The 2026 Paradox: Have We Forgotten How to “Prep”?
Do you still remember the days before generative AI? When we wanted a report, a deck, or a proposal, someone had to sit down and grind it out.
Because production was expensive, we prioritized preparation: thinking, planning, and alignment came first. We clarified assumptions, debated constraints, and socialized decisions, especially when clients were involved. At the time, we worked hard to avoid revisions caused by simple misunderstandings.
Then generative AI flipped the script
Now, the “doing” is cheap, but are we still preparing? While we are certainly generating more work, many teams are buried under a mountain of “AI slop”: high-fidelity, long-form content that is technically correct but functionally useless without hours of human review.
Today, anyone can generate a polished, 20-page strategy before the client even agrees on the problem. But then, you risk the client losing trust before they finish the first page. The moment they sense that “AI trace,” your expertise evaporates.
When people define productivity by the volume of work produced, we call it the Illusion of Productivity. Real productivity is defined by the value created for every hour worked. AI slop doesn’t bring much value; these “good-looking” drafts only lead to endless hours of re-doing, fine-tuning, editing, and re-aligning.
AI is not a magic spell
Too many teams still treat AI like a magic spell: expect the AI to be smart enough to understand everything they didn’t say.
But the truth is, the most critical work in the AI era happens before you ever hit “Generate.” When you prepare the inputs for AI, after thinking, planning, and aligning.
Think of AI as a master chef: if you ask for a dish, and dump a random bag of ingredients on their counter, you’ll get a meal, but it won’t be the one you wanted.
People has no incentive to go deep
In a 2026 workforce defined by high turnover and constant reconfiguration, this pattern has structural consequences. People are changing companies faster than ever; with no incentive to document the “prep” behind AI work, they focus solely on the deliverables.
This means the critical context prompted into AI is never saved. It’s lost. This leads to the erosion of industrial knowledge and long-term competitiveness. When no one can explain why a workflow exists or why a decision was made, strategy becomes fragile and adaptation slows.
The Agentic Reality Check
In response, many teams look to AI agents as the next “magic” solution. The promise is total autonomy: vertical agents that “just work” with minimal setup.
But here is the reality check: if everyone in your industry uses the same vertical agent, where is your competitive advantage?
The real value lies in the “Why”—the institutional knowledge behind your specific workflows. Without this, AI becomes nothing more than a glorified RPA script from a decade ago: a black box that no one understands and everyone is afraid to break.
The Last Moat: The Structured Context Layer
What should a company do? They must protect the last moat of business reality: operational data, industrial knowledge, and unique local know-how. This proprietary “soul” of the company must be kept in an AI-readable format that employees can access, reuse, and enhance.
In AI architecture, this is the Structured Context Layer.
With a context layer, employees can feed high-precision information—including their own personal expertise—directly into the AI. This results in high-quality assistance and eliminates “AI slop.” Furthermore, agentic workflows gain the guidance they need to perform every step with accuracy.
Rebuilding the Missing Layer
Teams can now deliberately rebuild what pre-AI manual work enforced implicitly: preparation before execution. This requires tools and practices that support the pre-generation stage, where intent and extended context is assembled and included into task inputs.
At least three areas should be covered in that stage:
- Context Assembly
Instead of encoding everything in long prompts, teams assemble context from modular elements such as research, data, and prior decisions. When these components are structured and connected, AI can interpret relationships directly, rather than inferring intent from prose alone. - Collaborative Guardrails
Effective AI use depends on shared constraints. Teams need a visible space to define tone, required sources, boundaries, and non-negotiables. Alignment happens before generation, not during post-hoc edits. - Context Reinforcement
High-quality outputs shouldn’t disappear once delivered. They should be cycled back into the context layer to reinforce it. This allows organizational knowledge to accumulate rather than resetting with every new project.
If your current tools only help you produce more, they may be accelerating the very problem you are trying to solve. But the next generation of AI tools should be collaborative and will help you and your team assemble, align, and preserve context - before the AI acts.
Otherwise, we will just keep moving faster without knowing where we are heading.
If this reflects challenges you are currently facing, we’d like to hear from you. We’re engaging with teams who are rethinking how context, alignment, and AI fit together, and are always open to thoughtful conversations. —> Book a time with us
