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Is Advertising in ChatGPT Worth It? Costs and Early Advertiser Experiences
Ads in ChatGPT give businesses a new way to reach potential customers. They can appear when users compare products, plan trips, or look for solutions to business problems. Early advertisers are already exploring whether these ads attract customers and increase sales. Their experiences offer insights into how much to spend on ChatGPT ads and when to include them in a marketing budget. In this article, we: examine campaign results shared by early advertisers; explain what ChatGPT ads look like and which campaign settings are available; compare costs and targeting with Google Ads and other channels; show how to measure performance and determine whether ChatGPT ads can be profitable for your business. What do ads in ChatGPT look like? A standard ad appears as a separate, labeled placement below a ChatGPT response. It includes the advertiser’s name, logo, headline, short description, image, and a link to a landing page. It may look like a product or service card relevant to the topic the user is exploring. For example, a user asks ChatGPT to help them choose an application for creating training courses. They describe their company’s size, the languages the courses need to support, and how frequently the content needs updating. An ad for a relevant application may appear below the response. Advertisers can describe situations in which their product would be useful, while the system decides whether to display the ad. According to OpenAI, ads do not influence ChatGPT’s responses. Businesses pay for an advertising placement, which does not buy a recommendation from the AI. An ad unit can contain one or more images. OpenAI is also testing multi-image ads displayed while users wait for an image to be generated. Example ad card with its elements explained. Image from OpenAI’s documentation. Product ads and chats with an AI brand representative Online retailers can upload a file containing product information and use it to create ads. These ads can include images, product names, prices, and promotional offers. Uploading a catalog supports paid advertising; it does not guarantee that ChatGPT will recommend those products in its responses. OpenAI is also testing a format called Sponsored Agents. It lets users move from an ad into a chat with an AI brand representative and ask questions about the company’s offering. The feature is in an early testing phase and available only to selected advertisers. Before including it in your first campaign, check whether your account has access. How much traffic does ChatGPT send to websites? When planning to advertise in ChatGPT, it helps to understand how small a share of website visits currently comes from the tool. In an Ahrefs analysis covering more than 115,000 websites, ChatGPT accounted for 0.34% of all visits in September 2026, compared with 23.96% for Google and 1.13% for Bing. That amounts to roughly three to four visits from ChatGPT per 1,000 website visits. These figures reflect the websites in the sample and use all visits, including paid traffic, as the denominator. The share attributable to each source can vary by industry. ChatGPT’s popularity has yet to translate into a comparable volume of visits to business websites. That matters when considering its potential to generate sales at scale. These figures do not establish how effective ads will be: their ability to acquire customers needs to be assessed through paid campaign results. What have early advertisers learned? Four examples We found early reports from businesses and agencies that have shared their ChatGPT advertising results. Some disclosed only part of their spending and sales data, but their experiences offer insights into what worked and where they encountered difficulties. These individual examples provide limited evidence of what a typical business in any given industry can expect. 1. Hostinger: nearly $70,000 spent, with profitability still unclear Hostinger spent nearly $70,000 on a ChatGPT advertising trial lasting several weeks. Hüseyin Ograk, who leads the company’s paid advertising team, shared several observations on LinkedIn. The cost per 1,000 impressions exceeded $65, and users clicked ads less frequently than in Google Search. Some visitors showed interest in the offering, while other visits brought no noticeable business benefits. Ads explaining how the product could address a specific need performed best. The landing page also mattered: it needed to address the same need as the ad. General messaging and links to a broad range of offerings produced weaker results. Was the campaign profitable? The published data does not provide a definitive answer. Ograk noted, however, that some visitors purchased later or returned to buy additional products. Assessing the value of those customers requires tracking their purchases beyond the campaign itself. 2. SE Ranking: 1,263 clicks, but too few sign-ups to continue advertising SE Ranking, a provider of tools that help improve website visibility in search engines, ended its trial because it generated few sign-ups. Over roughly two weeks, its ads received more than 97,000 impressions and 1,263 clicks. The click-through rate, or CTR—the percentage of impressions that resulted in a click—was 1.30%. The company ran three campaigns across the United States, Canada, Australia, and New Zealand. The authors suspect that the ads failed to reach the right audience. SEO professionals may use ad-free ChatGPT plans such as Plus, Pro, or Business. This is the company’s hypothesis rather than an established explanation for the weak results. The timing of the trial also matters. In June, SE Ranking set click bids manually, and the system had no sign-up data to use for campaign optimization. Advertisers now have a broader range of campaign capabilities. 3. Opascope: approximately $60,000 in spend and $89,000 in revenue Opascope disclosed results from 15 days of advertising for one client. Approximately $60,000 in ad spend generated $89,000 in attributed revenue. The average cost per click was $1.72, and the conversion rate was 2.35%. The agency reported a return on ad spend, or ROAS, of 1.49. This means that each dollar spent on advertising generated $1.49 in attributed revenue. Assessing profitability also requires margin data: that revenue must cover the cost of the product or service and other expenses. The selection of campaigns included in the report also matters. Opascope presented campaigns whose budgets had been increased after promising initial results. The figures therefore did not cover every trial. Results varied considerably from day to day, with daily ROAS ranging from approximately 0.2 to 2.9. The client’s name and profit margin were not disclosed. 4. InterTeam Marketing: a 60% lower cost per lead than Google Ads InterTeam Marketing shared more encouraging experiences from campaigns for three clients offering subscription-based business software. According to the agency, the ads generated more than 150 leads considered valuable by sales teams. The average cost per click was approximately $5, while the cost per lead was about 60% lower than in Google Ads. What did the campaigns promote? Among other things, e-books and webinars relevant to people looking for solutions to specific problems. The agency also sought to reach users comparing vendors or looking for an alternative to their existing software. Ads directed them to landing pages addressing those same topics. This is an encouraging result for B2B businesses, although a full assessment requires information about what happened to those leads. The agency did not disclose the number of signed contracts or each client’s full budget. It also did not explain its lead qualification criteria in detail. The value of advertising depends on the qualified leads and sales it generates. Other businesses’ results can help inform your own campaign, but they need to be considered alongside your offering, customers, and margins. How much do ChatGPT ads cost, and how do they compare with Google Ads? What does the advertiser pay for? When creating a campaign, you choose what you want to achieve: ad impressions, website visits, or specific actions such as purchases. The system adjusts ad delivery to support your chosen goal. Charges are based on clicks or impressions. ChatGPT Ads campaign goals and billing models Goal Billing model What does the system aim to increase? Views CPM: payment per 1,000 impressions Ad visibility Clicks CPC: payment per valid click Website traffic Conversions oCPC or oCPM: payment for clicks or impressions, depending on the available option A selected action, such as a purchase or form submission With the “Conversions” goal, the system optimizes ad delivery to increase the likelihood of a purchase, sign-up, or form submission. You pay for clicks or impressions. Before launching the campaign, set up tracking for your chosen action so the system receives data on ad performance. How much should you budget for your first ChatGPT campaign? According to OpenAI’s documentation, the minimum daily budget is $25 for accounts billed in USD or €15 for accounts billed in EUR. Plan your campaign budget around the number of leads or purchases you want to generate and how much you can afford to pay for each. Your daily budget sets your average daily spending. With a $25 daily budget, the system can spend up to $50 on a single day and less on other days. If the budget remains unchanged, total spending from Sunday through Saturday will not exceed $175. You can also set a lifetime budget to control spending over the full campaign period. Include ad creation, landing page development, and measurement tools in your budget. If an agency or independent specialist manages the campaign, factor in their fees as well. For