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How to build an AI strategy when the landscape changes daily

  • Keira Redmond
  • Apr 30
  • 2 min read

~ Written by Keira Redmond


New models are shipping almost every day. Here is how to build a strategy that can absorb the change without sending you back to square one.


A dynamic illustration of a business professional in motion, racing upward symbolising growth and progress, with an arrow indicating a sharp upward trajectory.

Updates constantly, started making a plan, now an update has ruined everything? Or just simply don't know where to start... How to build an AI strategy when the landscape changes DAILY. OpenAI dropped two major releases this week. Anthropic are shipping new models every other week. The pace is not slowing down. If anything, it is accelerating.

For business leaders, this raises an uncomfortable question: how do you commit to an AI strategy when the ground keeps moving underneath you?


Most AI strategies fail not because the technology stops working, but because they were built around what AI can do today, not what stays true regardless of what ships next. A team builds something clever to solve a real problem. Six weeks later, a new model does the same job out of the box, cheaper and faster. The custom build is made redundant overnight.

That is the trap. And the fix is simpler than most people expect.



The two-layer approach


The answer is to split your AI strategy into two distinct layers: one that you invest in heavily and rarely change, and one that you treat as temporary by design.


The durable layer


  • Clean, mapped data with clear ownership

  • Lean, connected systems

  • Documented SOPs

  • An AI usage policy

  • Role-based training, refreshed quarterly

  • Your core AI and tech infrastructure

The swappable layer


  • The specific model you are using

  • The specific tasks you hand to AI (these will grow over time)

Get the split right, and new releases stop being a panic. They become expected. You swap the swappable layer properly, not patch it together when something breaks.



Why the boring stuff is where the value lives


BCG's research on AI value creation makes this point clearly. The model you choose accounts for only a small fraction of the outcome. The real value sits in the foundations.


10%

The value of AI comes from algorithms and model choice

20%

comes from data quality and technical infrastructure

70%

comes from people, process, and adoption


This is why clean data ownership, documented processes, and a genuine usage policy matter far more than which model you are currently running. Those foundations compound. They get more valuable over time. Models, by contrast, get replaced.


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