Why context engineering will define the next era of enterprise AI

Enterprises have spent the past two years determining which large language model they should use. GPT or Claude? Gemini or an open-source alternative? But as these models begin to converge in terms of quality and capabilities, the question of which model to use becomes less important than how to actually use them.

Ultimately, the real competitive advantage for enterprises won’t come from choosing the right model. It will come from building the right context, including the proprietary data, documents, workflows, and domain knowledge that shape how AI systems reason and act.

We are entering a new discipline known as context engineering: the practice of designing, integrating, and orchestrating the information environment in which AI systems operate. If prompt engineering was about asking better questions, context engineering is about giving AI the foundation to answer them well.

A huge leap beyond prompt engineering

Prompt engineering was the early bridge between human intent and machine intelligence. It taught us a new skill: phrasing instructions in a way that can significantly impact output quality. But prompts alone are shallow, similar to asking for directions without showing a map.

As AI systems evolve into agentic systems capable of reasoning, planning, and executing tasks, they require more than clever phrasing. They need a deeper understanding of where they are, what they know, and the constraints that apply. 

That understanding comes from context.

Context includes everything that shapes intelligent behavior: documents, structured data, memory of previous interactions, workflows, policies, and even unstructured sources such as audio or video. Stitching together all the relevant context and feeding it to AI is what enables AI to make relevant, consistent, and responsible decisions.

In short, we’re moving from a prompted world to a contextual one, and from instructing AI to equipping it.

The challenge of data variety and scale

Building this context layer isn’t simple. For decades, enterprises have struggled to manage structured data from systems such as ERPs, CRMs, and various analytics platforms. Today, the picture is even more complex because context comes from everywhere: chat transcripts, support tickets, sensor feeds, video, contracts, PDFs, and more.

This explosion of data variety has outpaced traditional data engineering methods. AI doesn’t consume static tables; it does much better with live, dynamic information flows. That means organizations must design composable systems that can retrieve, transform, and deliver the right context to the right process, all in real time and at scale.

The scale problem isn’t just about computing power, which has become abundant. It’s about orchestration. When you stitch together dozens of data products, run them across distributed systems, and feed them into AI models, reliability and observability are paramount. Engineers used to spend days restarting failed pipelines or trying to trace lineage across systems. Now, that level of manual intervention simply doesn’t scale.

The next wave of data infrastructure must handle these orchestration challenges automatically by understanding dependencies, monitoring flows, and ensuring that the context being fed to AI is always complete, consistent, and compliant.

Going from data engineering to context engineering

In many ways, context engineering is the natural evolution of data engineering. Where data engineers build systems to collect, clean, and deliver information, context engineers design living data environments, complete with semantics, lineage, and rules.

They don’t just move data; they make it ready for AI consumption. That means packaging data into data products that are secure, well-documented, and embedded with metadata about how they can be used.

Take a financial institution that wants to feed its underwriting models with historical claims data. It needs to mask sensitive information, maintain lineage, and ensure compliance with regulations. Or take a healthcare provider integrating on-premises patient data with cloud-based AI models. It must ensure privacy while still delivering relevant context to the AI system. These are quintessential context engineering problems.

I’ve seen this evolution firsthand, and I’ve found that the goal should be to make data easier to consume, regardless of format or source. The idea is to enable even non-engineers to build and share governed data products, mask personally identifiable information (PII), and document lineage without writing code. This is what context engineering can look like in practice: lowering the technical bar while raising the quality and trust of the data ecosystem.

Teaching AI to operate in complex environments

Context matters for AI as much as it does for human workers. For example, imagine training a human for a complex role at a new company, such as managing procurement for a global manufacturer. It could take months for a new hire to become fully up to speed and understand the specific systems, policies, and nuances the company uses to make decisions. 

AI faces a similar learning curve.

In the same vein, AI agents need the same organizational context that humans rely on to reason effectively, including prior decisions, policies, and examples of exceptions. The better the context, the better the AI will perform.

This doesn’t mean giving AI control of critical systems, such as a general ledger. It means allowing AI to handle the connective tissue—think summarizing reports, flagging anomalies, drafting communications, or optimizing workflows. And it should all be guided by the same context humans would use to make these judgments.

The point isn’t full automation; it’s contextual augmentation. It’s about enabling AI to operate with awareness and reliability in complex, real-world settings.

A shift in methodology

This transition involves more than adding a new toolset; it’s about changing methodology. Traditionally, software and analytics relied on humans to decide what questions to ask, what data to gather, and how to visualize the results. The process was linear and prescriptive.

In the AI-driven world, that model has flipped. We can feed large amounts of contextual data into an intelligent system and let it infer which information is relevant to the outcome we want. Instead of programming every rule, we define boundaries, supply context, and let AI reason within those boundaries.

That’s a fundamentally different way of building software, blending data engineering, governance, machine learning operations, and domain expertise into a single, continuous system. It’s the foundation for truly agentic AI: systems that act with awareness rather than just react to instructions.

Success means lowering the bar for intelligent systems

The real promise of context engineering is access. It opens the door for people beyond data scientists or machine learning experts to work productively with AI. When the underlying systems handle complexity, domain experts such as analysts, product managers, and marketers can focus on applying insight rather than dealing with the complexity of pipelines.

Modern, composable tools make it possible for almost anyone to shape and improve the context behind intelligent systems. They can monitor flows, adjust rules, and refine the data that guides decisions. That’s how enterprises will responsibly scale AI: by making participation broader, not narrower.

The shift we’re seeing isn’t only about building smarter models. It’s about creating smarter ecosystems. Companies that learn to engineer context, connecting knowledge, data, and process into a living system, will be the ones that set the pace for enterprise innovation in the years ahead.

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