ChatGPT, an Integrated LLM, an SLM or Automation? How to Choose the Right AI for Your Business Process
In 2025, 20% of enterprises in the European Union used artificial intelligence, compared with 13.5% a year earlier. In Poland, the figure was 8.4%. The most common application was analysing written language, used by 11.8% of the companies surveyed. The authors of a study published in 2026 in Organization Science described the uneven boundaries of AI capabilities as a “jagged technological frontier”: tasks of similar difficulty for humans can pose very different challenges for a model. The next step requires answering a more specific question: which form of AI fits a particular task? In an experiment involving 758 consultants, participants using GPT-4 completed 12.2% more tasks and finished them 25.1% faster on average when the tasks fell within the model’s capabilities. For a complex task beyond those capabilities, the probability of reaching the correct solution fell by 19 percentage points. The authors of the study, published in 2026 in Organization Science, called this uneven boundary of AI capabilities the “jagged technological frontier”. Effectiveness therefore depends on matching the technology to the task, data, risk and approach to verifying results. One process may be best served by an enterprise LLM assistant. Another may require an application connected to a CRM, a knowledge base and an access control system. A third may benefit from a small model running locally. Operations governed by explicit rules can be handled by code, rules engines or robotic process automation (RPA). Traditional machine learning is suitable for tasks such as forecasting and data classification. 1. LLMs and SLMs in Business: Choosing the Model, Integrations and Where Data Is Processed Terms such as “LLM”, “deployed LLM” and “closed SLM” combine several layers of technology. In a business context, it helps to separate four decisions: Way of working: does an employee interact with a ready-made assistant, or does the process start automatically? Scope of integration: does the solution work with materials supplied by the user, or does it retrieve data and perform actions in company systems? Model type: does the task require the broad capabilities of a large language model, or would a specialised SLM be sufficient? Processing location: does the model run as a cloud service, in a dedicated environment, in a private cloud, on premises or directly on a device? SLM stands for Small Language Model, which typically requires fewer computing resources. The term “closed system” needs clarification: it may refer to restricted access, an isolated environment or data processing within the organisation’s own infrastructure. A large language model can run in a private environment, while an SLM can be available through a public API. Model size alone does not determine how data is secured. 2. Six Ways to Use AI in Business Processes Approach How It Works Best Fit Key Metric Enterprise LLM assistant An employee assigns a task and checks the result in an approved environment, such as ChatGPT Business or Enterprise Analysis, drafting, summarising, developing alternatives and ad hoc work Time saved per task after accounting for review and corrections Integrated LLM or RAG application The model uses company sources, rules, permissions and integrations Repeatable processes involving documents, knowledge and data from business systems Cost per successfully resolved case AI agent The solution plans its next steps, selects tools and pursues a goal within a defined scope Multi-step processes involving exceptions and a dynamic sequence of actions Percentage of tasks completed correctly SLM A smaller model handles a narrow range of tasks in the cloud, on a company server, at the edge or on a device High task volumes, a fixed subject area, short response times and offline operation Quality compared with an LLM within a specified cost budget and p95 response time limit Private or on-premises deployment An LLM or SLM runs in a controlled environment, private cloud, company network or on a device Requirements relating to data residency, business continuity, connectivity, infrastructure or security policies Compliance with requirements, quality, availability and total cost of ownership (TCO) Rules, RPA or traditional ML The process is defined through code, conditions, a predictive model or a state machine Calculations, transactions, fixed workflows and unambiguous decisions Accuracy, repeatability and completeness of the audit trail In a mature deployment, these approaches often work together. The language model interprets a message, rules check the conditions, an application retrieves data, a person approves the action, and the transactional system records the result. 3. Which Tasks Are Suitable for ChatGPT or Another LLM Assistant? A ready-made LLM assistant supports tasks where an employee is responsible for checking and using the result. The user initiates the work, provides context, evaluates the response and decides how to use it. The output takes the form of a draft, recommendation, analysis or working document. Examples include: preparing a first draft of a report, message, presentation or article, summarising documents and correspondence, comparing several materials supplied by the user, developing questions, scenarios and alternative solutions, translating content for subsequent review, exploring data and explaining findings, organising meeting notes, drafting a procedure or action plan. This approach works well in processes where every response undergoes human review, the result can easily be corrected or withdrawn, and the task does not require automatic writes to a critical system. Wide variation in the source material and the need for language-related work further increase the usefulness of an LLM. Security depends on the approved product and its configuration. OpenAI states that data from ChatGPT Business, ChatGPT Enterprise and the API is not used to train models by default. The API data controls documentation also describes separate retention policies, including standard abuse monitoring logs and a Zero Data Retention option for eligible customers. Before using the solution, an organisation should review data classification, contractual terms, processing region, retention, administrator permissions and policies for connected applications. For more examples, see our overview of 15 ChatGPT integrations with business applications. 3.1 How Can You Measure Time Savings and the Quality of Work with an LLM? Relying solely on employee surveys can overstate the benefits. In a METR experiment, experienced developers took 19% longer to complete the tasks studied when using AI tools, yet afterwards still estimated that AI had made their work 20% faster. The study involved 16 participants and 246 real tasks in repositories they knew well, so its findings apply to that specific setting. The methodological lesson has broader relevance: actual task duration and quality need to be measured before deployment. For an LLM assistant, useful metrics include median task completion time, the proportion of outputs accepted without changes, average correction time, quality assessed against consistent criteria, frequency of use and the number of cases in which users return to their previous way of working. 4. When Should You Integrate an LLM with Company Systems, and When Should You Deploy an AI Agent? Integration becomes justified when the value of a process depends on current company data, a repeatable workflow and coordination across several systems. The model then receives controlled access to documents, a knowledge base, CRM, ERP, a ticketing system or email. The application verifies the user’s identity, controls which data is shared, defines the response format, checks results, logs actions and routes selected operations for approval. A typical integrated AI application consists of five layers: Input: a message, document, form, system event or record. Context: data retrieved in line with access permissions, often using RAG. Model: an LLM or SLM selected for the specific stage. Validation: rules, completeness checks, source verification and risk classification. Output: a response for a person, a draft record or an approved action in a system. Use cases requiring this architecture include finding answers in an internal knowledge base, reviewing contracts against a company’s risk checklist, preparing quotations using CRM data and a price list, classifying support tickets, onboarding employees, checking procurement documents and drafting responses based on customer history. RAG retrieves up-to-date passages from approved sources and adds them to the context used to generate a response. An AI agent represents a further level of integration. It receives a goal, selects tools and plans a sequence of actions. According to the current Google Cloud guidance on agentic architecture, agents are suited to open-ended, multi-step problems that require external data and a degree of autonomy. An application with a predefined sequence of steps is usually sufficient for a single summary or translation. We explore the role of an advanced model as a reasoning layer connected to tools, data and permissions in our article GPT-5.5 for Business: A New Era of AI Agents. The scope of an agent’s autonomous actions can be expanded when test results confirm the required effectiveness and safety. Its permissions should be limited to the functions needed for the process, read access should be separated from write access, and actions with significant consequences for the company or customer should require approval. OWASP identifies excessive functionality, excessive permissions and excessive autonomy as the three main causes of Excessive Agency risk. The principle of least privilege limits the consequences of misinterpretation, fabricated information or an attack that feeds malicious instructions to the model (prompt injection). 