The Enterprise AI Paradox

Adoption is easy. Value is an operating-model problem, and other lessons from the Technology Leaders Network.

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Two years ago, boards asked how fast leaders could roll out AI. Now they ask: Where is the return?

Among the leaders in the room, AI is nearly universal, and teams feel faster. But a faster person is not a better-performing company. Call it the altitude gap: an analyst who saves six hours a week is more productive, yet the approvals, handoffs, and teams around her stay the same.

On October 8, more than 80 technology leaders from 12 industries gathered in San Francisco to tackle that gap. Attendees included Fortune 100 CTOs, CIOs, and CDOs from the CapitalG Technology Leaders Network (TLN), a curated community of senior executives, as well as CapitalG portfolio leaders and AI experts. The day ran under the Chatham House Rule, so attendee remarks are unattributed. It was candid about what worked, what didn’t, and what they’d change.

The culprit, leaders kept saying, wasn’t the models, which are remarkably capable. It was organizational: who owns a decision, who redesigns a process, and who decides what freed-up time is for.

The playbook: five things leading enterprises do differently

1. They make fewer, bigger bets and measure outcomes instead of activities.

One retailer asked every corporate team for AI ideas and got back more than 1,000. None had an owner, a baseline, or a path to reuse. “I thought I was collecting demand,” its AI leader said. “What I actually did was make a thousand promises.” The company cut the list to three projects that seemed viable and assigned each one an owner. The leader offered attendees a simple litmus test: “Have you ever pulled the plug on a well-funded project that wasn’t delivering? If not, you’re funding activity, not outcomes.”

2. They reimagine the process, not just the work.

“Automation asks how to do the same work faster,” another leader said. “Reimagination asks whether the work should happen that way at all.” Their organization stopped bolting AI onto old processes and now tracks handoffs removed, not pilots launched. The point: Freed-up time creates value only when someone decides what it’s for. Otherwise, as one speaker put it, you just “gave somebody a nicer week, but you have not improved your bottom line.”

Marianna Lopert-Schaye, Managing Director at BNY, on reimagining how work gets done, not just adding AI to old workflows.

3. They lay the plumbing once.

At another company, the first finance automation required connecting dozens of fragmented systems. It also meant untangling a single supplier spelled five different ways. It was tedious, unglamorous work, but it is the reason the next automation was cheaper to build. Every project that follows inherits clean data and working connections. The test is simple: If your tenth use case costs as much as your first, you aren’t building a scalable system.

4. They build in concert with the people closest to the work.

A restaurant leader learned this lesson the hard way: Tools built in a vacuum without user input sat unused, while tools built alongside them took off. “Build it with them, and it becomes their Iron Man suit.” The people closest to the work know where it breaks and what will actually help. The test: if you took the tool away tomorrow, would its users object?

5. They govern instead of babysit.

When one leader laid out this three-point framework for AI-enabled work, it drew nods around the room: An operator does the work. An administrator checks the output. A governor sets the rules, decides what the system handles on its own, and owns the result.

Many leaders admitted they were stuck in the middle role, reviewing every output and owning none of the outcomes. One energy software company is breaking that trap by designing for the governor role. Its AI model logs every step and explains its reasoning. People set the rules up front, decide which calls the system can make on its own, and step in on exceptions. They stay accountable for the result. As one leader put it, “It’s about whether anybody made the call.”

What leaders told us they came to solve

Before the summit, we surveyed the leaders we invited. Of the 60 who responded, 75% ranked scaling AI from pilot to production as their top challenge, with the operating model close behind. Governance, proving value, vendor lock-in, workforce design, and legacy systems rounded out the list.

What this means for founders

Buyers have stopped counting pilots launched. They count handoffs removed, and they ask four questions:

1. What will it change, and what is that worth?

2. Who owns the result?

3. What will it log and explain when it acts?

4. How does it fit our stack?

Two summit companies offered strong answers. Baseten, which builds inference infrastructure, answers the stack question: it keeps AI fast, reliable, and cost-efficient once it leaves the pilot and fits into what a customer already runs. Simile, which simulates how people will behave before an enterprise commits to a decision, answers the value and explainability questions: its value is specific, and its reasoning can be inspected.

Buyers also told us they fear vendor sprawl and lock-in. We expect the winning founders to sell a redesigned workflow, plug cleanly into the customer’s foundation, and be easy to audit rather than hard to leave.

Amir Haghighat, CTO and Co-Founder of Baseten, on the infrastructure that carries enterprise AI from pilot to production.

Joon Sung Park, CEO of Simile, traces AI human simulation from Stanford research to the Fortune 500.

Closing the altitude gap

The leaders in the room already have what startups spend years building: customer trust, distribution, and decades of domain knowledge. AI can make all of it move faster, but speed alone won’t close the altitude gap. That takes owners, redesigned processes, and a decision about what freed-up time is for.

The most useful ideas came from executives willing to say what went wrong. One Fortune 100 CTO summed up the day: “The real promise of AI in the enterprise is not smarter demos, but smarter institutions.”

Enterprise technology leaders listen and share insights at the CapitalG Summit.

Join the conversation

The CapitalG Technology Leaders Network is a community for senior technology leaders to compare notes on what’s working and what isn’t. If you’re an enterprise leader redesigning your workflows, or a founder building for these buyers, send me a message on LinkedIn. I’d love to compare notes and, for leaders, help you get a seat at a future gathering.