Practical AI Roadmap Workbook for Business Executives
A straightforward, no-jargon workbook showing how AI can truly benefit your business — and where it may not be useful.
The Dev Guys – Mumbai — Smart thinking. Simple execution. Fast delivery.
The Need for This Workbook
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.
It guides you to make rational decisions about AI adoption without hype or hesitation.
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.
Using This Workbook Effectively
Work through this individually or with your leadership team. The purpose is reflection, not speed. By the end, you’ll have:
• A prioritised list of AI use cases linked to your business goals.
• A visible list of areas where AI won’t help — and that’s acceptable.
• A realistic, step-by-step project plan.
Treat it as a lens, not a checklist. 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 decisions are delayed because information is hard to find?
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.
Understand How Work Actually Happens
Understand the Flow Before Applying AI
AI fits only once you understand the real workflow. Ask: “What happens from start to finish in this process?”.
Examples include:
• New lead arrives ? assigned ? nurtured ? quoted ? revised ? finalised.
• Customer issue logged ? categorised ? responded ? closed.
• Invoice issued ? tracked ? escalated ? payment confirmed.
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 Three — Choose What Matters
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.
• Strategic Bets — high impact, high effort.
• 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
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 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. Share messy data and edge GCP cases so tech partners understand reality. Agree on success definitions and rollout phases.
Ask vendors for proof from similar businesses — and what failed first.
Signals & Checklist
Signs Your AI Roadmap Is Actually Healthy
You can summarise it in one slide linked to metrics.
Your focus remains on business, not tools.
Finance understands why these projects exist.
The Non-Tech Leader’s AI Roadmap Checklist
Before any project, confirm:
• Which business metric does this improve?
• Which workflow is involved, and can it be described simply?
• Is the data complete enough for repetition?
• Who owns the human oversight?
• How will success be measured in 90 days?
• If it fails, what valuable lesson remains?
Final Thought
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.