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 — Built with clarity, speed, and purpose.
Purpose of This Workbook
If you run a business today, you’re expected to “have an AI strategy”. Everyone seems to be experimenting with, buying, or promoting something AI-related. But most non-tech business leaders face two poor choices:
• Agreeing to all AI suggestions blindly, expecting results.
• Rejecting all ideas out of fear or uncertainty.
It guides you to make rational decisions about AI adoption without hype or hesitation.
Forget models and parameters — focus on how your business works. AI should serve your systems, not the other way around.
Using This Workbook Effectively
Work through this individually or with your leadership 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.
Use it for insight, not just as a template. 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
Focus on Goals Before Tools
Most AI discussions begin with tools and tech questions like “Can we use ChatGPT here?” — that’s backward. 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 matters when it affects measurable outcomes like profit or efficiency. Ideas without measurable outcomes belong in the experiment bucket.
Start here, and you’ll invest in leverage — not novelty.
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
Assess Opportunities with a Clear Framework
Evaluate AI ideas using a simple impact vs effort grid.
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
Get the Basics Right First
Without clean systems, AI will mirror your chaos. 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 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.
Request real-world results, not sales pitches.
Evaluating AI Health
Indicators of a Balanced AI Plan
It’s simple, measurable, and owned.
Buzzword-free alignment is visible.
Ownership and clarity drive results. AI
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. It’s not a list of tools — it’s an execution strategy. True AI integration supports your business invisibly.