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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 business heads often struggle between two bad decisions:
• 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 provides a third, smarter path — a clear, grounded way to find genuine AI opportunities.

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 purpose is reflection, not speed. By the end, you’ll have:
• A prioritised list of AI use cases linked to your business goals.
• Recognition of where AI adds no value — and that’s okay.
• A realistic, step-by-step project plan.

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.

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 top objectives are driving your business now?
• Where are teams overworked or error-prone?
• Where do poor data or slow insights hold back progress?

It should improve something tangible — speed, accuracy, or cost. If an idea doesn’t tie to these, it’s not a roadmap — it’s just an experiment.

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 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 full stack product engineering 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?.

Small wins set the foundation for larger bets.

Foundations & Humans

Get the Basics Right First

AI projects fail more from poor data than bad models. Check data completeness, process clarity, and alignment.

Design Human-in-the-Loop by Default

AI should draft, suggest, or monitor — not act blindly. Build confidence before full automation.

Common Traps

Steer Clear of Predictable Failures

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.

Partnering with Vendors and Developers

Your role is to define the problem clearly, not design the model. State outcomes clearly — e.g., “reduce response time 40%”. Share messy data and edge cases so tech partners understand reality. Clarify success early and plan stepwise rollouts.

Transparency about failures reveals true expertise.

Signs of a Strong AI Roadmap

How to Know Your AI Strategy Works

It’s simple, measurable, and owned.
Buzzword-free alignment is visible.
Ownership and clarity drive results.

Essential Pre-Launch AI Questions

Before any project, confirm:
• What measurable result does it support?
• Is the process clearly documented in steps?
• Is the data complete enough for repetition?
• Where will humans remain in control?
• How will success be measured in 90 days?
• If it fails, what valuable lesson remains?

The Calm Side of AI

AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.

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