Practical AI Roadmap Workbook for Business Executives
A clear, hype-free workbook showing the real areas where AI adds value — and where it doesn’t.
The Dev Guys — 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:
• 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 should serve your systems, not the other way around.
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 realistic, step-by-step project plan.
Use it for insight, not just as a template. 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
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 processes are slowed by scattered information?
AI is valuable only when it moves key metrics — revenue, margins, time, or risk. Ideas without measurable outcomes belong in the experiment bucket.
Start here, and you’ll invest in leverage — not novelty.
Understand How Work Actually Happens
Understand the Flow Before Applying AI
full stack product engineeringAI 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.
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
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.
• Big strategic initiatives take time but deliver scale.
• Nice-to-Haves — low impact, low effort.
• Delay ideas that drain resources without impact.
Add risk as a filter: where can AI act safely, and where must humans approve?.
Small wins set the foundation for larger bets.
Foundations & Humans
Data Quality Before AI Quality
AI projects fail more from poor data than bad models. Check data completeness, process clarity, and alignment.
Human Oversight Builds Trust
Let AI assist, not replace, your team. Over time, increase automation responsibly.
The 3 Classic Mistakes
Avoid the Three AI Traps for Non-Tech Leaders
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.
Transparency about failures reveals true expertise.
Signs of a Strong AI Roadmap
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?
• Do we have data and process clarity?
• Where will humans remain in control?
• How will success be measured in 90 days?
• What’s the fallback insight?
Conclusion
Good AI brings order, not confusion. It’s not a list of tools — it’s an execution strategy. True AI integration supports your business invisibly.