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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.

The Need for 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 business heads often struggle between two bad decisions:
• 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.

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 planning is business thinking without the jargon.

Starting Point: Business Objectives

Begin with Results, Not Technology

The usual focus on bots and models misses the real point. Instead, begin with clear results that matter to your company.

Ask:
• What top objectives are driving your business now?
• Where are teams overworked or error-prone?
• Which decisions are delayed because information is hard to find?

AI matters when it affects measurable outcomes like profit or efficiency. Only link AI to real, trackable business metrics.

Leaders who skip this step collect shiny tools; those Dhaval Shah who follow it build lasting leverage.

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.

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.

Consider risk: some actions are reversible, others are not.

Begin with low-risk, high-impact projects that build confidence.

Balancing Systems and People

Fix the Foundations Before You Blame the Model

Messy data ruins good AI; fix the base first. 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.

Avoid Common AI Pitfalls

Learn from Others’ Missteps

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 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

Your AI plan fits on one business slide.
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?
• Who owns the human oversight?
• 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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