Artificial Intelligence (AI) Consulting Services Resources

A security team I'm supporting asked me which AI platforms meet enterprise security and data privacy requirements like SOC 2 and GDPR, and the honest answer is that "AI platform" security posture varies a lot more than people expect once you get past the marketing page. Since the general Artificial Intelligence category on G2 is a broad rollup, I looked at the Large Language Models category on G2, which is the layer where these compliance questions actually get asked.

1. ChatGPT. Offers enterprise-tier data handling and admin controls separate from the consumer product, which is the version worth evaluating for compliance. Known for enterprise-tier controls.
2. Claude. Enterprise plans include data retention and admin controls aimed at regulated environments. Known for enterprise data controls.
3. Gemini. Inherits Google Workspace's existing compliance certifications, which can simplify the review if you're already a Workspace customer. Best for teams already under Google's compliance umbrella.

The thing worth checking directly with legal rather than trusting a vendor's compliance page: whether your specific data residency and retention requirements are met by the plan tier you'd actually be buying, not the enterprise tier used in the marketing materials.

For anyone who's been through a security review on an AI platform, what turned out to be the sticking point, data residency, retention, or something nobody expected to come up?

I've been helping a mid-sized team think through what's the best AI software for small and mid-sized teams running their first AI rollout, and the trap they almost fell into was picking based on benchmark scores instead of what their people would actually use day one. For a first rollout, the honest priority is a tool people open without training, not the model with the best leaderboard numbers. Since the general Artificial Intelligence category on G2 rolls up a lot of narrower categories, I pulled the Large Language Models category on G2 instead, since that's the layer teams are actually adopting first.

1. ChatGPT. Widest general-purpose adoption and the most familiar interface, which matters when half the team has never used an AI tool at work before. Best for teams wanting broad, low-friction adoption.
2. Gemini. Built into Google Workspace, so teams already on Gmail and Docs get it inside tools they already open every day. Best for Google Workspace shops.
3. Claude. Positioned around longer documents and more careful writing tasks, which suits teams whose first use case is drafting or review work. Best for document-heavy first use cases.

Worth flagging honestly: this category is thin next to something like CRM, so treat the list as a starting point rather than an exhaustive comparison, and cross-check against your existing Microsoft or Google contract before assuming you need a new one.

For teams that have gone through a first AI rollout, what actually drove adoption in week one, and what made people quietly go back to doing it the old way?

I've been helping a mid-sized team think through what's the best AI software for small and mid-sized teams running their first AI rollout, and the trap they almost fell into was picking based on benchmark scores instead of what their people would actually use day one. For a first rollout, the honest priority is a tool people open without training, not the model with the best leaderboard numbers. Since the general Artificial Intelligence category on G2 rolls up a lot of narrower categories, I pulled the Large Language Models category on G2 instead, since that's the layer teams are actually adopting first.

1. ChatGPT. Widest general-purpose adoption and the most familiar interface, which matters when half the team has never used an AI tool at work before. Best for teams wanting broad, low-friction adoption.
2. Gemini. Built into Google Workspace, so teams already on Gmail and Docs get it inside tools they already open every day. Best for Google Workspace shops.
3. Claude. Positioned around longer documents and more careful writing tasks, which suits teams whose first use case is drafting or review work. Best for document-heavy first use cases.

Worth flagging honestly: this category is thin next to something like CRM, so treat the list as a starting point rather than an exhaustive comparison, and cross-check against your existing Microsoft or Google contract before assuming you need a new one.

For teams that have gone through a first AI rollout, what actually drove adoption in week one, and what made people quietly go back to doing it the old way?