AI is already embedded in the daily work of most insights teams. The speed of adoption is striking — but the path to ROI is less straightforward. Five moves that help connect AI use to clearer priorities, better measurement and stronger returns.
A way to think about progress over time rather than a fixed state of adoption. The point is to build the habits, skills and proof points that help AI deliver value at each step — not a race to the top.
Widespread usage does not automatically create measurable value. That is why the ladder is useful — it gives teams a way to think about progress from early wins and efficiency gains to stronger quality, better workflow integration and broader business impact.
Teams often get pulled toward new tools because the features look impressive or the use cases sound broad. A better starting point is to define the result that matters most — whether that is time saved, lower cost, better quality or faster delivery. Once the desired outcome is clear, it becomes easier to evaluate where AI can help and how success should be measured.
This kind of discipline makes it easier to separate useful experiments from random activity.
AI becomes far more useful when it is tied to real work, identifying where AI belongs based on “jobs to be done.” Looking at the work this way helps teams avoid vague AI ambitions and focus instead on the workflows that can actually benefit from support.
This is also where teams can identify which workflows offer the greatest opportunity for meaningful gains in efficiency, cost or quality.
Time and cost savings are often the easiest returns to spot, which is why they show up early on the ladder. AI can remove some of the mundane, low-judgment work that slows research teams down — report writing, summarizing long text, organizing inputs, packaging insights. Saving time matters, but the larger value comes from what that time makes possible.
The value of an insights team still rests on thinking, judgment and influence. AI supports that work best when it creates more room for it.
A faster workflow does not create value if the output cannot be trusted. Hallucinations, weak sourcing and polished but shallow output can undermine confidence quickly — which is why teams need review habits that hold up under real-world pressure.
If a team wants to operationalize AI, trust has to be built into the design through review, security and responsible use.
The teams making the most progress with AI are building the skills and habits that let the tools they're adopting produce lasting value. It also requires a mindset shift toward using AI as a copilot, building skills continuously and prioritizing critical thinking — so researchers can spend less time on busywork and more time on high-level strategy.
A mindset shift toward using AI as a copilot — building skills continuously and prioritizing critical thinking.
For insights teams, the strongest results tend to come from clear use cases, practical measurement and a willingness to build on what works.
A practical, fillable companion to this report — 12 questions to evaluate any AI use case before you test, adopt or scale it. Download it, share it with your team, and work through it together.
