Introduction
Over the last few years, I have watched AI move from an emerging technology to a boardroom discussion.
Executives want to understand it. Technology teams want to implement it. Software vendors want to position themselves around it. And almost every industrial company is trying to determine what role AI should play in its future.
Much of the discussion focuses on better search, better productivity, better automation and better decision support.
All of these goals are reasonable. Yet I have noticed something interesting.
Most organizations are asking whether AI has access to enough information. Far fewer are asking whether AI has access to enough context.
At first glance, those questions appear similar. In practice, they are fundamentally different.
And understanding that difference may determine whether AI becomes a transformational capability or an expensive disappointment.
The Information Problem
Traditionally, industrial organizations have struggled with information availability.
Documents were difficult to find. Knowledge was trapped within departments. Systems were disconnected. Engineering information existed in one place. Commercial information existed somewhere else. Service information existed somewhere else again.
Finding information often required knowing exactly where to look.
The latest generation of AI changes that equation dramatically. Suddenly vast amounts of information can be searched, summarized and presented almost instantly.
Documents that were previously difficult to find become accessible. Questions can be answered conversationally. Knowledge appears closer than ever before.
This is genuinely valuable. But it creates a new risk.
Organizations may begin confusing information access with understanding.
The Question That Sounds Easy
Imagine asking an AI assistant a seemingly straightforward question:
Can this component be used in this product configuration?
Many people assume the answer should be relatively simple. Find the part. Find the product. Check compatibility. Return an answer.
The reality is often far more complicated.
A reliable answer may depend on understanding:
- Which product platform is involved
- Which market the product is intended for
- Which model year applies
- Which software version is installed
- Which surrounding components are present
- Which engineering changes have been introduced
- Which regulatory constraints apply
- Which service implications exist
The answer may not exist in a single document. In fact, it often does not exist anywhere explicitly.
It emerges from the relationships between many different pieces of information.
And that distinction is critical. Because AI can access information without understanding those relationships.
Information Versus Context
This is where many AI discussions begin to lose precision.
Organizations often talk about data, documents, knowledge bases, content repositories and information lakes. The assumption is that if enough information becomes available, understanding will naturally follow.
Industrial products rarely work that way.
A product is not simply a collection of information objects. It is a network of relationships.
Requirements relate to functions. Functions relate to systems. Systems relate to components. Components relate to rules. Rules relate to configurations. Configurations relate to markets. Markets relate to regulations. Regulations influence commercial offerings. Service history influences future engineering decisions.
The product is not the information. The product is the context created by those relationships.
And context is significantly harder to manage than documents.
What Experienced People Know
When experienced engineers solve problems, they rarely rely on information alone. They rely on context.
They understand why a decision was made, which alternatives were considered, which exceptions exist, which assumptions are still valid and which historical decisions continue to influence current products.
Most of this knowledge was not acquired from reading documents. It was acquired through years of experience, projects, failures, discussions, trade-offs and lessons learned.
The challenge for AI is obvious.
AI does not automatically inherit organizational memory. It only sees what the organization has made explicit.
And many organizations have discovered that important decisions often remain surprisingly implicit.
The Limits of Retrieval
This is one reason I remain cautious when people describe AI primarily as a search problem.
Search is important. Retrieval is important. Access to knowledge is important. But decision-making requires something more.
Imagine giving someone access to every engineering document ever created. Would that automatically make them capable of designing the next product generation?
Of course not.
The documents explain what happened. They do not necessarily explain why.
This distinction becomes increasingly important as organizations move from information retrieval toward decision support.
Finding information and making decisions are different capabilities. And AI becomes significantly more difficult when the objective moves from one to the other.
The Product Context Problem
Many industrial companies face an additional challenge.
Product knowledge is distributed across the lifecycle.
Engineering understands the designed product. Sales understands the commercial product. Manufacturing understands the produced product. Service understands the operated product.
No single function possesses the complete picture.
This means the context required for effective decisions is often fragmented across multiple systems and organizational boundaries.
An AI assistant connected exclusively to engineering information may provide engineering-accurate answers and still produce business-inaccurate recommendations.
An assistant connected exclusively to commercial systems may produce commercially accurate answers and still ignore engineering constraints.
Both perspectives may be correct. Neither perspective may be sufficient.
Because the lifecycle itself extends beyond any individual system.
Why This Matters More Than Ever
Historically, human beings acted as the integration layer.
Engineers talked to engineers. Sales people consulted experts. Service organizations escalated questions. Knowledge moved through conversations. Slowly, imperfectly and sometimes inconsistently. But it moved.
Organizations now want AI to participate in those same workflows. Not just finding information, but supporting decisions, guiding actions, recommending solutions and predicting outcomes.
This changes the level of responsibility dramatically.
And the higher the responsibility, the more context becomes necessary.
A confidently wrong recommendation is often more dangerous than no recommendation at all.
The Governance Challenge
This is where many AI discussions become less exciting. And considerably more important.
Questions begin to emerge such as:
- Which source is authoritative?
- Which rule applies?
- Which information is current?
- Which version is valid?
- Who owns the decision logic?
- Can the reasoning be explained?
- Can the recommendation be traced?
These questions rarely appear in AI demonstrations. Yet they often determine whether AI can be trusted in real operational environments.
The challenge is no longer generating answers. The challenge is governing understanding.
A Different Definition of AI Readiness
Many organizations evaluate AI readiness through a technical lens.
Which models should we use? Which platform should we select? How much data do we possess? How quickly can we deploy?
Those questions matter. But I increasingly believe a more important question exists.
How ready is our product knowledge?
Can relationships be explained? Can decisions be reconstructed? Can product states be understood? Can context be trusted? Can accountability be identified?
Without those capabilities, even sophisticated AI will struggle. Not because the models are inadequate. Because the organization's knowledge remains difficult to interpret.
Final Thoughts
AI is exceptionally good at processing information.
Industrial companies, however, operate through products. And products are defined not only by information, but by relationships, decisions and context.
That distinction matters.
Because the most important question is rarely: "What information can we find?"
The more important question is: "What decision should we make?"
The first problem is largely about retrieval. The second is about understanding. And understanding requires context.
The organizations that gain the most value from AI will not necessarily be those with the largest data lakes or the newest platforms. They will be the organizations that understand how product knowledge, decision logic and lifecycle context fit together.
Because AI may understand language. But industrial companies compete through products.
And products only become understandable when their context is connected.