smaller campaigns, preparation and management can account for a substantial share of the total cost. How much does a website visit from a ChatGPT ad cost? For manual bidding, OpenAI suggests starting with a maximum cost-per-click bid of $3–$5. This sets the amount you are willing to pay for a click. Your actual cost depends on factors including the ad’s relevance to the user’s needs and competing advertisers’ bids. You can find it in your campaign results. Search advertising costs provide a useful reference point. WordStream and LocaliQ analyzed 13,474 US campaigns run in Google Ads and Microsoft Ads between April 2025 and March 2026. The results show what advertisers paid in that market. The reported figures are medians: half the results were lower and half were higher. Compare these benchmarks with your own campaign costs or forecasts for your industry and target market. Advertising costs: what the different figures mean Data source Figure How should you interpret it? OpenAI: manual CPC bidding $3–$5 Recommended starting bid limit, rather than an average cost per click WordStream/LocaliQ: Google and Microsoft Search CPC: $5.42; CPL: $66.69 Benchmarks from a study of US campaigns The same report: business services CPC: $5.87 Benchmark for one broad industry category Sources: OpenAI documentation and the WordStream/LocaliQ report. To assess whether ChatGPT advertising costs less than Google advertising, compare campaigns promoting similar offerings in the same country. Measure the same outcome in both, such as submitting a service inquiry form. When evaluating new customer acquisition, analyze campaigns targeting existing customers and searches for your company name separately. The cost per lead also depends on how many website visitors submit an inquiry. For illustration, suppose each ad click costs $10 and two out of every 100 visitors complete the form. You would spend $1,000 on 100 clicks and receive two inquiries, costing $500 each. If a click costs $15 but five out of every 100 visitors complete the form, you would spend $1,500 and receive five inquiries. Each would then cost $300. How do you set up an advertising campaign in ChatGPT? Start by creating an Ads Manager account, entering your business details, adding a payment method, and completing the required verification. The campaign builder then guides you through choosing your goal, budget, and audience settings. Within each campaign, you create ad groups, for example, for different products or customer needs. Each group contains ads with a headline, description, image, and link to a relevant landing page. OpenAI reviews ads for compliance with its advertising policies before they begin running. Core settings in ChatGPT Ads Manager Level Settings Practical use Campaign Goal, budget, dates, locations, and supported platforms Separating countries, products, or business goals Ad group Topic, context hints, and bidding strategy Addressing distinct needs, such as choosing a product or switching vendors Ad Headline, description, image, and landing page URL Testing different messages addressing the same customer need Source: OpenAI’s campaign setup guide. How do you explain who your product is for and when it is useful? In your ad group settings, you can add context hints to help the system match your ads to users’ needs. These describe what your product does, who it helps, and when someone might use it. For a training authoring application, context hints might include: “An application for businesses creating employee onboarding courses from their own documents.” “Creating and updating multilingual courses when company procedures change.” “Creating courses that can be published in a company’s learning management system (LMS).” Provide specific information that accurately reflects the application’s capabilities. The system uses these hints when selecting ads to display. Adding a hint does not guarantee that your ad will appear alongside a particular question or reach someone with a specific job title. If you promote several use cases, create a separate ad group for each. Each group should direct users to a landing page explaining how the product addresses that particular need. Where can your ads appear, and who can you reach? You select the country where you want your ads to run. Depending on the market, you may also be able to choose a region, city, or ZIP code. For new campaigns created from product catalogs, location targeting is currently limited to the country level. You can also choose whether ads appear in the iOS app, the Android app, or a desktop browser. Some new accounts initially allow advertising only in the country where the business is registered. Access to additional markets requires meeting further conditions. In markets that support this feature, you can upload your own list of customers or prospects. You can use it to show ads to those people, exclude them from campaigns, or adjust bids to reach them. To target this audience, the system must match at least 25,000 people on your list to ChatGPT users. A database containing 25,000 contacts may therefore be too small. Ad personalization remains unavailable in the European Economic Area and Switzerland. OpenAI recommends avoiding custom audience lists in campaigns targeting these markets. When planning campaigns in these regions, focus on location and matching your ads to the user’s topic and needs. How do you control where ads appear and set bids? The Negative Phrases feature allows selected advertisers to specify phrases associated with topics where they want to avoid showing ads. Check your account for availability and details. For bidding, you can use the Maximize results strategy. The system automatically adjusts bids to generate as many clicks or conversions as possible within your budget, depending on the campaign goal. This option may be enabled by default in some new ad groups. Before launching, check whether you are setting bids manually or letting the system adjust them. With automated bidding, regularly monitor your cost per website visit, lead, and sale. These costs can change throughout the campaign. ChatGPT, Google, Meta, or LinkedIn: which advertising channel should you choose? The choice depends on who you want to reach and when they are likely to be interested in your offer. In ChatGPT, an ad can appear while someone is looking for a solution or comparing products. Google lets you reach people entering specific search queries, while LinkedIn makes it easier to select audiences by job title or company. Channel How do you select your audience? When should you consider it? ChatGPT Ads Based on the topic and needs described by the user, information about your offering, and location When a purchase involves understanding a product, comparing options, or finding a solution that meets specific requirements Google Search Using keywords, match settings, locations, and exclusions When customers are already searching for a product, service, or solution to a specific problem Meta Ads Using factors such as location, interests, behaviors, and custom audiences, depending on campaign settings When an image, video, or product demonstration could interest people browsing Facebook and Instagram LinkedIn Ads Using factors such as job title, company, industry, and professional experience When your offering targets people in specific roles at particular types of organizations One important difference between Google and ChatGPT is how much you can learn about what triggered an ad. In Google, the search terms report shows some of the queries that led to your ads appearing. You can use this information to exclude irrelevant searches. In ChatGPT, advertisers cannot see users’ questions or conversations. You therefore assess audience relevance primarily through website activity and the inquiries people submit. Both platforms use AI to match ads to users. Google is expanding these capabilities through tools such as AI Max, which uses information from keywords, ads, and landing pages. It also offers bidding strategies based on a target cost per conversion or return on ad spend. In ChatGPT, the available settings depend on your campaign goal and billing method. When comparing results, account for how each campaign is configured and managed. Ads in Google’s AI Overviews are a separate consideration. In supported markets, ads from Search, Shopping, and Performance Max campaigns can appear in these responses. However, Google does not provide a separate report for their performance. This makes it difficult to compare the effectiveness of ads appearing exclusively within the two companies’ AI experiences. How do you measure ChatGPT ad performance? 1. Decide which outcomes you want to measure For an online store, focus primarily on paid orders. Account for margins and returns when assessing their value. For a business selling services to other companies, track inquiries, meetings, and signed contracts separately. An e-book download can serve as an early sign of interest. Agree with your sales team on what makes an inquiry valuable. Does it come from the type of company you serve? Does it concern a need your offering addresses? Is the person involved in choosing a vendor? Apply the same criteria to leads generated through ChatGPT, Google, and LinkedIn. This lets you compare the cost of acquiring similar prospects across channels. 2. Tag your links to identify visits from ads Add UTM parameters to your ad links. These short tags allow analytics tools such as Google Analytics 4 to identify the traffic source, campaign, and specific ad version. Use a consistent naming convention for campaigns and ads. This makes results easier to compare and prevents data for the same campaign from being split across several similar names. Before launching, open a tagged link and submit a test inquiry through your form. Check that your analytics tool records the correct traffic source and campaign name. Make sure this information is also saved with the inquiry in your customer relationship management system. These tags help distinguish paid ad traffic from visits through links in ChatGPT responses. They also help identify which ads generate inquiries and purchases. 