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 — Built with clarity, speed, and purpose.
The Need for This Workbook
Modern business leaders face pressure to adopt AI strategies. All around, people are piloting, selling, or hyping AI solutions. But business heads often struggle between two bad decisions:
• Saying “yes” to every vendor or internal idea, hoping some of it will succeed.
• Rejecting all ideas out of fear or uncertainty.
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.
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
Work through this individually or with your leadership team. The purpose is reflection, not speed. By the end, you’ll have:
• A prioritised list of AI use cases linked to your business goals.
• Recognition of where AI adds no value — and that’s okay.
• A realistic, step-by-step project plan.
Treat it as a lens, not a checklist. If your CFO can understand it in a minute, you’re doing it right.
AI strategy is just business strategy — minus the buzzwords.
Step One — Focus on Business Goals
Begin with Results, Not Technology
The usual focus on bots and models misses the real point. Start with measurable goals that truly impact your business.
Ask:
• What top objectives are driving your business now?
• Where are mistakes common or workloads heavy?
• Where do poor data or slow insights hold back progress?
It should improve something tangible — speed, accuracy, or cost. Ideas without measurable outcomes belong in the experiment bucket.
Leaders who skip this step collect shiny tools; those who follow it build lasting leverage.
Understand How Work Actually Happens
Visualise the Process, Not the Platform
You must see the true flow of tasks, not the idealised version. 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 generated ? sent ? reminded ? paid.
Inputs, actions, outputs — that’s the simple structure. AI belongs where the data is chaotic, the task is repetitive, and the result is measurable.
Rank and Select AI Use Cases
Score AI Use Cases by Impact, Effort, and Risk
Evaluate AI ideas using a simple impact vs effort grid.
Think of a 2x2: impact on the vertical, effort on the horizontal.
• Quick Wins — high impact, low effort.
• Strategic Bets — high impact, high effort.
• Nice-to-Haves — low impact, low effort.
• Avoid for Now — low impact, high effort.
Always judge the safety of automation before scaling.
Small wins set the foundation for larger bets.
Laying Strong Foundations
Fix the Foundations Before You Blame the Model
AI projects fail more from poor data than bad models. Clarity first, automation later.
Keep Humans in Control
Let AI assist, not replace, your team. 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
Your role is Gen AI consulting to define the problem clearly, not design the model. State outcomes clearly — e.g., “reduce response time 40%”. Share messy data and edge cases so tech partners understand reality. Clarify success early and plan stepwise rollouts.
Transparency about failures reveals true expertise.
Signs of a Strong AI Roadmap
Indicators of a Balanced AI Plan
You can summarise it in one slide linked to metrics.
Buzzword-free alignment is visible.
Finance understands why these projects exist.
The Non-Tech Leader’s AI Roadmap Checklist
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?
• Who owns the human oversight?
• How will success be measured in 90 days?
• What’s the fallback insight?
Conclusion
AI should make your business calmer, clearer, and more controlled — not noisier or chaotic. Focus on leverage, not hype. When executed well, AI simply amplifies how you already win.