5. Which Business Processes Are Suitable for a Small Language Model (SLM)? A Small Language Model uses fewer parameters and computing resources than a large language model. According to Microsoft Azure, this can support faster responses, lower infrastructure requirements and data processing close to where the data originates (edge computing), for example on industrial devices. A specialised SLM can be suitable for tasks with a fixed subject area, predictable inputs and a clearly defined output. An SLM is worth testing when a process meets several of the following conditions: a fixed set of categories, intents, fields or response types, a high and predictable volume of requests, a requirement for a short response time measured as p95, the threshold within which 95% of responses are completed, limited hardware resources, a need to operate offline or directly on a device, access to data from the relevant business domain and a set of reference answers, the ability to escalate difficult cases to a larger model or a person. Examples include classifying support tickets into 30 fixed categories, identifying a user’s intent, extracting field values from a single document type, generating short responses within a tightly defined subject area or analysing messages locally on an industrial device. The choice depends on testing with company data. An SLM should meet the required targets for quality, response time and cost per correctly handled case. For example, a company might require the smaller model to retain at least 98% of the reference LLM’s quality score, reduce the cost per correct result by at least 20% and stay within the p95 response time limit. These are illustrative decision thresholds that the process owner sets before the pilot. 5.1 When Should You Deploy an LLM or SLM On Premises or in a Private Cloud? A private or on-premises deployment may be driven by requirements relating to data sovereignty, security policies, business continuity, network latency or operation without internet access. Such a deployment can use an SLM or a larger model. An enterprise application can also use a managed API with encryption, retention controls, an appropriate processing region and contractual provisions governing data handling. A cascading architecture can be the most effective approach. Microsoft describes a hybrid model in which an SLM handles routine queries and passes more complex cases to an LLM. In a business setting, it is worth adding a third route to this cascade: referring the case to an employee when the result is uncertain, the risk is high or required data is missing. 6. Which Processes Should You Automate with Rules, RPA or Machine Learning? Processes governed by fixed rules require a clearly defined sequence of actions and conditions for carrying them out. AWS documentation on orchestration distinguishes between rule-based workflows, where successive states and transitions are explicitly defined, and agentic orchestration, where a model interprets the goal and dynamically selects tools. Both layers can operate within a single application. Process Recommended Mechanism Role of the Language Model Calculating tax, pay or a discount Code and a rules engine Explaining the result or interpreting the user’s query Executing a payment, refund or limit change A transactional process with access controls Identifying intent and preparing data for approval Granting or revoking permissions Identity and access management (IAM), role-based rules and approvals Handling a request expressed in natural language Checking that all required fields are complete A schema validator Extracting fields from an unstructured document Predicting customer churn or forecasting demand Traditional machine learning Explaining contributing factors and drafting communications Interpreting a free-form message An LLM or SLM Classifying intent and passing data to a controlled workflow In a financial process, an LLM can read a message, identify the request and prepare a proposal. Rules check the balance, limits, customer status and required approvals. The transactional system executes the operation once the conditions are met. Each layer performs a task for which clear criteria for correctness can be defined. 7. How Do You Match AI to a Business Process? Four Assessment Criteria An initial assessment can be carried out during a short workshop. The process owner evaluates the process across four areas, assigning a score from 0 to 3 in each. The individual scores help define requirements for the model, integrations, safeguards and infrastructure. Dimension 0 1 2 3 Complexity of content interpretation Fixed fields and rules A fixed set of categories Interpreting context Combining information and reasoning across multiple sources Integration and autonomy No access to systems Reading from a single source Reading from multiple sources or preparing data to be written to a system Transactions and dynamic tool selection Impact of an error Easily reversible Limited operational cost Significant financial, legal or reputational impact Critical or irreversible consequences, or an impact on rights and safety Infrastructure and data requirements A managed cloud meets the requirements A specific region or retention policy is required A private network or strict latency limit Operation offline, in an air-gapped environment, at the edge or directly on a device The scores can be interpreted as follows: Content interpretation complexity of 0-1 with stable rules: code, workflows, RPA or traditional ML. Complexity of 2-3, integration of 0-1 and error impact of 0-1: an enterprise LLM assistant with user review. Complexity of 2-3 and integration of 2-3: an integrated LLM application, RAG or an agent. Complexity of 1-2, a narrow domain and infrastructure and data requirements of 2-3: an SLM as a candidate for comparative testing. Error impact of 2-3: approval by an authorised person, safeguards based on predefined rules and a complete activity log, regardless of model type. The table helps identify solutions for a pilot. The final decision follows a comparison of their performance on the same set of real cases. 8. ChatGPT, Integrated LLMs, SLMs and Automation: Business Use Cases Example Process Recommended Architecture Key Performance Indicator Human Oversight Drafting marketing content An enterprise LLM assistant Median time saved and percentage of outputs accepted Approval of every publication Summarising a meeting and listing action items An enterprise assistant with access to an approved source Completeness of action items and number of corrections Verification of task owners and deadlines Answering questions about internal procedures An integrated LLM with RAG and source citations Percentage of answers grounded in sources and accuracy of citations Escalation when no source is available Assigning support tickets to predefined queues An SLM or classifier, with an LLM for exceptions Macro-F1 and the proportion of priority tickets correctly identified Review of uncertain cases Reviewing contracts against a company’s risk checklist An integrated LLM with RAG, rules and logging Rates of detected and missed risky clauses Decision by a lawyer Extracting fields from a single invoice type OCR, traditional ML or an SLM, combined with rule-based validation Field-level accuracy and cost per document processed Review of exceptions Preparing a quotation using CRM data and a price list An integrated LLM, RAG and price retrieval governed by predefined rules Preparation time and percentage of quotations requiring commercial corrections Approval of pricing and terms Checking refund eligibility Process rules Compliance with policy and time to decision Handling exceptions Executing a refund A transactional workflow with authorisation 100% accounting accuracy and a complete audit trail Depends on the amount and risk Analysing machine messages locally An SLM or specialised model running at the edge p95 response time, alarm detection rate and availability without an internet connection Escalation of critical alarms Revoking access for a departing employee IAM and a deterministic workflow Completeness of access revocation Approval in line with policy Handling a customer case across multiple steps An AI agent with a restricted set of tools Task success rate, correctness of tool use and percentage of cases referred to an employee Approval checkpoints for high-impact actions 9. How Do You Measure the Results of an AI Deployment? Quality, Time and Cost Metrics Measurement starts with the existing process. The Generative AI at Work study, involving 5,179 customer support employees, found an average increase of 14% in the number of issues resolved per hour. Less experienced employees saw the greatest improvement. Productivity defined this way has a clear numerator, denominator and comparison group. An enterprise pilot requires similar precision. Area Metric How to Measure It Scale Case volume Number of cases per month, seasonality and peak demand periods Time Case handling time Mean, median and p90 before and after deployment Quality Task success rate Percentage of cases meeting all criteria for correct task completion Usefulness Percentage of outputs accepted without substantive changes Proportion of outputs accepted without changes to their substance Oversight Percentage of AI decisions changed by an employee Proportion of decisions or proposals modified by an employee Risk Critical error rate Number of critical errors per 1,000 or 10,000 cases Classification Precision, recall and F1 score Measured separately for each important class, particularly rare events RAG Consistency of responses with sources Percentage of claims supported by a cited, up-to-date source Agent Correctness of tool selection and supplied parameters Whether the correct tool was selected and valid parameters were supplied Automation Percentage of cases handled entirely automatically Proportion of cases completed without manual intervention Performance Time from task initiation to the final result p50 and p95 of end-to-end task completion time Economics Cost per successfully completed task Total cost divided by the number of correct outcomes Stability Changes in system quality over time Variation in quality by time period, language, category and user type Google Cloud identifies cost per successfully completed task as a key metric for AI agents operating in real business processes. A model that costs USD 0.10 per run and achieves a 50% success rate incurs a model invocation cost alone of USD 0.20 per correct result. Human review, retries, integrations, monitoring and the cost of errors must also be included. 