen presence help learners follow the material, stay focused, and complete the course. In many traditional e-learning modules, attention drops after the first few screens. Learners start to skim, click through, or lose context. AI avatars can reduce this fatigue by turning passive content into a more guided experience. Instead of leaving employees alone with blocks of text, the avatar introduces topics, explains key points, and keeps the pace clear and consistent. For organizations training people at scale, this matters even more. When hundreds of employees go through onboarding, compliance training, or product updates, avatars help deliver the same message with the same tone, energy, and clarity across locations, languages, and time zones. 2. What AI Avatars in E-Learning Actually Are AI avatars in e-learning are digital characters powered by artificial intelligence that simulate human instruction within a course environment. They use technologies like natural language processing, text-to-speech synthesis, and adaptive learning logic to interact with learners in real time. What separates an AI avatar from a simple talking head video is interactivity. A talking head delivers a script. An AI avatar can respond to learner inputs, adjust pace based on performance data, offer feedback, and guide learners down different paths depending on their choices. 2.1 AI-Powered Avatars vs.Traditional Video Instruction Recorded video works well for straightforward content delivery, but it has a fixed ceiling. Once recorded, it cannot adapt or respond. An AI avatar changes that relationship entirely, bringing presence and responsiveness without requiring a live instructor. It can detect when a learner is struggling andofferan alternative explanation, or prompt reflection with a question rather than simply presenting answers. 2.2 Types of AI Avatars and Their Roles Instructor avatarsserve as theprimary guide through course content, presentinginformationand keeping learners oriented. A well-designedinstructoravatar carries authority without feeling distant, striking a tone that feels like a knowledgeable colleague rather than a textbook. Peerand coach avatarsaddress one of online learning’s most persistent challenges: isolation. Peer avatars simulate the social dimension of learning, encouragingreflectionand creating a sense of learning alongside someone. Coach avatars motivate, check in on progress, and celebrate milestones. Scenario-based character avatarsappear within simulated situations. A customer service course might feature a challenging customer the learner must respond to; a leadership course might include a team member presenting a workplace conflict. These let learners practice in realistic, low-stakes environments before the real thing. 3. Key Benefits of Using AI Avatars in E-Learning 3.1 Personalized Learning at Scale AI avatars analyze how each learner responds to content andadjustdelivery accordingly. A learner who breezes through foundational material can move faster, while someone needing reinforcement getsadditionalexplanation before advancing. This kind of adaptive instruction was once reserved for one-on-one tutoring. Withavatars, itscales tothousands of learners simultaneously. 3.2 Higher Learner Engagement and Completion Rates One of the biggest challenges in e-learning is keeping learners engaged until the end of a course. When training feels impersonal or repetitive, attention naturally starts to fade. AI avatars help create a more engaging learning experience by presenting information in a way that feels conversational rather than static. They can explain concepts, guide learners through scenarios, andmaintaina consistent presence throughout the course. As a result, employees are more likely to stay focused, complete the training, and remember what they have learned. 3.3 Faster Production and Lower Costs Traditional training videos are expensive to produce and difficult to update. They require recording sessions, presenters, editing, and often another round of production whenever the content changes. AI avatars make this process faster. Instead of recording a new video from scratch, teams can update the script, choose a digital presenter, and generatea new versionof the module much more quickly. This is especially useful for onboarding, compliance training, product updates, and other materials that need to stay current. For L&D teams, the main benefit is not only lower productioncost. It is the ability to refresh training content without restarting the whole video production process every time something changes. 3.4 Consistent Multilingual Delivery Global organizations face a recurring challenge: training that feels equally strong across languages and regions. AI avatars can speak dozens of languages fluently,maintainingconsistent tone and quality throughout. A learner in São Paulo and one inSingapore both receive instruction that feels native and natural, without multiplying production costs. 4. High-Impact Use Cases for Avatar-Based Training 4.1 Employee Onboarding and Orientation First impressions shape long-term retention. An avatar-guided onboarding journey delivers a structured introduction to company culture, processes, and expectations in a format new employees can engage with at their own pace. Rewe Group tookthis astep further with “goRobert,” a hyper-realistic digital twin of a management member that new hires can query both in person and via Microsoft Teams.The system lets employees ask sensitive or practical questions without fear of judgment, improving psychological safety and information access during onboarding. 4.2 Compliance and Mandatory Training An AI avatar changes the delivery of compliance content without changing the substance. It can present complex regulations clearly, check comprehension with interactive questions, and keep the experience from feeling punitive. The result is better retention andcompletionrecords that hold up in audits. 