Let’s be honest—most of us have experienced the letdown of AI-generated research. At first glance, it’s dazzling. A structured report appears in minutes. It cites dozens of sources. It seems comprehensive… until you look closer. Then come the dead links, the recycled content from the same three sites, and the sense that something’s missing.
What’s missing is everything that isn’t on the internet.
And if you’re trying to make a multimillion-dollar decision—whether it’s entering a new market, picking the right technology partner, or assessing regulatory risks—you can’t afford to get it wrong. That’s why today’s most sophisticated organizations are turning to a different approach:
Research Answers As Databases. Not just as repositories of data—but as intelligent, living dashboards that surface insights you won’t find anywhere else.
In a recent AI Unhyped interview, I spoke to two industry pros who’ve each worked on hundreds of projects, building decision-making databases that balance AI automation with real-world nuance:
- Gareth Armanious: Technical Director of Food & Beverage at PreScouter.
- Christian Salles: Technical Director of Natural Resources & Energy at PreScouter.
“Many times, the best thing isn’t necessarily the most advertised thing.”
– Christian Salles
The teams building these solutions aren’t just pulling public data and calling it a day. They’re identifying the real gaps—the unpublished data, the expert knowledge, the unstructured insights—and designing tools that can interpret and act on all of it.
Let’s walk through what that actually looks like.
The shift from data dumps to decision hubs:
Having a spreadsheet with thousands of entries isn’t valuable if your users don’t know how to act on it. Too often, that’s what traditional dashboards become: just another interface that overwhelms instead of clarifies.
The real shift happens when you design your database with a question in mind. You’re not just compiling facts. You’re organizing knowledge around the decision someone needs to make.
“You need to create an environment that makes it as easy as possible for the user to get to those insights.”
– Christian Salles
This requires understanding two things:
- What questions are users trying to answer?
- How can we simplify the interface so it reveals those answers—without needing a training manual?
Done right, these dashboards become more than data repositories. They’re strategic engines.
Why this works better than AI deep research:
AI-based research tools have improved. They’re fast. They provide good starting points. But they carry two fatal flaws when used alone: bias and superficiality.
“What’s often missing are the failures. And understanding those failures is often what makes the difference.”
– Gareth Armanious
AI tools pull what’s published—and that usually means what worked. That’s survivorship bias in action. The failed pilots, the unscalable experiments, the on-the-ground insights from experts—they’re invisible to most algorithms. Which is why the human layer matters so much.
Above: Data is pulled, compiled and analysed by AI, with expert oversight.
The hybrid model is the solution: AI does the legwork, humans do the thinking.
“We use AI to extract and classify information from thousands of sources, but then it goes through a funnel—where our researchers vet everything before it gets into the database.”
– Christian Salles
It’s fast, it’s scalable, and most importantly—it’s trustworthy.
From numbers to narratives: Making data actionable
All the data in the world is useless unless it’s aligned to the questions you’re trying to answer.
And sometimes, those questions aren’t quantitative.
“How do I know this carbon capture tech is viable for my region?”
“What incentives apply across my top three target markets?”
“What went wrong in past projects—and how do we avoid repeating it?”
These are questions that don’t live in tidy Excel rows. Which is why today’s best dashboards embed a layer of natural language analysis powered by AI agents—tools that can read thousands of descriptions and extract patterns.
“We trained an AI agent to read through thousands of project failures and extract insights. It’s the only way to make sense of qualitative data at scale.”
– Christian Salles
Combined with expert interviews—often structured using sentiment scores or follow-up queries—these databases become intelligent systems, capable of telling stories, not just showing charts.
Visualization isn’t just decoration:
Charts, heat maps, competitive matrices—these aren’t just bells and whistles. They’re essential for clarity.
But the key is restraint.
“Start with the high-level takeaways, then use progressive disclosure. Don’t overwhelm people with everything upfront.”
– Gareth Armanious
Whether it’s a PICO framework for comparing clinical interventions or a carbon capture calculator based on real project data, the goal is the same: start from insight, then let the user drill down.
Above: A visualization from PreScouter’s Carbon Capture & Sequestration Database.
Don’t just report the weather—Forecast it
Perhaps the most important evolution in this work is what comes next: keeping it current.
Different sectors require different cadences. Some datasets—like regulations or market entries—might need weekly updates. Others—like packaging or ingredient innovations—move more slowly.
The future here lies in active listening.
“Set up automated background searches for key triggers—IP filings, funding rounds, regulation changes—and let those drive database updates.”
– Gareth Armanious
This lets you stay relevant without trying to boil the ocean every month. And when a shift happens, you’ll be ready with current, structured insight—not another PDF from last quarter.
Where this is headed:
The outlook is clear: hybrid systems will win.
Not because AI isn’t improving—it absolutely is. But because human judgment, domain knowledge, and context still matter. The best systems won’t replace humans. They’ll make them exponentially better.
“Even as AI gets better at reading technical literature, the human-in-the-loop model isn’t going anywhere.”
– Gareth Armanious
Databases are no longer back-office tools. They’re front-line decision enablers. If you’re serious about using data to drive your strategy, the lesson is simple:
Start with the question. Build from there. And let humans and machines each do what they do best.