10. How Do You Calculate the ROI of an LLM or SLM Deployment? The full cost of the solution should include development and integration, the model or API, infrastructure, monitoring, updates, human review, corrections and expected losses resulting from errors. Cost per successfully completed task: C_success = (development cost allocated to the period + model/API + infrastructure + monitoring + human review + corrections + expected losses from errors) / number of successfully completed tasks Annual benefit: Benefit = value of working time saved + avoided correction and error costs + additional margin + avoided SLA penalties ROI: ROI = (Benefit - TCO of the AI solution) / TCO of the AI solution × 100% Suppose a team classifies 20,000 support tickets per month. Each ticket takes an average of 4 minutes, and the fully loaded hourly labour cost is PLN 120. The monthly cost of manual classification is approximately PLN 160,000. During the pilot, 88% of the system’s outputs are accepted without correction. Reviewing each output takes an average of 45 seconds, correcting the remaining cases takes 3 minutes each, and the monthly costs of the model, infrastructure, maintenance and amortised implementation total PLN 51,000. reviewing all cases: approximately PLN 30,000, correcting 12% of cases: approximately PLN 14,400, model, infrastructure, maintenance and implementation: PLN 51,000, total process cost after deployment: approximately PLN 95,400, monthly cost reduction: approximately PLN 64,600, or 40.4%. This example illustrates the calculation method using assumed values. A financial assessment of an actual deployment must account for changes in the cost of errors, seasonality, downtime, exception handling costs and the pace of employee adoption. For a revenue-generating process, the calculation should also include changes in margin, conversion rate or customer retention. 11. How Do You Run a Measurable LLM or SLM Pilot? Establish a baseline. Measure volume, time, quality, errors, escalations and the cost of the current approach over at least one full business cycle. Prepare a test dataset. Include typical tasks, difficult and rare situations, boundary cases and deliberate attempts to mislead the system. Google Cloud recommends a custom dataset that reflects the full range of intended uses. Set thresholds before testing. Document the minimum quality, maximum cost, acceptable latency, critical error limit and escalation rules. Compare several approaches. Include the current process, a capable LLM, a smaller model and a hybrid architecture. Run the system in shadow mode. AI generates outputs alongside the existing process. Decisions and actions continue under the established rules. This helps identify errors before AI is allowed to handle live operations. Deploy the system within a limited scope. Start with proposals and approvals. Expand automated actions based on the results. Monitor performance quality. Repeat testing whenever the model, AI instructions, tools, data sources, rules or types of input material change. 11.1 How Many Test Cases Do You Need to Evaluate an LLM or SLM? For a metric expressed as a proportion, a conservative sample size at a 95% confidence level and a margin of error of +/-5 percentage points is approximately 385 independent cases. A margin of +/-3 percentage points requires approximately 1,068 cases. The representative sample should be supplemented with a separate set of critical and edge cases. For rare errors, the so-called rule of three is useful. If no critical errors occur in 300 tests, the approximate upper bound on their true probability at a 95% confidence level is still around 1%. Zero errors in 3,000 tests reduces that bound to approximately 0.1%. High-risk processes therefore require much larger datasets and tests targeting specific threats. 11.2 When Should You Complete an AI Pilot and Move into Production? Example Criteria Process Example Criteria for Moving into Production Support ticket classification Macro-F1 of at least 0.90; recall for priority tickets of at least 0.99; override rate no higher than 8%; p95 response time no longer than 2 seconds Internal knowledge base Acceptance rate of at least 85%; citation accuracy of at least 98%; no unsupported claims in the critical test set; p95 response time no longer than 8 seconds Draft quotation Median preparation time reduced by at least 30%; at least 75% of drafts accepted with minor changes; 100% of prices retrieved from an authorised source; human approval for every quotation These values illustrate how to define acceptance criteria. The process owner sets the thresholds according to the cost of errors, required quality and the organisation’s risk tolerance. 12. How Do You Match Human Oversight to AI Risk? NIST defines risk as a combination of the likelihood of an event and the scale of its consequences. This principle translates general concerns about AI into a measurable model: error frequency, the value of funds or resources at risk, the ability to reverse an action, the time needed to detect an error and the cost of correcting it. In the consolidated text of the EU AI Act, requirements for high-risk systems include continuous risk management, appropriate levels of accuracy, robustness and cybersecurity, and effective human oversight. Oversight measures should be proportionate to the risk, degree of autonomy and context of use. Testing should use predefined metrics and thresholds appropriate to the intended purpose. In business practice, this means assigning a specific person responsibility for approvals, monitoring and intervention. The interface should display sources, actions taken and the level of uncertainty, and the user must be able to stop the process. The risk of serious consequences from an error justifies restricting model permissions, adding further checks and extending shadow-mode testing. The regulatory classification should be assessed separately for the intended use and the organisation’s role. 13. How Can You Combine LLMs, SLMs and Rules in a Single Business Process? Combining several technologies allows each stage of a process to be handled appropriately. For example, an SLM identifies the topic of a customer’s request, while an LLM drafts a response using the contact history and documents retrieved through RAG. If the case involves a refund, the system checks conditions and limits against established rules, then routes the operation for execution or approval by an authorised employee. This approach combines automation with oversight of decisions that have financial consequences. At TTMS, we can help you assess where a similar solution would deliver the greatest benefit. We will start with the tasks that take up the most time: repetitive activities, searching for information or correcting errors. During a consultation, we will examine your workflows, available data and existing systems. Based on this assessment, we will recommend the technology and pilot scope, then work with you to define the expected outcomes and how to measure quality, time and costs. Ready to choose your first process to improve? Book a consultation with us about implementing AI. Sources Eurostat, 20% of EU enterprises use AI technologies, 11 December 2025. Fabrizio Dell’Acqua et al., Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality, Organization Science. Erik Brynjolfsson, Danielle Li, Lindsey Raymond, Generative AI at Work, NBER Working Paper 31161, 2023. Joel Becker, Nate Rush, Beth Barnes, David Rein, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, METR, 10 July 2025. Microsoft Azure, What Are Small Language Models (SLMs)? Microsoft Azure, Boost processing performance by combining AI models, 8 January 2025. OpenAI, Enterprise privacy at OpenAI. Google Cloud, The KPIs that actually matter for production AI agents, 26 February 2026. NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1, July 2024. European Union, Regulation (EU) 2024/1689, consolidated text of 27 July 2026. OWASP GenAI Security Project, LLM06:2025 Excessive Agency, 2025. FAQ Do You Need to Run Your Own AI Model to Work with Company Data? Running your own model is one of several options. Companies can use an approved business environment, a managed API, a private cloud, dedicated infrastructure or a model running locally. The choice depends on data classification, processing location, retention, encryption, user identities, industry requirements and the agreement with the provider. For example, OpenAI states that business customer data is not used for training by default and offers additional retention controls for eligible API customers. Organisations should document the data flow for their specific configuration, as the model’s name does not describe the full security architecture. Can RAG Replace Fine-Tuning a Model on Company Data? RAG and fine-tuning address different needs. RAG retrieves current information from a controlled source when generating a response, making it well suited to knowledge bases, procedures, documentation and frequently updated content. Fine-tuning uses examples to adapt a model’s behaviour to a particular style, format or specialised task. AWS documentation comparing RAG and fine-tuning recommends starting with RAG for a question-answering system based on your own documents, particularly when up-to-date information and source references matter. Both approaches can work together when a process requires current knowledge and consistent behaviour within a specific domain. Can a Single Process Use Both an LLM and an SLM? Yes. A routing component can direct routine, clearly identified cases to an SLM and complex cases to a larger LLM. A third route passes cases to a person when the system detects missing data, low confidence or a high level of risk. Another option is to divide the process by function: an SLM classifies the document, an LLM prepares an explanation, code calculates values, and a workflow records the approved decision. This setup helps control costs and response times while retaining access to more advanced capabilities for difficult cases. How Many Examples Do You Need for an LLM or SLM Pilot? The number depends on the required measurement precision and how rare the errors are. When measuring the proportion of successful responses, a sample of approximately 385 independent cases provides an approximate margin of error of +/-5 percentage points at a 95% confidence level under conservative assumptions. A margin of +/-3 percentage points requires approximately 1,068 cases. The random sample should reflect the actual distribution of cases across languages, channels and user types, in proportion to their volumes. A separate test set should cover critical, edge and rare cases, along with attempts to manipulate the system. How Often Should You Retest an Application Built on a Language Model? A full evaluation should be run whenever the model, prompt version, tool, permissions, data source, business rules or input format changes. Production use also requires continuous monitoring of key performance indicators and regular regression testing. The frequency depends on the level of risk and how quickly the process changes. An application handling marketing content may follow a different schedule from a system supporting financial decisions. A practical approach is to run automated tests after every technical change, review trends monthly and conduct a business evaluation quarterly, with shorter cycles for high-risk applications.
