Every AI tool in your stack has to clear the same bar: enterprise-grade security, privacy, governance, and responsible AI. That bar rose this year because AI has started to take action. AI agents now read across your tools, complete steps on people's behalf, and connect to the systems that hold your most sensitive data. The questions most teams still bring to a vendor evaluation weren't written for that.
So we wrote new ones. Below are the questions to ask any AI vendor before you buy, written for a world where the software does more than make a suggestion when prompted. Ask these questions of every vendor you evaluate, including Superhuman. When you want to see how Superhuman answers these questions, the companion guide has every answer on record.
Core capabilities
Start with what the platform does and for whom. A tool that serves only one team becomes one more point solution to manage. These questions tell you whether the AI earns a place across your organization and whether you can shape it around the way your business runs.
1. What enterprise use cases does your AI support across different teams and roles?
2. Can the AI be tailored to our internal knowledge, guidelines, and brand?
Adoption and user experience
The most capable AI does nothing if it goes unused. Most enterprise tools reach only a fraction of the workforce because they wait for people to come find them. Plenty of employees know they're expected to use AI and still don't, because they don't know what tools to use or how to start. What they need is AI that comes to them, feels intuitive from the first try, and delivers real value on day one, even in nontechnical roles.
3. How does the AI fit into the tools and workflows employees already use?
4. How quickly can a nontechnical employee get real value?
Agents and control
These are the key questions that didn't exist two years ago, when companies were mostly shopping for generative AI tools. Protecting corporate data is core to IT's job, and agents change the terms of it. Agents now read across your tools, take steps on your behalf, and connect to the systems that hold your most sensitive data. That reach is what makes them useful and what makes them a risk, so it changes what you need to ask.
5. How are agents vetted before they reach our teams, and who decides which ones run?
6. What can a connector agent actually access, and how is that boundary enforced?
7. How are agents isolated from your core systems and from each other?
8. When can an agent act on its own, and when does it require approval?
9. Can we build and deploy our own agents for internal workflows?
Deployment and integration
Rolling out AI across an organization takes real effort, and most of that effort falls on your IT team. Before you commit to a new tool, get a clear picture of the work involved to deploy it and how well it fits the apps and systems you already have in place.
10. What does it take to deploy this across our organization?
11. How does the AI connect to the apps, systems, and data we already run?
Data privacy and handling
Once AI touches your data, vague assurances stop being good enough. What matters here is whether a vendor can be specific about how your data is handled and whether those specifics hold up in the contract.
12. What data does the AI collect, process, and store?
13. Is our content ever used to train your models or any third party’s models?
14. What are the retention rules for our data, and can we delete it on our terms?
Security and compliance
Every vendor can claim to be secure. The ones worth trusting can show you the audits, attestations, and reports that prove it, and they share them early rather than late in the procurement process.
15. How is our data encrypted in transit and at rest, and who can access it?
16. What certifications, attestations, and third-party audits does the product hold?
17. Which subprocessors and large language model (LLM) providers handle our data and under what terms?
18. What is your incident response and breach notification process?
Responsible AI
AI that acts on your behalf raises the stakes for how it's built. What matters is whether safety and human oversight are designed in from the start, not added once something has already gone wrong.
19. How transparent are your models, and where do people stay in control?
20. How do you test for bias, harmful output, and misuse?
The right AI partner won't flinch at any of these. Ask them of every vendor on your shortlist and pay close attention to who answers in specifics and who sidesteps real answers with vague replies. We put Superhuman's answers to all 20 questions in writing so you can hold us to the same bar. Download Superhuman's Enterprise AI Evaluation Snapshot to see how we respond to each question.
