Prompt engineering is dead. Long live context engineering!

For a while, prompt engineering felt like strategy.

Craft the perfect input, unlock perfect output. Add a few tokens here, adjust tone there and suddenly your chatbot sounds like a senior marketer. A productivity revolution. A creative partner. Maybe even a competitive edge.

But it wasn’t.

It was a placeholder—an interface trick for extracting meaning from a system that knew nothing about your business.

Prompting became popular not because it worked, but because it was the only tool available. It gave us the illusion of control while hiding a more significant truth: AI that doesn’t understand your context will never deliver your strategy.

Now the limitations are showing.

AI can’t scale relevance

Prompt-based tools scale content, but not relevance. They move faster, but not smarter. Ask them to reflect your differentiated value prop, pricing rationale and compliance nuance—and they improvise. Eloquently. Confidently. Wrongly.

What happens when you scale improvisation? You multiply risk.

Last year, McKinsey reported that 78% of enterprises are piloting GenAI in some form. Yet only 10% report material P&L impact. Why? Mass deployment without business alignment leads to surface activity, not bottom-line outcomes.

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Worse, early-stage experimentation often alienated stakeholders: security teams encountered compliance breaches, boards questioned ROI and marketing leaders found themselves producing more content with less impact.

We’ve reached the ceiling of the first generation of enterprise AI adoption. And that ceiling isn’t technical—it’s architectural.

Generic AI will give you generic outputs

Because if your AI isn’t trained on what your organization knows, believes and does best, then it isn’t your asset. It’s someone else’s.

And if go-to-market leaders don’t take ownership of this architecture, someone else will define what it becomes.

This is no longer an ops project or a digital pilot. This is a generational reset in how knowledge becomes revenue. And if Marketing, Sales and CX teams don’t reassert control, they’ll inherit a system built for someone else’s priorities.

That’s why the next era of AI doesn’t start with a better prompt. It starts with better design.

Context is the new code.

Context is king

AI systems that drive outcomes don’t rely on tricks. They rely on knowledge—specifically, your knowledge, structured and made accessible at scale. The shift we’re living through now is not from analog to digital, or manual to automated. It’s from prompting outputs to engineering context for your AI.

And that shift has enormous implications for go-to-market teams.

The deeper your offering, the more complex your market and the more differentiated your buyer journeys are, the more your AI needs to know. Not guess. Not generalize. Know.

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This isn’t about better fluency. It’s about better alignment.

AI that knows your ICPs. Your competitive edge. Your content strategy. Your pricing guardrails. Your win-loss logic. That’s what makes a machine intelligent.

Your AI needs to know what makes your company unique

When you build AI systems trained on your company’s proprietary intelligence, you stop chasing productivity and deliver precision. You stop asking, “How do we get the tone right?” and start asking, “How do we operationalize what we believe?”

You don’t get that with a better prompt. You get that with expert-trained AI.

This requires a change in posture: from experimentation to ownership.

The early phase of GenAI was about tool sprawl and tactical wins. Freelancers used free tools, agencies cobbled together assets and teams pasted prompts into interfaces and called it innovation.

It worked—until it didn’t.

Expert-trained models are not models trained on more data. They’re models trained on the correct data.

Your sales motion. Your brand voice. Your product roadmap. Your field insights. Your compliance framework. Your competitive playbooks.

Treat AI as infrastructure

These are your economic moats. Your AI should reflect them. And that means treating them like infrastructure: structured, versioned, governed, embedded.

To get there, organizations must build retrieval layers that pull relevant, governed knowledge. They must use systems that embed product data, sales logic and persona nuance. They must train models on proprietary corpora—not just web-scale content. And they must measure success not in speed but in signal: more resonance, less noise.

This isn’t a rejection of language models. It’s a rejection of generic deployment.

The foundation models are extraordinary. But if all they know is what they trained on—open-source text, scraped content and general web data—then they will never outperform your competitors because they trained on the same corpus.

The risk isn’t inefficiency. The risk is commoditization.

This is the moment to move from velocity to validity.

From velocity to validity

Expert-trained AI doesn’t just speed up creation—it raises the ceiling of what can be created. But it demands a strategy. It demands investment in knowledge capture. It demands rethinking governance, ownership and relevance.

Because the alternative is more of the same: more generalized models guessing at specialized tasks. More content. Less conversion. More outreach. Less engagement.

And here’s the deeper risk: You’re not just missing out on marginal performance. You’re letting someone else own your domain.

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If your knowledge isn’t part of the system, someone else’s will be. And their logic—not yours—will define what your customers hear, how your teams make decisions and what your future revenue engine looks like. Every quarter without re-architecting your AI stack is a quarter where generics are embedded deeper into your operating model. 

• Prompting becomes process. 
• Hallucinations become decisions. 
• And strategy becomes reactive.

We are at the inflection point.

Building with intent

You don’t need to start by building from. You need to start by building with intent.

What knowledge is unique to your company? Where does it live? How is it structured? Who validates it? And how does it get surfaced to the people—and systems—that need it most?

From there, the implementation roadmap becomes a function of design:

• Retrieval-augmented generation (RAG) pipelines aligned to strategic domains.
• Embedding vector stores that reflect your ICPs, playbooks and product truths.
• Governance structures that assign owners to key knowledge assets.
• Human-in-the-loop processes to ensure fidelity, quality and trust.

This is what it looks like to transition from AI as experimentation to AI as infrastructure.

And it’s not just feasible—it’s necessary. Because prompt engineering is dead. The future isn’t defined by who can write better prompts. It’s defined by who can embed better logic.

If you own pipeline, brand, content or customer experience, this shift belongs to you. Not to IT. Not to procurement. Not to legal. It is your strategy that will be scaled—or lost—based on what you build now.

Your team doesn’t need more AI. It needs the right AI, trained on the right knowledge, deployed in the right places.

When your knowledge becomes part of your architecture, AI stops sounding smart and starts being useful.