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GPT-6 Astra in Microsoft 365 Copilot: Access, Tasks and Cowork Costs
Does your company use Copilot, and would you like to try GPT-6 Astra? OpenAI’s model is also available in Copilot Cowork. This means you can try it when working with documents, email and calendars in Microsoft’s environment. Access to Astra depends on your organisation’s licences and settings, while tasks performed in Cowork are billed based on credit usage. What does your administrator need to enable? Which tasks can you delegate to Astra in Cowork, and how are they handled in ChatGPT Work? Below, we explain access requirements, differences in working with files and billing rules. For guidance on choosing an assistant for your organisation, see our comparison of Microsoft Copilot and ChatGPT for business. 1. What Does GPT-6 Astra Bring to Copilot Cowork? Microsoft lists GPT-6 Astra among the models available in Copilot Cowork. Users select a model from the list enabled by their organisation. The default Auto setting lets Cowork choose a model for the task; a label next to the response shows which model was used. Selecting Astra applies to work within Cowork. The availability of a particular model in other Copilot features needs to be checked separately. GPT-6 Astra is another model you can assign tasks to in Cowork. Cowork itself provides the tools for finding information, creating files and taking action in Microsoft 365. Your choice of model may affect how information is analysed, the level of detail in the response and the time taken to complete the task. When evaluating Astra, check whether it handles an existing task better: whether it brings together findings more accurately, accounts for exceptions and produces a result that requires fewer revisions. Work IQ gives Cowork access to the context of your organisation’s work. When preparing a project summary, the information needed may be spread across documents, correspondence and meeting materials. Cowork can search for the organisational resources required for the task. Before trying it, check that the employee’s account has access to the relevant materials and that they include the latest decisions and updates. This determines which information Astra will use to produce its result. We discuss the model’s test results and examples of its use in our article GPT-6 Astra: Impressive Achievements and New Possibilities for Business. 2. How Can You Access Astra in Copilot Cowork? For business users, Microsoft describes Cowork as a service that requires a Microsoft 365 Copilot licence and usage-based billing for task execution. An administrator then needs to configure employee access. There are two separate settings to configure: Access to Cowork. The employee must belong to a group covered by a spending policy that includes Cowork. The administrator configures this in the Microsoft 365 admin centre under Copilot, Cost Management, Configuration. This is where they specify the users, budget and billing method. Access to models provided by OpenAI. In the Copilot settings, the administrator specifies which users can use OpenAI as a Microsoft subprocessor. Once you have access, open Cowork and select Astra from the model list. For your first task, check the model label next to the response. If an employee can see Cowork but cannot find Astra, the administrator should check the model provider settings. To make Cowork available to a specific team, the administrator must grant access to the relevant user group. A low credit limit restricts spending while still allowing employees covered by it to get started. 3. Astra in Cowork and ChatGPT Work: Differences in Task Execution Preparing a report involves finding up-to-date data, processing it and saving the result somewhere the team can access. At each stage, the tools available to the model matter. The comparison below shows how the two environments work with the materials needed for a task. Working with Materials in Copilot Cowork and ChatGPT Work Task Component Copilot Cowork ChatGPT Work Finding materials Searches Microsoft 365 resources accessible to the user, including email and files. Plugins can provide access to additional sources. Uses files provided for the task and information retrieved through enabled apps and authorised accounts. Working on documents Creates and modifies documents, spreadsheets and presentations. Output files are saved to the workspace in OneDrive or SharePoint. Creates and edits files. Transferring them to another system depends on the operations supported by the connection to that system. Files stored on the computer A file can be uploaded to the session. Cowork does not edit files directly on the user’s drive. Work in a supported desktop app can use local files once the appropriate access has been granted. Using an application through a browser The local Edge browser uses the employee’s existing sign-in. The feature must be enabled by an administrator. Access depends on the browser tool selected and the permissions granted. A cloud task requires separate authorisation to access company resources. When working through a browser, you need to consider where the task is running. Cowork supports the local browser when its web version is open in Edge. This feature is currently unavailable in the Copilot desktop app and on mobile devices. Cowork and Edge must also use the same work account. If the computer goes to sleep, actions requiring the local browser may be paused. In ChatGPT Work, the model can use shared files and applications on the computer during a local task. A task launched in the cloud runs in a separate environment. If the required materials are stored only on the employee’s drive or are accessible through a company VPN, they need to be made available to that environment through a supported method. As it works, Cowork displays the successive stages of the task. You can interrupt the session, clarify your instructions or provide missing information. Before taking significant actions, such as sending a message or scheduling a meeting, Cowork asks for approval. The additional confirmations it requests also depend on permissions granted earlier. For your first trial, choose a task for which you can clearly identify both the source materials and where the result should be saved. You can find examples of responsibilities in sales, HR, finance and other departments in our overview of 10 practical uses of Microsoft Copilot in an organisation. 4. How Much Does It Cost to Use Astra in Cowork and ChatGPT Work? Your budget needs to cover both the subscription and the use of tools to carry out tasks. For a company that already has the appropriate licences, enabling Cowork primarily means budgeting for usage charges. Below are the public prices for selected business plans. Subscription prices. Charges for task execution are explained below. Plan Monthly Price per User Terms Microsoft 365 Copilot Business EUR 18.20; currently EUR 15.60 under a promotional offer Billed annually, excluding tax. A separate qualifying Microsoft 365 licence is required. Available for up to 300 users. Microsoft 365 Copilot for enterprise EUR 26 Billed annually, excluding tax. A separate qualifying Microsoft 365 licence is required. ChatGPT Business USD 20 when billed annually or USD 25 when billed monthly A minimum of two users. Public pricing in USD; the final amount depends on factors including taxes and the market where the subscription is purchased. ChatGPT Enterprise Custom pricing Usage limits and billing are defined in the agreement. The Copilot Business promotion applies to the first year with an annual commitment and runs from 1 July to 31 December 2026. 4.1 How Are Cowork Tasks Billed? Cowork charges for factors including model usage, context retrieval, tool calls and runtime. Usage is converted into Copilot Credits; under the published pay-as-you-go pricing, one credit costs USD 0.01. One thousand credits therefore cost USD 10. The cost of an individual task depends on the number of credits consumed. The selected reasoning level also affects usage. Cowork offers Light, Medium, High, Extra High and Max settings. A higher level may increase task duration and credit consumption. For recurring work, check whether increasing this setting improves the result enough to justify the cost. 4.2 What Does Credit Usage Mean in ChatGPT Work? ChatGPT Work follows the usage limits and billing rules of the relevant plan. Under agreements based on a shared credit pool, tasks reduce the available balance. Credits already paid for under the agreement are covered by that payment. Additional charges may arise once those credits run out, if the agreement and settings allow work to continue. When comparing costs, use the same set of tasks and output requirements. Record usage, the number of retries and the extent of any revisions needed. Calculating the cost per successfully completed task shows how much you pay for a result your team can use. First, convert each service’s credit usage into a monetary amount using its own pricing. 5. What Data Protection Rules Apply to Astra in Cowork? In Copilot Cowork, Astra is provided by OpenAI as a Microsoft subprocessor. According to the documentation, this use of the model is governed by Microsoft’s terms and Data Protection Addendum, subject to specified exclusions. These services fall within the EU Data Boundary, with documented exceptions. Microsoft currently excludes them from its commitments to process data in a specific country. This detail matters to organisations that require processing exclusively in Poland, for example. When enabling Astra, the administrator should therefore consider the model provider’s policies and access to the materials used in the task. In ChatGPT Work, whether the task runs locally or in the cloud also matters. During a local task, file excerpts, screenshots and tool outputs may be sent to OpenAI. Company AI policies should account for this method of sharing information as well. 6. Which Task Should You Start with When Trying Astra? Start with a responsibility that regularly involves an employee gathering information and preparing material for other people. This workflow lets you assess both Astra’s analysis and the tools available in Cowork or Work. A weekly project summary is one example. A sample prompt for your own trial: Using the project folder [link] and correspondence about this project from the past seven days, prepare a report for the manager. List revised deadlines, pending decisions and the people responsible for next steps. Provide a source and date for each finding. If the materials contain conflicting information, show the discrepancy and explain what you need to resolve it. Save the report as a DOCX file using the attached template in the folder [link]. Draft a message to [recipients] with a link to the report. Leave sending it subject to my approval. Check whether the report reflects the latest decisions and updates, provides sources and dates, identifies conflicting information and assigns responsibilities correctly. Also assess whether it follows the template and whether the file has been saved in a folder accessible to its recipients. After a successful trial, you can consider running the task regularly. Cowork supports scheduled tasks and tasks triggered by events such as an email or a Teams post. By default, event-triggered tasks prepare actions for approval. We discuss how to design the entire process in our guide to business process automation with Copilot. 7. Prepare Your First Astra Tasks with TTMS Through our AI consulting services, we help you determine which data a task requires, which tools need to be made available and how to assess the result. We also analyse the required licences and usage billing arrangements. We combine consulting with AI solution design and the integration of business systems. TTMS was the first company in Poland to obtain accredited ISO/IEC 42001 certification for its artificial intelligence management system. The TÜV Nord Poland audit covered AI design and usage policies, including risk management and project documentation. Tell us which task you would like to delegate to Astra and which applications your team uses. Talk to TTMS about AI consulting for your business. GPT-6 Astra in Copilot Cowork: Frequently Asked Questions Does selecting Astra in Cowork change the model across all Copilot applications? The selection applies to work within Cowork. Microsoft describes a separate model selection option for this environment. To find out which model powers a particular feature in Word, Excel or Teams, check that feature’s documentation. When reviewing a Cowork task, you can see which model was used by checking the label next to the response. Will the same model give an identical response in Copilot and ChatGPT? The result may differ. The model works with the information provided by each product and uses its tools, instructions and reasoning settings. When comparing results, check which materials the model received and which actions it could perform. Only then can you meaningfully assess the differences in the outputs. Why can I see Cowork but cannot select GPT-6 Astra? Access to Cowork and access to OpenAI models are controlled by separate settings. Your administrator should confirm that your account is allowed to use models provided by OpenAI as a Microsoft subprocessor. The model list displayed in Cowork reflects the access granted by your organisation. Does a Microsoft 365 Copilot subscription cover all Cowork tasks? Cowork tasks incur additional usage-based charges. Copilot Credit consumption depends on factors including the model, information retrieval and tools used. Administrators can set spending policies for users and groups. Your budget should account for both the subscription and expected Cowork usage. Can Astra in Cowork edit a document saved on my computer? You can upload a document to a Cowork session. According to the current FAQ, the service does not open or edit files directly on your local drive. Cowork works with the materials you provide and files available in OneDrive and SharePoint. Support for the Edge browser is a separate feature. Will the same Astra model produce the same result in Cowork and ChatGPT Work? The result also depends on the available data, instructions, tools and reasoning settings. A task performed using the same model may therefore proceed differently in the two environments. Comparing results using the same materials will reveal differences in the completeness of the output and the actions performed.
