Key Takeaways:
- A 90-day pilot, broken into four phases (picking a bounded question with a known answer, running the tool alongside your existing method, testing the tool’s edges, and making a documented decision) validates an agentic AI tool against real use before a team commits to a full rollout.
- The first two weeks of a pilot should test a business question the team has already answered through traditional methods, so there’s a known result to compare the tool’s output against.
- Five practical use cases for agentic AI in research and insights (refreshing a stale segmentation, prepping for a stakeholder conversation, answering a time-boxed business question, generating multiple audience options for a pitch, and building a first-pass persona from a plain-language brief) give teams a starting point once a pilot has validated a tool.
This is part two of our series on adopting agentic AI as a research and insights team. In part one, we walked through six questions to ask before choosing a tool, covering methodology, population limits, ranking logic, workflow fit, exportability, and what “agentic” actually means for a given vendor.
Let’s say you’ve asked all six of those questions from part one and got good answers. Before you commit to a particular agentic AI tool, take it for a test drive in the form of a 90-day pilot. Let it earn broader adoption and explore what it would really be like to use.
In part two, we’ll share an outline for structuring your pilot. Then, we’ll look at five core uses cases for agentic AI that can help research and insights teams identify the opportunity, articulate the need to the business, and maximize value from the tool.
How Is a 90-Day Agentic AI Pilot Structured?
It is increasingly common to stress test substantial investments in a real-world setting before committing to an expense or long-term contract. This is especially the case for complex solutions like agentic AI.
There are several ways this test drive may be structured, including:
- Proof-of-concept (POC) agreement — This agreement is separate from the master service agreement and typically covers scope, duration, data handling, confidentiality, and output parameters. This is the most common approach.
- Trial under the vendor’s standard terms — Some vendors have a click-through or short trial agreement they use for all prospects, with basic usage and liability terms predetermined.
- Sandbox/limited-access environment — The vendor gives you a scoped environment with sample or synthetic data rather than your real systems, which sidesteps a lot of the contracting overhead entirely.
- Paid pilot — This gives you full access to the product or solution for a smaller fee and duration than a full contract, sometimes creditable toward the eventual contract if you move forward.
Regardless of which path you choose, you’ll want to clearly document and define success criteria, data ownership and deletion obligations after the pilot is complete, pilot renewal terms, and IP ownership of any outputs the pilot generates.
Building the pilot calendar
Once you’re aligned on the formalized agreement, you’ll need a clear rubric for structuring your pilot so you can most effectively determine value. We’ve outlined a 90-day approach that marketing research teams can apply to agentic solutions you’re considering.
Days 1-15: Pick one bounded question. Choose a business question your team has already answered before through traditional methods, so you have a strong known result to use as a control group. This is important because you need to test whether the tool’s answer matches your own, improves upon it, or gets the answer wrong entirely before you proceed.
Days 16-45: Run the tool and your existing method side by side. Don’t retire the old method yet. Run both and compare not just the headline finding but the reasoning behind it. Does the tool’s ranked list of attributes make sense to someone who knows this population well? Does the population it’s modeling actually match the one you intended to study?
Days 46-75: Test the edges. Push the tool toward the population and sample-size limits you identified in question two. Try a narrower audience than the flagship use case. Try exporting output into a real deliverable you’d actually present, with the goal of finding out what the tool’s limitations are. Knowing how far you can go will help you figure out what its actual use cases are.
Days 76-90: Decide what to do next and be specific about your reasons. Decide whether to expand, adjust scope, or walk away, based on where the tool matched your existing standard, where it fell short, and whether those gaps are ones you can work around or ones that disqualify the use case. Document this. It becomes the basis for training the rest of your team and for negotiating contract terms if you move forward.
What Are the Top Five Agentic AI Use Cases for Research and Insights Teams?
After you’ve evaluated the solution in the pilot environment and put it through its paces, it’s still useful to have a clear sense for how your research team can apply the tool to maximize value. It can be difficult to see a return on your investment without an organizational plan for usage.
Here are five of the most common use agentic use cases for research and insight teams.
Use Case 1: Refreshing a segmentation that’s gone stale.
A segmentation built a year ago describes customers who have almost certainly changed since then. Depending on the vendor you’re working with, you can use the agentic AI tool to not only update the audience, but to compare the two segments and point out what’s changed, thus saving yourself time and increasing speed to insight.
Use Case 2: Prepping for a specific stakeholder conversation.
Preparing for a presentation is an extra job for your team, and it’s one an agentic AI tool was built to help with. Use the tool to pull together a summary of the key attributes and signals of the audiences you’ll be discussing and ask it to write up some talking points that are specific to the person or people you’re presenting to. This frees up your team’s time to focus on other things, like performing valuable research.
Use Case 3: Answering a specific business question under a deadline.
Agentic AI can remove the need to field a new weeks-long study every time marketing asks, “Who’s the right audience for this campaign?” The right technology can give you a profile of the next-best customers as well as their top attributes and media consumption habits. This ensures not only that you get the answers faster than you would the traditional way, but also that marketing has relevant insights they can use to target consumers based on who they are right now, not outdated ones that speak to who customers were months ago.
Use Case 4: Generating multiple audience options for a pitch or proposal.
When a team needs several candidate audiences for a new business pitch or planning conversation, an AI workflow can quickly produce a handful of options as a starting point. That way, you can put your energy into simply refining the list, thus saving yourself hours of work.
Use Case 5: Building a first-pass persona from a plain-language brief.
With an advanced agentic AI tool, you can just describe the audience you’re trying to understand and let it build a persona for you. Then, you can use the output as a working draft to react to and refine. In this use case, the agent is more of a helpful coworker than a simple replacement for a particular software.
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Frequently Asked Questions
How long should a pilot take before we commit to a broader rollout?
Ninety days is generally enough to test a bounded question against a known answer, push the tool toward its population limits, and evaluate export and workflow fit. Shorter pilots often don’t surface edge-case limitations until after a broader commitment is already in place.
What’s the single biggest mistake research and insights teams make when adopting agentic AI?
Trusting an output without understanding the population it can and can’t reliably speak to, particularly for niche or low-incidence audiences where sample size is a real constraint the tool may not surface on its own.
Should we pilot with our easiest use case or our hardest one?
Easiest first. Choose a use case where you already know what a good answer looks like, so you’re evaluating the tool’s reasoning rather than guessing at whether an unfamiliar answer is correct.
What should disqualify a vendor during evaluation?
An inability to explain their ranking methodology on a real example, no clear answer on minimum viable sample size for your population, or output that can’t be exported into a usable format for stakeholders without platform access.
