# BEYOND AI PILOTS

## The enterprise AI race will not be won by the company with the most pilots. It will be won by organizations that learn how to turn AI into business value, repeatedly and at scale.

Most large enterprises do not have an AI ideas problem. Across engineering, operations, customer experience and corporate functions, teams are testing copilots, generative AI, machine learning and increasingly AI agents. The harder question begins after the demonstration: how do we convert a promising experiment into a secure, dependable capability embedded in the way the enterprise works?

This scaling gap is visible in market research. McKinsey’s 2025 State of AI survey reports that 88 percent of respondents say their organizations regularly use AI in at least one business function. Yet nearly two-thirds say their organizations have not begun scaling AI across the enterprise, and only 39 percent report enterprise-level EBIT impact from AI. The message is not that AI lacks potential. It is that adoption and enterprise value are not the same thing.

BCG’s 2024 research presents a similar contrast: 98 percent of companies studied were at least experimenting with AI, but only 26 percent had developed the capabilities to move beyond proofs of concept and begin extracting value. The next leadership challenge is therefore not simply more experimentation. It is industrialization.

# I have seen this pattern before

Over more than 25 years in technology, I have worked through several waves of enterprise change: databases and data modeling, enterprise architecture, data platforms, analytics, Big Data, statistical modeling, machine learning, deep learning and now generative and agentic AI.

Each wave is different, but the adoption pattern is familiar. A new capability emerges. Teams experiment. Early successes create excitement. Demand rises quickly. Different groups build independently, using different platforms, methods and controls. Eventually, the organization discovers that innovation cannot scale through isolated heroics.

That is the point at which the enterprise needs a repeatable system, not simply another pilot.

# What I mean by an AI Factory

> An AI Factory is a repeatable enterprise system that converts business demand into governed, production-grade AI capabilities and measurable business outcomes at scale.

The term “factory” should not imply rigid standardization or a central team building everything. It signals repeatability: shared infrastructure, reusable components, operating disciplines, quality controls, feedback loops and measurable outputs.

<table style="min-width: 125px;"><colgroup><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"><col style="min-width: 25px;"></colgroup><tbody><tr><td colspan="1" rowspan="1"><p><strong>BUSINESS DEMAND</strong></p></td><td colspan="1" rowspan="1"><p><strong>DATA &amp; KNOWLEDGE</strong></p></td><td colspan="1" rowspan="1"><p><strong>MODELS &amp; AGENTS</strong></p></td><td colspan="1" rowspan="1"><p><strong>AI ENGINEERING</strong></p></td><td colspan="1" rowspan="1"><p><strong>BUSINESS VALUE</strong></p></td></tr><tr><td colspan="1" rowspan="1"><p>Prioritize problems<br>Define outcomes</p></td><td colspan="1" rowspan="1"><p>Trusted data<br>Enterprise context</p></td><td colspan="1" rowspan="1"><p>LLMs, ML, agents<br>Reusable patterns</p></td><td colspan="1" rowspan="1"><p>Build, evaluate<br>deploy, operate</p></td><td colspan="1" rowspan="1"><p>Growth, productivity<br>experience, risk</p></td></tr></tbody></table>

*Foundation across every stage: Security • Privacy • Responsible AI • Governance • Cost and performa*

## 1\. Start with business demand, not the model

The first question should not be “Which model should we use?” It should be “Which business, customer, engineering or operational outcome should change?” Use cases should be prioritized against strategic relevance, measurable value, feasibility, data readiness, adoption complexity and risk.

This prevents an enterprise from accumulating many interesting demonstrations with no clear route to an operating outcome.

## 2\. Build trusted data and enterprise context

AI needs more than model intelligence. It needs authorized access to the right information, context and business rules. The factory should make trusted data, knowledge retrieval, APIs, metadata, identity and access controls reusable across solutions.

The objective is not to connect every model to every data source. It is to provide the right context, with the right permissions, for the right purpose.

## 3\. Industrialize AI engineering

A successful prototype is not a production system. Production-grade AI requires evaluation, testing, deployment, observability, cost management, resilience, security, human oversight and operational support. These disciplines belong in the normal engineering lifecycle, not in a separate innovation lane.

