ChatGPT Team - Enterprise is already a powerful language model tool, but I think there could be a few areas of improvement. One key area is greater transparency and control over model reasoning. Although the system provides high-quality output, in academic and machine learning contexts, it would be beneficial to have more explainability features, such as clear traceability of how conclusions are derived or optional structured reasoning views. This would strengthen trust, especially when the tool is used for research synthesis or decision support. Another area for improvement is deeper integration with domain-specific tools and workflows. While the current integrations are useful, tighter coupling with platforms such as ML pipelines would make it more seamless to integrate ChatGPT Team - Enterprise directly into end-user research and production environments. Citation accuracy and source attribution is also an area that could be enhanced in literature review and academic writing. Having more reliable, automatically verified references or direct links to indexed research databases would reduce the need for manual validation and make the tool more robust for scholarly use.
In terms of improvements needed for ChatGPT Team - Enterprise, accuracy still requires enhancement in complex or high-domain specific scenarios, particularly architecture and security topics. Responses sometimes need validation, especially for production-level requirements, architecture, or security-related inquiries. More fine-grained controls over knowledge boundaries and model behaviors per team would also be beneficial. ChatGPT Team - Enterprise has demonstrated strong productivity gains, but there are areas where further enhancements could significantly boost enterprise adoption and trust. Improvements should focus on complex domain-specific and production-level use cases, as outputs still require validation for architecture, security, and compliance-sensitive topics. Additionally, more granular controls over shared team workspaces, model behaviors, and knowledge boundaries would help teams scale their usage with stronger governance and security while maintaining brand consistency.
ChatGPT Team - Enterprise is already a very strong AI tool, but some improvements could enhance it further. Currently, context depends on what is provided, the prompts, and what is in memory. An improvement could be the automatic suggestion of relevant files or context based on queries. Cross-document semantic linking could be beneficial. Better handling of evolving knowledge would be useful, such as showing what changes have been made since last month. Additionally, teams want finer permissions such as read-only memory groups and scoped templates by role, departments, and temporary access tokens for contractors. I feel that more granular access controls are needed. Beyond APIs, teams now want no-code automations, including the ability to trigger summary generation after meetings, classification of support tickets, and scheduling of reports. Another improvement that comes to mind is the implementation of an in-app learning and training program. Built-in tutorials could help the team discover more advanced features and learn how to provide better prompts. The platform should provide best practices that show users what they need to do to get the best results. An in-app learning or training program would be valuable.
ChatGPT Team - Enterprise is currently working well, and I do not see any need for improvements because it handles every project I run without complexity or delay. Everything on ChatGPT Team - Enterprise is functioning at its best.
Head of Marketing at a tech company with 51-200 employees
Real User
Dec 4, 2025
One area where ChatGPT Team - Enterprise can be improved is hallucinations. They have gotten slightly better, but sometimes it provides information that is either not relevant or made up. There is a little bit of spot check work required. However, it has been getting better, and that would be the main thing. There have been a couple of times I have used it and the results I have gotten are either completely made up or not true.
Senior Analyst at a healthcare company with 5,001-10,000 employees
Real User
Top 5
Oct 10, 2025
In our case and scenario, the implementation is good with enterprise data, but if we could set up a small language model for ChatGPT Team - Enterprise to work with in a very bounded and independent way, that would be great. There are some limitations where we can leverage ChatGPT Team - Enterprise for some of our business workflows. There are issues of cost and viability, with various concerns around these aspects, but that would be a great enhancement for us.
There are areas where ChatGPT can still improve. If you ask it the same question twice, it gives you a slightly different answer, which even a human being does unless it memorizes something. As information changes, ChatGPT is obviously going to do it differently, slightly different each time. If you ask it to, the more simple your question is, the more chance you're going to get multiple answers. For instance, if you tell it to draw a green box with a white circle, it might make it bigger or smaller or one shade of green versus another. The more specific your query is, your question, the better the result you're going to get. ChatGPT is kind of already getting better, so just continuous improvements are needed.
Senior Investment Analyst at a financial services firm with 1-10 employees
Real User
Top 10
Jun 24, 2025
Some areas that could be improved with ChatGPT include the accuracy aspect. Sometimes when we are trying to backtrack sources or where data came from, it can be difficult to navigate.
Some areas that could be improved with ChatGPT include the information they provide, as sometimes they hallucinate something that's not real. Sometimes they comment positively without being asked, and I have questioned ChatGPT that I don't want the prompt to agree with me but rather to challenge me. If I provide a slide and ask to compare it to McKinsey or other big companies and scale it from zero to 10, they might say it's an eight and suggest improvements to reach nine or ten. However, these evaluations aren't always realistic because if you're not a specialist on the subject, you might create something that's not accurate. This becomes problematic because we are working with real clients, not experiments. When working on real cases, I have to be the one to validate that the information is accurate. Sometimes they provide information that isn't real. You have to be the gate to validate that this information is correct before proceeding. For simpler tasks adjusting communication style or making text more formal, they perform perfectly. However, for complex cases, I don't use ChatGPT as the source of truth because it's not always accurate. If you delegate it to make decisions in your place, it could create significant problems to solve.
Director Metropolitano de Gobierno Digital at a government with 10,001+ employees
Real User
Top 5
Jun 23, 2025
The information base of ChatGPT has to grow because, in some cases, it does not include information about my city. I am referring specifically to information about my city.
