Technology buyers increasingly use AI assistants to research vendors before they ever fill out a form, reply to an email, or agree to a sales meeting.
The questions are rarely broad category queries.
They're specific:
- Will this work with Salesforce and SAP?
- How difficult is migration?
- What will it actually cost at 500 users?
- Who are the serious alternatives?
- What happens to our data if we leave?
- Has a company like ours actually deployed it? That creates a new problem for vendors. The shortlist can be formed before the vendor knows there is a deal. This article lists 20 questions that repeatedly appear in buyer conversations, explains two ways to discover the questions relevant to your own category, and gives you a process for finding the six questions most worth winning.
The 20 questions buyers are asking
The easiest way to understand the shift is to stop thinking in terms of categories and start thinking in terms of purchase decisions.
A buyer doesn't necessarily ask:
"What's the best data platform?"
They ask:
"Does this data platform work with the systems we already run?"
That difference matters.
Fit — Will this work where I already am?
Does this tool connect properly to Salesforce and SAP without custom work?
How long does a typical rollout take for a company with 3,000 employees?
Can it handle 10 million records a day without the price jumping?
Which tools in this category suit a mid-sized manufacturer rather than a large bank?
What has to be in place before we start — clean data, a dedicated administrator, anything else?
These questions are really about implementation risk.
The buyer isn't asking whether the product has a feature.
They're asking whether their environment will work with it.
Risk — What goes wrong?
What do customers complain about most with this vendor?
Why do companies leave this vendor after two or three years?
How hard is it to move our data out if we change our minds?
Is this vendor stable enough to still be here in five years?
Who owns the data, and what happens to it when the contract ends?
This is where AI-assisted research becomes particularly interesting.
Vendor websites are naturally optimized to explain strengths.
Buyers want to understand failure modes.
Those are not the same information problem.
Cost — What will this really cost?
What does this actually cost for a company with 500 users?
Which costs show up later that are not in the first quote — training, support, extra modules?
Is the more expensive option worth the difference over the cheaper one?
How long before this pays for itself in a normal deployment?
Notice that none of these questions is simply "What is your price?"
The buyer wants the economic outcome, not just the list price.
Proof — Has anyone like me done this?
Which companies in manufacturing have deployed this successfully?
Has any company our size done this, or only large enterprises?
What results have real customers reported, not the vendor's own claims?
Who are the serious alternatives, including ones the vendor would not mention?
This is the point where being "well known" and being "credible for this buyer" become two different things.
A vendor can have hundreds of enterprise customers and still be a poor fit for a specific industry, company size, architecture, or use case.
Process — What do I do next?
What should we ask in a first meeting to find out quickly whether this is a fit?
How do we compare three vendors fairly when each measures things differently?
These are particularly important because they move the AI assistant from researcher to decision-support tool.
The buyer isn't just asking which vendor is good.
They're asking how to run the buying process.
The pattern behind all 20 questions
Almost none of these questions are answered cleanly on a vendor's homepage.
Vendor websites answer:
"What do we do?"
Buyers are asking:
"What happens to me?"
That's the fundamental gap.
And it creates two different ways for vendors to understand what buyers are actually asking.
Two ways to find your own list
There are two useful methods.
They don't produce the same information.
The strongest approach is to use both.
What machine-based tools do well
Platforms such as Profound can query AI assistants at scale and track which sources get used in their answers.
That creates something valuable:
a measurable baseline.
Instead of asking, "Does AI know about us?", you can track questions such as:
- How often is our company mentioned?
- Which competitors appear?
- Which sources are being cited?
- Which pages are influencing the answer?
- What changes over time? For a company managing dozens or hundreds of buyer questions, doing this manually doesn't scale.
Machine-based discovery gives you breadth and repeatability.
If you want to know where you stand today, this is the fastest route.
What machine-based tools cannot see
There is an important limitation.
They can only observe questions that have already been asked and answered in some measurable environment.
- They cannot see the question a buyer asks a colleague.
- They cannot see the question a buyer thinks about but never types.
- They cannot see the concern that makes someone quietly remove a vendor from the shortlist. And those questions can be the most commercially important ones. For example: "Would our security team actually approve this?" may never appear in public AI queries. But it can still determine whether a vendor gets to the next stage.
What buyer interviews add
This is where direct buyer research becomes useful.
A structured buyer conversation is not a product demo.
The researcher asks about the actual evaluation:
- What triggered the purchase?
- What did you compare?
- What did you ask an AI assistant?
- What almost disqualified a vendor?
- What information was missing?
- When did a vendor disappear from the shortlist?
- What question did you wish someone had answered earlier? That produces something machine-based monitoring cannot: the language buyers actually use. It also surfaces questions that never became public queries.
Use both: machines for breadth, interviews for depth
The two approaches work best together.
