The demos are not lying.
AI capability and maturity are advanced enough today that the technology already produces the outputs you see in a proof of concept (POC).
What happens after that is where enterprise AI stops. Most POCs never see the light of production. That distance, between AI capability and AI running in production at enterprise scale, is the last mile.
I see that gap from the inside. I work with clients across their large AI transformations, from the first conversation through consulting and advising to executing the work itself. AI is evolving fast and everybody is adopting. Some are getting wins, while others are struggling.
The Three Questions Enterprise AI Leaders Are Asking
Three questions dominate the market right now.
- Which tool is best in the market, and how does it compare against the one we already chose?
- Where can I implement AI so that I get immediate ROI and early wins?
- Where can I cut my costs? This is a more recent question. Everybody’s costs are growing, when the whole premise was that they would do the opposite.
In isolation, these are valid, fair questions. On the first, the technology is rarely what is failing, so choosing a different tool rarely fixes anything. On the second, yes: you want quick ROI and quick wins so that there is a business case for your organization to start. On the third, it is very important to be conscious about your costs, but you should not be cutting costs just because. The cut should be controlled, so that you are not losing the value AI delivers to people.
The question leaders should be asking is, what does AI mean for the enterprise? Consider the enterprise as a persona and ask: how can I redesign my organization, my workflows, and my processes to involve AI, so that I get the best benefit at the optimal cost? That is the ROI question, and it gets asked at the level of the enterprise, not for individual teams or for individuals. It paves the way into the right approach to adopting AI.
The Biggest Mistake in Enterprise AI Adoption
The constant pattern I see with AI adoption is that it comes down to a technology selection problem. Success then gets measured on gut feel, or a piece of technology goes out to the teams and gets judged on spot successes and spot failures.
Measuring that way leaves you with a fragmented picture. There are teams saying they see wins. They feel they are getting good output, and then there is rework, and there is peripheral work around it, and the efficiency goes down. Others become naysayers immediately, because they are unable to use the technology properly. That is the mistake organizations make when they get into AI adoption.
What leaders should focus on is going to sound boring. Apply basic software engineering to the adoption of AI. It has always worked. Through every industry revolution, basic software engineering has held. The way you apply it here is by adopting an operating model that works for your organization.
Four Principles of an Agentic AI Operating Model
Businesses need to know what that operating model consists of and how to run it. Start with four principles. Build them out and you will have a real operating model for the organization.
- Agentic Processes and Context Engineering: A pilot that stays inside one team delivers nothing to the rest of the organization. Unless the work converts into an agentic process that different teams can use to deliver the same consistent output, it does not help anyone beyond that team. You do not want the same team rediscovering and reinventing how to deliver test automation.
Contextualize those processes at the same time. We call it context engineering, and it is one of the biggest improvements AI adoption has made in the industry today. It is what allows teams to reuse these processes. - Orchestration and Integration Across a Multi-LLM Environment: Orchestration is often used to mean agentic orchestration alone, one multi-agentic system coordinating with another. But I mean something wider. Orchestrate with your SDLC tools and with your other agentic tools.
Leadership should assume they will always be dealing with a multi-platform, multi-technology, multi-LLM environment. With that assumption, orchestration has to hold these tools together, coexisting and delivering the output as one. Any technology you think about using in isolation takes you nowhere. - Cost Control and Model Routing: With the first two principles in place, you can use model routing to send different tasks to different models. You do not want the frontier model performing a simple task like summarization or classification. A faster, smaller model handles that, which cuts your cost. Token usage has to be designed into the agentic process from the start. Gartner now forecasts that inference costs per agentic workflow will rise more than fivefold through 2028, and points to tiering, routing, and orchestration as the way to hold them down.
- AI Governance, Guardrails, and Measurability: None of this works without people. New roles are taking shape; they differ from the roles you had before, and the skills your teams need differ too, so training and change management matter.
Then look at your agentic tools. What are they taking as inputs? What are they delivering as outputs? What is the traceability in between? How are you auditing them? How are you guardrailing what they do? An RBAC model that governs who creates agents and which agents get published and reused is a very important part of this.
Measurement belongs in the operating model too. Adoption percentage, productivity boost, and cost are the ROI calculus. Unless that gets measured as part of the regular process, it stays a gut feel.
Why AI Demos Fail to Scale to Production
A team builds a POC, it succeeds, but it never scales up. Then the skilled people who built it, your architects, move quickly on to something fancier. The first POC gets left behind.
When the demo has to transfer into a real production use case with another team, the context is lost. That team never went through the process of discovering the tool. The scale fails and the transfer fails.
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, and attributes it to escalating costs, unclear business value, and inadequate risk controls rather than to model capability. This is not a technology problem. DORA surveyed nearly 5,000 technology professionals and found that AI amplifies what a team already is, which puts the return in the quality of your platforms, workflows, and team alignment rather than in the tool you pick.
I watch organizations diagnose it as a tool problem anyway, then select another tool to do the same thing, which is a vicious cycle. You never get out of it if you are chasing your own tail.
How to Scale AI Pilots into Enterprise Impact
You do not have to design the entire operating model up front, sink a large investment into it, and sit on it for a year before running your first pilot. The operating model is the North Star. What matters is that everything you do at a smaller scope becomes a stepping stone toward it.
Choose use cases that are able to scale to larger teams. Pilots are fine, but they have to be designed around the outcomes they are supposed to deliver and around how they get scaled beyond the team running them. Ask which technologies, tools, and standards they implement, and how those become transferable. Define that, and a pilot becomes a stepping stone toward the larger goal. From there you go through the normal steps: a smaller pilot, expanded to two, three, four teams, and eventually the enterprise level. The knowledge translates, instead of leaving pieces of effort scattered everywhere.
Define the North Star for your enterprise and walk toward it in smaller steps.