3. Check that purchases and inquiries are being recorded To assess campaign performance, you need to know what people do after clicking an ad. OpenAI Pixel, a piece of code installed on your website, collects this information. It can record actions such as purchases, sign-ups, and form submissions. You can also send data directly from your business systems through the Conversions API. Ask the person responsible for your website or analytics to handle the implementation. They should ensure that actions are correctly attributed to ads and that the same purchase cannot be counted twice. It is also important to preserve the oppref parameter, a click identifier added to the page URL. Redirects should not remove it. Data collection and transmission must also account for appropriate user notices and required consent. Before launching the campaign, place a test order or submit a form. Check that the action is recorded, the amount and currency are correct, and reopening the confirmation page does not record another purchase. OpenAI also offers a beta integration between Ads Manager and Google Analytics 4 to help compare results across the two tools. Access requires contacting your account representative. You can track visits from UTM-tagged links in GA4 independently of this integration. 4. Understand what the reported results include Ads Manager shows impressions, clicks, conversions, and spending. You can also check the average cost per click and per thousand impressions. Results are available at the campaign, ad group, and individual ad levels, and you can export them to a CSV file. Reporting is delayed. Clicks and impressions typically update about every 15 minutes, costs may take 7–8 hours to appear, and conversions may take 24–48 hours. Results from the most recent hours may therefore be incomplete. The time between an ad click and a purchase or inquiry also matters. In the reporting settings, you choose whether to include actions taken within 7, 14, or 30 days after a click. Separately, you can enable reporting for conversions that occur within one day after an ad impression, even without a click. This is part of attribution: the rules used to credit ads with results. For example, a customer might see an ad in ChatGPT, later find the company through Google, and then make a purchase. Both systems could claim credit for that purchase. Confirm order counts and revenue in your store or sales system. When comparing channels, use the same evaluation period and rules for counting results. 5. Calculate customer acquisition costs and profitability Compare campaign spending with inquiry and sales data. Check how many leads met your criteria and how many became customers. What do you want to measure? How do you calculate it? Cost per lead, or CPL Divide ad spend by the number of leads generated Cost per qualified lead Divide ad spend by the number of leads meeting your agreed sales criteria Full customer acquisition cost Divide the marketing and sales costs associated with the campaign by the number of new customers acquired Revenue generated per dollar of ad spend, or ROAS Divide revenue attributed to ads by ad spend Profitability depends on how much revenue remains after covering costs. Suppose that $100 in revenue leaves $40 after paying for the product, order fulfillment, payment processing, and returns. If acquiring that order costs $40, the entire remaining amount goes toward advertising. This corresponds to a ROAS of 2.5: every $40 spent on ads generates $100 in revenue. To make a profit, the campaign must also cover ad creation, campaign management, and the share of business overhead allocated to those sales. For subscription sales, include subsequent customer payments and the cost of serving those customers. Base your calculations on actual data about how long customers continue using the service. Also check how many months it takes to recover the money spent acquiring them. In B2B, revisit the results after the campaign ends to include contracts signed following longer negotiations. How do you plan your first ChatGPT campaign? Decide what you want to learn, how much you can spend, and what results would justify continuing. Keep the initial campaign focused so its performance is easier to assess. Choose one offering and one market. Decide whether you want purchases, product demo bookings, or sales inquiries. Check ad availability in your target market and the policies that apply to your industry. Build a landing page that expands on the ad. If you are promoting an application that helps update training courses, show how that feature works. Visitors should be able to find the information they need and understand how to book a demo or contact your company. Test your measurement setup. Check link tags, purchase or form tracking, and the recording of lead sources. Agree with sales on which inquiries will count as qualified leads. Set your budget and an acceptable cost per lead. If you can pay $200 for a qualified inquiry and want to generate 20, your starting ad budget would be $4,000. The campaign will show whether that cost is achievable. Budget separately for preparation and management. Explore several product use cases. Prepare two or three ad groups, each addressing a different customer need. Match the ad copy and landing pages to each one. Choose the number of variations to suit your budget so each can collect enough data to assess its performance. Set a review date and conditions for pausing the campaign. Account for the time customers need to make a decision. Monitor spending and lead quality throughout the campaign. Pause it if tracking fails or spending reaches your agreed limit without delivering the expected results. Compare performance with other campaigns. If you use Google Ads, compare the cost of acquiring similar leads and customers. Include the value of sales and the work required to handle inquiries. With a larger budget, you can also assess how much additional sales your ads generate. This requires a carefully designed test: run the campaign for some comparable audiences or markets and leave others as a control group. Comparing their results helps estimate how many purchases the advertising generated. Is advertising in ChatGPT worth it? The strongest case for ChatGPT advertising is for businesses whose customers compare products against specific requirements. Hostinger’s and InterTeam’s experiences suggest that these situations offer better opportunities to generate qualified inquiries and purchases. Advertisers need to show exactly what their product can do. General brand promotion or ads covering an entire product range may make less effective use of this opportunity. The early results also reveal the limits of the channel’s promise. Users may describe their needs in detail while still gathering information. SE Ranking generated more than 1,200 clicks but very few sign-ups. A detailed description of someone’s needs helps match an ad, but that person may still be a long way from buying. ChatGPT deserves consideration as an additional source of customers, particularly for products that involve comparison and explanation. The available reports offer little basis for moving budget away from profitable Google campaigns. A lower cost per inquiry at one company and ad-attributed revenue at another provide limited evidence for judging ChatGPT a more profitable sales channel. Talk to us about using AI in your business At TTMS, we help businesses implement AI solutions and integrate them with their existing systems. We were the first company in Poland to obtain accredited certification for an artificial intelligence management system compliant with ISO/IEC 42001. If you want to use AI in marketing, organize your data, or improve how your team works, talk to TTMS experts about your business needs. What are ChatGPT ads? ChatGPT ads are paid placements displayed alongside answers to users’ questions. They can feature products or services related to the topic a user is exploring. Ads are labeled as sponsored and include a link to the advertiser’s website. How can I advertise my business in ChatGPT? You can create a campaign in OpenAI’s Ads Manager. After setting up and verifying your account, you add a payment method, choose your campaign goal and budget, and create your ads. You can also describe the customer needs your offering addresses to help the system match your ads to relevant questions. How much does advertising in ChatGPT cost? Costs depend on your billing model, competition, and how relevant your ads are to users’ needs. You can pay for clicks or impressions. Minimum daily budgets vary by account currency, so check the requirements for your account before planning a campaign. Include ad creation, landing page development, and campaign management in your total budget. Does paying for ads make ChatGPT recommend my business? According to OpenAI, advertising does not influence ChatGPT’s answers. Ads appear as separate, clearly labeled placements. To support your business’s visibility in answers, make reliable information about your products and services available online. Advertising spend does not guarantee recommendations. Are ChatGPT ads more effective than Google Ads? Early campaign results vary. Some advertisers report a lower cost per lead than Google Ads, while others received few inquiries despite generating many clicks. Compare campaigns promoting similar offerings in the same market. Focus on the cost of acquiring qualified leads and paying customers, along with the sales those campaigns generate.
