Enterprise AI Has an Overthinking Problem. The Cure Is Scope.

A recent Forbes Technology Council piece argues enterprise AI fails from over-engineering, not lack of capability. We agree — and the next phase of enterprise AI will be won by products built around one defined job, not copilots on every screen.

On September 1, 2026, Pega's Don Schuerman published a Forbes Technology Council piece with a title we wish we'd written: "Enterprise AI Has An Overthinking Problem." His argument: many enterprise AI deployments fail not from lack of capability, but from over-engineering — chasing generality and complexity instead of scoping AI tightly to a defined business problem.

We've been making a version of this argument since we founded Pyra. It's worth spelling out why the overthinking failure mode is so common, and what the alternative actually looks like.

How Overthinking Happens

Nobody sets out to over-engineer. It happens through a sequence of individually reasonable decisions. The team starts with a real problem — say, RFP responses take three weeks. Then someone asks: "While we're at it, shouldn't the agent also handle security questionnaires? And sales proposals? Shouldn't it be a general document intelligence platform?"

Eighteen months later there's an architecture diagram with twelve integration points, a steering committee, and no shipped workflow. Meanwhile the reports of errant agents and malfunctioning models keep arriving — nearly weekly, as Schuerman notes — because systems built for generality have far more surface area for things to go wrong.

• General systems have unbounded failure modes. An agent scoped to one workflow can be tested against that workflow. An agent scoped to "anything" can't be tested against everything.
• Generality delays value. Every additional use case pushes the first measurable outcome further out — and executive patience runs out before the platform ships.
• Nobody owns "everything." A general capability has no single accountable owner. A product built for the RFP team has one.

"The next phase of enterprise AI won't be won by adding copilots to every screen. It will be won by building trustworthy AI products around specific business outcomes — one defined job, clear inputs and outputs, and human approval exactly where it matters. Scope is not a limitation. Scope is the strategy."

From AI Features to AI Products

The distinction we draw with clients is between AI features looking for a use case and AI products built around a defined job. A feature is a text box that can do anything and is accountable for nothing. A product has:

• A defined job. "Draft winning RFP responses from our knowledge base" — not "assist with documents."
• Clear inputs and outputs. You know what goes in, what comes out, and what "good" looks like.
• Human approval where it matters. The agent does the work; a person owns the consequential decisions.
• A measurable outcome. Cycle time, coverage, win rate — something a CFO can audit.

This is how we build every agent in our lineup — from RFP response to lead generation. Each one does a job. None of them tries to be a platform for everything.

The Uncomfortable Corollary

Here's what the overthinking framing implies, and few vendors will say plainly: most enterprises should be buying less ambitious AI than they're being sold. Not less capable — less general. A tightly scoped agent that removes 90% of the manual work from one real workflow beats a general platform that demos beautifully and never reaches production.

Start with the workflow that hurts. Ship the narrow thing. Measure it. Then expand. That sequence sounds obvious, and it's the opposite of how most enterprise AI programs are structured today. If you want to see what a scoped-first approach looks like in practice, our solutions page shows the workflows we start with — or bring us the one that hurts most.

Referenced: Don Schuerman, "Enterprise AI Has An Overthinking Problem," Forbes Technology Council, September 1, 2026. Views summarized with attribution; commentary is our own.