Key Takeaways:
- Agentic AI can meaningfully increase speed to insight, qualitative research capacity, overall research capacity, population coverage, and speed to activation, but only if a team evaluates a tool’s fit for their specific workflow.
- Six questions separate a defensible agentic AI purchase from a risky one: methodology transparency, population and sample-size limits, ranking/weighting logic, workflow fit alongside existing tools, output exportability, and a concrete definition of what “agentic” actually means for that specific tool.
- A demo is designed to show a tool at its best, not to reveal its limitations, so evaluation questions need to be asked directly and in writing.
Like the other members of a marketing team, research and insights professionals throughout the industry are exploring agentic AI with interest. When used properly, the technology can add a lot of value, increasing:
- Speed to insight
- Capacity for qualitative research without adding budget
- Overall research capacity without adding headcount
- Population coverage, even for audiences that were previously too expensive
- Speed to activation
But reaping all these benefits requires the team to understand how the agent fits into their specific workflow and meets their specific needs. In this blog, we’ll suggest an evaluation framework research and insights professionals can use as they consider various agentic tools. In an accompanying blog, we’ll talk about how you can better understand how agentic AI fits into your workflow, as well as practical use cases.
How Research and Insights Teams Can Evaluate Agentic AI Tools
You probably have a list of agentic AI tools you’re considering, or at least an idea of some vendors you’d like to know more about. Demos are valuable, but it’s important to remember that their purpose is to show you the tool at its best, not to reveal where its limitations are. You need a clear rubric for evaluating the utility of an agentic solution aligned to your specific business needs. With that in mind, here are six questions to ask yourself before you buy.
Question 1: What’s the methodology behind the tool?
If your team is going to be able to back up the insights you’re giving other colleagues or clients, they need to be able to explain how they got them, even if an agentic AI tool is doing some of the actual tasks.
Find out how the underlying data was collected, how the models were built and trained, and what validation exists behind the outputs. It’s best to request documentation rather than just a verbal explanation so you can review it in detail and without the pressure of a salesperson sitting in front of you.
A good answer includes a specific description of the data sources, the sample composition, and how the model was validated against known outcomes. If the response changes each time you ask or sounds like “the AI figures it out,” that’s a red flag.
Question 2: What population can this tool actually speak to, and where does it run out of data?
Research and insights teams trying to use agentic AI often run into a frustrating limitation: When they’re working with niche or low-incidence populations, the insights they can glean are constrained due to small sample sizes.
Remember that an AI tool is only as good as the data it’s built on. Ask for the minimum viable sample size the tool needs to produce a reliable read. Then, test it against the smallest population on the list of audiences you care about (not just the vendor’s standard use case) and evaluate the insights you get against the ones you would want.
Question 3: How does the tool rank or weight what it shows you?
“AI-generated insights” can mean the tool is surfacing findings by statistical index (how disproportionately a trait appears in this group versus the general population), by raw composition (how common a trait is within the group only), or by some blended logic in between.
A trait can be common inside your audience without being distinct. For example, if 70% of your audience shops on Ebay, that’s a high composition. But if 70% of the general population also shops on Ebay, the index is close to 100 and tells you nothing about what makes your audience unique. It works the other way, too: If only 9% of your audience uses Instagram, but that’s three times more than the general population, it’s a stronger signal of who those individuals really are.
You actually need both composition and index for the fullest view of your audience, but ranking algorithms may default to one or the other and lead you to false assumptions. You should know what you’re getting up front to ensure you’ll be able to use the insights once you’re locked into a contract with the vendor. Ask them to walk you through one real, ranked output, attribute by attribute, and explain why each item ranked where it did.
Question 4: What does this replace in our workflow?
Agentic AI tools tend to work best as an addition to an existing research stack, filling speed and scale gaps, rather than a wholesale replacement that does away with tools your team already trusts for specific use cases. Map any tool you’re seriously considering against your current tech stack: which existing tool does each new capability actually compete with, and which gap does it fill that nothing else covers?
A word of advice: Treat any claims that an agentic AI tool will do “everything your current tech stack does, but faster” with skepticism. We haven’t yet reached a point where agentic AI can replace an entire tech stack and perform all of its functions perfectly.
Question 5: What happens to the output once it leaves the platform?
You need to be able to present your findings, whether you’re just talking to internal stakeholders at your own company or are in a room with clients. To that end, find out whether outputs can be exported into an editable format your team can use or if they remain locked inside the tool’s own interface. If it’s the latter, you may want to reconsider.: This will quickly become an operational headache and make passing insights to marketing teams a constant frustration.
Question 6: What do you mean by “agentic”?
The term “agentic” is currently so broad it’s not safe to assume it means just one thing. Sometimes, it means the tool generates several audience options from a plain-language brief and stops there. At other times, it means something closer to end-to-end workflow automation, brief in, activated audience out. Ask for a concrete walkthrough and think about if it matches what you need.
Methodology, population limits, ranking logic, workflow fit, exportability, and a concrete definition of “agentic” are the questions that determine whether the tool will produce work your team can stand behind. Ask for documentation, not just a verbal answer, and test claims against your own smallest and most demanding use cases before you sign anything.
Once you’ve gotten straight answers to all six, the next step is figuring out how the tool actually fits into your day-to-day workflow, which is where a structured pilot and a set of practical use cases come in. Stay tuned for part two of this blog, which will cover both of those topics.
The Resonate Difference
Resonate Cortex is built to answer these six questions directly: methodology and data sourcing are documented and available on request; population coverage and sample-size guidance are specific to the audience you’re evaluating; and outputs move straight into an activatable segment rather than staying locked inside a single interface.
For a research and insights team, that means less time spent untangling a vendor’s claims and more time acting on insight your team can actually stand behind. To see how Cortex holds up against your own evaluation criteria, schedule a consultation with a Resonate data expert today.
Frequently Asked Questions
Why shouldn’t we rely on a vendor demo alone to evaluate an agentic AI tool?
A demo is built to show the tool at its best, not to reveal where its limitations are, so the six evaluation questions need to be asked directly rather than assumed from what a demo shows.
What’s a red flag when asking a vendor about their methodology?
An answer that changes each time you ask, or one that amounts to “the AI figures it out,” rather than a specific, documented description of data sources, sample composition, and model validation.
Why do population and sample size matter so much for research and insights teams specifically?
Niche or low-incidence populations can hit real sample-size limits that constrain the reliability of a tool’s insights. A tool is only as good as the data it’s built on, so it’s important to test the tool against your smallest audience of interest, not just the vendor’s flagship use case.
Why ask how a tool ranks or weights its findings?
“AI-generated insights” can mean different things depending on whether findings are ranked by statistical index, raw composition, or a blend of both. These produce different answers to what actually matters most about an audience.
Should we expect an agentic AI tool to replace our existing research stack?
No. These tools tend to work best as an addition that fills speed and scale gaps, rather than a wholesale replacement for tools your team already trusts. Be skeptical of any claim that a tool does “everything your stack does, but faster.”