Machine-based discovery tells you what the market is already answering.
Buyer interviews tell you what the market is still struggling to answer.
The intersection is where the most useful content opportunities usually appear.
That intersection is also where buyer intelligence becomes more valuable than simply tracking visibility, because the questions buyers ask reveal what they are actually trying to decide and where existing vendor information falls short. Analyst Layer combines technology intelligence with primary, field-grounded research to understand what buyers are comparing, what concerns shape their decisions, and where vendors need better evidence to influence the shortlist.
How to build your own list in five steps
You don't need a sophisticated system to start.
1. Write down 30 candidate questions
Take the five groups above:
- Fit
- Risk
- Cost
- Proof
- Process Rewrite each question around your category, competitors, and the systems your buyers already use.
"Does this work with Salesforce and SAP?" is more useful than "Is this platform good?"
Make the questions specific enough that a buyer could actually use them during an evaluation.
2. Run each question through four AI assistants
Record:
- which vendors are mentioned
- which sources are cited
- which companies appear repeatedly
what information is missing
the date of the query
Keep the date.
AI-generated answers change.
A position you observe today is not a permanent ranking.
3. Mark where you are absent
This is important.
Absence is the finding.
If buyers are asking a commercially important question and your company never appears in the answer, that is useful information.
Don't immediately interpret it as a content problem.
First determine why you're absent.Maybe nobody has published the answer.
Maybe competitors have stronger evidence.
Maybe the assistant relies on third-party sources.
Maybe your existing content answers the wrong version of the question.
4. Test the list against real buyers
Take the questions to five recent buyers.
Ask:
"Which of these did you actually ask?"
Then ask:
"What did we miss?"
This is usually where the interesting information appears.
The questions buyers remember asking are often more specific than the questions a marketing team expects them to ask.
5. Pick the six worth winning
Don't try to own 100 questions.
Score each question on two dimensions:
Then prioritize the high-closeness, low-competition questions.
Those are the questions closest to an actual buying decision where relatively few strong sources currently exist.
What to do once you know the questions
Being named in an AI answer is the beginning.
It isn't the finish line.
A mention puts you on the shortlist.
It doesn't create a conversation.
The next question is:
Where does the buyer go from there?
This is where many vendors make the wrong move.
They turn every research question into a CTA for a sales meeting.
But the buyer may not be ready for that.
They may still be trying to understand the problem, compare options, or validate a decision they already have in mind.
In our work, one useful next step is an independent analyst conversation where the buyer can work through their own criteria without being pushed into a product pitch.
That kind of conversation also produces better intelligence for the vendor.
The buyer explains what they are actually trying to decide.
And that brings us back to the original problem.
The questions that determine the outcome are often not the questions vendors have answered anywhere.
The shift toward AI-assisted buyer research changes the visibility problem from simply being discoverable to being relevant at the exact moment a buyer is forming a shortlist. This analysis looks at that shift in detail, examining how buyers are using AI assistants to compare technology vendors before engaging sales and what companies can do to understand and influence the questions shaping those decisions.
Common questions about this method
How many questions should we track?
Start with 6–20.
Beyond that, most teams struggle to turn the findings into action.
The goal isn't to monitor everything.
It's to identify the questions closest to a purchase that are currently underserved.
How long before a new page shows up in AI answers?
Usually weeks, sometimes longer, and never guaranteed.
Treat any position as temporary.
Re-check important questions regularly rather than assuming that publishing once means the problem is solved.
Do we need software?
No.
The first pass can be done manually in a day.
Software becomes useful once you're tracking many questions repeatedly and need consistent measurement across assistants and time.
Is this the same as SEO?
No.
Traditional search optimization is largely about getting a page to rank for a search query.
This is about becoming a source that an AI assistant uses when it constructs an answer.
There is overlap in the underlying fundamentals — useful content, authority, accessibility, and evidence — but the target is different.
Who should own this inside a company?
Usually, whoever owns early pipeline should care about it.
It often gets pushed into product marketing because it looks like a content problem.
But the commercial outcome is bigger than content.
If a buyer forms a shortlist before talking to sales, then understanding what appears in that shortlist is ultimately a pipeline question.
The shift buyers have already made
The important change isn't that buyers are using AI assistants.
It's when they're using them.
The vendor used to enter the process relatively early.
A buyer had a problem, searched for vendors, visited websites, downloaded material, and eventually spoke to sales.
Now there is another layer before that.
The buyer can ask:
"Which three vendors should I consider?"
Then:
"Which one works best with our stack?"
Then:
"What do customers complain about?"
Then:
"What happens if we need to leave?"
All before a vendor receives a form submission.
That's why the most important AI visibility question for a technology company isn't:
"Does AI know who we are?"
It's:
"When a buyer asks the question that could determine whether we make the shortlist, what answer does the AI give — and are we part of it?"
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