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ChatGPT for financial services: what does combining GPT with professional data sources offer?
On 10 September 2026, OpenAI announced ChatGPT for Financial Services, a solution combining GPT-6 Astra with professional financial data sources and tools for preparing analyses. The product was developed in collaboration with Morgan Stanley and Evercore. It is designed for financial institutions, with an initial focus on investment banking and equity research. It allows analyst teams to find data, perform calculations and prepare client materials in one place. This could reduce the time spent gathering information and transferring it between tools. In this article, you will learn: what data and features ChatGPT for Financial Services offers, what preparing a company analysis with GPT could look like, why metric calculations and data sources need to be checked, which stages require an analyst’s review, how to assess whether implementation is worthwhile for your company. How does ChatGPT for Financial Services support analysts? Materials published by OpenAI and its data providers describe several specific use cases: Comparing companies. Daloopa, a provider of company financial data, makes selected data and metrics available for comparing business performance. Source references help analysts verify where the figures come from. Finding companies that meet specific criteria. Daloopa also describes searching for companies by business activity or geographical region. The resulting list can provide a starting point for further market analysis. Preparing client materials. OpenAI describes creating valuation models, research notes and presentations using company templates for Excel, Word and PowerPoint. ChatGPT for Financial Services provides access to selected data from Daloopa, PitchBook, LSEG News and Crunchbase. OpenAI is also developing integrations that will allow institutions to use data covered by their existing subscriptions. These include S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody’s. From data to company analysis: five steps in the workflow Let’s walk through preparing a comparison of two industrial companies ahead of a client meeting. The analyst needs to assess profitability, explain material differences and prepare a short note with a results table. Using fictional data, we will show what to check during a pilot, from selecting information to approving the final material. 1. Defining the question and scope of the comparison First, we establish which period to compare: the last full year, a six-month period or the trailing twelve months. We also check whether the figures cover the entire corporate group or an individual company, which currency they use and how each metric was calculated. In our example, we use consolidated data for both groups for the same calendar year. Amounts are stated in millions of euros. Before comparing results, we need to check the start and end dates of the reporting periods. One company’s financial year may end in December, while another’s ends in March. The documentation for the US SEC’s EDGAR database also highlights these differences. The analyst must then decide how to account for the mismatch and whether additional data is needed. The agreed approach should be recorded in the instructions for the AI and included with the completed analysis. This gives the model clear guidance and helps the reviewer understand which data was compared and why. 2. Gathering data and identifying its sources For each important figure, record the company and period it relates to, the units used and how it was calculated. A reference to the specific table or explanatory note in the report is also needed. Keeping the source document and its retrieval date makes it easier to review or update the analysis later. According to OpenAI’s description, ChatGPT for Financial Services lets users locate specific tables and document passages, highlighting the information used in the analysis. In our example, we compare EBITDA, or earnings before interest, taxes, depreciation and amortisation. The reviewer should be able to trace a reported value back to the company’s report and check how it was calculated. This also allows them to confirm that the figure covers the correct period and scope of operations. 3. Aligning definitions before comparing margins Companies may report adjusted EBITDA that excludes selected costs. These adjustments increase the value of the metric. Before comparing profitability, it is therefore necessary to check which adjustments have been applied. The US SEC also highlights differences in how individual companies calculate financial measures. Let’s look at two fictional companies. We assume that both calculate EBITDA before adjustments using the same principles. Company A then adds back EUR 4.59 million in costs that it excludes when calculating adjusted EBITDA. As a result, the metric rises from EUR 27.57 million to EUR 32.16 million. Company B has no such costs, so its figure remains unchanged. Illustrative example. Consolidated data for the same calendar year. Amounts are stated in EUR million. Item Company A Company B Revenue 229.73 183.78 EBITDA before adjustment 27.57 23.89 Costs excluded when calculating adjusted EBITDA 4.59 0.00 Adjusted EBITDA 32.16 23.89 EBITDA margin before adjustment 12% 13% Adjusted EBITDA margin 14% 13% After the adjustment, Company A’s margin is 14%, exceeding Company B’s margin of 13%. Before the adjustment, Company B has the higher margin: 13% compared with 12%. In this example, the treatment of costs determines which company has the higher EBITDA margin. The analyst should therefore check which costs make up the EUR 4.59 million adjustment and whether they also occurred in previous years. This helps them assess whether excluding these costs is justified for the analysis being prepared. They can also present both scenarios and explain the difference to the client. AI can help gather data and recalculate margins, while the expert assesses whether the adjustment is justified and how it affects the conclusions. 4. Verifying calculations in the spreadsheet In our example, simply divide EBITDA by revenue: 27.57 ÷ 229.73 gives a margin of approximately 12%, while 32.16 ÷ 229.73 gives approximately 14%. The displayed amounts are rounded; the spreadsheet should retain full precision for its calculations. More complex analyses may require currency conversion, alignment of reporting periods or the preparation of several forecast scenarios. The reviewer should be able to trace each of these steps. It is therefore worth asking AI to create a spreadsheet in which source data, assumptions and formulas are clearly separated. The analyst can then check the calculations and see how changing a single value affects the result. To test the spreadsheet, you can halve the adjustment, reducing it from approximately EUR 4.59 million to EUR 2.30 million. Company A’s adjusted EBITDA should then be approximately EUR 29.86 million, with a corresponding margin of 13%. These amounts are rounded for presentation; the spreadsheet should calculate the change using unrounded values. After making this change, check the comparison table and the commentary on the results as well. Both companies would now have the same margin, so the conclusion that Company A has a higher margin would need updating. This is a simple way to assess whether the calculations and accompanying text remain consistent. 5. Preparing client materials and reviewing conclusions The completed analysis can be presented in the company’s preferred format. According to OpenAI’s description, an administrator can share Excel, Word and PowerPoint templates with the team for the tool to use when creating documents and presentations. In our example, the client should receive a results table and a short explanation of how the cost adjustment affects the margin comparison. The expert reviewing the material checks whether the conclusions match the calculations and answer the client’s question. If anything needs clarification, they can request a further explanation or another version of the analysis. Time measurements should also include reviewing the material and making corrections before approval. How can you protect data and preserve a record of the analysis? When preparing a client analysis, the team may use public reports, paid databases and confidential documents. It is necessary to establish who can access this information, where it will be stored and who can receive the finished material. According to the ChatGPT Work security documentation, business data is encrypted and is not used to train models by default. Data retention periods, processing locations and the scope of recorded activity depend on the settings and connected services. Before implementation, check which user and tool actions are logged and which records can be exported. During the pilot, keep the source documents, successive versions of the spreadsheet and the final material, together with a record of who approved it and when. Then check whether this documentation allows you to reproduce the calculations and trace the approval of the analysis. Separately, verify whether system logs allow user and tool activity to be traced to the extent required by the company. How can you assess whether implementation is worthwhile? Start by choosing a task the team performs regularly, such as updating a company comparison after quarterly results are published. Before testing, measure how long this analysis takes using the existing method and define its quality requirements. These findings will provide a baseline for comparison with AI-assisted work. The time needed to review and correct the analysis must be added to the preparation time. OpenAI highlights this in its guidance on assessing the business value of AI, also recommending that implementation and ongoing usage costs be included. In practice, it is worth comparing: Metric What does it tell us? Time from starting the task to approving the analysis Does the client receive the finished material sooner? Include waiting time between stages. Total time spent by the analyst and reviewer Does the team spend fewer hours on preparation, review and corrections? Number of errors affecting the results or conclusions Does the analysis meet the same quality requirements as the existing approach? Accuracy and completeness of source references Can the origins of key figures and information be verified? Time needed to update the analysis How efficiently can new data be incorporated and the calculations and conclusions that depend on it be updated? Cost per approved analysis What is the cost of the finished material, including team time, the tool, data and the share of implementation and maintenance