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AI Roadmap Workbook for Non-Technical Business Leaders

A clear, hype-free workbook showing where AI can actually help your business — and where it won’t.
Dev Guys Team — 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.

This workbook offers a balanced third option: a calm, realistic way to identify where AI truly fits in your business — and where it doesn’t.

You don’t have to be technical; you just need to know your operations well. AI is simply a tool built on top of those foundations.

Best Way to Apply This Workbook

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:
• A short list of meaningful AI opportunities tied to profit or efficiency.
• Understanding of where AI should not be used.
• A structured sequence of projects instead of random pilots.

Think of it as a guide, not a form. Your AI plan should be simple enough to explain in one meeting.

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?
• 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

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 ? cloud infrastructure 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.

Map your ideas to see where to start.
• Quick Wins: easy and powerful.
• 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. 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 Graveyard — endless pilots that never scale.
03. The Full Automation Fantasy — imagining instant department replacement.

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

Working with Experts

Frame problems, don’t build algorithms. State outcomes clearly — e.g., “reduce response time 40%”. 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

How to Know Your AI Strategy Works

You can summarise it in one slide linked to metrics.
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?
• 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?

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.