Practical AI Roadmap Workbook for Business Executives
A straightforward, no-jargon workbook showing how AI can truly benefit your business — and where it may not be useful.
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. 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.
• 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 have to be technical; you just need to know your operations well. AI is simply a tool built on top of those foundations.
How to Use This Workbook
You can complete this alone or with your management 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 planning is business thinking without the jargon.
Starting Point: Business Objectives
Start With Outcomes, Not Algorithms
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 is valuable only when it moves key metrics — revenue, margins, time, or risk. Ideas without measurable outcomes belong in the experiment bucket.
Skipping this step leads to wasted tools; doing it right builds power.
Understand How Work Actually Happens
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 generated ? sent ? reminded ? paid.
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.
Step 3 — Prioritise
Assess Opportunities with a Clear Framework
Choose high-value, low-effort cases first.
Think of a 2x2: impact on the vertical, effort on the horizontal.
• Quick Wins — high impact, low effort.
• Reserve resources for strategic investments.
• AWS Minor experiments — do only if supporting larger goals.
• Avoid for Now — low impact, high effort.
Always judge the safety of automation before scaling.
Begin with low-risk, high-impact projects that build confidence.
Balancing Systems and People
Fix the Foundations Before You Blame the Model
Without clean systems, AI will mirror your chaos. Ask yourself: Is the data 70–80% complete? Are processes well defined?.
Keep Humans in Control
Keep people in the decision loop. As trust grows, expand autonomy gradually.
Avoid Common AI Pitfalls
Learn from Others’ Missteps
01. The Shiny Demo Trap — getting impressed by flashy demos with no purpose.
02. The Pilot Problem — learning without impact.
03. The Automation Mirage — expecting overnight change.
Fewer, focused projects with clear owners and goals beat scattered enthusiasm.
Collaborating with Tech Teams
Non-tech leaders guide direction, not coding. Focus on measurable results, not buzzwords. Expose real examples, not just ideal scenarios. Clarify success early and plan stepwise rollouts.
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.
Essential Pre-Launch AI Questions
Before any project, confirm:
• What measurable result does it support?
• Which workflow is involved, and can it be described simply?
• Is the data complete enough for repetition?
• Who owns the human oversight?
• What is the 3-month metric?
• If it fails, what valuable lesson remains?
Conclusion
AI done right feels stable, not overwhelming. A real roadmap is a disciplined sequence of high-value projects that strengthen your best people. When AI becomes part of your workflow quietly, it stops being hype — it becomes infrastructure.