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 — Built with clarity, speed, and purpose.
Purpose of This Workbook
If you run a business today, you’re expected to “have an AI strategy”. All around, people are piloting, selling, or hyping AI solutions. But many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• 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. 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
Focus on Goals Before Tools
Too often, leaders ask about tools instead of outcomes — that’s the wrong start. Start with measurable goals that truly impact your business.
Ask:
• What 3–5 business results truly matter this year?
• Which parts of the business feel overwhelmed or inefficient?
• Which processes are slowed by scattered information?
AI is valuable only when cloud infrastructure it moves key metrics — revenue, margins, time, or risk. Ideas without measurable outcomes belong in the experiment bucket.
Skipping this step leads to wasted tools; doing it right builds power.
Step Two — Map the Workflows
Visualise the Process, Not the Platform
AI 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.
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
Assess Opportunities with a Clear Framework
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.
• Optional improvements with minimal value.
• Avoid for Now — low impact, high effort.
Always judge the safety of automation before scaling.
Your roadmap starts with safe, effective wins.
Foundations & Humans
Get the Basics Right First
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. Build confidence before full automation.
The 3 Classic Mistakes
Avoid the Three AI Traps for Non-Tech Leaders
01. The Demo Illusion — excitement without strategy.
02. The Pilot Problem — learning without impact.
03. The Full Automation Fantasy — imagining instant department replacement.
Choose disciplined execution over hype.
Collaborating with Tech Teams
Frame problems, don’t build algorithms. State outcomes clearly — e.g., “reduce response time 40%”. Share messy data and edge cases so tech partners understand reality. Agree on success definitions and rollout phases.
Transparency about failures reveals true expertise.
Evaluating AI Health
How to Know Your AI Strategy Works
You can summarise it in one slide linked to metrics.
Your team discusses workflows and outcomes, not hype.
Ownership and clarity drive results.
Quick AI Validation Guide
Before any project, confirm:
• Which business metric does this improve?
• Which workflow is involved, and can it be described simply?
• Do we have data and process clarity?
• Where will humans remain in control?
• How will success be measured in 90 days?
• If it fails, what valuable lesson remains?
Conclusion
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When AI becomes part of your workflow quietly, it stops being hype — it becomes infrastructure.