Latest News on Gen AI consulting

Wiki Article

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

A simple, practical 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

In today’s business world, leaders are often told they must have an AI strategy. AI discussions are happening everywhere—from vendors to competitors. But business heads often struggle between two bad decisions:
• Accepting every proposal and hoping it works out.
• 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.

Start here, and you’ll invest in leverage — not novelty.

Understand How Work Actually Happens

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.

Step Three — Choose What Matters

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.
• High cost, low reward — skip them.

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

AI projects Dhaval Shah fail more from poor data than bad models. 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.

Partnering with Vendors and Developers

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

It’s simple, measurable, and owned.
Buzzword-free alignment is visible.
Ownership and clarity drive results.

Quick AI Validation Guide

Before any project, confirm:
• What measurable result does it support?
• Which workflow is involved, and can it be described simply?
• 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.

Report this wiki page