costs allocated to that analysis? The test should cover several tasks of varying difficulty. Define the assessment criteria before it begins. Someone performing the same analysis for a second time already knows the data and some of the answers, which may shorten the time needed. It is therefore worth using comparable tasks and varying the order in which participants work with AI and with the existing method. If AI saves time, check how the team used it. They may have prepared more analyses, responded to clients sooner or reduced overtime. The implementation assessment should show separately how the time saved was used and whether company spending decreased, and by how much. When is it worth starting a pilot? Consider a pilot if the team regularly gathers data from multiple sources and updates similar analyses. Choose a task that takes analysts a significant amount of time, such as comparing data from company reports. Assign a person to lead the pilot and experts to review the results. If the team only occasionally analyses a few annual reports, check whether tools already approved for use within the company are sufficient. Where data retrieval and calculations are already automated, identify a specific task that the new tool could improve. OpenAI makes the product available to financial institutions that meet its access requirements and directs interested companies to its sales team. Pricing, detailed terms and availability for a particular institution in Poland must be confirmed with the provider. Availability information. The implementation decision should be based on the pilot results: the quality of the analyses, the time needed to prepare and review them, and the total cost of the work. The test will also show whether the tool provides access to the data the team needs. Want to explore where AI could improve analysts’ work in your organisation? Talk to the TTMS team about choosing a task for a pilot, connecting the necessary data sources and assessing the results. How does ChatGPT for Financial Services differ from analysing reports in ChatGPT? ChatGPT for Financial Services provides access to selected professional financial data directly within the tool. It also supports references to specific tables and document passages, as well as the preparation of materials using company templates. When assessing its suitability for a team, check whether the available sources cover the companies, periods and metrics the team needs. Does ChatGPT for Financial Services require separate financial data subscriptions? Selected datasets are included in the product. These cover some of the information supplied by the providers named by OpenAI. The company is also developing integrations intended to let institutions use data covered by their existing subscriptions. Before purchasing, confirm which data is included in the offering and which requires additional access rights. Can ChatGPT for Financial Services be used to analyse companies listed on the Warsaw Stock Exchange? This depends on the availability of data for individual companies. Check whether the tool provides their financial statements, relevant metrics and historical data. The launch announcement alone does not confirm full coverage of the Warsaw Stock Exchange. The best way to assess the product’s suitability is to test it on several companies the team regularly analyses. What should you do if data from ChatGPT differs from the figures in a company’s report? Start by comparing the sources, reporting periods, units and definitions of the metrics. A discrepancy may arise, for example, from using standalone rather than consolidated financial data, or from including EBITDA adjustments. Also check whether the company has published an updated report. The analyst should explain the discrepancy and document which value they used and why.
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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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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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ChatGPT 5.6 in Practice: Initial Compliments and Disappointments
OpenAI rolled out GPT-5.6 in stages. It first appeared in limited test access for selected partners. Access to ChatGPT 5.6 reached Europe, including Poland, gradually, so only recently have teams been able to test the model in everyday work. Expectations are high. In the second half of 2026, businesses expect language models to handle multi-step tasks and work with extensive context. Ease of use matters too. GPT’s interface has undergone a major redesign. Has it improved the user experience and the quality of responses? This article explores that question, as well as: which business processes ChatGPT 5.6 can support by improving productivity and the quality of working materials, how to plan an AI pilot in your organisation, measure results and maintain quality control, which limitations of ChatGPT 5.6 to consider before a wider rollout, how to establish a shared standard for prompts and output validation across the team, what early users think about working with ChatGPT 5.6. If you are looking for a full overview of the changes, pricing, models and capabilities of GPT-5.6, see our article GPT-5.6 from OpenAI: what has changed, pricing, capabilities and business applications. ChatGPT 5.6: our first impressions and early industry feedback Early expert reviews focus primarily on context handling. Reviewers note that when working with substantial material that goes through multiple rounds of edits, ChatGPT 5.6 is better at keeping the task on track. Most of us have experienced earlier OpenAI models losing their “bearing”. On top of that, the model itself encouraged endless revisions, which could pull the material away from the original intent of the prompt. GPT 5.5 had an irritating habit of suggesting more and more variations. Almost every response ended with a clickbait-style suggestion along the lines of: “If you want, I can help you add two elements that will create a wow effect and give the text around 50% more SEO power.” As a result, instead of closing the topic, we were drawn into the model’s endless doubts: could the material really not be improved further? GPT 5.6 is no less capable than the older model, but it finally respects what matters most: the intent behind the prompt and our time. Kajetan Terlecki SEO Specialist, TTMS Another recurring observation concerns the quality of the first draft—the material GPT produces after the first prompt. Reviewers emphasise that the model’s draft is usually well structured and much closer to a final version than it was with GPT 5.5. It is not a perfect ten yet, but a solid eight. In other words, a final version may be within reach after a relatively short time. With earlier GPT models, the “brainstorming” phase took much longer. The third—and most immediately noticeable—area is the way we use the tool, which we can simply call the “interface”. It is admittedly quite complex. Beyond writing a prompt, users must make a series of decisions: which workspace should I choose: Chat or Work? which model best fits my request: Luna, Terra or the most advanced Sol? Or is the older GPT 5.5 enough? does the task require Deep Research? how much effort should the model put into the task: low, medium, high, very high, max or ultra? should I use Turbo mode and generate a response 50% faster at the cost of higher token use? If we add the almost endless range of available plugins, writing the prompt turns out to be only half the work required to get a useful result. I would welcome an automatic mechanism that reads the prompt and selects the right settings on its own. One that uses a sufficiently capable GPT model without wasting tokens when they are not needed. How do you navigate all this? We have outlined a suggested configuration here, including which modes to use for different types of tasks. Where does GPT 5.6 outperform the previous version? 1. GPT 5.6 is better at preserving document layout and formatting The previous version of GPT had something of a goldfish memory. You could also compare it to a short blanket: pull it over one part, and another is left exposed. When we asked the model to update data in a document it had generated, it produced a factually correct response, but one that no longer followed the original format. It might use a different heading hierarchy, rearrange the information or omit elements that are essential for the company. GPT 5.6 is much better at preserving the structure of reference material. OpenAI illustrated the difference in materials introducing GPT-5.6. The company placed three slides side by side: the reference file, the GPT-5.5 output and the GPT-5.6 output. The task was to update figures in a presentation while retaining the original template. In the comparison, GPT-5.5 omitted some template elements, while GPT-5.6 preserved the slide structure more faithfully: layout, typography, spacing, colours and recurring template elements. OpenAI states that GPT-5.6 can also interpret rules saved in the slide template, including the Slide Master. In practice, this matters when a presentation needs to retain not only its colours and fonts, but also defined layouts, spacing and mandatory components. 2. GPT-5.6 moves beyond the chat window GPT-5.6 shows its greatest potential when it works not only with a single instruction, but also with files and tools made available by the user. It can then move quickly through a task: from gathering the materials to preparing a first draft. The new GPT model can identify related files in a project folder, flag places that need updating and prepare working versions of documents. There is a catch: the process still needs human oversight. Someone must check whether GPT found all the relevant files, understood the context correctly and left unchanged the elements that were meant to remain unchanged. Still, instead of manually digging through documents, the team starts with a list prepared by the model. 3. From an idea to a version you can show the team Experts testing GPT 5.6 point out that the first version of a simple application, dashboard or website is now more often suitable for showing to a team and collecting specific feedback. It is somewhat like an MVP: good enough to test an idea, present it to the team and gather initial comments. A product owner can see the whole process, a designer can assess the layout and usability, and a developer can spot technical constraints sooner. This does not mean that GPT-5.6 creates a finished product. The initial prototype still needs to be assessed for security, quality and architecture. The difference is concrete, however: the team can evaluate an actual solution earlier, rather than debating assumptions alone. 