4.3 Sales, Product, and Customer Service Training AI avatar courses can simulate realistic customer conversations, allowing sales and service teams to rehearse objections and handle difficult interactions beforeencounteringthem live. Research on AI avatars in hospitality employee training found that avatar-led instruction improved learning outcomes and engagement compared to static e-learning while also reducingreliance on live facilitators. This scenario-driven approach builds both skill and confidence, with real-world performance improving as a direct result. 4.4 Soft Skills and Leadership Practice Teaching soft skills through traditional e-learning has always beenhard. Avatar simulations create situations where learners must respond, make decisions, and experience consequences. A manager in a leadership course might face a difficult performance conversation with an AI avatar playing a resistant employee. That emotional realism makes the learning stick in ways a lecture cannot. 5. How to Create and Deploy AI Avatars for Your Courses 5.1 Choose the Right AI Avatar Tool Platforms range from template-based avatars to fully customizable digital humans, so evaluating options requires a clear framework. Four criteria matter most for corporate training contexts: Check whether the platform supports SCORM orxAPIstandards for reliable integration and learner data tracking. Assess interactivity depth. Some platforms support branching scenarios and adaptive pathways; others offer only linear delivery. Consider language coverage and how naturally the synthetic voices perform in each language your teamsactually use. Evaluate avatar customization. Some platforms let you reflect your brand andlearnerdemographics; others lock you into templates. Aligning the platform’s strengths with your specific training goals, whether that’s compliance delivery, onboarding, or sales simulation, makes a meaningful difference in outcomes. TTMS has direct experience evaluating and integrating avatar platforms intoexisting learning environments, which helps organizations avoid costly mismatches between tool capabilities and training needs. 5.2 Design Avatar Appearance and Persona Visual design choices, including gender presentation, age, style, and cultural representation, shape how learners perceive and relate to the avatar. For global programs, building a diverse set of avatars ensures more learners see themselves reflected in the instruction. The persona matters equally: a compliance avatar might project calm authority, while an onboarding avatar might lean warmer. Whenpersonamatches context, the experience feels intentional rather than generic. 5.3 Script and Integrate Avatars into Your LMS Good avatar scripting reads naturally when spoken, avoids passive constructions, andbuilds innatural pauses and branching points where learner input changes the direction of instruction. Once the content is ready, integration into your LMS ensures learner progress is tracked, completion is recorded, and data flows into reporting dashboards. 6. Best Practices for Effective Avatar-Based Learning A strong AI avatar program requires more than choosing the right tool. Before designing any interaction, start with a clear answer to one question: what does this learner need to be able to do, and how does this avatar help them get there? When the purpose is clear, the experience feels cohesive. Whenit’svague, learners notice and disengage. Consistency matters just as much. If an AI avatar shifts tone or appearance between modules without explanation,learnertrust erodes. Maintaining visual and persona consistency across a course reinforces the mental model learners build early on and reflects organizational culture in corporate training contexts. Accessibility and cultural inclusivityaren’toptional extras. Caption options, visual contrast, and avatar personas that reflect the diversity of the learner population all ensure the course functions for everyone. Treatlaunchas the beginning of an iterative cycle, not the finish line. Completion data, quiz performance, and learner feedback reveal where the experience breaks down and where it earns the most engagement. 7. Frequently Asked Questions About AI Avatars in E-Learning What makes an AI avatar different from a simple animated character? An AI avatar uses artificial intelligence to generate speech, adapt responses, and interact with learner inputs in real time. A simple animated character is scripted and static. The intelligence layer is what enables personalization, real-time feedback, and adaptive learning pathways. Can AI avatars work across different languages and regions? Yes. Modern platforms support dozens of languages, and avatars can be localized not just linguistically but culturally, adapting tone and examples to suit regional audiences. How much does it cost to build avatar-based e-learning? Costs vary by platform and interactivity complexity. In general, avatar-based production is significantly faster and less expensive than traditional video, particularly for content that needs regular updates. Do learners actually respond well to AI avatars? Research and real-world deployments consistently show stronger engagement with avatar-guided content than with text-only or static video formats. The key is designing avatars that feel genuine, with strong scripts, clear purpose, and a persona appropriate to the subject matter. How does TTMS support organizations adopting avatar-based learning? TTMS provides end-to-end e-learning services covering course development, avatar integration, LMS administration, and performance analytics. As a partner with hands-on experience in both AI implementation and learning system integration, TTMS helps organizations build AI avatar training programs that are practical, scalable, and tied to measurable business outcomes.
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