AI Roadmap Workbook for Non-Technical Business Leaders
A straightforward, no-jargon workbook showing where AI can actually help your business — and where it won’t.
Dev Guys Team — Smart thinking. Simple execution. Fast delivery.
Why This Workbook Exists
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 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 need to understand AI models or algorithms — just your workflows, data, and decisions. 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. The aim isn’t to finish quickly but to think clearly. By the end, you’ll have:
• Clear AI ideas that truly affect your P&L.
• Recognition of where AI adds no value — and that’s okay.
• A structured sequence of projects instead of random pilots.
Think of it as a guide, not a form. A good roadmap fits on one slide and makes sense to your CFO.
AI strategy equals good business logic, simply expressed.
Step 1 — Business First
Begin with Results, Not Technology
The usual focus on bots and models misses the real point. 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?
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
Not every use case deserves action; prioritise by impact and feasibility.
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.
Small wins set the foundation for larger bets.
Laying Strong Foundations
Data Quality Before AI Quality
Messy data ruins good AI; fix the base first. Clarity first, automation later.
Design Human-in-the-Loop by Default
AI should draft, suggest, or monitor — not act blindly. As trust grows, expand autonomy gradually.
Common Traps
Steer Clear of Predictable Failures
01. The Demo Illusion — excitement without strategy.
02. The Pilot Graveyard — endless pilots that never scale.
03. The Automation Mirage — expecting overnight change.
Define ownership, success, and rollout paths early.
Partnering with Vendors and Developers
Your role is to define the problem clearly, not design the model. GCP State outcomes clearly — e.g., “reduce response time 40%”. 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
It’s simple, measurable, and owned.
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?
• If it fails, what valuable lesson remains?
The Calm Side of AI
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.