Step-by-Step AI Guide for Non-Tech Business Owners
A simple, practical workbook showing the real areas where AI adds value — and where it doesn’t.
The Dev Guys — Built with clarity, speed, and purpose.
The Need for This Workbook
If you run a business today, you’re expected to “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 short list of meaningful AI opportunities tied to profit or efficiency.
• Understanding of where AI should not be used.
• A clear order of initiatives instead of scattered trials.
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 top objectives are driving your business now?
• Where are teams overworked or error-prone?
• Where do poor data or slow insights hold back progress?
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
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 RAG ? resolved.
• Invoice issued ? tracked ? escalated ? payment confirmed.
Inputs, actions, outputs — that’s the simple structure. Ideal AI zones: messy inputs, repeatable steps, consistent outputs.
Step Three — Choose What Matters
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.
• Strategic Bets — high impact, high effort.
• Optional improvements with minimal value.
• High cost, low reward — skip them.
Consider risk: some actions are reversible, others are not.
Begin with low-risk, high-impact projects that build confidence.
Foundations & Humans
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.
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%”. Share messy data and edge cases so tech partners understand reality. Clarify success early and plan stepwise rollouts.
Ask vendors for proof from similar businesses — and what failed first.
Evaluating AI Health
How to Know Your AI Strategy Works
It’s simple, measurable, and owned.
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?
• What’s the fallback insight?
The Calm Side of AI
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When AI becomes part of your workflow quietly, it stops being hype — it becomes infrastructure.