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AI Strategy For Business: How To Do IT Right
The AI hype is real, but so is the failure rate. If you want to be part of the 6% of companies actually seeing returns, you need to stop chasing the magic and start fixing the mechanics. Here is the strategy guide.
AI in 2026: Beyond the Hype
The year is 2026.
AI is the thing. ChatGPT runs the economy. Everyone is rich!
...Except, that’s not exactly what’s going on.
The Reality Check
The year is indeed 2026, and AI is the hottest thing in town. Truly, the progress made by these tools is nothing short of terrifying. We are living in the future we were promised. And yet!
Companies who tried to blindly take advantage of this AI boom are learning that not all is so rosy. According to McKinsey’s 2025 deep dive:
Only about 6% of enterprises are seeing significant, transformative returns.
The vast majority are stuck in "Pilot Purgatory."
Organizations are burning cash on models that look cool but do nothing.
This failure isn't about the code or the compute. It is a Lack of Proper Strategy. I break things for a living, but even I know you can't build a house if you don't know what the bricks are for.
The Framework for Surviving the Hype
1. Define the Problem (Actually)
Companies need to know exactly what problem their AI implementation aims to solve. You cannot fire a missile if you don't have a target.
Ask yourself, are you trying to:
Free up creative uptime for a copywriter?
Drastically reduce the "Mean Time to Resolution" for your support team?
Parse thousands of legal documents to find a specific clause?
IMPORTANT
If you can't write the specific problem on a post-it note, you aren't ready for the API key.
2. Do It Dumber
You might find that once you have clearly identified the problem, a simple automation might cure you of whatever ill.
This is my favorite hill to die on: Boring Automation > Sexy AI.
Rather than doing AI for the sake of AI, ask yourself: "Can this be solved with a simple script?" If the answer is yes, do that. It’s cheaper, it’s faster, and it breaks less often. Save the Heavy Lifting (LLMs) for the problems that actually require reasoning. For everything else? Keep it dumb.
3. Data Makes The Dream Work
You can have the smartest model in the world, but if you feed it garbage, you will get faster, more confident garbage. The reality of 2026 is simple:
1. Models are commodities: Everyone has access to GPT-5 or the latest Gemini.
2. Your Data is the moat: This is your only true competitive advantage.
If your internal data is messy, siloed, or unstructured, an AI project will only amplify the chaos. Clean the data first. Build the pipes. Then—and only then—turn on the brain.
Final Thoughts
The technology is there, and yes, we live in exciting times. But if we are to make the most of it, we need to understand where the pieces fit together.
Stop chasing the magic. Start building the momentum.
— Mr Elokusa