Practical AI Roadmap Workbook for Business Executives
A straightforward, no-jargon workbook showing how AI can truly benefit your business — and where it may not be useful.
The Dev Guys – Mumbai — Think deeply. Build simply. Ship fast.
Purpose of This Workbook
Modern business leaders face pressure to adopt AI strategies. 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.
• Declining AI entirely because of confusion or doubt.
This workbook offers a balanced third option: a calm, realistic way to identify where AI truly fits in your business — and where it doesn’t.
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 clear order of initiatives instead of scattered trials.
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 3–5 business results truly matter this year?
• Which parts of the business feel overwhelmed or inefficient?
• Which processes are slowed by scattered information?
It should improve something tangible — speed, accuracy, or cost. If an idea doesn’t tie to these, it’s senior engineering team 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
You must see the true flow of tasks, not the idealised version. Simply document every step from beginning to end.
Examples include:
• New lead arrives ? assigned ? nurtured ? quoted ? revised ? finalised.
• Support ticket ? triaged ? answered ? escalated ? 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
Assess Opportunities with a Clear Framework
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
Fix the Foundations Before You Blame the Model
Messy data ruins good AI; fix the base first. Clarity first, automation later.
Design Human-in-the-Loop by Default
Keep people in the decision loop. As trust grows, expand autonomy gradually.
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
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?
• How will success be measured in 90 days?
• 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.