Practical AI Roadmap Workbook for Business Executives
A straightforward, no-jargon workbook showing the real areas where AI adds value — and where it doesn’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 many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• Rejecting all ideas out of fear or uncertainty.
It guides you to make rational decisions about AI adoption without hype or hesitation.
Forget models and parameters — focus on how your business works. 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:
• A prioritised list of AI use cases linked to your business goals.
• A visible list of areas where AI won’t help — and that’s acceptable.
• A realistic, step-by-step project plan.
Treat it as a lens, not a checklist. If your CFO can understand it in a minute, you’re doing it right.
AI strategy is just business strategy — minus the buzzwords.
Step One — Focus on Business Goals
Start With Outcomes, Not Algorithms
Too often, leaders ask about tools instead of outcomes — that’s the wrong start. Start with measurable goals that truly impact your business.
Ask:
• Which few outcomes will define success this year?
• Where are mistakes common or workloads heavy?
• Where do poor data or slow insights hold back progress?
It should improve something tangible — speed, accuracy, or cost. Only link AI to real, trackable business metrics.
Leaders who skip this step collect shiny tools; those who follow it build lasting leverage.
Step 2 — See the Work
Understand the Flow Before Applying AI
AI fits only once you understand the real workflow. Simply document every step from beginning to end.
Examples include:
• New lead arrives ? assigned ? nurtured ? quoted ? revised ? finalised.
• Customer issue logged ? categorised ? responded ? closed.
• Invoice issued ? tracked ? escalated ? payment confirmed.
Every process involves what comes in, what’s done, and what moves forward. AI belongs where the data is chaotic, the task is repetitive, and the result is measurable.
Rank and Select AI Use Cases
Assess Opportunities with a Clear Framework
Evaluate AI ideas using a simple impact vs effort grid.
Use a mental Azure 2x2 chart — impact vs effort.
• Focus first on small, high-impact changes.
• Big strategic initiatives take time but deliver scale.
• Nice-to-Haves — low impact, low effort.
• Delay ideas that drain resources without impact.
Consider risk: some actions are reversible, others are not.
Small wins set the foundation for larger bets.
Laying Strong Foundations
Get the Basics Right First
AI projects fail more from poor data than bad models. Clarity first, automation later.
Keep Humans in Control
Let AI assist, not replace, your team. Build confidence before full automation.
The 3 Classic Mistakes
Avoid the Three AI Traps for Non-Tech Leaders
01. The Shiny Demo Trap — getting impressed by flashy demos with no purpose.
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
How to Know Your AI Strategy Works
It’s simple, measurable, and owned.
Buzzword-free alignment is visible.
Ownership and clarity drive results.
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?
• Where will humans remain in control?
• What is the 3-month metric?
• 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.