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.
The Dev Guys – Mumbai — 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 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 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. 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.
Leaders who skip this step collect shiny tools; those who follow it build lasting leverage.
Step 2 — See the Work
Map Workflows, Not Tools
Before deciding where AI fits, observe how work really flows — not how it’s described in meetings. 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.
Each step has three parts: inputs, actions, outputs. 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
Evaluate AI ideas using a simple impact vs effort grid.
Use a mental 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.
Begin with low-risk, high-impact projects that build confidence.
Laying Strong Foundations
Fix the Foundations Before You Blame the Model
Messy data ruins good AI; fix the base first. Clarity first, vectorization automation later.
Design Human-in-the-Loop by Default
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 Graveyard — endless pilots that never scale.
03. The Automation Mirage — expecting overnight change.
Define ownership, success, and rollout paths early.
Working with Experts
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.
Transparency about failures reveals true expertise.
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.
Essential Pre-Launch AI Questions
Before any project, confirm:
• What measurable result does it support?
• Is the process clearly documented in steps?
• Is the data complete enough for repetition?
• Where will humans remain in control?
• How will success be measured in 90 days?
• What’s the fallback insight?
The Calm Side of AI
AI done right feels stable, not overwhelming. Focus on leverage, not hype. True AI integration supports your business invisibly.