Brendan McSheffrey
The Kendall Project
The $400 Billion Misunderstanding
In 1950, W. Edwards Deming arrived in Japan to teach statistical quality control. The Japanese executives who attended his lectures didn't just train their workers. They built systems, documented processes, measurement frameworks, and continuous improvement infrastructure that persisted long after Deming returned home.
The American companies that later tried to copy Japan's success made a critical error. They sent managers to seminars. They hired consultants for awareness training. They invested millions in knowing about quality but not in the operational infrastructure that produces quality.
We’re watching the same pattern unfold with AI.
The global corporate training market exceeds $400 billion. A substantial and growing portion of that spend now flows toward AI literacy, teaching employees what generative AI is, how to write prompts, how to avoid hallucinations. These are reasonable investments. But they're solving the wrong problem.
The Accuracy Ceiling isn't a technology limitation. It's an infrastructure gap that no amount of training will close.
In my work with Fortune 500 organizations implementing enterprise AI, I've observed a consistent pattern: AI accuracy plateaus at 65-75% regardless of which model, vendor, or prompting technique is deployed. I call this the Accuracy Ceiling. And the enterprises that break through it aren't the ones with the best-trained employees. They're the ones that built context infrastructure.
This paper examines what that infrastructure looks like, why training alone can't produce it, and how to evaluate investments that actually move the needle on AI performance.
The Training Gap: What AI Literacy Delivers vs. What AI Systems Require
Let me be clear: AI literacy training has value. Employees who understand how large language models work, what prompts produce better outputs, and where AI tends to fail make better decisions about when and how to use these tools.
But literacy and operational readiness are different capabilities.
I recently reviewed the offerings from nine leading AI literacy providers serving Global 2000 companies. The landscape ranges from self-paced micro-courses at under $15 per employee to cohort-based programs exceeding $500 per person. The content quality varies, but the fundamental value proposition is consistent: educated employees.
Each of these addresses a legitimate need. None of them produces what I've come to understand as the critical missing layer in enterprise AI: structured organizational context that AI systems can reliably interpret.
What AI Systems Actually Require
When a generative AI system underperforms, the instinctive response is to improve the prompt, fine-tune the model, or switch vendors. These are tool-first solutions to what is fundamentally a context problem.
AI accuracy depends on the system having access to the right context, defined processes, role specifications, terminology standards, policy constraints, and decision frameworks that govern how work actually happens in a specific organization. This context exists inside the heads of experienced employees, buried in unstructured documents, or not documented at all.
Your AI is only as good as your clarity. And clarity is infrastructure, not intuition.
Training employees to write better prompts doesn't create this infrastructure. It assumes the infrastructure already exists and simply needs to be communicated more effectively. In most organizations, it doesn't.
The pattern is instructive. Programs that produce both certified practitioners and organizational artifacts command premium pricing and deliver premium value. Organizations aren't just buying knowledge transfer. They're buying operational capability that persists.
Context Engineering: The Missing Infrastructure Layer
Over the past three years, I've developed a discipline I call Context Engineering, the systematic capture, structuring, and governance of organizational knowledge for AI consumption. It emerged from a simple observation: the enterprises achieving consistent AI performance weren't just training people better. They were building context infrastructure.
Context Engineering borrows heavily from operational excellence traditions. Deming's emphasis on documented processes. Ohno's insight that improvement requires making work visible. The quality management principle that variation is the enemy of performance.
Applied to AI, these principles suggest that accuracy problems are often clarity problems in disguise. The model isn't failing to understand. The organization hasn't articulated what there is to understand.
What Context Infrastructure Looks Like
In practice, context infrastructure consists of structured documentation across several categories:
If procured separately through traditional process documentation or knowledge management consulting, this infrastructure typically costs $50,000-250,000 or more for a comprehensive implementation. And critically, traditional documentation wasn't designed for AI consumption, it's optimized for human readers, not machine interpretation.
The Accuracy Ceiling exists because organizations are asking AI to work with context that doesn't exist in any structured form.
Evaluating Your Options: A Framework for AI Readiness Investment
When evaluating AI readiness investments, I recommend executives consider four questions:
1. What problem are you actually solving?
If employees don't understand what AI is or how to use it safely, literacy training addresses that gap. If AI systems are underperforming despite competent users, the gap is likely context infrastructure. These require different investments.
2. What do you own when the engagement ends?
Training produces educated people. Infrastructure produces organizational assets. Both have value. Be clear which you're buying.
3. Does the investment build internal capability to maintain and extend the asset?
Consulting engagements that produce documentation but not the internal capability to maintain it create dependency. The best investments develop both assets and people.
4. Can you measure the impact on AI performance?
Awareness is difficult to measure. Accuracy is not. Investments that produce structured context should demonstrate measurable improvement in AI output quality.
A Comparative View
The Path Forward
The enterprises that will succeed with AI aren't necessarily the ones with the largest training budgets or the most sophisticated models. They're the ones that recognize AI accuracy as an infrastructure problem and invest accordingly.
This doesn't mean AI literacy has no place. It means literacy is prerequisite, not solution. Employees need to understand AI. But understanding AI won't make AI understand your organization.
The Accuracy Ceiling that 65-75% plateau where most enterprise AI stalls exists because we've been solving the wrong problem. We've been teaching people to communicate better with AI when we should have been building the context infrastructure that gives AI something meaningful to understand.
Good enough is choosing to fail. The organizations that treat context as infrastructure will outperform those that treat it as an afterthought.
Deming understood this sixty years ago. The Japanese executives who attended his lectures didn't just learn statistical methods. They built systems. The enterprises that break through the Accuracy Ceiling will do the same.
The question isn't whether to invest in AI readiness. It's whether that investment produces trained people, operational infrastructure, or both.
Choose accordingly
About The Kendall Project
The Kendall Project helps Fortune 1000 organizations break through the Accuracy Ceiling through systematic context engineering. Our methodology, developed across dozens of enterprise implementations, produces both trained Context Curators and structured context assets that permanently improve AI system performance.
Executive AI Leadership Workshops align senior and cross-functional leaders around how AI will create real business advantage. These sessions help leadership teams understand AI through a context-first lens, identify high-impact opportunities, set priorities, and establish the operating model required to move from experimentation to enterprise-scale execution.
Context 360 Workshops bring cross-functional teams together to capture, structure, and validate organizational context across critical process areas. Participants learn the methodology while producing production-ready context assets.
Context Curator Certification develops internal practitioners who can lead context engineering initiatives, conduct Context Sprints, and maintain context infrastructure over time.
The Kendall Alliance provides organizations with ongoing access to methodology updates, practitioner community, and quality standards as context engineering matures as a discipline.
Contact:
Brendan McSheffrey
The Kendall Project