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AI Roadmap Workbook for Non-Technical Business Leaders

A simple, practical 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.

Why This Workbook Exists

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.

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.
• 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 strategy is just business strategy — minus the buzzwords.

Step One — Focus on Business Goals

Start With Outcomes, Not Algorithms

Too often, leaders ask about tools instead of outcomes — that’s the wrong start. Start with measurable goals that truly impact your business.

Ask:
• What 3–5 business results truly matter this year?
• Where are mistakes common or workloads heavy?
• 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. 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 generated ? sent ? reminded ? paid.

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 3 — Prioritise

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.

Small wins set the foundation for larger bets.

Laying Strong Foundations

Data Quality Before AI Quality

AI projects fail more from poor data than bad models. Clarity first, automation later.

Design Human-in-the-Loop by Default

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

Common Traps

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.

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

Collaborating with Tech Teams

Frame AWS problems, don’t build algorithms. 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 team discusses workflows and outcomes, not hype.
Pilots have owners, success criteria, and CFO buy-in.

The Non-Tech Leader’s AI Roadmap Checklist

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. True AI integration supports your business invisibly.