What questions should you ask an AI vendor?

1 of 6

Start with the business problem
Good vendor questions start with the work, not the demo
Before you compare tools, make the vendor explain the real workflow you want to improve.
The workflow: What exact task are we trying to make faster, easier, or more accurate?
The users: Who does this work today, and who will use the tool tomorrow?
The baseline: How do we do it now, and what are the current time, error, or cost levels?
The goal: What would success look like in 30, 60, or 90 days?
The exceptions: What cases still need a person, judgment, or approval?
If the business problem is fuzzy, the vendor answer will be fuzzy too.

Ask about data & inputs
A tool is only as good as the information you can feed it
Many AI projects fail because the data is messier, thinner, or harder to access than the sales demo suggests.
Data source: Where will the inputs come from, and who owns them?
Data quality: How clean, complete, and consistent does the data need to be?
Access: What systems, files, or permissions are required to make the tool useful?
Privacy: Will our prompts, files, or outputs be stored, shared, or used to improve the model?
Missing data: What happens when the input is incomplete, unusual, or low quality?
Ask for the ugliest real input, not just the prettiest demo example.

Ask about integration & workflow
The tool has to fit your real process, not just look smart
A useful AI system has to connect to your existing work, handle exceptions, and fit inside the way your team already operates.
Current tools: What systems does it connect to on day one?
Human review: Where can a person check, edit, approve, or override the result?
Exceptions: What happens when the tool is unsure, wrong, or out of scope?
Audit trail: Can we see what happened, when it happened, and why?
Change effort: How hard is it to update prompts, rules, or workflows after launch?
The best AI tool is the one your team can actually run every day.

Ask about cost, support & risk
Look past the license price to the full project cost
A smart vendor conversation should cover not just price, but also internal effort, support, uptime, and what happens if the relationship changes.
Pricing model: How are we charged: by seat, usage, workflow, or outcome?
Internal time: What work will our own team still need to do each week?
Support: Who helps when something breaks, and how fast do they respond?
Uptime: What reliability, monitoring, and backup process do you provide?
Exit risk: If we stop using the vendor, how do we keep our data and transition cleanly?
The cheapest quote can still become the most expensive project.

The small-business takeaway
Use questions to turn a sales call into a decision tool
Define the job: Start with one real workflow, not a vague interest in AI.
Compare fairly: Ask every vendor the same core questions.
Insist on a pilot: Test with real inputs before expanding.
Include the users: The people doing the work should judge the fit.
Measure the result: Track time, errors, and rework after launch.
Scale only what works: Grow the tool only if the whole process improves.
Good questions reduce bad buying decisions.
A strong vendor is willing to answer hard questions before you sign anything.

Lesson done.

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The short answer

Ask every AI vendor the same hard questions: what exact problem it solves, what data it needs and whether your files are stored or used to improve the model, how it connects to your tools, who supports it, and what it really costs. Their answers show how the tool will work in your business before you sign.

Three things to remember

01Make the vendor explain your real workflow, your current baseline, and which cases still need a person.
02Ask for your ugliest real input, and whether your prompts and files are stored, shared or used for training.
03Ask about pricing model, support speed, uptime, and how you keep your data if you leave.

Every slide, in words

01
What questions should you ask an AI vendor?
Start with the business problem, not the sales pitch
A good vendor conversation should help you understand the workflow, the data, the real cost, and what happens after launch.
Business fit: What problem are we solving?
Data: What does the tool need to work well?
Integration: How does it fit our current systems?
Ownership: Who supports it after launch?
Risk & cost: What could go wrong, and what will it really cost?
The best vendor questions reveal how the tool will work in your real business.
02
Start with the business problem
Good vendor questions start with the work, not the demo
Before you compare tools, make the vendor explain the real workflow you want to improve.
The workflow: What exact task are we trying to make faster, easier, or more accurate?
The users: Who does this work today, and who will use the tool tomorrow?
The baseline: How do we do it now, and what are the current time, error, or cost levels?
The goal: What would success look like in 30, 60, or 90 days?
The exceptions: What cases still need a person, judgment, or approval?
If the business problem is fuzzy, the vendor answer will be fuzzy too.
03
Ask about data & inputs
A tool is only as good as the information you can feed it
Many AI projects fail because the data is messier, thinner, or harder to access than the sales demo suggests.
Data source: Where will the inputs come from, and who owns them?
Data quality: How clean, complete, and consistent does the data need to be?
Access: What systems, files, or permissions are required to make the tool useful?
Privacy: Will our prompts, files, or outputs be stored, shared, or used to improve the model?
Missing data: What happens when the input is incomplete, unusual, or low quality?
Ask for the ugliest real input, not just the prettiest demo example.
04
Ask about integration & workflow
The tool has to fit your real process, not just look smart
A useful AI system has to connect to your existing work, handle exceptions, and fit inside the way your team already operates.
Current tools: What systems does it connect to on day one?
Human review: Where can a person check, edit, approve, or override the result?
Exceptions: What happens when the tool is unsure, wrong, or out of scope?
Audit trail: Can we see what happened, when it happened, and why?
Change effort: How hard is it to update prompts, rules, or workflows after launch?
The best AI tool is the one your team can actually run every day.
05
Ask about cost, support & risk
Look past the license price to the full project cost
A smart vendor conversation should cover not just price, but also internal effort, support, uptime, and what happens if the relationship changes.
Pricing model: How are we charged: by seat, usage, workflow, or outcome?
Internal time: What work will our own team still need to do each week?
Support: Who helps when something breaks, and how fast do they respond?
Uptime: What reliability, monitoring, and backup process do you provide?
Exit risk: If we stop using the vendor, how do we keep our data and transition cleanly?
The cheapest quote can still become the most expensive project.
06
The small-business takeaway
Use questions to turn a sales call into a decision tool
Define the job: Start with one real workflow, not a vague interest in AI.
Compare fairly: Ask every vendor the same core questions.
Insist on a pilot: Test with real inputs before expanding.
Include the users: The people doing the work should judge the fit.
Measure the result: Track time, errors, and rework after launch.
Scale only what works: Grow the tool only if the whole process improves.
Good questions reduce bad buying decisions.
A strong vendor is willing to answer hard questions before you sign anything.

More in part 4

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