AI Roadmap Workbook for Non-Technical Business Leaders
A straightforward, no-jargon workbook showing how AI can truly benefit your business — and where it may not be useful.
The Dev Guys – Mumbai — Smart thinking. Simple execution. Fast delivery.
Why This Workbook Exists
Modern business leaders face pressure to adopt AI strategies. 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.
• Declining AI entirely because of confusion or doubt.
This workbook offers a balanced third option: a calm, realistic way to identify where AI truly fits in your business — and where it doesn’t.
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.
Best Way to Apply This Workbook
You can complete this alone or with your management team. 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.
Skipping this step leads to wasted tools; doing it right builds power.
Step Two — Map the Workflows
Visualise the Process, Not the Platform
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:
• New lead arrives ? assigned ? nurtured ? quoted ? revised ? finalised.
• 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.
Step 3 — Prioritise
Assess Opportunities with a Clear Framework
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.
• Reserve resources for strategic investments.
• 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. 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 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.
Signs of a Strong AI Roadmap
How to Know Your AI Strategy Works
You can summarise it in one slide linked full stack product engineering 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.