AI isn't coming.

It's here. In your tools. In your vendors' roadmaps. In the way your best people already work when nobody is watching.

How a company approaches it will decide whether the next two years compound or stall.

Resistance will backfire. Fear will delay. Intentional adoption will accelerate.

But adoption of what?

That is the question most leadership teams still answer too narrowly. I have spent the last several years answering it from the inside: as a director who built and led a global technical organization, owned budget and execution when the market was still arguing about priorities, and now as an operator rolling AI into real workflows end to end.

Most "AI strategies" are unfinished experiments

Chat is an interface. A pilot is a test. Neither is a transformation program.

A lot of companies will tell you they have an AI strategy.

What they usually have is a pilot that stalled, or an isolated solution not built to scale or evolve.

AI as chat. Useful. Limited. Easy to demo in a board meeting. Easy to mistake for transformation.

AI as a pilot that never reaches production. A use case gets funded. A vendor gets selected. A team gets excited. Then the work sits in a sandbox because nobody owns the path into real workflows, real data, real accountability, and real KPIs.

That is not prudence. That is unfinished strategy dressed up as experimentation.

I learned that the hard way in enterprise infrastructure. When partners still treated AI as optional, I used my budget to acquire the NVIDIA stack and started building and delivering AI solutions without waiting for consensus. Two years later, the urgency arrived industry-wide. The lesson was not about being early for sport. It was about what happens when strategy waits for permission.

The companies pulling ahead are not the ones with the flashiest model access. They are the ones who treat AI as operating capacity across the business, with someone accountable for turning conviction into production.

Headcount is the wrong economic frame

Same people. More valuable work. Attention reclaimed for what compounds.

There is a second mistake that usually travels with the first.

Leaders frame AI as a headcount play. Same work. Fewer people. Cleaner spreadsheet.

Wrong economic frame.

Used well, AI is a force multiplier. Same people. More valuable work. Faster cycles. Better decisions. Less time burned on remedial tasks that never needed a human in the first place.

I have run large technical teams through that kind of retooling before — not as a slide exercise, as an operating change. At NetApp I led an organization of 21+ across the US, EMEA, and India and helped take our product to an added ~$400M ARR in three years. The pattern that scaled was not "work harder." It was clarity of ownership, ruthless prioritization, moving budget and people toward the work that compounded, and senior leadership willing to execute on long-term strategy.

AI raises the same leadership problem, faster.

Every hour a senior engineer spends on routine review is an hour not spent on architecture. Every hour a sales leader spends digging for pipeline truth is an hour not spent closing. Every hour an ops team spends stitching reports is an hour not spent fixing the process that created the mess.

The ROI conversation worth having is not "who can we cut." It is "whose attention can we reclaim for work that compounds."

Not a cubicle initiative

Field, ops, product, customer, back office — anywhere attention gets wasted.

I see the same pattern at home and in the field.

At home, lights, thermostat, security, and daily routines are not a pile of apps I babysit. They are an ecosystem I can instruct.

Multiply that instinct by a company. Then look past the knowledge-worker default.

The technician whose diagnostic system tells him what is failing before he opens the unit. The job site where vision systems flag safety issues in real time instead of after the incident report. The property or facilities team that treats access, operations, and exceptions as a living system instead of disconnected tools and tribal knowledge.

AI is not a cubicle initiative. It is a horizontal transformation. Field. Ops. Product. Customer. Back office. Anywhere work creates value and attention gets wasted.

Production is the difference between a story and a capability

Three use cases. Real owners. An operating layer that can ship.

In a smaller organization I have built, we did not wait for a perfect enterprise program. We put AI into the operating stack and measured what changed.

Engineering moved faster across the lifecycle because AI supported build, review, test, and pipeline work that used to consume senior bandwidth. Leadership and go-to-market stopped waiting on slow exports and tribal report-building. People could ask questions of their data and get a usable answer while the decision was still open.

That is the difference between AI you can demo and AI you can run.

So what does a real answer look like when you are accountable for the outcome?

  1. Map where work actually happens. Where time, money, risk, and customer experience are created or destroyed. Include the workflows that never touch a desktop.

  2. Pick three high-impact use cases for the next 12 months. Three. Not thirty. Each gets an owner, a production path, a success metric, a budget line, and kill criteria. If it cannot leave the sandbox, it is theater.

  3. Install the operating layer that makes adoption safe and fast. Intake and prioritization. Tooling budget. Enablement. Guardrails that protect the company without freezing the work. Data access with accountability. Clear rules for what can be automated, what needs human approval, and what never leaves a controlled environment.

If AI lives only in IT, or only with one enthusiastic VP, it stalls. The mandate has to span technical reality, operating redesign, people, and policy. Someone has to own that translation end to end and have the authority to fund it.

Inside those use cases, AI should show up as more than a chat box. Research partner. Memory layer for institutional knowledge. Workstream owner for discrete sub-projects that report back to humans. Decision support in the room when the call gets made.

Most companies activate one of those. The gap between one and several is where advantage hides. Production is the difference between a story and a capability.

Catch-up forms in slow motion

Accept the scope. Fund production. Put a leader in the seat.

If your AI strategy is still mostly chat and pilots that never ship, you do not have a wait-and-see strategy.

You have a catch-up problem forming in slow motion.

AI is here. The companies that win will not be the ones who accepted the idea of AI. They will be the ones who accepted the real scope of it, put production and budget behind their bets, and put a leader in the seat who can turn resistance into an operating plan.

That is the work. That is the standard I hold myself to.

If you want a smaller next step

Start with how people actually use the tools — then wire production around that.

You do not need a thirty-slide AI program to start. Clear use cases, clear owners, and a path out of the sandbox beat another demo.

If your team is still living mostly in chat, the guides on how chat differs from tools that can take steps and how to talk to AI clearly are a practical floor. When you are picking models for real workloads instead of novelty demos, the model picker is there for that.

Originally published on LinkedIn.