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AI Roadmap Workbook for Non-Technical Business Leaders

A clear, hype-free workbook showing where AI can actually help your business — and where it won’t.
The Dev Guys — Built with clarity, speed, and purpose.

The Need for This Workbook

If you run a business today, you’re expected to “have an AI strategy”. All around, people are piloting, selling, or hyping AI solutions. But most non-tech business leaders face two poor choices:
• Agreeing to all AI suggestions blindly, expecting results.
• Saying “no” to everything because it feels risky or confusing.

It provides a third, smarter path — a clear, grounded way to find genuine AI opportunities.

Forget models and parameters — focus on how your business works. AI is only effective when built on your existing processes.

How to Use This Workbook

Either fill it solo or discuss it collaboratively. It’s not about completion — it’s about clarity. By the end, you’ll have:
• A prioritised list of AI use cases linked to your business goals.
• Understanding of where AI should not be used.
• A clear order of initiatives instead of scattered trials.

Think of it as a guide, not a form. Your AI plan should be simple enough to explain in one meeting.

AI strategy equals good business logic, simply expressed.

Step 1 — Business First

Begin with Results, Not Technology

Most AI discussions begin with tools and tech questions like “Can we use ChatGPT here?” — that’s backward. Instead, begin with clear results that matter to your company.

Ask:
• What top objectives are driving your business now?
• Where are teams overworked or error-prone?
• Where do poor data or slow insights hold back progress?

It should improve something tangible — speed, accuracy, or cost. If an idea doesn’t tie to these, it’s not a roadmap — it’s just an experiment.

Skipping this step leads to wasted tools; doing it right builds power.

Step Two — Map the Workflows

Visualise the Process, Not the Platform

You must see the true flow of tasks, not the idealised version. Pose one question: “What happens between X starting and Y completing?”.

Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• Support ticket ? triaged ? answered ? escalated ? resolved.
• Invoice generated ? sent ? reminded ? paid.

Inputs, actions, outputs — that’s the simple structure. AI adds value where inputs are messy, actions are repetitive, and outputs are predictable.

Rank and Select AI Use Cases

Evaluate Each Use Case for Business Value

Not every use case deserves action; prioritise by impact and feasibility.

Use a mental 2x2 chart — impact vs effort.
• Focus first on small, high-impact changes.
• Big RAG strategic initiatives take time but deliver scale.
• Optional improvements with minimal value.
• High cost, low reward — skip them.

Add risk as a filter: where can AI act safely, and where must humans approve?.

Small wins set the foundation for larger bets.

Foundations & Humans

Data Quality Before AI Quality

AI projects fail more from poor data than bad models. Check data completeness, process clarity, and alignment.

Human Oversight Builds Trust

AI should draft, suggest, or monitor — not act blindly. Build confidence before full automation.

The 3 Classic Mistakes

Avoid the Three AI Traps for Non-Tech Leaders

01. The Demo Illusion — excitement without strategy.
02. The Pilot Problem — learning without impact.
03. The Full Automation Fantasy — imagining instant department replacement.

Choose disciplined execution over hype.

Collaborating with Tech Teams

Frame problems, don’t build algorithms. Focus on measurable results, not buzzwords. Expose real examples, not just ideal scenarios. Agree on success definitions and rollout phases.

Ask vendors for proof from similar businesses — and what failed first.

Signals & Checklist

Signs Your AI Roadmap Is Actually Healthy

You can summarise it in one slide linked to metrics.
Your team discusses workflows and outcomes, not hype.
Finance understands why these projects exist.

The Non-Tech Leader’s AI Roadmap Checklist

Before any project, confirm:
• Which business metric does this improve?
• Which workflow is involved, and can it be described simply?
• Is the data complete enough for repetition?
• Who owns the human oversight?
• How will success be measured in 90 days?
• If it fails, what valuable lesson remains?

Final Thought

AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.