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


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A straightforward, no-jargon workbook showing where AI can actually help your business — and where it won’t.
Dev Guys Team — 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 is only effective when built on your existing processes.

How to Use 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.

Treat it as a lens, not a checklist. Your AI plan should be simple enough to explain in one meeting.

AI strategy is just business strategy — minus the buzzwords.

Step One — Focus on Business Goals


Focus on Goals Before Tools


Too often, leaders ask about tools instead of outcomes — that’s the wrong start. Start with measurable goals that truly impact your business.

Ask:
• What 3–5 business results truly matter this year?
• Which parts of the business feel overwhelmed or inefficient?
• Which processes are slowed by scattered information?

AI is valuable only when it moves key metrics — revenue, margins, time, or risk. Ideas without measurable outcomes belong in the experiment bucket.

Skipping this step leads to wasted tools; doing it right builds power.

Step Two — Map the Workflows


Visualise the Process, Not the Platform


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 generated ? sent ? reminded ? paid.

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

Step 3 — Prioritise


Assess Opportunities with a Clear Framework


Choose high-value, low-effort cases first.

Think of a 2x2: impact on the vertical, effort on the horizontal.
• Quick Wins — high impact, low effort.
• Reserve resources for strategic investments.
• Minor experiments — do only if supporting larger goals.
• Avoid for Now — low impact, high effort.

Always judge the safety of automation before scaling.

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

Balancing Systems and People


Fix the Foundations Before You Blame the Model


Messy data ruins good AI; fix the base first. Ask yourself: Is the data 70–80% complete? Are processes well defined?.

Keep Humans in Control


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

Avoid Common AI Pitfalls


Learn from Others’ Missteps


01. The Shiny Demo Trap — getting impressed by flashy demos with no purpose.
02. The Pilot Graveyard — endless pilots that never scale.
03. The Automation Mirage — expecting overnight change.

Define ownership, success, and rollout paths early.

Working with Experts


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.
Buzzword-free alignment is visible.
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?
• Do we have data and process clarity?
• Where will humans remain in control?
• What is the 3-month metric?
• What’s the fallback insight?

Conclusion


Good AI brings order, not confusion. It’s not a list of tools — it’s an execution strategy. True AI integration supports your business invisibly.

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