The Case for “Intelligence-Grade AI”

Here's what we mean when we say "intelligence-grade AI" and why your decisions are only as smart as the intelligence they're built on.

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, the idea of it being derailed because an LLM decided to hallucinate a few facts and figures is a lot harder to laugh about.

What is “intelligence-grade AI”?

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 — especially the high-stakes world of global manufacturing — 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’s given. When you’re trying to get a live view of your market while up against an especially complex business decision, “broadly available” simply isn’t good enough. You need to understand what’s moving in your markets, with your specific competitors, in languages your team doesn’t speak, in trade publications that never make it to page one of Google.

I can only speak for Valona here, but we’ve spent more than a quarter century building our source base because the most valuable intelligence often sits in places that are difficult to discover and even harder to monitor consistently. Over 200,000 validated sources across 115 languages: licensed trade publications, regulatory filings, financial data, specialist industry media.

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. Six weeks earlier, you’re making a decision. Six weeks later, 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.

When the decision gets challenged (and the decisions worth making always 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

For decades, intelligence and strategy teams have been doing their work with infrastructure that was never built for the job. As a result, the people closest to the intelligence spend most of their time finding it.

Most intelligence and strategy professionals spend around 80% of their time gathering data and only 20% analyzing it. Monitoring earnings calls within hours of release, combing through trade publications in languages they don’t speak, and manually packaging analysis for whoever asked first (or loudest) leaves less time for the work that moves the business. At Valona, we believe this ratio should be completely flipped.

What changes with intelligence-grade AI isn’t just speed—it’s how intelligence work gets done. Valona’s AI monitors continuously, without being prompted, helping teams identify significant developments and distinguish signal from noise.

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

One of our customers put it simply: “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.”

That’s exactly what we’re building toward. Not a replacement for human judgment, but the infrastructure that makes human judgment faster, sharper, and better informed.

“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.”
Global manufacturing leader

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 a continuously maintained intelligence foundation that enterprise AI can build on. 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:

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General-purpose AI synthesizes what’s broadly available and gives you a reasonable answer fast. Intelligence-grade AI starts with a different foundation — verified sources, human validation, and context specific to your markets and competitors. The distinction isn’t the model. It’s what the model has to work with.

They can, and many do. But general-purpose AI draws from what’s publicly available and broadly indexed. It has no visibility into licensed trade publications, regulatory filings in 115 languages, or the signal buried in a Dutch industry journal on a Tuesday morning. For ad-hoc questions, it’s fine. For decisions worth hundreds of millions, the gaps become expensive.

Every data point in Valona traces back to its original source. Human analysts are accountable for every decision point. When someone asks “where did this come from?” — in a board meeting, in a legal review, in a budget conversation — the answer is always there.

It’s not a replacement for your team — it’s the infrastructure that changes what they spend their time on. Most intelligence professionals currently spend 80% of their time gathering data and 20% analyzing it. Valona inverts that. Your analysts stop doing the archaeology and start doing the strategy.

Valona’s MCP server connects directly to Microsoft Copilot, Claude, and other enterprise AI tools and agent frameworks. Rather than asking your AI to reconstruct analysis from raw data each time, it gives it access to intelligence that’s already been built, curated, and validated. The analysis is there when the agent needs it.

The ones where being wrong has consequences — entering a new market, responding to a competitor move, anticipating a regulatory shift, deciding where to build. Decisions where “my AI hallucinated” is not an acceptable answer in the boardroom.