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Practical AI Roadmap Workbook for Business Executives

A straightforward, no-jargon workbook showing where AI can actually help your business — and where it won’t.
Dev Guys Team — Smart thinking. Simple execution. Fast delivery.

The Need for This Workbook

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 most non-tech business leaders face two poor choices:
• Accepting every proposal and hoping it works out.
• 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

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.

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

Start With Outcomes, Not Algorithms

The usual focus on bots and models misses the real point. Non-technical leaders should start from business outcomes instead.

Ask:
• Which few outcomes will define success this year?
• Where are mistakes common or workloads heavy?
• 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.

Start here, and you’ll invest in leverage — not novelty.

Understand How Work Actually Happens

Understand the Flow Before Applying AI

Before deciding where AI fits, observe how work really flows — not how it’s described in meetings. Ask: “What happens from start to finish in this process?”.

Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• Support ticket ? triaged ? answered ? escalated ? resolved.
• Invoice issued ? tracked ? escalated ? payment confirmed.

Inputs, actions, outputs — that’s the simple structure. Ideal AI zones: messy inputs, repeatable steps, consistent outputs.

Rank and Select AI Use Cases

Evaluate Each Use Case for Business Value

Not every use case deserves action; prioritise by impact and feasibility.

Map your ideas to see where to start.
• Quick Wins: easy and powerful.
• Big strategic initiatives take time but deliver scale.
• 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

Data Quality Before AI Quality

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.

Collaborating with Tech Teams

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

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 AI can it be described simply?
• Do we have data and process clarity?
• Who owns the human oversight?
• What is the 3-month metric?
• If it fails, what valuable lesson remains?

Final Thought

AI should make your business calmer, clearer, and more controlled — not noisier or chaotic. A real roadmap is a disciplined sequence of high-value projects that strengthen your best people. When AI becomes part of your workflow quietly, it stops being hype — it becomes infrastructure.

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