The Adoption Velocity metric: A CTO's guide to turning AI pilots into strategic assets

You’ve seen it happen. A team of your sharpest engineers spends six months building a sophisticated prediction engine. The demos are impressive, the accuracy metrics are in the high 90s, and the PowerPoints are flawless. The pilot is declared a "success."

And then… nothing.

The tool sits on a server, its insights ignored. The intended users revert to their old spreadsheets. The project, once a source of innovation pride, quietly fades into the background, becoming another ghost in the graveyard of successful pilots. This is the expensive, default outcome of confusing technical achievement with business progress.

Your leading indicator of value is adoption velocity

The fundamental flaw in most AI governance is that we measure the wrong things. We obsess over technical KPIs—precision, recall, latency—because they are easy to quantify. But these metrics say nothing about whether the solution will ever actually be used to make a better decision.

To de-risk AI investments, you need a leading indicator of business value. That indicator is Adoption Velocity.

Adoption Velocity isn't a vanity metric like user logins; it’s the rate at which an AI tool becomes indispensable to a core business workflow.

It’s a composite measure of an initiative’s organizational momentum. Think of it as the answer to the question: "Is the business pulling this solution from the lab, or is the lab pushing it on the business?"

You can measure it by tracking three simple factors:

1. Workflow integration: How deeply and frictionlessly is the tool embedding into an existing business process? Is it saving steps, or adding them?
2. Decision influence: Are people using the tool's output to make specific, measurable business decisions? How many decisions per week are being influenced?
3. User pull: Are users proactively asking for new features and finding new use cases, or do they need to be constantly reminded to use the tool?

A project with high Adoption Velocity might have a technically inferior model, but it’s solving a real, painful problem. A project with low Adoption Velocity, no matter how technically elegant, is a science project. And science projects belong in R&D, not on your strategic roadmap.

High technical maturity without adoption is a liability

By mapping your AI initiatives on two axes—Technical Maturity and Adoption Velocity—you can instantly clarify your portfolio's health. This reveals where your real strategic assets are, and which projects are actually hidden liabilities.

Figure 1: High technical maturity without high adoption velocity creates a costly "science project."

• Y-Axis: Technical Maturity (Low to High)
• X-Axis: Adoption Velocity (Low to High)
• Bottom-left quadrant (Lab experiment): Low maturity, low adoption. This is the natural starting point. Keep the investment small and focused on learning.
• Top-left quadrant (Science project - the danger zone): High maturity, low adoption. This is the trap. The tech is great, but it solves a nonexistent or misunderstood problem. This is where you must have the discipline to kill the project.
• Bottom-right quadrant (Simple tool): Low maturity, high adoption. A great signal! The problem is real and users are hungry for a solution. This is a prime candidate for more investment.
• Top-right quadrant (Strategic asset - the goal): High maturity, high adoption. The solution is technically sound and deeply integrated. This is a true competitive advantage.

Using this framework, you can establish clear go/no-go gates. If a project can't demonstrate a minimum threshold of Adoption Velocity after a set period, it doesn't get more funding. It’s not a punishment; it’s a strategic reallocation of capital and talent to projects with genuine momentum.

Where this is headed: From adoption to autonomy

In the near future, the conversation will shift beyond user adoption. As AI systems prove their value and reliability through metrics like Adoption Velocity, we will begin granting them true operational autonomy over specific, well-defined business processes.

This can only happen after a tool has demonstrated deep workflow integration and earned organizational trust. Adoption Velocity, therefore, isn't just a project metric; it’s a leading indicator of your organization’s readiness to embrace true AI-driven automation. The projects that show high velocity today are the ones that will become the autonomous agents running parts of your business tomorrow.

However, raw adoption is only the first signal. To truly understand the strategic impact, we must look deeper. The most critical metric is not just if a tool is being used, but where and how deeply. This is the concept of Usage Density. This is the organizational 'heat map' that reveals what your people truly value, and where your real transformation is happening. I explore this advanced concept in my follow-up post, Usage Density: the north star metric for AI adoption.

Start the conversation

1. Contrarian question: What if the biggest risk in your AI portfolio isn't a project that fails, but one that technically succeeds and consumes resources for years without ever impacting a single business metric?
2. Actionable challenge: Pick one active AI pilot this week. Ignore its technical KPIs. Instead, interview two of its intended end-users for 15 minutes. Ask them: "If this worked perfectly, what part of your day would it eliminate?" The clarity of their answer is your first, raw reading of its Adoption Velocity.