Across industries, leaders are investing billions in AI, building models, training data pipelines, piloting agents and integrating tools. Yet the great majority of efforts fall short of expectations.
The reason isn’t the technology. It’s the missing context.
AI doesn’t fail because it’s incapable. It fails because it doesn’t understand your business, your team members, workflows, rules, and success criteria. Without that structure, AI operates in the dark - like a brand new, really smart employee, producing outputs that sound right but miss the mark.
Context Turns AI From Generic to Strategic
Think of context as the connective tissue that makes AI useful. It’s what allows a model to interpret a question the same way your people do.
When AI knows who is asking, what process they’re working in, what problem they’re trying to solve and which rules apply, its answers become precise, aligned, and actionable. Context gives AI a sense of purpose, it tells the system not just what data means, but why it matters.
This is how organizations move from experimentation to execution. Context transforms AI from a tool that reacts to a prompt into a partner that performs like part of the business.
The Business Impact of Context
Structured context has measurable business value. When organizations capture and standardize the language of their operations, processes, policies, and priorities
AI can:
- Reduce variation in outputs across teams
- Accelerate decision-making with aligned reasoning
- Scale knowledge across functions and geographies
- Improve governance, compliance, and accuracy
- Deliver consistent experiences to employees and customers
In short, context drives both performance and trust.
From Context Chaos to Context Engineering
Most companies already have the right information, it’s just hidden in people’s heads or scattered across systems, slides, and spreadsheets. The work of Context Engineering is to bring that fragmented knowledge together into a structured format AI can understand.
This is the foundation of the Kendall Framework:
- Source context from people, processes, and data.
- Assemble context into modular building blocks.
- Govern context to keep it accurate, current, and secure.
Once this structure is in place, AI can finally operate within the real parameters of the business, not assumptions.
Why Context Is the New Competitive Edge
In the next phase of enterprise AI, the differentiator won’t be access to data or compute power. It will be the ability to operationalize context at scale.
Companies that treat context as a first-class asset will see faster adoption, higher ROI, and fewer failed experiments. They’ll be ready for the era of intelligent agents and AI copilots because their systems already understand how the business runs.
The takeaway is simple: AI performance begins and ends with context.
Structure it, govern it, and teach your AI to think like your business and it will perform like one of your best teams.