Context Is What Holds the System Together

In my AI and Product Leadership series I counted twenty-seven references to “system.” I think that is because the real journey for me was moving from product-led to systems-led thinking, a necessary shift in mindset before we can really make the most of AI.

Another recurring theme from the series was that keeping everyone on the same page across systems and teams matters even more once AI enters the picture. Everyone in the business has to see the same picture to succeed.

With this article, I would like to build on those ideas. What do we need to do to effectively put “context” at the center of how our businesses work? How can that context help our systems actually make sense and work together?

To be sure we are using the same language, here are two working definitions:

1. A system is a set of interrelated elements that work together as a unified whole.
2. Context is the frame that shows what happened, where the data came from, and why it matters.

Think of a system as the way we arrange information to get work done, and think of context as the shared understanding that tells us what that information means.

The Evolution of Context in Systems

Early systems worked alone and their context stayed inside clear boundaries. A customer database had customer information. An inventory system tracked products. Each did its own job and usually did it well.

As systems began talking to each other, we focused on moving data across them and assumed meaning would follow. Imagine one database keeping a customer marked active because an outstanding balance remains, while another flags the same customer closed after a cancellation request. An AI service that pulls those conflicting signals might send a loyalty offer and a farewell email on the same day. The data travelled just fine, the context did not.

Platforms (such as Enterprise Resource Planning or ERP systems) tried to solve this by creating shared environments where applications could exchange both data and meaning. Unfortunately, most of these platforms force organizations to use a standard data model to make that exchange work. When departments have to use the same template, it can have the opposite effect, forcing important information into spreadsheets and siloed databases. The one-size-fits-all approach loses the rich context that makes each department work.

Now AI has entered the picture, bringing both opportunities and challenges. AI systems are great at finding patterns across lots of data, potentially spotting connections humans might miss. However, AI also works based on statistical pattern matching rather than human understanding, sometimes making connections that sound right but aren’t, or missing obvious nuance a human would catch.

When we look at how systems connect and share information, coherence emerges as our fundamental challenge. Coherence means every part of the system keeps just enough shared context so people and machines can act without stopping to fix mismatches. Think of coherence like an orchestra. Each player has their own notes, but they follow one score and one tempo. Without that shared guide, music turns to noise.

Designing for Coherence

Over time, we start to recognize that our most important design work happens at the connective layer, how information maintains meaning as it flows between systems, teams, and time periods. Creating the conditions for context to persist requires attention to data models, integration patterns, and organizational structures that can be easy to overlook in feature-focused development. Systems with well-maintained context deliver better experiences for users while providing AI models with the coherence they need to generate reliable insights.

This kind of coherence doesn’t happen by accident. It needs to be built into the system. Data models, integration patterns, team structures, and governance all affect how well context flows through a business. Every decision about how systems connect, what information they share, and how they handle edge cases affects this critical layer.

Shifting from feature-focus to context-focus changes how we approach our work. We move from optimizing individual touchpoints to considering how the whole system holds together through change. We think less about specific features and more about relationships between them. We build for flexibility rather than rigid requirements.

This likely explains why so many digital transformation efforts struggle. Organizations implement new systems without thinking about how information will flow between them. They often focus on enabling individual capabilities or specific departments without considering what connects them, and context becomes an afterthought.

A Quiet Statement of Purpose

If systems are how our organizations work, then context is what makes that work make sense. The ability to maintain coherence across boundaries, whether between teams, technologies, or time periods, increasingly can separate successful organizations from struggling ones. Good context helps AI models work better and helps prevent hallucinations. It creates the right conditions for AI to truly enhance what humans do rather than just automating existing processes with their narrow focus.

Context engineering starts with one idea: meaning matters as much as mechanics, and the links between things shape outcomes as much as the things themselves. Focusing on context asks us to view our work differently, ensuring we create environments where information flows naturally, and people maintain shared understanding, even as complexity grows.