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AI Roadmap Workbook for Non-Technical Business Leaders

A clear, hype-free workbook showing the real areas where AI adds value — and where it doesn’t.
Dev Guys Team — Think deeply. Build simply. Ship fast.

The Need for This Workbook

Modern business leaders face pressure to adopt AI strategies. 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.
• Saying “no” to everything because it feels risky or confusing.

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 is only effective when built on your existing processes.

How to Use This Workbook

Either fill it solo or discuss it collaboratively. It’s not about completion — it’s about clarity. By the end, you’ll have:
• A short list of meaningful AI opportunities tied to profit or efficiency.
• 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. If your CFO can understand it in a minute, you’re doing it right.

AI planning is business thinking without the jargon.

Starting Point: Business Objectives

Begin with Results, Not Technology

Most AI discussions begin with tools and tech questions like “Can we use ChatGPT here?” — that’s backward. Start with measurable goals that truly impact your business.

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.

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

Step Two — Map the Workflows

Visualise the Process, Not the Platform

You must see the true flow of tasks, not the idealised version. Pose one question: “What happens between X starting and Y completing?”.

Examples include:
• Lead comes in ? assigned ? follow-up ? quote ? revision ? close/lost.
• 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.

Rank and Select AI Use Cases

Evaluate Each Use Case for Business Value

Evaluate AI ideas using a simple impact vs effort grid.

Think of a 2x2: impact on the vertical, effort on the horizontal.
• Quick Wins: easy and powerful.
• Reserve resources for strategic investments.
• Optional improvements with minimal value.
• Delay ideas that drain resources without impact.

Always judge the safety of automation before scaling.

Small wins set the foundation for larger bets.

Balancing Systems and People

Fix the Foundations Before You Blame the Model

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

Design Human-in-the-Loop by Default

Keep people in the decision loop. Over time, increase automation responsibly.

The 3 Classic Mistakes

Avoid the Three AI Traps for Non-Tech Leaders

01. The Demo Illusion — excitement without strategy.
02. The Pilot Graveyard — endless pilots that never scale.
03. The Automation Mirage — expecting overnight change.

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%”. Expose real examples, not just ideal scenarios. Clarify success early and plan stepwise rollouts.

Request real-world results, not sales pitches.

Evaluating AI Health

Indicators of a Balanced AI Plan

It’s simple, measurable, and owned.
Your team discusses workflows and outcomes, not hype.
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?
• 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. A real roadmap is RAG 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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