4. GPT 5.6: “I don’t know” — is this the end of answers given for the sake of answering? We all know the old classified ad: “Encyclopaedia Britannica, 40 volumes for sale. I got married a week ago, so I no longer need it. My wife knows everything better.” The know-it-all syndrome is a nuisance not only in old marriage jokes, but also for people who work with language models every day. GPT often lacks the information needed to give a reliable answer. GPT-5.5, like earlier versions, would rather provide an incorrect—yet convincing-sounding—answer than admit it did not know. What about the new version? The change is visible at first glance, even though it is hard to capture in a benchmark and easy to appreciate in day-to-day work. Our first days of working with the two most advanced models, Terra and Sol, suggest that GPT 5.6 is more likely to say “I don’t know”, “I don’t have enough data” or “I could not find anything else on this topic”. People still need to add or verify information manually, but this reduces the risk of an embarrassing error in material prepared for a client, the board or a project team. Before you give GPT-5.6 an important task: what to watch out for in early testing 1. A working prototype is not yet a finished product GPT-5.6 can prepare a website, dashboard or simple application that can be launched and shown to the team. This is a major step forward, particularly when testing an idea. The tests also reveal the other side: elements can become misaligned, interactions do not always work as intended, and visual details still require refinement. The first version can be an excellent starting point, but it should not automatically be sent to clients or other external audiences. Before treating it as finished, we need testing, a security assessment and, in some cases, a developer’s review. 2. The new Work environment can still be frustrating Model quality is one thing. The way we use it in practice is another. One reviewer pointed out that, in Work, it was difficult to access generated files and open a preview of the finished result. Others criticised the number of settings—discussed earlier in this article—as well as the unclear distinction between Chat, Work and Codex. GPT-5.6 may complete a task correctly, while the working environment still makes it difficult to retrieve or review the result. It is worth testing the entire process, not only the quality of the response in the chat window. 3. GPT needs clear boundaries One reviewer tested how GPT-5.6 would handle a complex mathematical problem. The model produced correct parts of the solution, but surrounded them with definitions, digressions and comments that added little value. Only after the instruction was made more specific did it produce a useful result. The same applies in a business context. We should not leave the model too much room for interpretation. It is better to state the expected result directly: “Prepare a one-page summary. Include the decision, three arguments, risks, missing information and next steps.” GPT then has fewer opportunities to pad the topic with peripheral content. 4. GPT can still be wrong The fact that GPT-5.6 appears more likely to signal that it lacks data or a basis for drawing a conclusion does not mean it is free from hallucinations. Luna, Terra and Sol—with Sol seemingly the least prone to this—can still provide an incorrect date, number, source or conclusion without batting an eyelid. The rule to “check after AI” still applies and will likely remain relevant for many future GPT releases. 5. Start with one problem, not a large system Once GPT-5.6 has access to files, a browser and company tools, it is easy to imagine a system that instantly organises the inbox, analyses team communication, updates the CRM and writes responses to clients. This vision can quickly turn into a project larger than the problem it was meant to solve. One expert working with an extensive Codex environment recommends starting with a single, repeatable task. It might be preparing a meeting summary, gathering open project issues or updating an offer after data changes. Only once the team sees measurable results and understands the tool’s limitations is it worth adding further automations. How should you run your first ChatGPT 5.6 test in the company? A pilot should answer one straightforward question: does GPT-5.6 genuinely improve a selected stage of work, and does the benefit justify the time, cost and additional quality control? The first test should not begin with building an extensive automation system. It is better to choose one repeatable task that currently takes up the team’s time and has a clearly defined outcome. This might be a meeting summary, a brief or a status report. What matters is that the team knows which materials it provides to the model, what result it expects and who reviews the final document. Before starting the pilot, answer five questions: Choose one process: for example, preparing meeting summaries, sales briefs or materials for project decisions. Set a baseline: measure the time needed to prepare the material, the number of revisions, the number of people involved and the most common errors. Prepare a shared prompt: use the same input materials and clearly describe the outcome the team expects. Assign expert review: nominate a person who will verify the facts, assess quality and approve the result before it is used further. Assess the outcome: compare time, the number of iterations, completeness of the material and the usefulness of the result for the next stage of the process. Pilot element Question for the team Process Which stage of work do we want to shorten or organise? Outcome What should be produced: a brief, decision list, analysis, recommendation or communication draft? Data Which materials are needed, and can they be used in the selected AI environment? Quality control Who confirms the facts, completeness and alignment of the material with the process? Metric How will we compare working time, the number of revisions and the usefulness of the result? After a few attempts, it becomes easier to assess whether the model is genuinely helping. Compare the time needed to prepare the material, the number of revisions and the effort required to verify the result. Only then decide whether to extend the pilot to further tasks. Three processes worth starting with 1. Summaries after client meetings The model can organise notes, gather decisions, identify open questions and prepare a list of next steps. The team confirms the arrangements and assigns task owners. This helps them move from discussion to action more quickly. 2. A brief for a sales conversation Based on selected sales materials, previous arrangements and public information about the company, GPT-5.6 can prepare a brief, discovery questions and a list of topics that require clarification. The salesperson remains responsible for the client relationship and decisions regarding the offer. 3. A status report for the project team The model can organise information about progress, blockers, risks and planned actions. The project owner confirms that the information is up to date before the report is shared further. This reduces the time the team spends manually consolidating data from several sources. How do you embed AI in a business process? After the pilot, it becomes clear whether ChatGPT 5.6 genuinely shortens the preparation of materials, reduces the number of revisions and helps the team move more quickly to the next stage of work. It also reveals where the model needs a better brief, access to data or expert oversight. Proven use cases can then be extended to other processes. At this stage, it is worth addressing data security, integration with existing tools, output quality and a clear division of responsibilities. These factors determine whether AI becomes lasting support for the organisation. At TTMS, we help organisations identify processes where automation and AI create business value. We then design solutions tailored to their data, regulatory requirements and ways of working. We combine engineering experience with a responsible approach to AI governance, confirmed by ISO/IEC 42001 certification. Let’s discuss the processes AI could support in your organisation. FAQ How do you choose a process for your first ChatGPT 5.6 test? The best candidate is a repeatable process that requires gathering several pieces of information and producing a predictable result. Examples include meeting summaries, sales briefs, status reports and document analysis. The team should know the current turnaround time and typical issues, as these provide the baseline for assessing the test. Start with one process and expand the use of AI only after evaluating the outcome. How do you measure the business value of ChatGPT 5.6? During a pilot, measure the time needed to prepare the first version of the material, the number of revisions before approval, the completeness of the output and the expert time required for verification. It is also useful to track metrics related to the next stage of the process – for example, faster meeting preparation, a shorter time to close agreed actions or fewer missing details in a report. This data helps assess team productivity based on actual results and supports decisions about integrating AI into further processes. What data should you prepare for working with ChatGPT 5.6? The model produces better results when the team provides current, well-organised source materials. Before starting, identify which documents take priority, which data must remain unchanged and how unverified information should be marked. The organisation should also define which data can be shared in the chosen AI environment. For personal, financial and confidential data, access rules, retention and compliance are essential. How do you maintain human oversight of the model’s work? Human oversight should be part of the process from the start. The process owner defines the task scope, an expert verifies facts and alignment with requirements, and an authorised person approves external actions. This division of responsibilities is particularly important for client communication, publications, data changes in systems and materials with legal or financial implications. It allows the team to use automation while retaining responsibility for the outcome. Where can I find information about GPT-5.6 pricing, models and capabilities? We have covered the changes in GPT-5.6, pricing, the Sol, Terra and Luna models, and business applications in a separate article: GPT-5.6 from OpenAI: what has changed, pricing, capabilities and business applications. This article focuses on the practical use of ChatGPT 5.6 in team workflows, early user experiences and how to run an AI pilot in an organisation.
