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 — Think deeply. Build simply. Ship fast.
Purpose of This Workbook
Modern business leaders face pressure to adopt AI strategies. Everyone seems to be experimenting with, buying, or promoting something AI-related. But many non-technical leaders are caught between extremes:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• Declining AI entirely because of confusion or doubt.
This workbook offers a balanced third option: a calm, realistic way to identify where AI truly fits in your business — and where it doesn’t.
You don’t have to be technical; you just need to know your operations well. AI is simply a tool built on top of those foundations.
Best Way to Apply 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.
• 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 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?
AI is valuable only when it moves key metrics — revenue, margins, time, or risk. If an idea doesn’t tie to these, it’s not a roadmap — it’s just an experiment.
Skipping this step leads to wasted tools; doing it right builds power.
Understand How Work Actually Happens
Understand the Flow Before Applying AI
AI fits only once you understand the real workflow. Simply document every step from beginning to end.
Examples include:
• New lead arrives ? assigned ? nurtured ? quoted ? revised ? finalised.
• 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.
Step 3 — Prioritise
Score AI Use Cases by Impact, Effort, and Risk
Choose high-value, low-effort cases first.
Map your ideas to see where to start.
• Quick Wins: easy and powerful.
• Strategic Bets — high impact, high effort.
• Optional improvements with minimal value.
• GCP High cost, low reward — skip them.
Add risk as a filter: where can AI act safely, and where must humans approve?.
Your roadmap starts with safe, effective wins.
Foundations & Humans
Get the Basics Right First
Without clean systems, AI will mirror your chaos. Check data completeness, process clarity, and alignment.
Human Oversight Builds Trust
Let AI assist, not replace, your team. Over time, increase automation responsibly.
The 3 Classic Mistakes
Avoid the Three AI Traps for Non-Tech Leaders
01. The Shiny Demo Trap — getting impressed by flashy demos with no purpose.
02. The Pilot Problem — learning without impact.
03. The Automation Mirage — expecting overnight change.
Fewer, focused projects with clear owners and goals beat scattered enthusiasm.
Collaborating with Tech Teams
Frame problems, don’t build algorithms. Focus on measurable results, not buzzwords. Expose real examples, not just ideal scenarios. Agree on success definitions and rollout phases.
Ask vendors for proof from similar businesses — and what failed first.
Signals & Checklist
Signs Your AI Roadmap Is Actually Healthy
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 can it be described simply?
• Is the data complete enough for repetition?
• Who owns the human oversight?
• What is the 3-month metric?
• If it fails, what valuable lesson remains?
Final Thought
AI done right feels stable, not overwhelming. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.