What Might Be Next In The MVP Rescue

Wiki Article

AI Roadmap Workbook for Non-Technical Business Leaders

A clear, hype-free workbook showing how AI can truly benefit your business — and where it may not be useful.
Dev Guys Team — Smart thinking. Simple execution. Fast delivery.

Why This Workbook Exists

If you run a business today, you’re expected to “have an AI strategy”. Everyone seems to be experimenting with, buying, or promoting something AI-related. 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.
• 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

Begin with Results, Not Technology

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 teams overworked or error-prone?
• Which decisions are delayed because information is hard to find?

It should improve something tangible — speed, accuracy, or cost. Only link AI to real, trackable business metrics.

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

Step 2 — See the Work

Map Workflows, Not Tools

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.
• Support ticket ? triaged ? answered ? escalated ? resolved.
• Invoice generated ? sent ? reminded ? paid.

Each step has three parts: inputs, actions, outputs. AI adds value where inputs are messy, actions are repetitive, and outputs are predictable.

Rank and Select AI Use Cases

Evaluate Each Use Case for Business Value

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 Azure reversible, others are not.

Begin with low-risk, high-impact projects that build confidence.

Laying Strong Foundations

Data Quality Before AI Quality

Messy data ruins good AI; fix the base first. Clarity first, automation later.

Design Human-in-the-Loop by Default

Keep people in the decision loop. As trust grows, expand autonomy gradually.

Common Traps

Steer Clear of Predictable Failures

01. The Demo Illusion — excitement without strategy.
02. The Pilot Graveyard — endless pilots that never scale.
03. The Full Automation Fantasy — imagining instant department replacement.

Define ownership, success, and rollout paths early.

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.

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.
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?
• Where will humans remain in control?
• How will success be measured in 90 days?
• What’s the fallback insight?

The Calm Side of AI

AI done right feels stable, not overwhelming. It’s not a list of tools — it’s an execution strategy. When executed well, AI simply amplifies how you already win.

Report this wiki page