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Microsoft Copilot vs ChatGPT – Which AI Assistant Is Better for Business?
Key Takeaways Microsoft Copilot works best in Microsoft 365-centric organizations. It is designed for companies where daily work happens mainly in Outlook, Teams, Word, Excel, PowerPoint, and SharePoint. ChatGPT Enterprise is better suited to broader, cross-platform workflows. It can support research, analysis, writing, coding, deep research, and AI-powered work across multiple tools and data sources. The main difference between ChatGPT and Copilot is their operating model. Copilot is more deeply grounded in Microsoft Graph and Microsoft 365 permissions, while ChatGPT relies more on enabled connectors, apps, workspace controls, and user authentication. Copilot is stronger as an in-flow productivity assistant. ChatGPT is stronger as a flexible AI workspace for cross-functional reasoning, experimentation, and custom workflows. For many companies, the best answer is not Copilot or ChatGPT, but both. A hybrid approach can combine Microsoft-native productivity with broader AI capabilities for research, analysis, automation, and custom enterprise use cases. When companies compare Copilot vs ChatGPT, they are not just comparing two chat interfaces. They are comparing two different enterprise AI operating models. Microsoft 365 Copilot is designed to work inside Microsoft 365 apps and can ground answers in organizational context through Microsoft Graph, while ChatGPT Enterprise is a broader AI workspace built around advanced models, data analysis, deep research, apps, and agents that connect to company systems. For many firms, that distinction is more important than raw prompt quality. Microsoft positions Copilot around secure work inside Word, Excel, Outlook, Teams, search, and agents, while OpenAI positions ChatGPT around cross-functional AI work such as writing, analysis, coding, research, deep research, and connected workflows through apps and agents. That suggests a simple rule of thumb: if the center of gravity is Microsoft 365, Copilot usually feels more native; if the goal is a flexible AI workspace across many tools and tasks, ChatGPT usually feels broader. That conclusion is an inference from how both vendors describe their products and enterprise architectures. 1. What Is the Difference Between ChatGPT and Copilot? The first difference between ChatGPT and Copilot is where each product lives. Microsoft 365 Copilot is embedded in the applications people already use for daily work, including Word, Excel, PowerPoint, Outlook, and Teams. Microsoft’s documentation says it can generate responses grounded in organizational data such as documents, emails, calendar items, chats, meetings, and contacts through Microsoft Graph. ChatGPT Enterprise, by contrast, is a managed ChatGPT workspace for organizations with centralized administration, security controls, and access to advanced ChatGPT capabilities. The second difference is the data-access and knowledge model. Microsoft distinguishes between web-based Copilot Chat and the licensed Microsoft 365 Copilot experience: web chat can be included at no extra cost for eligible Microsoft 365 organizations, while work-based chat and full Microsoft 365 Copilot experiences rely on a Copilot license and deeper grounding in Microsoft Graph data. Microsoft also says Copilot uses an advanced lexical and semantic index over organizational data and respects the same user permission boundaries already enforced in Microsoft 365. ChatGPT handles enterprise knowledge access differently. OpenAI’s company knowledge and apps rely on enabled integrations, existing permissions, and user authentication. OpenAI says ChatGPT can only access what each user is already allowed to view, while Enterprise admins can manage apps, require SSO and SCIM, and control access using RBAC. In practice, one of the biggest differences between ChatGPT and Copilot is that Copilot is more natively grounded in the Microsoft work graph, while ChatGPT is more connector- and app-driven. The third difference is workflow style. Copilot is strongest when the task starts inside Microsoft 365: summarizing a meeting, drafting an email, refining a PowerPoint, or generating formulas and insights in Excel. ChatGPT is broader by design: OpenAI describes it as a workspace for writing, research, coding, data analysis, deep research, and agentic tasks, and OpenAI’s own enterprise adoption data shows early usage clustering around writing, research, programming, and analysis across departments. In short, copilot ai vs chatgpt is often a choice between an in-flow productivity layer and a more general AI operating environment. The fourth difference is extensibility. Microsoft offers Copilot Studio and Agent Builder for organizations that want custom agents grounded in business data and published across employee or customer channels. OpenAI offers apps, custom MCP-powered apps, and workspace agents that can connect to tools, run on schedules, and operate inside ChatGPT or Slack. That means the difference between ChatGPT and Copilot is not only about the base assistant, but also about the ecosystem you want to build around it. 2. Microsoft Copilot for Business – Use Cases In practice, microsoft copilot for business starts with two entry points. Microsoft says eligible organizations can use web-based Copilot Chat at no extra cost, while paid Microsoft 365 Copilot unlocks work-based chat, app experiences, and deeper organizational grounding. Microsoft also sells Microsoft 365 Copilot Business for organizations of up to 300 users, which gives smaller and mid-sized companies a packaged way to adopt the same in-app Copilot experience. The most obvious use case is productivity inside familiar apps. In Word, Copilot helps draft and edit documents; in Excel, it supports formula suggestions, trend analysis, and visualizations; in Outlook, it summarizes email threads and drafts messages; and in Teams, it summarizes meetings and helps create action items. This is where Microsoft has its clearest advantage: employees do not need to leave the workflow surface they already know. Sales and commercial teams are another strong fit. Microsoft’s scenario library highlights use cases such as accelerating customer research and sales preparation, creating customized pitches, and responding to RFPs. Some of those workflows can be handled directly in Microsoft 365 Copilot, while others can be extended through Copilot Studio or Copilot for Sales, where agents can connect to line-of-business systems through connectors and APIs. Finance, operations, and service workflows are also central to the Microsoft story. Microsoft’s official scenario pages describe Copilot use cases for budgeting, forecasting, financial analysis, planning, risk management, customer service problem resolution, issue diagnosis, and frontline assistance in financial services. That makes enterprise copilot especially attractive in environments where internal policies, structured records, and regulated processes matter as much as content generation. Finally, Microsoft positions Copilot as more than a personal assistant. Copilot Studio lets organizations build and manage custom agents connected to business data, while Microsoft 365 Copilot includes access to built-in and custom agents and Microsoft provides Copilot analytics and usage reporting for adoption tracking. For companies that want AI to move from experimentation into governed process automation, that combination of app-native assistance, agent building, and admin reporting is a major selling point. 