ChatGPT Team - Enterprise offers fast query processing and seamless integration, emphasizing efficient knowledge access and customizable processes. It facilitates swift idea organization and code generation, delivering quick insights to streamline workflows, benefiting users from diverse backgrounds. Designed for enterprises seeking operational efficiency, ChatGPT Team - Enterprise enhances workflows by providing fast query processing and easy integration. With capabilities in chat, talk,...
ChatGPT Team - Enterprise is already a powerful language model tool, but I think there could be a few areas of improvement. One key area is greater transparency and control over model reasoning. Although the system provides high-quality output, in academic and machine learning contexts, it would be beneficial to have more explainability features, such as clear traceability of how conclusions are derived or optional structured reasoning views. This would strengthen trust, especially when the tool is used for research synthesis or decision support. Another area for improvement is deeper integration with domain-specific tools and workflows. While the current integrations are useful, tighter coupling with platforms such as ML pipelines would make it more seamless to integrate ChatGPT Team - Enterprise directly into end-user research and production environments. Citation accuracy and source attribution is also an area that could be enhanced in literature review and academic writing. Having more reliable, automatically verified references or direct links to indexed research databases would reduce the need for manual validation and make the tool more robust for scholarly use.
In terms of improvements needed for ChatGPT Team - Enterprise, accuracy still requires enhancement in complex or high-domain specific scenarios, particularly architecture and security topics. Responses sometimes need validation, especially for production-level requirements, architecture, or security-related inquiries. More fine-grained controls over knowledge boundaries and model behaviors per team would also be beneficial. ChatGPT Team - Enterprise has demonstrated strong productivity gains, but there are areas where further enhancements could significantly boost enterprise adoption and trust. Improvements should focus on complex domain-specific and production-level use cases, as outputs still require validation for architecture, security, and compliance-sensitive topics. Additionally, more granular controls over shared team workspaces, model behaviors, and knowledge boundaries would help teams scale their usage with stronger governance and security while maintaining brand consistency.
ChatGPT Team - Enterprise is already a very strong AI tool, but some improvements could enhance it further. Currently, context depends on what is provided, the prompts, and what is in memory. An improvement could be the automatic suggestion of relevant files or context based on queries. Cross-document semantic linking could be beneficial. Better handling of evolving knowledge would be useful, such as showing what changes have been made since last month. Additionally, teams want finer permissions such as read-only memory groups and scoped templates by role, departments, and temporary access tokens for contractors. I feel that more granular access controls are needed. Beyond APIs, teams now want no-code automations, including the ability to trigger summary generation after meetings, classification of support tickets, and scheduling of reports. Another improvement that comes to mind is the implementation of an in-app learning and training program. Built-in tutorials could help the team discover more advanced features and learn how to provide better prompts. The platform should provide best practices that show users what they need to do to get the best results. An in-app learning or training program would be valuable.
ChatGPT Team - Enterprise is currently working well, and I do not see any need for improvements because it handles every project I run without complexity or delay. Everything on ChatGPT Team - Enterprise is functioning at its best.
One area where ChatGPT Team - Enterprise can be improved is hallucinations. They have gotten slightly better, but sometimes it provides information that is either not relevant or made up. There is a little bit of spot check work required. However, it has been getting better, and that would be the main thing. There have been a couple of times I have used it and the results I have gotten are either completely made up or not true.
In our case and scenario, the implementation is good with enterprise data, but if we could set up a small language model for ChatGPT Team - Enterprise to work with in a very bounded and independent way, that would be great. There are some limitations where we can leverage ChatGPT Team - Enterprise for some of our business workflows. There are issues of cost and viability, with various concerns around these aspects, but that would be a great enhancement for us.
There are areas where ChatGPT can still improve. If you ask it the same question twice, it gives you a slightly different answer, which even a human being does unless it memorizes something. As information changes, ChatGPT is obviously going to do it differently, slightly different each time. If you ask it to, the more simple your question is, the more chance you're going to get multiple answers. For instance, if you tell it to draw a green box with a white circle, it might make it bigger or smaller or one shade of green versus another. The more specific your query is, your question, the better the result you're going to get. ChatGPT is kind of already getting better, so just continuous improvements are needed.
Some areas that could be improved with ChatGPT include the accuracy aspect. Sometimes when we are trying to backtrack sources or where data came from, it can be difficult to navigate.
Some areas that could be improved with ChatGPT include the information they provide, as sometimes they hallucinate something that's not real. Sometimes they comment positively without being asked, and I have questioned ChatGPT that I don't want the prompt to agree with me but rather to challenge me. If I provide a slide and ask to compare it to McKinsey or other big companies and scale it from zero to 10, they might say it's an eight and suggest improvements to reach nine or ten. However, these evaluations aren't always realistic because if you're not a specialist on the subject, you might create something that's not accurate. This becomes problematic because we are working with real clients, not experiments. When working on real cases, I have to be the one to validate that the information is accurate. Sometimes they provide information that isn't real. You have to be the gate to validate that this information is correct before proceeding. For simpler tasks adjusting communication style or making text more formal, they perform perfectly. However, for complex cases, I don't use ChatGPT as the source of truth because it's not always accurate. If you delegate it to make decisions in your place, it could create significant problems to solve.
The information base of ChatGPT has to grow because, in some cases, it does not include information about my city. I am referring specifically to information about my city.
I think it's pretty good. No recommendations.