From AI Experiments to Outcomes: A Practical Model for Achieving Real Value for Your Business -- Part 1

You stare numbly at the presentation you’ll deliver tomorrow. The dashboard is packed with colorful AI pilots—chatbots, auto‑draft tools, categorization models—but every line that truly matters hasn’t budged. Cycle times, backlog, customer satisfaction, cost per ticket… all flat. You can already hear the looming question from your executive peers: “When are we going to see results?”

In that moment, you’re caught between pressure and possibility. You know the promise of AI is real—but so is the gap between experimentation and transformation. And now, staring at that silent, unchanging dashboard, you can feel it: something deeper needs to change.

A recent study done by MIT called “GenAI Divide” found that ~95% of enterprise pilots fail to produce measurable ROI, while only ~5% scale to meaningful value. [1][2]  Why is this happening?  Why is it that for most organizations, the “AI promise” feels stuck in neutral? 

The pattern highlighted in the MIT study is a familiar one if you are familiar with the concepts in Geoffrey Moore’s book Crossing the Chasm.  While the book focuses on marketing high-tech to mainstream consumers, internally most organizations are at the edge of a “chasm” like what Geoffrey Moore described in his classic book. Successfully crossing that gap isn’t about more experiments; it’s about making a few key strategic choices and applying a disciplined operating model.

The Chasm

Geoffrey Moore’s Crossing the Chasm frames tech adoption in five segments: Innovators → Early Adopters → (Chasm) → Early Majority → Late Majority → Laggards.

The “chasm” is the tough transition from visionary experimentation to pragmatic mainstream use. Visionaries (innovators and early adopters) readily accept risk for potential advantage; pragmatists (early majority) demand proof, completeness, and low disruption. [3][4]

It seems AI in organizations today mirrors this progression: abundant proofs‑of‑concept (POCs) and pockets of individual productivity enhancement, but comparatively few repeatable deployments that change P&L— which is consistent with the MIT “GenAI Divide” findings.

We are standing at the edge of a chasm in our organizations when it comes to AI and the pressure is mounting on leaders to find a way to cross it and enjoy the real business results at scale that we were promised; however, many POCs can’t get across.

Why AI POCs Fall Into the Chasm

In his book, Moore identified several reasons that technologies struggle to “cross the chasm”. These can easily be applied to AI POCs in organizations:

a) No Beachhead
Moore emphasizes starting with a narrow, high‑value niche and dominating it before expanding. [5][6] Many programs try to “AI everything” across sales, marketing, ops, and service all at once.

b) Not a Whole Product
Moore’s “whole product” concept is central to crossing the chasm. [7][8] Flashy AI models and demos aren’t enough. The whole solution is needed: integration, data pipelines, governance, security, enablement, and support. 

c) Vague Outcomes
POCs often measure “time saved” or “coolness,” not business outcomes (cycle‑time, error rate, cost per ticket, revenue lift). MIT’s study supports this as well finding that pilots stall where workflows are brittle and results aren’t tied to real operational KPIs. [9][10]

d) Risk Perception
MIT’s analyses of the failed pilots highlight integration gaps, governance issues, and nondeterminism risks that must be actively addressed to reach production. [11]  Pragmatic early adopters rightly worry about compliance, brand safety, hallucinations, and process brittleness. In addition, there is a growing perceived risk that AI will cause disruption for employees or even job loss for some employees in organizations.  This growing “trust gap” only serves to widen the chasm.

Understanding why AI POCs fall into the chasm is the first step in understanding how to cross over into widespread adoption of the AI feature and the true business value that we seek.  In part 2 of this article, we'll discuss the practical steps we can take to help get our AI efforts successfully across the chasm.