I just wrapped up hosting a roundtable at AI4 called "Signal over Noise: Unlocking Enterprise-scale ROI from Agentic AI." The room was packed with enterprise leaders who are knee-deep in AI deployments. What we discussed won't make it into the glossy conference recaps, but it's exactly what every enterprise leader needs to hear.
Here's the unfiltered truth that we discussed.
Let me start with the reality check that had the room nodding in uncomfortable agreement. One leader shared data that stopped the conversation cold: they're running 150 narrow AI pilot projects. Want to guess how many show real business value?
Seven to eight percent.
That's not a typo. Out of 150 pilots, only about 12 projects delivered enough value even to consider moving to production. This wasn't an outlier story---every head in the room was nodding. A leader from a large US financial institution confirmed they're seeing a 95% failure rate when AI tools get embedded into actual business processes.
Everything is in pilot mode. Scaling takes time, and so does perfecting the use case with demonstrable value.
The leaders who see results follow a clear pattern. They democratize AI internally first, then expand.
Here's their playbook: Create an intake funnel for employee-submitted use cases. Let your teams experiment and submit ideas. Then pick the best ones and have those teams present globally. Show off working prototypes to encourage the rest of your organization.
But here's the key insight: Start with finance use cases. Why? Your finance team already knows how to book gains and measure value. They understand ROI calculations better than anyone. When finance gets comfortable with AI ROI, they become your internal advocates for future projects.
This isn't just about technology. It's about organizational courage.
A leading biotechnology company shared their structure, and it's telling. They didn't just create an AI Center of Excellence. They built three connected teams: an AI COE, an Automation COE, and a Data COE. All three are glued together by a central enablement team.
That's not cheap. That's not simple. That requires serious executive sponsorship and the courage to invest before you see returns.
Without that level of commitment from the top, these initiatives die slow deaths in committee meetings.
We talked openly about what "savings" actually means in AI projects. The room got quiet when someone said it out loud: there are no hard savings unless you cut people.
The professional services you eliminate? Those count as hard savings, too. But the productivity gains everyone loves to talk about? Those are soft savings at best.
This is why so many pilots don’t justify production deployment. Teams get excited about time savings, but CFOs want to see real cost reductions. The math rarely works without difficult workforce decisions.
The leaders seeing success focus on specific types agentic AI use cases: compliance reviews, clinical trial operations, legal document handling. These workflows involve judgment, coordination, and variable inputs. They can't be solved with simple automation.
But here's what makes them viable: clear measurement, not standardization. You need visibility into how work moves across systems and teams. Process telemetry matters more than perfect workflows.
The most effective teams measure task durations, handoffs, exceptions, and rework. This gives them a grounded way to select use cases instead of guessing.
Everyone in the room agreed: finding the right talent isn't just about technical skills. You need people who can navigate internal politics thoughtfully.
AI projects touch every department. Success depends on getting buy-in from teams that see AI as either a threat or just another technology fad. The technical implementation is often the easy part. Managing the human side is where most projects break down.
This conversation felt different from the usual AI conference panels. No one was selling anything. No one was pitching their company's solution.
Instead, we talked about what's actually happening when the cameras aren't rolling and the press releases aren't being written. We shared the projects that didn't work, the pilots that got killed, and the uncomfortable conversations happening in executive meetings.
The enterprise AI revolution is still coming. But it's going to look different than what the headlines suggest. It's messier, slower, and requires more organizational change than anyone wants to admit.
The companies that get this right will have a massive advantage. The ones still chasing the hype will keep adding to that 95% failure rate.
The signal is there. You just have to know how to filter out the noise.
If you want to hear how we're helping our customers through this transition, let's talk.