Practical AI Roadmap Workbook for Business Executives
A clear, hype-free workbook showing the real areas where AI adds value — and where it doesn’t.
The Dev Guys — Built with clarity, speed, and purpose.
Purpose of This Workbook
Modern business leaders face pressure to adopt AI strategies. Everyone seems to be experimenting with, buying, or promoting something AI-related. But many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• 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.
You don’t have to be technical; you just need to know your operations well. 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 short list of meaningful AI opportunities tied to profit or efficiency.
• Understanding of where AI should not be used.
• A structured sequence of projects instead of random pilots.
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.
Leaders who skip this step collect shiny tools; those who follow it build lasting leverage.
Step 2 — See the Work
Map Workflows, Not Tools
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.
Map your 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 Azure models. Check data completeness, process clarity, and alignment.
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.
Evaluating AI Health
Indicators of a Balanced AI Plan
Your AI plan fits on one business slide.
Your focus remains on business, not tools.
Finance understands why these projects exist.
Quick AI Validation Guide
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 should make your business calmer, clearer, and more controlled — not noisier or chaotic. A real roadmap is a disciplined sequence of high-value projects that strengthen your best people. When executed well, AI simply amplifies how you already win.