Without first answering why, AI becomes an expensive distraction.No cost-benefit analysis. No connection to real business outcomes. Just a vague sense that “we should be doing something.”
Executives are telling tech teams to go build AI. But what for? What’s the actual job to be done? And what outcomes or decisions are most relevant?
There’s an assumption that:
- AI is cheap (it isn’t), or that
- AI will eliminate costs overnight (rarely, and only if you’ve done the groundwork).
Here’s what that looks like in practice:
Case Study #1: The Compliance Agent That Cost Too Much
The idea was straightforward: an internal AI assistant to help employees pull up compliance documents quickly. On paper, it sounded efficient, cut down search time, improve response rates.
But once the project was scoped, the reality set in. Dev time ballooned. Hosting and token costs piled up. Integration with internal systems wasn’t seamless. What looked like a quick win turned into tens of thousands of dollars in spend. All for a tool that saved only minutes per week, per user.
The result? Not useless, but not worth it.
Case Study #2: The Inventory AI That Got It Wrong
A global retailer deployed an AI tool to automate stock reordering.
Goal: less waste, smarter forecasting.
Reality: It over-ordered massively.
Why? They forgot to teach it the difference between:
- a sales spike due to promotion, and
- a sales spike due to real demand.
Tech? Functional. Thinking? Missing.
Human Clarity First. Always.
In both cases, the missing ingredient wasn’t capability. It was clarity:
- What decision is the AI supporting?
- What criteria guide that decision?
- Who’s validating the output?
AI doesn’t work in a vacuum.
It’s not a shortcut around operational thinking.
If You Want AI to Work, Start Here
The AI Clarity Checklist
- Ask Why:
- What’s the real problem you’re solving?
- Define the Decision
- If your team can’t explain how they decide, AI definitely can’t.
- Check Your Foundations:
- Is your data clean?
- Are your teams ready?
- Is your process even worth automating?
- Do the Maths
- Account for the full cost; dev, tokens, licenses, training.
- Integrate It
- Don’t bolt AI on. Build it into the flow of work.
- Pilot with Purpose
- No random experiments. One use case. Learn fast. Scale smart.
Interest → Impact. Potential → Practical.
AI has massive potential. But it’s not plug-and-play. It’s not automatic ROI. It’s work. Strategic work.
That’s where we come in. At Twenty44, we turn curiosity into human clarity, and AI hype into business value.
Want to Build AI with Purpose? Start by asking the right questions. Let’s talk.
