How Much Do You Know About Enterprise Automation?

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

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.

You don’t need to understand AI models or algorithms — just your workflows, data, and decisions. AI should serve your systems, not the other way around.

Using This Workbook Effectively

Work through this individually or with your leadership team. The aim isn’t to finish quickly but to think clearly. By the end, you’ll have:
• Clear AI ideas that truly affect your P&L.
• Recognition of where AI adds no value — and that’s okay.
• A realistic, step-by-step project plan.

Use it for insight, not just as a template. A good roadmap fits on one slide and makes sense to your CFO.

AI planning is business thinking without the jargon.

Starting Point: Business Objectives

Start With Outcomes, Not Algorithms

The usual focus on bots and models misses the real point. 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.

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. 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 ? AI systems 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. Build confidence before full automation.

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%”. 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?
• 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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