Look at any agentic AI website and you'll see the same four use cases: outbound marketing, sales discovery, meeting summaries, code generation.
These are the easily automatable problems. They have standard patterns, clear inputs and outputs, and low stakes when the AI gets it wrong. They're valuable — nobody wants to write follow-up emails by hand — but they're not where the real leverage is.
The easy-problem trap
The AI industry has optimized for problems that are easy to demo. An AI that writes a cold email in 3 seconds makes a great product video. An AI that summarizes a meeting and extracts action items is immediately understandable.
But these are also the problems where AI creates the least differentiation. If every company has the same AI writing the same cold emails, where's the competitive advantage?
The hard problems look different.
What hard problems actually look like
How do you improve a hardware company across the entire value chain — from supplier quality to final assembly to field service — when the knowledge about what's working and what isn't lives in 50 different people's heads across 5 plants?
How do you enable bespoke processes across five business units in a regulated environment, where each unit has different requirements but needs to meet the same compliance standards?
How do you scale the judgment of a 25-year veteran quality engineer who can look at a part and know something's wrong before any measurement confirms it?
These problems share some characteristics:
- They're cross-functional. The answer isn't in one system or one department.
- They depend on expertise. The "right" answer requires human judgment that was built over years.
- They're specific to your operation. No generic model captures your particular constraints, processes, and relationships.
- They're high-stakes. Getting it wrong isn't a bad email — it's a quality escape, a compliance violation, or a missed quarter.
Why most AI tools can't solve them
The standard approach to AI in the enterprise is: connect to your data, train a model, deploy an agent. This works for standardized problems because the data tells the whole story.
For hard problems, the data tells maybe 30% of the story. The rest lives in tribal knowledge, expert judgment, contextual understanding, and organizational relationships that no database captures.
An AI that only sees your data will automate the data-driven parts of the problem. It will generate reports, flag anomalies, and extract patterns. These are useful outputs. But they're not the hard part.
The hard part is knowing what to do about it. That's where expertise comes in.
A different approach
We focus on the hard problems because that's where the value is. Not automating what's already well-defined, but scaling judgment that took decades to build.
That means starting with the expert, not the data. Understanding how they think, what they prioritize, what tradeoffs they make. Then building AI that mirrors that expertise — so it's available to the organization at scale.
It's harder to build. It's harder to demo. It's harder to explain in a 30-second video.
But it's also harder to commoditize. And the problems it solves are worth orders of magnitude more than another automated email.