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Practical AI Roadmap Workbook for Business Executives

A straightforward, no-jargon workbook showing where AI can actually help your business — and where it won’t.
The Dev Guys – Mumbai — Think deeply. Build simply. Ship fast.

Why This Workbook Exists

Modern business leaders face pressure to adopt AI strategies. AI discussions are happening everywhere—from vendors to competitors. 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.

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 structured sequence of projects instead of random pilots.

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 top objectives are driving your business now?
• Which parts of the business feel overwhelmed or inefficient?
• Where do poor data or slow insights hold back progress?

It should improve something tangible — speed, accuracy, or cost. 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.

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.

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.

Think of a 2x2: impact on the vertical, effort on the horizontal.
• Quick Wins — high impact, low effort.
• Reserve resources for strategic investments.
• Optional improvements with minimal value.
• Avoid for Now — low impact, high effort.

Always judge the safety of automation before scaling.

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 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

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 MVP Building 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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