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GPT-6 Astra: Impressive Achievements, New Opportunities for Business
OpenAI unveiled GPT-6 Astra on 3 September 2026, and early research findings and user reports show why the release is generating so much excitement. The new model solved mathematical problems that earlier GPT models and competing models failed to solve in the same test. Users have already tested GPT-6 across a range of tasks, from rebuilding a sales workflow in a CRM system and creating a detailed steam locomotive model to analysing a novel spanning more than 500 pages. What exactly has it achieved, and how can businesses put these capabilities to use? 1. GPT-6 Astra’s early achievements are impressive Early tests and user reports show how Astra handles complex tasks. These include findings from a test conducted by Epoch AI and accounts from people who put the model to work on their own projects. 1.1 Solving two previously unsolved mathematical problems Epoch AI tasked five models with solving 68 Erdős problems, giving each the same time and budget limits. Only the pre-release version of Astra solved two of them and produced solutions in a form that passed automated mathematical verification. GPT-5.6 Sol, GPT-5.5, Claude Fable 5.1 and Claude Fable 5 did not solve any of the problems in this test. Astra had already recorded other mathematical achievements. Before its release, OpenAI presented ten further results involving problems in mathematics and theoretical computer science. We covered them in our article “Astra, the future GPT-6: a new model from OpenAI?”. 1.2 A 3D steam locomotive model with 3,295 editable components Tom Krcha, a designer and creator of the AI-assisted interface design tool Pencil, gave Astra an old drawing of a steam locomotive. The task was to recreate the machine in Blender, an application for building 3D models, animations and scenes. Within a few minutes, it produced a model containing 3,295 separate objects. The wheels, axles, boiler components and other parts can be selected, moved and modified independently. Astra had to interpret a flat drawing, reconstruct the machine’s three-dimensional structure and preserve the relationships between thousands of components. According to Krcha, it did this mainly by writing Python scripts that built the geometry piece by piece. The resulting model can serve as a starting point for an animation or a game project. 1.3 Rebuilding a sales workflow directly in a CRM system Claire Vo, creator of the ChatPRD platform, gave Astra access to her customer relationship management system, or CRM. Working through Codex, the model was tasked with changing how new sales leads were handled. It had to understand the existing workflow, find the relevant settings and rebuild the rules in a visual editor. After the changes, the system automatically routed leads to Claire or Zach, inserted a link to book a meeting with the appropriate person and sent the draft message to Slack for approval. The new rules would also apply to future leads. 1.4 Adding new capabilities to a Bluetooth speaker with Astra Claire Vo also used the model to experiment with a small Divoom speaker fitted with a colour pixel display. She wanted to show her own images and messages on it. According to her account, the device had no public API, an interface that would allow other software to control it. This meant working out how to communicate with the hardware. Astra built an application that displayed drawings made with a computer mouse on the speaker’s screen. It then created a tool for controlling the speaker. The model also looked up information about the latest podcast episode and sent scrolling text and an animated graphic to the display. Vo noted that earlier attempts with other models had only allowed her to display a simple greeting. This time, she received custom software for controlling the device that she could develop further to suit her ideas. 1.5 Checking plot consistency in a novel spanning more than 500 pages Jakub Szczęsny of Antyweb gave Astra an extensive draft of his own book. The model was asked to check the chronology of events, the logic of the plot and storylines introduced in one part of the text and developed many chapters later. Analysing a manuscript of this length requires tracking the characters’ stories, the sequence of events, their motivations and the consequences of earlier decisions at the same time. Astra mapped these connections and flagged passages that needed further work, consistently checking the entire text for the specified issues. Szczęsny particularly valued its ability to connect information scattered across hundreds of pages. A similar skill is useful when reviewing contracts, project documentation and reports, where details in one section affect how others should be interpreted. 1.6 An AI agent completes the entire game Portal A creator publishing as CozyBlaze connected Astra to Portal, a spatial puzzle game in which players create passages between distant locations and use the laws of physics to overcome obstacles. The model received screenshots and information about the player character’s position and the direction they were facing. It used this information to plan moves, execute them and check the results. After approximately 23 hours and 43 minutes, including waiting time and technical interruptions, it reached the end credits. The creator developed custom controls and settings to make precise movement easier, and the game paused while the model was processing its next actions. The creator also resumed the session following service availability issues, while the agent made the gameplay decisions. Completing the game required interpreting the situation on screen, spatial awareness and hours of planning moves and checking their effects. These examples help explain the enthusiasm around Astra. The model analyses a problem, carries out successive actions and uses the results to guide what it does next. For businesses, this opens up the possibility of assigning AI more complex tasks involving information and applications. Comparisons with GPT-5.6 show where performance has improved and what those gains could mean for businesses. 2. GPT-6 Astra in business: what does better performance mean for companies? Businesses using AI also bear the cost of checking responses, correcting errors and stepping in when the model cannot finish a task. Better performance from the next generation could therefore make more tasks cost-effective to delegate to AI. Astra’s results give businesses reason to reconsider how responsibilities are divided, how existing systems are used and how much time employees spend reviewing AI output. GPT-6 Astra vs GPT-5.6 Sol: test results and their business implications Skill tested GPT-6 Astra GPT-5.6 Sol Business implications Successfully completing a task across several applications, AutomationBench 41.4% 28.77% Reason to test whether AI can handle a larger part of a process independently. Locating the correct elements on screen, ScreenSpot-Pro 92.7% 76.9% Greater precision when AI interacts with business software. Detecting bugs that require analysing several files, the more challenging CodeRabbit subset 57.1% 47.6% Better performance when analysing complex dependencies in software. The results come from different tests and measure distinct skills. AutomationBench: Zapier’s leaderboard as of 9 September 2026, with both models set to Max. ScreenSpot-Pro: OpenAI’s comparison. CodeRabbit: the more challenging subset of code reviews. The business implications are interpretations of the results; any savings need to be assessed within the company’s own process. 2.1 A broader range of tasks to delegate The more stages a task involves, the more opportunities there are for mistakes. The model needs to find information, apply the right rules, perform actions in the correct order and use the same assumptions consistently throughout. These are the demands placed on models by AutomationBench, Zapier’s benchmark for business processes. On the leaderboard dated 9 September 2026, Astra successfully completed 41.4% of tasks, compared with 28.77% for GPT-5.6 Sol, with both models using their highest reasoning setting. A task counted as successful only when all required conditions were met. That amounts to roughly 13 more successfully completed tasks out of every 100. For businesses, this is an opportunity to test whether AI can complete more stages without employee assistance. It is worth reviewing tasks where an employee repeatedly prompts the model to take the next step, supplies more data or transfers the output to another application. Each intervention takes time and reduces the benefit of automation. Astra gives businesses reason to test which of these stages it can now handle together. The 41.4% result also shows how demanding these tasks remain. The level of autonomy should be determined by the model’s performance within the company’s own process. 2.2 Automating more tasks in existing business software Companies use many applications introduced at different stages of their development. AI’s access to these resources plays a significant role in how useful it can be. When a model can navigate an application’s interface effectively, businesses can consider using it for tasks that employees currently perform through forms, buttons and menus. In ScreenSpot-Pro, a test of locating elements in screenshots, Astra achieved 92.7% accuracy, compared with 76.9% for GPT-5.6 Sol. The test covers complex applications with densely packed controls. The result measures one specific skill needed to operate software. This improvement gives businesses reason to consider automating more tasks in the applications they already use. Automation planning should include tasks that currently require employees to navigate software manually. Better interface recognition could help AI carry them out when equipped with the appropriate tools and permissions. Cost-effectiveness will depend on the success of the entire operation, including reading data, making changes and checking the result. Accurately locating a button is one requirement for completing that operation successfully. 2.3 Detecting errors that require connecting information from several places Business tasks are often difficult because of the connections between pieces of information. A change to one agreed detail can affect subsequent decisions, documents and team activities. The person responsible for the overall task needs to identify these dependencies and assess their consequences. In CodeRabbit’s evaluation, Astra’s advantage was particularly clear when detecting bugs that required examining several code files. The model detected 57.1% of labelled bugs, compared with 47.6% for GPT-5.6 Sol. Across the full evaluation, the difference was smaller: 61.3% versus 59.0%. The greatest improvement was therefore seen in the more challenging cases. For those overseeing AI adoption, this offers a useful lesson: evaluate the new generation on tasks where the earlier model missed connections between pieces of information or required extensive corrections. With simple prompts, the difference between models may be small. Materials containing exceptions, interdependent conditions and information spread across several places can reveal more about Astra’s usefulness. CodeRabbit documents this improvement in software analysis. Establishing whether similar gains apply to documentation, reports or business agreements requires testing on the company’s own materials. This is a useful way to assess the model in organisations where employees spend considerable time working out how different pieces of information affect one another. 