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.
• 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 is only effective when built on your existing processes.
How to Use This Workbook
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 strategy is just business strategy — minus the buzzwords.
Step One — Focus on Business Goals
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. 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 ? resolved.
• Invoice issued ? tracked ? escalated ? payment confirmed.
Inputs, actions, outputs — that’s the simple structure. Ideal AI zones: messy inputs, repeatable steps, consistent outputs.
Rank and Select AI Use Cases
Evaluate Each Use Case for Business Value
Not every use case deserves action; prioritise by impact and feasibility.
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.
Add risk as a filter: where can AI act safely, and where must humans approve?.
Small wins set the foundation for larger bets.
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. Build confidence before full automation.
Common Traps
Steer Clear of Predictable Failures
01. The Demo Illusion — excitement without strategy.
02. The Pilot Problem — learning without impact.
03. The Full Automation Fantasy — imagining instant department replacement.
Choose disciplined execution over hype.
Collaborating with Tech Teams
Frame problems, don’t build algorithms. State outcomes clearly — e.g., “reduce response time 40%”. Share messy data and edge cases so tech partners understand reality. Agree on success definitions and rollout phases.
Request real-world results, not sales pitches.
Evaluating AI Health
Indicators of a Balanced AI Plan
Your AI plan fits on one business slide.
Your focus remains full stack product engineering 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 should make your business calmer, clearer, and more controlled — not noisier or chaotic. A real roadmap is 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.