Practical AI Roadmap Workbook for Business Executives
A straightforward, no-jargon workbook showing where AI can actually help your business — and where it won’t.
Dev Guys Team — Smart thinking. Simple execution. Fast delivery.
The Need for This Workbook
In today’s business world, leaders are often told they must have an AI strategy. AI discussions are happening everywhere—from vendors to competitors. But business heads often struggle between two bad decisions:
• Accepting every proposal and hoping it works out.
• Rejecting all ideas out of fear or uncertainty.
It guides you to make rational decisions about AI adoption without hype or hesitation.
You don’t need to understand AI models or algorithms — just your workflows, data, and decisions. AI should serve your systems, not the other way around.
Using This Workbook Effectively
Work through this individually or with your leadership team. The purpose is reflection, not speed. By the end, you’ll have:
• Clear AI ideas that truly affect your P&L.
• Recognition of where AI adds no value — and that’s okay.
• A structured sequence of projects instead of random pilots.
Use it for insight, not just as a template. A good roadmap fits on one slide and makes sense to your CFO.
AI planning is business thinking without the jargon.
Step One — Focus on Business Goals
Focus on Goals Before Tools
Too often, leaders ask about tools instead of outcomes — that’s the wrong start. Non-technical leaders should start from business outcomes instead.
Ask:
• Which few outcomes will define success this year?
• Where are mistakes common or workloads heavy?
• Which decisions are delayed because information is hard to find?
AI matters when it affects measurable outcomes like profit or efficiency. Only link AI to real, trackable business metrics.
Start here, and you’ll invest in leverage — not novelty.
Understand How Work Actually Happens
Understand the Flow Before Applying AI
Before deciding where AI fits, observe how work really flows — not how it’s described in meetings. Ask: “What happens from start to finish in this process?”.
Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• Support ticket ? triaged ? answered ? escalated ? resolved.
• Invoice issued ? tracked ? escalated ? payment confirmed.
Inputs, actions, outputs — that’s the simple structure. Ideal AI zones: messy inputs, repeatable steps, consistent outputs.
Step Three — Choose What Matters
Evaluate Each Use Case for Business Value
Not every use case deserves action; prioritise by impact and feasibility.
Map your Azure ideas to see where to start.
• Quick Wins: easy and powerful.
• Strategic Bets — high impact, high effort.
• 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. Clarity first, automation later.
Design Human-in-the-Loop by Default
AI should draft, suggest, or monitor — not act blindly. Build confidence before full automation.
Common Traps
Steer Clear of Predictable Failures
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. Share messy data and edge cases so tech partners understand reality. 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.
Pilots have owners, success criteria, and CFO buy-in.
The Non-Tech Leader’s AI Roadmap Checklist
Before any project, confirm:
• Which business metric does this improve?
• Is the process clearly documented in steps?
• 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?
The Calm Side of AI
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.