5 Questions to Ask AI Vendors Before Buying a Tool

Introduction

There are many ways to use AI in marketing, and it feels like for every smart initiative, 10 AI vendors appear with a tool that claims to solve it.

Early in this wave, I had more calls and answered more emails than I could keep up with. Over time I realized I was asking every vendor the same questions to evaluate whether their tools actually met the need.

If you are in the same place and flooded with vendor outreach, here are five questions that will help you decide whether they are worth your time, along with my reasoning for asking them and what I expect to hear — or not hear.

1. What problem does your tool solve?

This question should help you understand the tool's purpose and — critically — whether the value it creates translates into real business outcomes.

If the vendor cannot clearly articulate the challenges or specific use cases the tool addresses, it was not built specifically to solve a real problem your team faces. Beware of vendors who try to impress you with feature language but do not explain the business benefits those features provide.

If a vendor identifies at least one existing team problem the tool solves and explains how it improves business outcomes, it is worth continuing the conversation. A strong follow-up is to ask for a case study showing how the tool was used and what results it delivered for an organization similar to yours in size and industry.

Look for benefits like "increases output" or "identifies gaps in data tracking to streamline troubleshooting." But do not rush to invest in tools that promise to "save time" (even if they really do) unless you have a precise plan for how you will use that extra time.

2. What expertise do you have in the domain where the tool solves a problem?

The answer to this question should reveal, for example, whether the vendor built the tool specifically for media buyers or is simply selling to media buyers.

Technical capability matters, but so does understanding what a media buyer actually does day to day. If the vendor has no personal experience in media buying, they should explain how they researched the media-buying market and how they incorporated those insights into the tool.

If they show shallow understanding or lack domain expertise, that is a red flag. It is fine for a salesperson not to have that expertise directly, but someone on their team should be a domain expert, and you should get access to that person as early as possible if you plan to continue the conversation.

If the vendor has a background story that led them to identify a problem you closely recognize and decide to build the solution themselves, that is compelling. Founders who faced the same challenges as your team are a solid basis for believing the tool can make a difference in your team's performance.

3. What case studies, real use cases, and results can you share?

I touched on case studies a few paragraphs above, and they are mandatory in a new, fast-moving industry. I want to understand whether the vendor has professional experience with customers like me or whether we will be among the first.

If you fall into the latter category, there are pros and cons, just like with any public beta you get access to before competitors. You may gain an edge by finding an important growth lever before rivals, or you may stumble while trying to work through bugs, or you may discover the tool simply does not live up to its promises.

If the tool is not yet reliable or there is a risk you will need to provide detailed feedback to make it work, it may not be the best use of your time and money — unless you believe what it can eventually deliver will be a game-changer.

If it is clear you will be early customers and the vendor is not willing to be flexible on contract terms that reduce risk, that is a trust issue. More established tools will likely deliver more consistent value, even if they have less pricing flexibility because of it. Newer tools that offer no flexibility on pricing and contract terms, by contrast, are probably not good long-term partners.

For established vendors, you should see specific, relevant case studies and use cases with real numbers from customers in a similar industry, similar size, or similar usage.

If they are early-stage companies, the best answer is honesty: "You will be among our first customers in this space. Here is what we have seen elsewhere, and here is what this partnership will look like." That transparency is a green flag to continue.

4. Who owns my information, and how is it used to train models?

It is striking how easily people share data with AI and AI tools in pursuit of a competitive edge. This is something I would recommend potential buyers consider before signing anything.

Watch for any answer that suggests your data is used to train shared or third-party models without your explicit consent. Another red flag is vague or evasive answers, or terms of service that do not match what the salesperson tells you verbally.

Your data belongs to you, full stop.

The vendor should be able to explain data-handling practices clearly, including where your data is stored, how long it is retained, whether it is used to train models (and if so, only to improve your experience), and what happens to your data if you stop using the tool. All of this should be in the contract, not just an oral promise. If it is not there, insist it be included before you sign.

5. What does using the tool look like in practice, and what does success require from our team?

Before you commit money, you need to understand the true cost of adopting the tool. That cost includes more than price. It is time, internal lift (including integration, training, and QA), and any possible disruption to your existing technical stack.

If it requires resources your team does not have, or you truly cannot dedicate the time to use the tool well, it is not worth the investment. Many marketers could avoid unnecessary martech spend if they asked this question and weighed the answer seriously.

No tool should be one-size-fits-all, but easy implementation and intuitive design are critical to getting your team to adopt the tool and stick with it.

Do not let AI hype rush your decision

I know from personal experience that many of these tools sound too good to be true — and often, they are.

You need to balance ambition for growth and curiosity with a little caution.

Remember we are still in the early stages of AI tool adoption. If a tool seems too expensive or hard to implement, or the contract is stiffer than it should be given the tool's maturity, a more attractive solution may appear in the coming months.

When in doubt, ask for a free trial. Assuming it does not create too much integration work for your team, that can be the right step to determine whether you have found your next competitive edge.