I see, that in companies, the discussion about AI often does not begin with „which AI model Is it the best?”, only „as over master it?”. Studies show that users with the approval of IT, or also without IT oversight, benefit from the various tools available AI or dedicated applications z AI to creation e.g. images or presentations.
Often the resolution is not at all to say that if you work on M365 then Copilot will BEST. Practice shows that you need to prepare to use different models AI for various scenarios. Plan please also Training users to educate them, what can, what not is advisable when it comes to copying data to the models used. It is necessary to ralso making aupdatei security policy and the introduction ofe rules and regulations for the use of with AI.
Consider „where do we work and how do we protect data?”. Below practical comparison 4 mostly options under consideration AI dedicated more to applications linking get in touch with your environment office, than considering, what model to put in the organization z short pros and consi:
1) Microsoft Copilot with an indication of working in M365
Best when: organization works in Outlook/Teams/SharePoint/OneDrive and wants AI „built in” to the daily work.
Advantages:
- Works on data that the user already has access to (e.g. Exchange, SharePoint, Teams
- protection of critical resources: prompts and answers covered are these aremy terms of protection data, What M365 (in practice: the same „compliance boundary”) – tHowever, it must be remembered that many The organization turns on the Copilot license without preparing PureView DLP and access rules. It needs first inventory and exclusion of the most critical data
- natural adoption: AI is showing up where humans already work (Word/Excel/PowerPoint/Teams). W my opinion is strong advantage, although the first uses in some Office applications made me a bit have failed.
Disadvantages:
- Quality outcome depends on the order of authority (SharePoint/Teams): if access is „spilled,” Copilot will only make this more apparent.
- Strongly ecosystemy: the biggest advantage is inside the M365, completely misses the point buying This license if you do not have integration with M365. We happened to try to test Copilot for separated tenant, but there was no company data there so the use was weaker than with ChatGPT
2) Google Gemini
Gemini gains the most when integrated with Google Workspace, but it can also work as a separate tool. In model quality rankings is high evaluated, but evaluations change from month to month (worth take a peek at LLM Arena for specific tasks).
Advantages:
- Google declares that data and interactions in the corporate domain are not used to train models outside the domain without permission and are not shared outside the organization.
- Inherit existing security controls with Workspace (DLP/IRM and policies, among others. security)
- The increasingly powerful „enterprise” layer (including agent-based approaches and applications in Gemini for Workspace).
- Very powerful for generating images (NanoBanana).
Disadvantages:
- pWith normal access (without integration z Workspace) don't get company context from M365
- This is a „second approved model” instead of ChatGPT, not a full replacement for Copilot
3) ChatGPT (Business/Enterprise).
The best when: you need a strong „universal” assistant for writing, analysis, researchu, prototyping Regardless of the office suite. Many people privately depend on it just she began. Thus, there are often scenarios that if there is no policy for the use of the AI tools, users That's where they work on their private accounts.
Advantages:
- Clear enterprise declarations: no training on business data „by default”, encryption And compliance/attestations (including SOC 2 Type 2) communicated by OpenAI
- Versatility: great for work outside of office documents (strategies, analysis, code, Synthesizing knowledge).
- Functions of teamwork and administration in Business/Enterprise plans (workspace, control access, etc.).
Disadvantages:
- Corporate context does not „happen on its own”: without a good connection to sources of knowledge and AI governance easy with „nice answers” however unsupported realities companies.
- Risks of shadow AI: When there are no policies and training, people throw in sensitive dane
4) DeepSeek (models/services)
Best when: technical team seeks low-cost, strong model base for experiments or deployments.
Advantages:
- Strong models in the cost relationship – scorei (often considered by R&D teams more for putting open model source and realization of own project)
- Technological flexibility: It's easier to think of the „model as a component,” not just the application office
Disadvantages:
- The biggest question mark: data and regulations: zevelopmental analyses indicate risks Legal and the fact that personal data may be stored on servers in China, and that privacy policy provides for extensive use of data
- Harder to get enterprise–grade governance out–of–the–box compared to Copilot/Gemini/ChatGPT Business.
- The application itself has excessive permissions. So convenient access, as has the application ChatGPT or Gemini does not go hand in hand with what the application for Deepseak can do on your phone. Security indications for the app caused me to uninstall the app from the Phone.
Regarding the selection of, if you choose a solution AI for your organization, consider Copilot as a natural M365 ecosystem integration. Take a peek at LLM Arene If you are interested in specific applications.
The most important recommendation afterper tools – Apply DLP policies, protect information and build a essential processes and procedures.
Make an appointment with an ISCG consultant i learn the process safeof the deployenia AI in the company:https://outlook.office.com/book/Konsultacjewobszarzeaplikacjibiznesowych@ISCG.onmicrosoft.com/s/fKddgNyppECh3KThb8VNMw2?ismsaljsauthenabled