AI & Competitive Intelligence

From Individual Research to Organizational Impact

What agentic AI means for the next chapter of competitive and market intelligence: a practical guide on what to automate, what to keep human, and how to make the case for both.

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Generating a competitive analysis has never been easier

A product manager. A sales director. An intern with a free AI account. Give any of them ten minutes and a decent prompt, and they’ll come back with something that looks like real analysis.

That didn’t used to be possible. Research demanded time and brain-power and sources were scattered. The teams with the best access to data had a real edge over everyone else.

“More than 80% of tasks face high or medium exposure to AI automation.”
— BCG, 2026

That edge is mostly gone now. AI has gotten dramatically better in the last 18 months, and most organizations have already started using it for exactly this kind of work.

So the question CMI functions have to answer is a new one: if research isn’t scarce anymore, what does your function still uniquely provide? This whitepaper is about that question — and what it takes to build intelligence people can trust and act on, now that everyone has access to the same tools.

What’s inside

Where AI is already being used for CMI, and where it quietly breaks down: reactive research, information silos, governance friction, and cost at scale, mapped against what we see across the CMI teams and senior leaders Valona works with
The three barriers to scaling AI in CMI: visibility, trust, and continuity — and why solving them is a technology problem as much as a process one
A working explanation of agentic AI and MCP for CMI leaders who need to be able to hold their own in discussions with IT or their CAIO
A four-part framework for what agentic AI means for CMI: setting the intelligence agenda, thinking strategically about delivery, getting close to the business, and drawing the line between where AI stops and human judgement starts

How to use it

Read it start to finish if you’re building the case for how your function should evolve. Short on time? Skip to the three barriers and the four-part framework — those are the two sections you can bring into a conversation with your leadership team this week.

Who it’s for

CMI, CI, and MI leaders who want to shape how their function uses AI instead of finding out after the decision’s already been made. Also useful if you’re a strategy lead or Chief AI Officer trying to figure out where AI genuinely helps intelligence work, and where it doesn’t.

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Agentic AI refers to AI systems that plan and execute multi-step tasks on their own, rather than responding to a single prompt. In competitive and market intelligence, an agent can monitor sources, flag relevant developments, trigger further research, and deliver a packaged output as part of a configured workflow without someone manually starting each step.

AI can automate the parts of CMI that benefit from scale and consistency: continuous monitoring, searching large source sets, structuring incoming signals, drafting first-pass summaries, and distributing outputs on a set cadence. What it can’t automate is judging what a signal means for your specific strategy, deciding what matters for which decision-maker, and validating outputs before they reach an executive.

Fine-tuning adjusts the model itself by training it on domain-specific material, so it develops a more accurate understanding of a field like financial or competitive intelligence data. Grounding controls what the model draws from when it generates a response — pulling from a curated knowledge base instead of relying on open web search or training data alone. They solve different problems, and CMI intelligence generally needs both.

No. MCP (Model Context Protocol) is a connectivity standard that lets AI systems access external data sources in a standardized way — for example, inside Microsoft Copilot, Claude, or ChatGPT. It determines how data moves, not whether that data is accurate, verified, or safe to use.