3. Copilot Enterprise vs ChatGPT Enterprise: Which One Fits Larger Organizations? To keep terminology precise, it is worth clarifying that copilot enterprise is usually a shorthand for Microsoft 365 Copilot and Copilot Chat deployed in a commercial or enterprise Microsoft tenant. Microsoft’s enterprise materials present those workplace offerings as the relevant enterprise Copilot layer, rather than a separate standalone product with a different name. That framing matters because companies often compare “Copilot Enterprise” with ChatGPT Enterprise even though Microsoft’s official product naming centers on Microsoft 365 Copilot. On privacy and compliance, both vendors make strong enterprise commitments, but the language is different. Microsoft says enterprise use of Microsoft 365 Copilot and Copilot Chat is covered by its Data Protection Addendum and Product Terms, with Microsoft acting as a data processor; prompts and responses are protected by enterprise data protection, and Microsoft says that prompts, responses, and Microsoft Graph data are not used to train its foundation models. OpenAI says organizations own and control their business data, OpenAI does not train models on business data by default, and ChatGPT Enterprise adds encryption at rest and in transit, custom data-retention policies, and support for data residency in ten regions. On governance, Microsoft and OpenAI emphasize different strengths. Microsoft’s big advantage is inheritance from the Microsoft 365 security and permissions model: Copilot only surfaces content the current user is already authorized to access, and its grounding is tied to Microsoft Graph and semantic indexing. OpenAI’s enterprise advantage is administrative breadth inside its own workspace: domain verification, SSO, SCIM, role-based access controls, user analytics, and a Global Admin Console that can span multiple ChatGPT workspaces and API organizations under one tenant. On integrations and knowledge access, the trade-off is depth versus breadth. Microsoft’s workplace strength is native depth in Outlook, Teams, Word, Excel, PowerPoint, SharePoint, and Microsoft Search, plus agent creation through Copilot Studio and Agent Builder. OpenAI’s strength is cross-platform connectivity: ChatGPT supports apps for tools such as SharePoint, Slack, Airtable, Google Drive, GitHub, and more; OpenAI also supports company knowledge, deep research with internal connectors, custom MCP-powered apps, and workspace agents for repeatable workflows. That leads to the most useful business interpretation of copilot enterprise vs chatgpt enterprise. If your organization already runs most collaboration, files, meetings, and internal knowledge discovery in Microsoft 365, Copilot will usually feel lower-friction and more native. If your teams work across Microsoft, Google, Slack, GitHub, CRM, analytics tools, and external research at the same time, ChatGPT Enterprise will often feel more flexible as a central AI workspace. That is an inference, but it follows directly from the integration patterns and admin models described in the official documentation. 4. Is Copilot Better Than ChatGPT for Companies? The honest answer to is copilot better than chatgpt is no, not universally. The better fit depends on where work happens, how sensitive the data is, which systems employees use all day, and whether the company wants AI embedded in existing software or centralized in a new AI workspace. In other words, chatgpt vs microsoft copilot is not a single winner-takes-all decision for every enterprise. Copilot is often better for Microsoft-first organizations. If employees live in Outlook, Teams, Word, Excel, PowerPoint, and SharePoint, Microsoft 365 Copilot offers a highly natural adoption path because it works inside those products, uses Microsoft Graph context, and respects the existing permission model. It is particularly compelling for meeting-heavy organizations, document-centric operations, and teams that want AI embedded directly in everyday processes rather than accessed through a separate destination. ChatGPT is often better for cross-functional reasoning and mixed-tool environments. OpenAI’s own enterprise usage data shows that early adoption spans writing, research, programming, and analysis, while the product itself combines advanced models, data analysis, deep research, apps, and agent features. For strategy teams, product teams, analysts, marketers, researchers, and software groups that constantly move between internal sources, external information, and multiple software stacks, ChatGPT can offer a broader working environment than Copilot alone. In many companies, the best answer is hybrid rather than binary. A practical setup is to use Copilot for Microsoft-native productivity such as email, meetings, documents, spreadsheets, and internal knowledge retrieval, while using ChatGPT Enterprise or OpenAI-based custom solutions for deep research, coding, experimentation, agentic workflows, and broader cross-system reasoning. For firms evaluating microsoft copilot vs chatgpt, that layered approach is often the most realistic way to capture the strengths of both platforms without forcing one tool to do everything. That recommendation is an inference grounded in the official feature sets of both ecosystems. 5. How Can Companies Turn AI Comparison Into Real Business Value? If your company is deciding between Copilot, ChatGPT, or a hybrid setup, the real challenge is rarely the tool alone. The real challenge is identifying the right business workflows, connecting AI to the right systems, and turning experimentation into measurable operational value. That is exactly the space where TTMS AI Solutions for Business positions its offer: TTMS describes its services as AI solutions aimed at improving operational efficiency and decision-making, ranging from intelligent chatbots to advanced analytics, and its published case studies include enterprise implementations such as AI-supported tender analysis integrated with Salesforce and Azure AI-based sales automation. Contact us! Can a company use both Microsoft Copilot and ChatGPT Enterprise at the same time? Yes, and in many organizations this may be the most practical approach. Copilot can support employees directly inside Microsoft 365, while ChatGPT Enterprise can serve broader tasks such as research, analysis, coding, content work, or cross-tool workflows. The key is to define clear usage policies, so teams know which tool should be used for which type of task. Which tool is easier to adopt across non-technical teams? Microsoft Copilot may be easier for teams that already work mainly in Outlook, Teams, Word, Excel, and PowerPoint, because it appears inside familiar applications. ChatGPT Enterprise may require more onboarding, but it can also be more flexible for teams that need a general AI workspace. Adoption depends less on the tool itself and more on training, governance, and real use-case mapping. Does ChatGPT Enterprise replace Microsoft Copilot? Not necessarily. ChatGPT Enterprise and Microsoft Copilot solve overlapping but different business problems. Copilot is closer to a productivity layer inside Microsoft 365, while ChatGPT Enterprise is closer to a flexible AI workbench. In many companies, one will not fully replace the other. What should companies check before choosing an enterprise AI assistant? They should review where employees actually work, what data the assistant needs to access, which systems must be integrated, what compliance requirements apply, and how success will be measured. A good choice should be based on business processes, not only on model quality or brand recognition. Which AI assistant is better for custom business workflows? It depends on the workflow. If the process is strongly connected to Microsoft 365 data and applications, Copilot Studio may be a natural fit. If the workflow spans many tools, external research, code, documents, and custom agents, ChatGPT Enterprise or a custom OpenAI-based solution may be more suitable.
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