2.4 When does better AI performance translate into savings? Even a small improvement in quality can matter when a task is repeated hundreds of times a month. If employees spend less time correcting outputs and helping AI finish tasks, the team gains time back. The scale of that benefit depends on how often errors occur, how long they take to correct and the consequences of those mistakes. Astra’s results justify reassessing applications of AI that previously proved too unreliable or required too much supervision. A company can revisit a shelved idea and test it on a set of real cases, including more difficult ones. Three measures matter in this assessment: the proportion of tasks completed correctly, employee time spent on checks and corrections, and the total cost of handling each case. These indicators help establish whether the model’s better performance benefits the team as a whole. Astra’s progress could therefore make tasks that were previously too costly to automate economically viable. Tasks that have required constant human assistance are worth testing again, particularly when they are frequent, time-consuming and have a clearly defined outcome. How can your business put GPT-6 Astra to use? Our AI consulting services help you identify the tasks where improvements would deliver the greatest business benefit and plan the implementation. At TTMS, we work with clients to analyse the process, identify the data and integrations required, and agree on how to measure results. We combine consulting with designing AI solutions and integrating them with business systems. We were the first company in Poland to obtain accredited ISO/IEC 42001 certification for our artificial intelligence management system. For clients, this confirms that our practices for risk assessment, project documentation and AI oversight have been reviewed by independent auditors. Tell us about a task you would like to improve. Together, we will explore how AI could help and where to begin. Talk to TTMS about AI consulting for your business. GPT-6 Astra in business: frequently asked questions What does GPT-6 Astra change for businesses already using AI? GPT-6 Astra gives businesses reason to test whether AI can handle a larger share of a task with less employee assistance. Comparisons with GPT-5.6 Sol show improvements in completing tasks across several applications, locating on-screen elements and analysing dependencies in code. It is therefore worth revisiting processes where the team frequently corrects outputs or guides the model step by step. The benefit comes when better performance reduces the work needed to achieve a correct result. Can GPT-6 Astra work in business systems without building a new integration? In some cases, yes, provided its environment gives Astra tools to operate a browser or computer. The model can then interact with applications through forms, menus and buttons. It needs access to the system and the appropriate permissions. Whether this approach is suitable depends on the application and the task. For frequent, repetitive operations, it is worth comparing it with an API integration, which allows software systems to exchange data directly. Which tasks are worth trying with AI again if earlier automation attempts failed? Start with tasks where the earlier model lost track of the steps, confused interface elements or missed connections between pieces of information. These are areas where the tests discussed in this article show Astra’s progress. Use the same materials and assessment criteria for the new trial, including cases that previously proved difficult. If outdated data, conflicting instructions or a lack of application access caused the original failure, those issues also need to be resolved. How can you decide what GPT-6 Astra can do independently and what needs employee approval? The level of autonomy should depend on the consequences of a mistake, whether an action can be reversed and the results of tests using company data. Drafting a document or organising a copy of a dataset allows the output to be checked before use. Sending a proposal, changing commercial terms or deleting records may require prior approval. Process instructions should clearly define when AI can proceed, when it should request a decision and when it should stop because information is missing. How can you tell whether GPT-6 Astra saves your business money after checks and corrections are included? Compare the total cost of completing the same set of tasks with and without Astra. Include data preparation, tool use, reviewing outputs, making corrections and any cases that employees have to redo. A useful measure is the cost per successfully completed task that meets the required quality standard. Base the assessment on a representative set of tasks, including exceptions. This will help establish whether the savings hold up in day-to-day work.
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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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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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15 ChatGPT Integrations with Business Apps in 2026
How can ChatGPT integrations with business applications simplify everyday work in 2026? Here is a simple example: a client emails us asking for a project status update. At this point, we face half an hour of clicking between Google Drive, Slack, Asana and the CRM system. What if ChatGPT could collect information from all these sources in a single conversation and immediately prepare a summary, response or plan for the next steps? In this article: we examine 15 ChatGPT integrations with popular business applications that can make the scenario described above part of a company’s everyday workflow, we explain the differences between apps, integrations, plugins, GPTs and MCP servers, we present specific business use cases and highlight what should be checked before implementation, including permission scopes, data security, availability and costs. How do business application integrations extend ChatGPT’s capabilities? In the client enquiry scenario described above, the right set of integrations could work as follows: ChatGPT would find documents in Google Drive, summarise conversations in Slack, check task statuses in Asana and analyse the client’s data in the CRM system. Individual integrations may be available in ChatGPT as apps, connectors or MCP-based solutions. They make it possible to use data and selected functions from external services without leaving the conversation, and then prepare an up-to-date summary, a response for the client or a plan for the next steps. The available capabilities depend on the specific solution. Some integrations are more “passive” and are used mainly for searching and reading data. More “active” integrations support creating, updating and sending data. GPTs, apps, connectors, plugins and MCP – how do these concepts differ? The terminology surrounding ChatGPT extensions includes several related concepts. In our previous article, we described the ecosystem of the most useful ChatGPT plugins. In this comparison, we use the term “ChatGPT integrations” as an umbrella term for the different ways of connecting ChatGPT to business applications, their data and their functions. Concept Proposed definition ChatGPT integration An umbrella term for connecting ChatGPT to an external application, its data or its functions. An integration may be implemented as an app, connector, plugin, MCP server or GPT Action. App A function of an external service available directly in ChatGPT, sometimes with an interactive interface. Connector A ready-made connection that gives ChatGPT access to the data or functions of a specific service. Depending on the solution, it may support search, synchronisation or actions. MCP server A layer that gives ChatGPT access to selected tools, data and operations from an external system in accordance with the Model Context Protocol standard. Plugin An installable package that extends ChatGPT or Codex and may include instructions, skills, an MCP connection and an optional interface. These concepts describe different elements of the same ecosystem and are not always completely separate. Integration is the umbrella term for connecting ChatGPT to an external service. It may be available as an app, use a connector or MCP server, while a plugin may combine several of these elements into a ready-made workflow. How do you connect an app to ChatGPT step by step? Define the task the integration should perform. Open the app or plugin directory in ChatGPT. Select the appropriate service and start the connection process. Sign in to the external application and approve the required permissions. Open a new conversation and select the connected app. Test the integration using a limited dataset before deploying it across the entire team. How did we select 15 ChatGPT integrations with business applications? This comparison covers integrations that support recurring business processes and are available directly in ChatGPT or through documented MCP-based solutions. We considered five criteria: Frequency of use: the tool stores data or supports tasks performed by teams every day. Value of context: the connection gives ChatGPT access to information that significantly improves the quality of its output. Scope of actions: the integration supports searching, analysing, creating or updating data. Access control: the provider describes authentication, user permissions or administrative controls. Usefulness across multiple roles: the solution can support sales, marketing, operations, IT, product development or knowledge management. The availability of individual features depends on the ChatGPT plan, the external service plan, the country, workspace settings and administrator decisions. The catalogue and permission scopes should be checked immediately before implementation. 15 ChatGPT integrations with business applications in 2026 1. Google Drive integration with ChatGPT – searching and analysing company documents The Google Drive integration with ChatGPT enables users to work with materials stored in Drive, Docs, Sheets and Slides. Users can search for files, combine information from several documents, analyse spreadsheets and use existing materials as sources for a new report, brief or presentation. It provides the greatest value to teams with well-organised folders and consistent document naming conventions. ChatGPT can then locate the correct versions of proposals, reports, meeting notes and project materials more quickly. Best use case: preparing a project summary based on documents, a results spreadsheet and a status presentation. Example prompt: “Find materials in Google Drive related to Project X from the last 30 days and prepare a summary of decisions, risks and next steps.” The video shows how to connect Google Drive to ChatGPT, create an SEO-optimised blog post and save it as a document in Google Drive. It also highlights the importance of detailed prompts for improving the quality of generated content. 2. SharePoint integration with ChatGPT – access to organisational knowledge, procedures and files SharePoint is a natural source of information for organisations using Microsoft 365. It stores documents, intranet pages, procedures, policies and project materials. The SharePoint integration with ChatGPT enables users to find these resources and use them when preparing responses or documents. It is particularly useful in larger organisations where knowledge is distributed across sites, document libraries and teams. The SharePoint permission structure continues to determine which information is available to each employee. Best use case: finding current policies, instructions, templates and project documentation. Example prompt: “Based on the current procedures in SharePoint, prepare an onboarding checklist for a new supplier.” 