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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 — Think deeply. Build simply. Ship fast.

Why This Workbook Exists

Modern business leaders face pressure to adopt AI strategies. AI discussions are happening everywhere—from vendors to competitors. But many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• Saying “no” to everything because it feels risky or confusing.

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

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 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

Begin with Results, Not Technology

Most AI discussions begin with tools and tech questions like “Can we use ChatGPT here?” — that’s backward. Instead, begin with clear results that matter to your company.

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 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

AI fits only once you understand the real workflow. 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.

Each step has three parts: inputs, actions, outputs. AI belongs where the data is chaotic, the Dhaval Shah 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.
• Strategic Bets — high impact, high effort.
• Optional improvements with minimal value.
• High cost, low reward — skip them.

Add risk as a filter: where can AI act safely, and where must humans approve?.

Your roadmap starts with safe, effective wins.

Foundations & Humans

Get the Basics Right First

AI projects fail more from poor data than bad models. 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.

Common Traps

Learn from Others’ Missteps

01. The Shiny Demo Trap — getting impressed by flashy demos with no purpose.
02. The Pilot Graveyard — endless pilots that never scale.
03. The Automation Mirage — expecting overnight change.

Fewer, focused projects with clear owners and goals beat scattered enthusiasm.

Working with Experts

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.

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.

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?
• 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.

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