Why Context, Not Just Data, Will Define AI-Ready Product Teams
You've probably had this conversation recently: a stakeholder, maybe even your CEO, pulls you aside and asks, "What do we need to consider now to make our organization AI future-ready?"
The answers usually gravitate toward the obvious suspects—data quality, model choice, infrastructure investments. Sometimes data security or moral and ethical considerations also make the list. All important, sure. But there's one ingredient I keep hearing in my coaching conversations that might surprise you: context. I want to make sure you have this on your radar so you're better equipped for these types of conversations.
Everyone talks about feeding AI systems more data. But what I see in teams that actually succeed with AI isn't just clever data work. They're obsessed with something much more fundamental: context—the invisible glue that makes their agents useful.
Think of AI systems like kids when they interact
Picture this: You ask a 7-year-old to "clean up the living room." What happens? They might stuff the books under the couch cushions, toss the TV remote into the toy box, and proudly call it done. Technically, they followed your instructions. The room looks "clean." But they completely missed the point because they lack the shared context you take for granted—what "clean" means in your household.
Adults fill these gaps instinctively: "Books go on the shelf, remotes on the table, toys in the chest." We don't get frustrated that kids don't know this. We recognize the gap and close it.
AI systems behave the same way. They start with no shared context about your organization's knowledge, rules, or objectives. Left unguided, they'll take plausible but often wrong paths. Here are just a few examples of how this might play out in real life:
Tell an AI to “analyze our user engagement trends,” and it might confidently report that engagement is up 40%—because it's counting every page refresh as an active session, not distinguishing between meaningful interactions and bots, or comparing apples to oranges across different product areas.
Ask an AI system to “rank feature requests by impact,” and without context about your strategic direction, it might prioritize the most-requested features—even though half come from a market segment you're deliberately exiting, or conflict with platform changes you've already committed to.
Ask an AI to “estimate complexity and timelines” for the next ten items on your backlog, and without knowing your team's non-negotiable principles around data privacy, maintainability, and extensibility standards, it might confidently tell you everything can ship in half the time—completely missing that every feature needs privacy impact assessments, comprehensive test coverage, and architecture that supports future scaling.
Closing those gaps by deliberately supplying context is what makes AI systems reliable.
Why AI product management is different
Here's where traditional product management gets turned upside down.
Traditional product management assumes your users already bring context. Doctors understand medicine. Accountants know balance sheets. Your job was to design smooth interactions and business logic for people who get the domain.
With AI in the mix, the "executor" is the system itself. And it starts from zero task context. Large language models bring general world knowledge from pretraining, but they don't know your policies, constraints, data, or current state. Without explicit guidance, they'll confidently march in the wrong direction.
That fundamentally changes our craft. Now we need to:
Reverse-engineer what a human would need to succeed at the task
Engineer context delivery systems—the instructions that guide AI behavior (system prompts), the knowledge bases it searches (retrieval pipelines), the tools it can use (tool schemas), and what it remembers between interactions (memory stores)
Design the human-AI handoff so users can easily add missing context during interactions
Iterate relentlessly with evaluation suites to measure improvements in accuracy, latency, and cost
This is less about UI polish and more about context engineering.
Domain expertise becomes the differentiator
Think about why coding copilots actually work. They succeed because their builders deeply understood the developer's world. Plus the domain had abundant training data and tight feedback loops through compilers, linters, and tests.
That combination of domain knowledge plus mechanisms to deliver it explains their early success. And it's exactly what we'll need in healthcare, finance, legal, and every other domain where AI is about to become essential.
The PMs who thrive in this shift will be those who can act as context engineers—people who are able to surface, structure, and inject the invisible knowledge that makes work actually work.
What this means for you as a product leader
Let's get practical. Preparing your org for AI isn't just about better data pipelines, model choices, or slick integrations. Those are important, but everyone is already talking about them. The deeper shift is learning to create, maintain, and model context. Because that's what both your people and your AI systems depend on.
Four habits will serve you well:
Define the playground clearly – Make goals, principles, and constraints explicit. Don't assume anyone (human or AI) automatically knows the boundaries.
Be explicit and repetitive – Document the "why" behind decisions and share it constantly. What feels obvious to you is invisible to others and completely invisible to machines.
Re-contextualize continuously – Context decays as markets shift and tools evolve. Actively refresh the baseline instead of letting assumptions drift.
Teach teams to spot context gaps – Build the muscle of "What context would an AI agent need to do this task well?" This skill serves you whether you're building AI products or just working alongside AI tools.
Here's the thing: Humans and machines both thrive when context is rich. They both struggle when context is thin.
Context design is a real superpower
In the AI era, a big part of the work of your product organization will be designing context. And designing context is methodical, iterative, sometimes invisible. But it's also the difference between expensive prototypes and AI systems that actually deliver value.
So here are my AI and context-related coaching questions for you:
Where in your organization is context too thin today?
How self-explanatory and context-rich are your strategy documents? Metrics definitions? User insights? Are they well documented?
What assumptions are you making about shared understanding that may not actually hold?
Start with these questions and you’ll discover something powerful: Strengthen context for your people, and you'll naturally strengthen it for your AI systems. That combination will do more to future-proof your org than any flashy model or infrastructure upgrade.
The teams that master context design won't just be AI-ready. They'll be unstoppable.