3. Box integration with ChatGPT – secure analysis of company documents Box combines content management with access controls and is often used by organisations working with confidential documents. The Box integration with ChatGPT can retrieve data on demand or synchronise selected content. On-demand access retrieves the required information while a prompt is being processed, while synchronisation indexes approved resources in advance and speeds up searches across large repositories. The choice of access mode should take into account data classification, retention requirements and the expected response time. Best use case: analysing contracts, project materials, client documentation and approved company resources. Example prompt: “Find the current versions of documents for Client X in Box and identify discrepancies in the project scope.” 4. Gmail integration with ChatGPT – summarising correspondence and preparing replies The Gmail integration with ChatGPT enables users to search for messages, summarise long threads and prepare draft replies based on their email history. Gmail in ChatGPT is useful in sales, customer service, recruitment and day-to-day coordination when important decisions are distributed across multiple messages. To help the Gmail connector return an accurate result, specify the relevant period, senders, subject and expected outcome. ChatGPT can then find the appropriate messages and turn them into a summary, list of decisions or ready-to-use draft reply. Best use case: summarising an email thread, preparing a follow-up and identifying the commitments made by each party. Example prompt: “Summarise the correspondence with Company X from the last two weeks. List the agreed actions, deadlines and questions that still require a response.” The video shows how to connect Gmail to ChatGPT step by step using the official app. Once the Gmail integration with ChatGPT has been configured, users can search for messages, summarise long threads, find important information and prepare draft replies directly within the conversation. 5. Outlook Email integration with ChatGPT – analysing messages in Microsoft 365 The Outlook Email integration with ChatGPT enables users to find messages, analyse long email threads and prepare replies that take the conversation history into account. Outlook in ChatGPT is particularly useful for organisations using Microsoft 365. If ChatGPT is also connected to SharePoint and Microsoft Teams, it can combine email discussions with documents and team conversations. The Outlook Email integration operates only within sources approved by the organisation and available to the individual user. Best use case: preparing a client response based on email history and current project materials. Example prompt: “Find the latest email thread about renewing the contract with Company X and prepare a draft reply that addresses the outstanding issues.” 6. Slack integration with ChatGPT – summarising team conversations, decisions and actions The Slack integration with ChatGPT gives the model access to context from messages, files, channels and team member profiles. Slack in ChatGPT helps reconstruct the history of decisions, prepare project status updates and identify recurring problems in team conversations. The Slack MCP server also supports selected actions, such as sending messages and creating or viewing Canvas documents. The Slack integration with ChatGPT only uses channels available to the authenticated user and operates according to the rules configured by the administrator. Best use case: preparing a weekly status update covering decisions, blockers, owners and open questions. Example prompt: “Review the project channel from Monday onwards and prepare a status update covering completed actions, risks, decisions and tasks for the coming week.” 7. Microsoft Teams integration with ChatGPT – analysing conversations, meetings and tasks The Microsoft Teams integration with ChatGPT enables users to search and analyse messages from individual chats, group conversations and channels available to them. Microsoft Teams in ChatGPT can also work with Microsoft Planner plans and tasks. When the relevant actions are enabled, it can create chats and channels, as well as send messages and replies. On the Enterprise plan, the integration can also retrieve transcripts from scheduled meetings if the user has the appropriate permissions. Files shared in Teams channels are usually stored in SharePoint, so analysing them requires an additional connection between ChatGPT and SharePoint. Best use case: finding decisions in team conversations and turning them into summaries, tasks and status materials. Example prompt: “Review the conversations in the project channel from the last five days and prepare a list of decisions, open questions, responsible individuals and deadlines.” 8. Notion integration with ChatGPT – creating and updating company knowledge The Notion integration with ChatGPT enables users to read, create and update content on Notion pages directly from a conversation. Notion in ChatGPT can support product documentation, campaign plans, knowledge bases, feature specifications and implementation checklists. The Notion MCP server operates within the permissions of the signed-in user. A person with broad access to the workspace gives the integration an equally broad scope of data and operations, so it is worth beginning the implementation with clearly limited use cases and accounts with appropriately assigned roles. Best use case: transforming notes and analysis results into structured pages, databases and action plans. Example prompt: “Create a feature specification in Notion based on these notes. Add objectives, requirements, acceptance criteria, risks and open questions.” The video shows how to connect Notion to ChatGPT and work with content stored in a workspace. The Notion integration with ChatGPT enables users to search for information and create or update pages directly from a conversation. 9. Atlassian Rovo integration with ChatGPT – working with Jira, Confluence and Bitbucket The Atlassian Rovo integration with ChatGPT connects the model to Jira, Jira Service Management, Confluence and Bitbucket. Jira and Confluence content can be searched and summarised in ChatGPT, while users can also create and update tasks, tickets and pages using natural language commands. The Atlassian Rovo MCP server supports software development, ticket management, change management and documentation processes. OAuth 2.1 authentication preserves existing user roles and permissions, while actions affecting data should be subject to approval and monitoring. Best use case: creating tickets from meeting notes, updating statuses and connecting Confluence documentation with Jira tasks. Example prompt: “Based on this specification, create five Jira tasks with descriptions, acceptance criteria and priorities. Show me the proposed tasks before saving them.” 10. Asana integration with ChatGPT – creating tasks and managing projects The Asana integration with ChatGPT provides information about projects and portfolios, and allows users to create and assign tasks, set up new projects and monitor progress. Asana in ChatGPT can turn decisions made during a conversation into a structured plan saved directly in the work management system. The Asana integration with ChatGPT is useful for planning campaigns, implementations, product launches and cross-departmental initiatives. The integration produces the most accurate results when projects, owners and custom fields have clear and consistent names. Best use case: creating a project plan and turning decisions into assigned tasks. Example prompt: “Create a plan in Asana for launching a new product page. Divide the work into stages, tasks, dependencies and responsible team members. Show me the proposed structure for approval before saving it.” 11. HubSpot integration with ChatGPT – CRM analysis and record updates The HubSpot integration with ChatGPT provides information about contacts, companies, sales opportunities, tickets and customer interaction history. HubSpot in ChatGPT can analyse the sales funnel, campaign results and customer activity, as well as create and update selected records and log activities. The HubSpot integration with ChatGPT is one of the most extensive solutions available to sales and marketing teams. The quality of its results depends on the completeness of CRM data, consistently defined funnel stages and correctly assigned permissions. Best use case: preparing an account brief, analysing the pipeline, updating an opportunity and creating a follow-up. Example prompt: “Analyse the sales opportunities in HubSpot that have had no activity for 14 days. Identify the priorities and prepare a plan for the next contact.” 12. Salesforce Agentforce Sales integration with ChatGPT – opportunity analysis and CRM management The Salesforce Agentforce Sales integration with ChatGPT combines information about customers, sales opportunities and the pipeline with analysis and planning capabilities. Salesforce in ChatGPT allows sales representatives to prioritise opportunities, prepare account plans, update records and run Agentforce actions directly from a conversation. The Agentforce Sales app for ChatGPT is currently available through the Open Beta programme to eligible customers using the required Agentforce add-ons. Before implementation, organisations should verify their Salesforce edition, access requirements and regional availability. Best use case: preparing a sales representative for a meeting, prioritising opportunities and updating the CRM after a client conversation. Example prompt: “Show me five Salesforce opportunities that require attention this week. Include their value, stage, most recent activity, risk and recommended next step.” 13. GitHub integration with ChatGPT – analysing code, issues and project changes The GitHub integration with ChatGPT gives the model access to context from repositories, code, issues, proposed changes and automated test results. GitHub in ChatGPT can help analyse code changes, organise issues, prepare documentation and identify dependencies between project components. Administrators can specify which repositories the GitHub integration with ChatGPT can access and which operations it can perform. This makes it possible to test the integration on a small number of selected projects before gradually making it available to additional teams. Best use case: analysing proposed code changes, organising issues, reviewing automated test results and preparing change documentation. Example prompt: “Review the open pull requests in the mobile application repository. Identify risks, missing tests and issues blocking the release.” The video shows how to connect GitHub to ChatGPT and give the integration access to selected repositories. The GitHub integration with ChatGPT enables users to explore project structures, analyse code and documentation, and summarise changes, commits and pull requests directly within a conversation. 14. Canva integration with ChatGPT – creating and editing visual content The Canva integration with ChatGPT enables users to search and summarise existing materials, as well as create, edit and display designs directly within a conversation. Canva in ChatGPT is useful for preparing presentations, social media posts, documents and other visual materials. Designs created through the Canva app for ChatGPT remain editable in Canva, allowing the team to continue refining their content and appearance. The best results can be achieved by specifying the intended audience, objective, format, source materials and brand requirements. Best use case: presentations, social media content, sales documents and visual summaries. Example prompt: “Create a presentation in Canva for the management team based on this report. Use eight slides, concise conclusions and one chart on the results slide.” 