Key Takeaways
- Look past polished demos by asking hard questions about day-one value, data grounding, and the experience of the average user—not just power users
- Trust depends on citations and source verification—ask how users will know AI answers are accurate
- The most predictive question: "What would make us cancel in a year?" reveals vendor honesty and product limitations
Every AI vendor has a great demo. The slides are polished. The use cases sound transformative. The ROI projections are compelling.
Then you buy it, and six months later, you're trying to figure out why nobody uses the thing.
The problem usually isn't the technology. It's that nobody asked the right questions before signing. The demo showed you what the AI could do. Nobody explored whether it would actually work in your environment, for your people, with your constraints.
Here are ten questions worth asking before you commit. Not the questions vendors expect—the ones that actually predict whether you'll get value.
1. What happens on day one?
Not day 90 after full implementation. Day one.
Can employees start using this immediately, or does it require weeks of setup, integration, and training before anyone sees value? Is there a clear first use case that works out of the box, or are you buying potential that may never be realized?
The longer the time to first value, the higher the risk that the initiative loses momentum, champions move on, and the tool becomes shelfware.
What you want to hear: Specific examples of what users can do immediately, without waiting for custom configuration.
Red flag: "Once we complete the implementation phase and integrate with your systems and train your team, you'll be able to..."
2. What does the average employee experience?
Not your most tech-savvy power user. Not the person who volunteered for the AI task force.
The median employee has fifteen minutes to try something new and no particular enthusiasm for technology. What do they see when they open this tool? Do they know what to do? Is there an obvious starting point, or a blank text box and infinite possibilities?
What you want to hear: Pre-built workflows, guided experiences, specific use cases by role. "A new HR coordinator can immediately do X, Y, and Z."
Red flag: "It's very flexible—users can customize it to do almost anything." (Translation: users have to figure it out themselves.)
3. How do users know they can trust the answers?
This is the question that separates AI tools that get used from AI tools that get abandoned.
When the AI gives an answer, how does an employee know it's correct? Can they see where the information came from? Can they click through to verify? What happens when the AI doesn't know something—does it say so, or does it make something up that sounds plausible?
The Citation Mechanism: Pay close attention to how the tool displays sources. A footnote that links to a general document is not enough. You want granular citations—direct links to the specific paragraph or policy section the AI used. This is the difference between a tool that builds trust and one that merely suggests accuracy.
What you want to hear: Answers grounded in your content, with citations. Clear behavior when information isn't available.
Red flag: "Our model is very accurate." (Every vendor says this. It doesn't mean anything.)
4. What systems does this connect to?
AI in isolation is AI that creates extra work. If employees have to copy data from your CRM, paste it into the AI tool, then copy the output back somewhere else, most of them will just skip the AI step entirely.
What integrations exist today—not on the roadmap, today? How deep are they? Can the AI pull context from your existing systems, or just receive pasted text?
What you want to hear: Native integrations with your actual tech stack (not just "we integrate with 500+ tools" but specifically the ones you use). Ability to query across systems.
Red flag: "We have an open API, so your team can build any integration you need." (Translation: the integrations don't exist yet, and building them is your problem.)
5. Who controls what the AI can access?
This matters for security, compliance, and basic trust.
Can you control which content sources the AI draws from? Can you ensure it only answers from approved materials, not the entire internet? Who has permission to add or modify those sources? Is there an audit trail?
What you want to hear: Granular admin controls. Clear content governance. Audit logs. The ability to create different access levels for different user groups.
Red flag: Vague answers about "enterprise-grade security" without specifics. No clear explanation of how the content is scoped.
6. What happens to our data?
This is the question that gets IT and Legal involved, and it should.
Is your data used to train the AI model? Where is it stored? Who can access it? What happens to it if you cancel? Can you get data residency in your required regions?
These aren't just compliance checkboxes. They determine whether you can actually use the tool for sensitive business content.
What you want to hear: "Your data is never used for training. Here's our SOC 2 report. Here's where the data is stored. Here's our data processing agreement."
Red flag: Hedging. "We take privacy very seriously" without specifics. Reluctance to provide documentation.
7. How does pricing actually work?
Not the headline number. The real math.
If you're paying per seat: what happens when you want to expand? How do you handle users who barely use it versus power users? What's your effective cost per active user if adoption is 50%? 30%?
If you're paying for usage: is there a cap? What alerts exist? Can you set budgets by team or department? What happens if you hit your limit mid-month?
What you want to hear: Clear, simple pricing you can model. Controls that prevent surprises. A path to scale that doesn't require a new procurement battle every time.
Red flag: Pricing that requires a spreadsheet to understand. "Contact us for enterprise pricing" without any published numbers.
8. What does adoption actually look like at other companies?
Not the cherry-picked case study. Real numbers.
What percentage of licensed users are active after 6 months? What does "active" mean—logging in once, or using it regularly? What are the most common use cases in practice, not in theory? What do companies struggle with?
Vendors will resist this question. Push anyway.
What you want to hear: Honest numbers, even if they're not perfect. Specific examples of what worked and what didn't. Acknowledgment that adoption requires effort.
Red flag: Only showing you the home run case studies. Unable to provide usage data. "Every customer's situation is different" as a way to avoid the question.
9. What does your team need to maintain this?
AI tools aren't set-and-forget. Someone needs to keep the content updated. Someone needs to manage user access. Someone needs to monitor what's working and what isn't.
What's the ongoing operational lift? Do you need dedicated staff, or can this be managed as part of existing roles? What happens when something breaks?
What you want to hear: Realistic assessment of admin requirements. Clear documentation. Support that's actually responsive. Self-serve capabilities that reduce the need for ongoing vendor involvement.
Red flag: "It's fully automated, you don't need to do anything." (This is never true.) Support tiers that require expensive upgrades to get actual help.
10. What would make us cancel in a year?
This is the question vendors never expect, and the answers are revealing.
Why do customers leave? What do they complain about? What functionality do people expect that you don't have? Where does the product fall short?
Every product has weaknesses. A vendor who can't articulate theirs either doesn't know their product or isn't being straight with you.
What you want to hear: Honest self-assessment. "We're not the best fit for X use case." "Customers sometimes struggle with Y." "We're investing in Z because we know it's a gap."
Red flag: "We don't really have customers who cancel." "Our NPS is very high." Answers that sound like marketing copy instead of honest conversation.
The Question Behind the Questions
All of these really come down to one thing: will this actually work, for our people, in our environment, given our constraints?
Demos show idealized scenarios. Sales teams show the best cases. Your job is to pressure-test whether the reality will match. That means asking uncomfortable questions. Pushing past polished answers. Talking to references who weren't hand-picked. Piloting with skeptics, not just enthusiasts.
The vendors who survive that scrutiny are usually the ones worth buying from. The ones who deflect, hedge, or get defensive are telling you something important about what the relationship will be like after you sign.
The Checklist
- What can users do on day one?
- What does the average employee experience look like?
- How do users verify that answers are accurate?
- What systems does this integrate with today?
- What controls exist for content access and governance?
- What happens to our data?
- How does pricing work at realistic adoption rates?
- What does adoption actually look like at similar companies?
- What ongoing effort does this require from our team?
- What would make us cancel in a year?
Print this out. Bring it to your next vendor meeting. See what happens. For a complete framework on evaluating and deploying AI that your team will actually use, see our comprehensive AI workplace assistant guide.
At JoySuite, we built the product around these questions. Day-one value with pre-built workflows. Answers grounded in your content with citations. Integrations with the systems you already use. Unlimited users so you don't have to ration access. And we're happy to answer question ten—just ask.
