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Step-by-Step AI Guide for Non-Tech Business Owners

A straightforward, no-jargon 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.

The Need for This Workbook

In today’s business world, leaders are often told they must have an AI strategy. All around, people are piloting, selling, or hyping AI solutions. But most non-tech business leaders face two poor choices:
• Agreeing to all AI suggestions blindly, expecting results.
• Rejecting all ideas out of fear or uncertainty.

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 planning is business thinking without the jargon.

Starting Point: Business Objectives

Start With Outcomes, Not Algorithms

Too often, leaders ask about tools instead of outcomes — that’s the wrong start. 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.

Step 2 — See the Work

Map Workflows, Not Tools

Before deciding where AI fits, observe how work really flows — not how it’s described in meetings. 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 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

AI should draft, suggest, or monitor — not act blindly. As trust grows, expand autonomy gradually.

Avoid Common AI Pitfalls

Steer Clear of Predictable Failures

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

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

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.

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?
Gen AI consulting • If it fails, what valuable lesson remains?

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.

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