The factory creates a dependable path from idea to production while reducing the need for each team to rebuild foundational capabilities.

## 4\. Embed governance by design

Governance should not be a final approval gate after a solution has already been designed. Security, privacy, Responsible AI and risk controls should be incorporated throughout the lifecycle.

NIST’s Generative AI Profile, NIST AI 600-1, is a cross-sector companion to the AI Risk Management Framework. It is intended to help organizations incorporate trustworthiness considerations into the design, development, use and evaluation of generative AI systems. For an AI Factory, the practical principle is straightforward: make governance part of the production system, not an external layer around it.

## 5\. Create a learning and reuse loop

Every production implementation should add to the enterprise’s reusable assets: evaluation methods, data products, prompts, agent patterns, components, architecture decisions, controls and operating lessons.

The lifecycle becomes: discover, build, evaluate, deploy, measure, learn and reuse. The organization’s AI capability should compound with every implementation.

# Not another centralized AI CoE

An AI Factory should not become a large central organization through which every initiative must pass. That model can concentrate scarce expertise early, but it can also create a bottleneck and separate AI delivery from domain ownership.

> Centralize the guardrails and reusable capabilities. Decentralize innovation, execution and outcome ownership.

| **CENTRALIZE** | **DECENTRALIZE** |
| --- | --- |
| Platforms and common services | Business problems and priorities |
| Architecture and engineering patterns | Domain knowledge and product ownership |
| Security, Responsible AI and governance | Solution execution and workflow redesign |
| Evaluation and observability | Adoption and change |
| Reusable components and standards | Business outcome accountability |

The central capability builds the highway. Business and technology teams decide where to go, contribute domain expertise, redesign the workflow and remain accountable for the outcome.

# The people and process dimension is decisive

BCG’s leadership guidance uses a 10-20-70 rule for AI transformation: 10 percent of effort on algorithms, 20 percent on technology and data, and 70 percent on people and processes. This should be treated as a management heuristic rather than a universal accounting formula, but its message is valuable: model capability alone does not transform an enterprise.

Workflow redesign, ownership, incentives, skills, adoption and management cadence determine whether AI becomes part of the operating model. An AI Factory therefore needs product leadership, engineering, data, risk, change and business ownership working as one system.

# Measure outcomes, not AI activity

Pilots, agents, active users and model calls can help track activity. They do not establish enterprise value. Significant AI initiatives should show an explicit chain:

<div data-node-type="callout">
<div data-node-type="callout-emoji">💡</div>
<div data-node-type="callout-text"><p>Use case&nbsp; →&nbsp; Operational outcome&nbsp; →&nbsp; Business value</p></div>
</div>

<mark>·         Growth or revenue enablement</mark>

<mark>·         Productivity and cost</mark>

<mark>·         Customer or employee experience</mark>

<mark>·         Engineering velocity and cycle time</mark>

<mark>·         Quality and reliability</mark>

<mark>·         Risk reduction and compliance</mark>

<mark>·         Decision speed and responsiveness</mark>

McKinsey’s 2025 survey notes that 80 percent of respondents said their companies set efficiency as an objective for AI initiatives, while companies seeing the most value often also pursued growth or innovation. The survey also identifies workflow redesign as a key success factor among high performers. The larger point is that value comes from changing how work gets done, not merely adding AI to the existing process.

# The leadership shift

AI experimentation should continue. Discovery and learning are essential. But experiments should increasingly feed a system that can identify the best opportunities, turn them into reliable solutions, govern them responsibly, integrate them into workflows and measure their outcomes.

That is how AI moves from a portfolio of projects to an enterprise capability.

The leadership conversation should therefore shift from “How many AI use cases do we have?” to “How repeatable is our ability to turn AI into value?”

> The goal is not to scale AI for its own sake. The goal is to scale the value the enterprise creates through AI.

# References and further reading

1. McKinsey & Company, “The state of AI in 2025: Agents, innovation, and transformation,” November 5, 2025.
2. Boston Consulting Group, “Where’s the Value in AI?” October 2024.
3. Boston Consulting Group, “The Leader’s Guide to Transforming with AI,” December 12, 2024.
4. National Institute of Standards and Technology, NIST AI 600-1, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile,” July 2024.