AI tends to demo really well. Use cases inside a proof of concept are compelling. The ROI projections look great on a slide. Then the pilot ends. And nothing ships. This pattern has become a trend.
I’ve discussed this with technology and business leaders across every major industry. They ran a GenAI pilot. It worked. Everyone was excited. And then, somewhere between “this is promising” and “this is in production,” the whole thing stalled. Budget review. Governance questions. Integration complexity. Change management. The reasons vary. The outcomes don’t.
This is the operationalization gap – and it persists as one the most pervasive AI challenges.
The Numbers Are Hard to Ignore
Last year, Presidio surveyed more than 1,000 IT decision-makers – CIOs, CTOs, technology leaders across industries. The headline finding? 80% of organizations have adopted generative AI. But 50% of them admit they launched before they were ready.
Half of the companies running AI programs built them on foundations that couldn’t support them. MIT’s GenAI Divide: State of AI in Business 2025 report found that 95% of generative AI pilots fail to deliver measurable financial return.
These are operationalization failures. The technology may have worked, but the architecture didn’t. The governance wasn’t there. The data wasn’t clean or connected. The orchestration layer – the thing that makes a pilot into a product – was never built.
My colleague Rob Kim, Presidio CTO, calls what happens next the AI Graveyard: a new pilot here, a new tool there, three teams running three different LLMs with no shared governance, shadow AI spreading with no data controls. Every experiment looks reasonable in isolation, but none of them connect. Disconnected AI investments don’t produce ROI that aggregates into enterprise-wide impact.
Every experiment looks reasonable in isolation, but none of them connect. Disconnected AI investments don’t produce ROI that aggregates into enterprise-wide impact.
enterprise AI deployment challenges: 
The Gap Isn’t Where You Think it Is
Most organizations approach AI like a project. Define a use case, stand up a model, run a pilot, measure results, declare success. What they don’t account for is everything that happens next.
Production AI is a fundamentally different animal than a pilot.
In a pilot, you control the inputs. You pick the clean data. You narrow the scope. You have a dedicated team focused on making it work. In production, the data is messy. The workflows span multiple systems. The compliance team has questions. The people using it every day aren’t the people who built it. The model you trained on six months ago may already be showing its age.
Pilots fail to cross the line because the organization wasn’t built to carry them forward.
The organizations that successfully operationalize AI share three characteristics:
- Infrastructure designed for AI from the start.
- Governance built into the architecture by design rather than retrofitted.
- Orchestration layer that connects agents, data, and workflows into something that runs at scale.
Related Read: FROM BOLT-ON TO BUILT-IN: THE CASE FOR SECURE-BY-DESIGN PLATFORMS
What Production “Ready” Actually Looks Like
At Cisco Live this year, we saw the infrastructure side of this story get serious attention. Cisco’s announcements — agentic orchestration, unified operations, and on-prem AI intelligence for air-gapped environments via Cisco IQ — are aimed squarely at the operationalization problem.
Cisco IQ On-Prem brings full agentic intelligence into fully isolated environments, delivering AI-driven insights locally with no external cloud connectivity required. That matters for organizations stalled by data sovereignty requirements, compliance constraints, or legacy infrastructure that couldn’t move to the cloud on anyone’s timeline.
Those were real reasons AI stayed in the pilot lane. And they’re being systematically removed.
As Liz Centoni, Cisco’s Executive Vice President and Chief Customer Experience Officer, announced at Cisco Live, “Every agentic action is still deterministic, validated, and governed before it touches your infrastructure. AI surfaces the insight. You make the decision. Accountability stays with Cisco.” Governance is designed in. That’s what makes production possible.
What We Built PRESIDIO P.A.T.H. to Do
At Presidio, we built the Programmable AI Technology Hub (P.A.T.H.) because we kept watching the same pattern: great pilots, stalled deployments. The problem was never the model. It was everything the model needed to run in the real world.
P.A.T.H. is a hands-on AI innovation environment built on Cisco compute and RoCE networking, NVIDIA GPUs, and a hybrid infrastructure stack that mirrors what our clients actually run. The point is to take a real use case and stress-test it against actual operational conditions (e.g., data integration, governance, orchestration, security) before committing to full deployment.
The organizations that successfully operationalize AI treat production readiness as part of pilot design. They build for governance from day one. They build for the model swap they’ll need in six months. They build for the compliance question they’ll get asked the day they want to go live. Presidio helps companies work through those decisions before they become the reason a good pilot never ships.
If you’re looking for a framework to take the next step — from identifying where to start to proving value in one part of the business before scaling — Rob Kim’s Clarify-Realize-Scale framework for CFOs and CTOs goes deeper on how to sequence that work and what the foundational investment looks like in real terms.
Related Read: FROM AGENT SPRAWL TO COMPOUNDING VALUE: WHY AI ORCHESTRATION IS THE ONLY STRATEGY THAT SCALES
The Decision in Front of You
The AI opportunity is real. The competitive pressure is real. The urgency to move fast is real.
Speed without the right foundation produces pilots, not outcomes.
The organizations winning right now aren’t the ones who launched the most AI projects. They’re the ones who built the infrastructure, governance, and orchestration layer that let them finish those projects and scale them — moving from experiment to operation, demo to deployed, lab result to business result.
That gap is worth closing. And it’s closer than most organizations think. 
Presidio’s P.A.T.H. is built to help organizations move from AI proof of concept to production-ready deployment. Learn more at presidio.com/solutions/ai.