15. Adobe integration with ChatGPT – editing photos, videos, graphics and PDF documents Adobe for ChatGPT is a package that brings together features from Adobe applications, including Photoshop, Premiere, Firefly, Express and Acrobat. It supports photo editing, consistent batch processing, preparation of social media formats, video shortening, work with PDF documents and searches across Creative Cloud assets. The solution supports workflows intended to produce a finished file. For example, a workflow may begin with a set of employee photos, include lighting correction and consistent cropping, and finish with the export of materials ready for publication. Best use case: repeatable photo editing, adapting content for different channels, working with PDF documents and quickly creating designs from templates. Example prompt: “Standardise the lighting and colours in these photos, apply consistent cropping and prepare versions for employee profiles on the company website.” 15 ChatGPT integrations with business applications – comparison table No. ChatGPT integration Area Best use case Primary type of work 1 Google Drive Documents and knowledge Analysing files from Drive, Docs, Sheets and Slides Search, reading and analysis 2 Microsoft SharePoint Organisational knowledge Working with controlled Microsoft 365 resources Search, reading and analysis 3 Box Content management Secure work with company files and folders On-demand access or synchronisation 4 Gmail Email Summarising email conversations and preparing replies Search, analysis and drafting 5 Outlook Email Microsoft 365 email Analysing email in a business environment Search, analysis and drafting 6 Slack Team communication Finding decisions and summarising channels and messages Search, reading and actions 7 Microsoft Teams Collaboration Analysing conversations, meetings and team context Search and summarisation 8 Notion Knowledge and documentation Creating and updating pages, databases and plans Real-time reading and writing 9 Atlassian Rovo Projects and IT Working with Jira, Confluence, Jira Service Management and Bitbucket Search, creation and updates 10 Asana Work management Managing project portfolios and creating tasks Analysis and project actions 11 HubSpot CRM, marketing and sales Analysing customers, the sales funnel and contact history Analysis, record creation and updates 12 Salesforce Agentforce Sales Enterprise sales Prioritising opportunities, planning accounts and updating the CRM Analysis and sales actions 13 GitHub Software development Working with repositories, issues, proposed changes and automated tests Search, analysis and issue organisation 14 Canva Design and communication Creating editable presentations and marketing materials Search, generation and editing 15 Adobe Creative work and documents Photos, videos, social media content, PDF files and Creative Cloud assets Search, generation, editing and export Security of ChatGPT integrations in a business environment Secure integration of ChatGPT with company systems requires appropriate permission management, separation of read and write operations, selection of the right data access method, approval of actions and operation logging. Data processing terms, OAuth scopes, retention and data residency requirements should also be reviewed for every connected service. In ChatGPT Business, Enterprise and Edu plans, data retrieved through integrations is not used to train OpenAI models. When is it worth building a custom ChatGPT integration? Ready-made ChatGPT integrations cover popular business applications and common use cases. A custom integration becomes justified when critical data is stored in an internal system, the process requires specific logic or the organisation needs greater control over its architecture and information flows. The most common reasons include: a private API, legacy system, internal database or on-premises solution; a workflow involving several systems and rules specific to the organisation; requirements concerning data residency, auditability and approval of operations; the need to combine RAG-based search, business logic and actions performed in external systems; a regulated environment requiring risk assessment, documentation and controlled implementation; a scale at which a custom integration simplifies access and cost management. Such a solution may use a dedicated integration, MCP server, GPT Actions, API layer or an architecture combining several approaches. The starting point should be a specific process, a clearly identified data owner and the expected business outcome. ChatGPT integrations as part of a secure enterprise AI ecosystem ChatGPT integrations provide the greatest value when the connection supports a real process, respects user roles and produces an output that is ready to use. For one organisation, this may mean faster knowledge retrieval. For another, it may involve CRM updates, document automation or a controlled process spanning several systems. Transition Technologies MS designs and implements AI solutions for business tailored to an organisation’s data, architecture, security requirements and operating model. The scope of a project may include API and MCP integrations, RAG solutions, action automation and a model for managing access, risk and accountability. Our approach to AI has been confirmed by ISO/IEC 42001 certification for our Artificial Intelligence Management System (AIMS). TTMS was the first company in Poland to obtain accredited certification for compliance with this standard and is among the first organisations in Europe operating within its framework. This means that we deliver AI projects according to structured principles covering security, accountability, documentation and risk management. TTMS also develops proprietary AI products that support specific business processes: AI4Content analyses documents and creates structured reports; AI4Knowledge helps employees use company knowledge more effectively; AI4E-learning transforms source materials into editable online training courses; AI4Localisation supports the translation and adaptation of content for different markets; AI4Legal automates document analysis and selected legal processes; AML Track supports customer verification, risk monitoring and compliance with AML obligations; AI4Hire structures application analysis and supports the initial assessment of candidates; QATANA uses AI to create test cases and manage the software testing process. This expertise allows us to combine integration, product and regulatory experience. We can help organisations establish a single connection to a company data source or design a solution spanning several systems, access controls and end-to-end process automation. FAQ: Frequently asked questions about ChatGPT integrations Can ChatGPT use multiple connected apps in a single task? Yes. Supported ChatGPT environments can use several approved sources within a single task. For example, a workflow could collect project decisions from Slack, retrieve a report from Google Drive and prepare an action plan in Asana. Availability depends on the ChatGPT plan, the interface or mode being used and the workspace configuration. The prompt should clearly identify the required sources, expected result and point at which ChatGPT should request approval. The organisation should also define which types of data may be combined in a single output. Does an integration give ChatGPT access to all of a user’s data? The scope of access depends on the permissions granted to the integration and the user’s role in the source system. Many integrations respect existing permissions for folders, channels, repositories and CRM records. An administrator account may therefore expose significantly more data than an employee account assigned to a specific team. During configuration, review the OAuth scopes, user roles and options for restricting access to selected resources. A pilot should ideally use an account with permissions corresponding to the intended user role. Can ChatGPT send messages and modify data in external applications? Selected apps, integrations and MCP servers support write actions such as sending messages, creating tasks, updating CRM records or adding pages. The available actions vary by provider, subscription plan and integration version. Some tools show the proposed change and request confirmation before completing it. Administrators may also restrict an integration to read-only access or allow only selected operations. Actions affecting customers, financial data, publications or regulated processes should always have clearly defined human approval requirements. Do I need a paid ChatGPT plan to use integrations? Not always. A limited selection of apps may also be available on the free ChatGPT plan, although search and analysis features may have lower usage limits. Broader access, including data synchronisation and custom MCP-based integrations, usually requires a paid plan such as Plus, Pro, Business, Enterprise or Edu. Availability may also depend on the user’s region, administrator settings and subscription to the external service. The current requirements for a specific integration should be checked directly in the ChatGPT app or plugin directory. Can ChatGPT be connected to a company’s internal system? Yes. An organisation can build a custom MCP server, dedicated integration or API connection that gives ChatGPT access to selected data and actions. A private system may remain behind a firewall or operate on-premises if the architecture uses a secure tunnel and controlled authentication. The project should define tool schemas, roles, logging, action approvals, error handling and protection against prompt injection. Before production deployment, the integration should be tested using valid requests, edge cases and tasks that it is expected to refuse. How do you connect an app to ChatGPT? First, define the task the integration should perform and the data required to complete it. Then open the app or plugin directory in ChatGPT, select the appropriate service and start the configuration process. Sign in to the external application, carefully review the requested permissions and approve only the access that is necessary. Once configuration is complete, open a new conversation, select the connected app and test it using a limited dataset. In a business environment, it is best to begin with a pilot for a small group of users before making the integration available to the wider organisation. Can a company administrator restrict access to apps in ChatGPT? Yes. A workspace administrator can decide which apps and plugins are available within the organisation, who may use them and which actions they can perform. For example, the administrator may allow read-only access while blocking message sending or CRM record updates. In managed workspaces, access can also be assigned according to user roles and groups. Integrations continue to respect permissions in the source system, so users should not gain access through ChatGPT to information they cannot view in the connected application. Does ChatGPT store copies of data retrieved from connected systems? It depends on how the integration works. With on-demand access, data is retrieved when a specific request is processed and is not indexed in advance. Integrations that use synchronisation may create an indexed copy of selected content to speed up searches and improve response quality. Disconnecting an app prevents further access, while its synchronised index is scheduled for deletion from OpenAI systems, typically within 30 days. Information previously used in conversations may remain in chat history, so removing it may also require deleting the relevant conversations and saved memories.
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