The Case for “Intelligence-Grade AI”

A few weeks back, I was having lunch with a friend. Eventually (inevitably, really), the conversation shifted to our ongoing experiments in the world of artificial intelligence. Knowing what I do for a living, she confided that she’d recently used AI for an ad-hoc market research project. 

At first, everything looked exactly as it should. Better, even. My friend described how impressed she felt reviewing clean charts, well-structured analysis, beautiful slides that would have cost many hours (and Euros) to assemble in “the before times.”  

Then she asked the all-important question: “Where did this data come from?” 

The AI’s reply was essentially “Oh, that data doesn’t exist. I made it up.” 

We had a good laugh about this. At this point, anyone who has used AI could probably share a similar story.

Still, I’ve spent the last few years sitting alongside the kinds of decisions our customers in global manufacturing are up against every day. Whether to acquire a competitor, move into a new market, shut down a production line. When a decision is worth hundreds of millions and thousands of jobs, it being derailed because an LLM decided to hallucinate is a lot harder to laugh about.

What is “intelligence-grade AI”?

Intelligence-grade AI is AI built on verified, source-traceable, continuously monitored intelligence rather than whatever information it can reach — so the output can be defended, not just delivered.

We use the term “intelligence-grade AI” across everything Valona does: our platform, our sales conversations, our product thinking. I’ve been wanting to write this piece for a while now, because I think it’s worth being precise about what we mean as an architectural distinction — not just more “marketing fluff.”

The place to start is with the word “intelligence” itself. Information is what’s available. Intelligence is what’s been verified, contextualized, and shaped for a specific decision.

A news article is information, a signal. But an analyst who has read that same article cross-referenced it against three competitors’ recent moves, and told you what it means for your pricing strategy next quarter? That’s intelligence.

Modern AI is remarkably good at synthesizing information. The models have become incredibly capable, but they can only work with the information and context they have.

And to be clear, I’m not coming for ChatGPT, or any of the general-purpose AI tools that have changed not only how we work, but how we move through the world. I use them every day. And in certain contexts there are structural limits that general-purpose AI is not designed to solve. These are precisely the gaps that intelligence-grade AI is built to fill.

Intelligence-grade AI is relevant

Ask your average LLM about pretty much anything and it’ll give you a good, general-purpose answer at lightning speed. It’s like having an expert researcher who can rapidly synthesize whatever information it’s given. That’s not a flaw, it’s exactly how these tools are built to work.

But even the best AI can only work with the information it can reach. When you need a live view of your market and a complex decision waiting on it, broadly available isn’t good enough. You need to know what’s moving in your markets, with your specific competitors, in different languages and in media and sites that never reach page one of Google.

I can only speak for Valona here. We’ve spent more than a quarter century building the source base behind our analysis. Every one of those years has trained our AI on which information matters to a C-suite decision and what form it needs to arrive in. The most valuable intelligence usually sits where it’s hardest to find and harder still to monitor week after week.

Sometimes it’s the one story buried in a Dutch trade publication on a Tuesday morning that tells you a competitor is quietly entering your market. Read it that morning and you’re making a decision. Read it six weeks later and you’re managing a surprise.

Intelligence-grade AI is accountable

A federal judge in Mississippi recently canceled a civil trial and barred two lawyers from her courtroom for two years. Why? Because all four attorneys — both sides of the bench — had been caught citing AI-generated cases that were completely fabricated, but nobody had taken the time to check. 

As cases like these continue to proliferate, the takeaway is clear: “My AI hallucinated” doesn’t hold up as an excuse in the courtroom. Or in the boardroom, for that matter. 

The decisions worth making always do get challenged. And when they do, you need intelligence that’s not only early enough to act on, but also verified enough to defend.  

At Valona, every insight is traceable back to its underlying sources. Human expertise remains central to the intelligence process, so whenever the question “where did this come from?” is asked, there’s always a verified answer waiting.

Intelligence-grade AI is efficient

Intelligence-grade AI monitors continuously rather than answering on request.

Our research put the old number at 80% of the week gathering data and 20% analyzing it. That number is finally moving, and anyone who has watched a research task finish in an afternoon instead of a week can feel it move.

What hasn’t moved is the work that comes after. Collecting information got easier. Deciding which of it deserves your attention did not. The analysts I talk to now open a folder of overnight summaries and spend the morning on a harder set of questions. Is this the development that matters, or the one that was easiest to write about? Would this source hold up if the CFO asked where it came from? Which of the forty things that surfaced this week actually changes the decision on the table next month?

That triage is real analytical work, and almost none of it reaches the leadership meeting. It’s the price of information being abundant.

The best market and competitive intelligence is continuous

There’s also a question nobody in the folder has asked yet, and that’s where the structural difference sits. General-purpose AI waits to be asked. It answers a question well and has no view on what moved in your market while nobody was asking. Valona’s AI monitors continuously and without being prompted, against your competitor set, your markets and the questions your organization keeps returning to, across 200,000+ sources including 280 premium paywalled ones. It works out which developments are significant enough to raise. It does it again next week and the week after, so the market picture accumulates instead of being rebuilt from scratch every time someone opens a chat window.

That accumulation is what makes the market and competitive intelligence forward-looking. A one-off answer tells you where things stand this morning. Watching the same markets and the same competitors week after week is what lets a weak signal in March become something you can defend in May, while there are still choices left. Not prediction. Earlier sight of what’s changing, and enough evidence to act on it.

By the time intelligence reaches the people who need to act, it’s already shaped for the decision on the table.

“I don’t want AI to tell me where to build my next factory. I want AI to help my people tell me where I should build my next factory.”

leading Global manufacturing enterprise, vp of operations

What’s next for intelligence-grade AI

The world our customers operate in makes this more urgent every year. Today, 82% of companies operate in uncertain or unpredictable conditions, while 84% don’t feel confident in their ability to anticipate what’s coming next.

As decision windows get shorter, the cost of late (or worse, fabricated) intelligence continues to rise. 

According to McKinsey, nearly nine in ten organizations now use AI, but most are deploying the same models to improve productivity. The advantage now lies in what you give it to work with. 

Recently we announced the launch of our MCP server — making Valona’s intelligence available directly inside Microsoft Copilot, Claude, and the agent frameworks enterprises are already building. Rather than asking AI to reconstruct market understanding from raw information every time, it provides to your AI ecosystem a continuously maintained intelligence foundation that enterprise AI can build on, and people can trust. Continuously updated, human validated and specific to your markets.  

This means in practice that critical intelligence finds you before you think to ask for it. And when someone asks where it came from, you always have a solid answer. 

In the end, it doesn’t matter how good your models are, how fast your agents run, or how clean your dashboards look.  

Your decisions are only as smart as the intelligence they’re built on.  

Intelligence-grade AI in practice:

• Read our whitepaper From Individual Research to Organizational Impact: What agentic AI means for CMI
• Watch webinar playback: What Agentic AI Means for Competitive Intelligence
• Read our blog post on Learn How to Build a Competitive Intelligence Program That Shapes Decisions 

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