AI Roadmap Workbook for Non-Technical Business Leaders
A clear, hype-free workbook showing the real areas where AI adds value — and where it doesn’t.
The Dev Guys — Think deeply. Build simply. Ship fast.
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 most non-tech business leaders face two poor choices:
• Accepting every proposal and hoping it works out.
• Rejecting all ideas out of fear or uncertainty.
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 simply a tool built on top of those foundations.
Best Way to Apply 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 prioritised list of AI use cases linked to your business goals.
• Recognition of where AI adds no value — and that’s okay.
• A realistic, step-by-step project plan.
Treat it as a lens, not a checklist. Your AI plan should be simple enough to explain in one meeting.
AI strategy is just business strategy — minus the buzzwords.
Starting Point: Business Objectives
Start With Outcomes, Not Algorithms
The usual focus on bots and models misses the real point. 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.
Start here, and you’ll invest in leverage — not novelty.
Step Two — Map the Workflows
Understand the Flow Before Applying AI
Before deciding where AI fits, observe how work really flows — not how it’s described in meetings. Ask: “What happens from start to finish in this process?”.
Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• 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
Score AI Use Cases by Impact, Effort, and Risk
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.
• Minor experiments — do only if supporting larger goals.
• Avoid for Now — low impact, high effort.
Always judge the safety of automation before scaling.
Your roadmap starts with safe, effective wins.
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.
The 3 Classic Mistakes
Steer Clear of Predictable Failures
01. The Demo Illusion — excitement without strategy.
02. The Enterprise Automation Pilot Graveyard — endless pilots that never scale.
03. The Full Automation Fantasy — imagining instant department replacement.
Define ownership, success, and rollout paths early.
Partnering with Vendors and Developers
Your role is to define the problem clearly, not design the model. 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.
Buzzword-free alignment is visible.
Ownership and clarity drive results.
Essential Pre-Launch AI Questions
Before any project, confirm:
• What measurable result does it support?
• Is the process clearly documented in steps?
• Do we have data and process clarity?
• Where will humans remain in control?
• What is the 3-month metric?
• What’s the fallback insight?
Conclusion
Good AI brings order, not confusion. 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.