From Interested to In-Progress

You've read the book. You understand the opportunities, the pitfalls, and the process. Now it's time to act.

This 30-day sprint takes you from "we should do something with AI" to a concrete, prioritized plan you can execute.

Before You Start

Commitment Required

This sprint requires:

• 1-2 hours daily from you
• Access to key stakeholders
• Authority to convene meetings
• Willingness to make decisions

If you can't commit these, wait until you can.

What You'll Have at the End

• Prioritized list of AI opportunities
• Clear first initiative to pursue
• Understanding of data and capability gaps
• Initial governance framework
• 90-day action plan
• Stakeholder alignment

Week 1: Discovery

Day 1: Define Your AI Ambition

Time: 1 hour

Exercise: Answer these questions:

• Why are you pursuing AI? (Specific business reasons, not "everyone else is")
• What would success look like in 1 year? 3 years?
• What's your risk tolerance?
• What resources could you commit?
• What's your timeline expectation?

Write a half-page AI ambition statement.

Day 2: Stakeholder Mapping

Time: 1 hour

Exercise: Identify the people you need:

Sponsors: Who has budget authority?

Champions: Who will advocate for AI?

Skeptics: Who needs to be convinced? (They're often right about something)

Users: Who would use AI solutions?

Experts: Who understands the data, processes, and technology?

Map each person: Their role, attitude toward AI, what they care about, how to engage them.

Day 3: Pain Point Collection (Part 1)

Time: 2 hours

Exercise: Schedule and conduct 3-4 conversations with operational leaders.

Ask:

• What tasks consume the most time?
• What's frustrating or repetitive?
• Where do errors happen?
• What decisions are hard to make?
• What information do you wish you had?
• Where is quality inconsistent?

Listen for volume, repetition, frustration, and cost.

Day 4: Pain Point Collection (Part 2)

Time: 2 hours

Exercise: Continue conversations with another 3-4 people.

Target different functions: Sales, operations, customer service, finance, HR.

Add to your list from yesterday.

Day 5: Data Landscape Overview

Time: 2 hours

Exercise: Meet with IT/data leadership.

Understand:

• What major data systems exist?
• What data is centralized vs. scattered?
• What data quality issues are known?
• What data governance exists?
• What data projects are in progress?
• What are the biggest data challenges?

You don't need details — you need the landscape.

Day 6: Capability Assessment

Time: 1 hour

Exercise: Assess your organization's AI readiness:

Talent:

• Do you have data scientists or ML engineers?
• Do you have data engineers?
• What analytical skills exist?

Technology:

• What cloud platforms are used?
• What data infrastructure exists?
• What analytics tools are in place?

Culture:

• Is data-driven decision-making normal?
• Is there appetite for experimentation?
• How are technology projects typically received?

Rate each area: Strong / Adequate / Weak / Missing

Day 7: Week 1 Review

Time: 1 hour

Exercise: Synthesize your week:

• What pain points emerged most often?
• What data exists vs. what's missing?
• What capability gaps are clear?
• What surprised you?
• What concerns emerged?

Write a summary document.

Week 2: Opportunity Development

Day 8: Opportunity Brainstorm

Time: 2 hours

Exercise: Generate potential AI use cases.

Using pain points from Week 1, brainstorm where AI could help:

• For each pain point, ask: Could AI help here?
• Reference the high-value use cases from Chapter 3
• Consider: What would delight customers? What would cut costs? What would reduce risk?

Generate 15-25 potential opportunities. Don't filter yet.

Day 9: Quick Assessment

Time: 2 hours

Exercise: For each opportunity from yesterday, quick-assess:

• Data exists? Yes / Partially / No / Unknown
• Business impact? High / Medium / Low
• Feasibility? High / Medium / Low / Unknown
• Effort? Low / Medium / High / Unknown

Eliminate obvious non-starters. Reduce list to 10-12.

Day 10: Deep Dive on Top Opportunities (Part 1)

Time: 2 hours

Exercise: Select 4-5 highest-potential opportunities.

For each, develop:

• Clear problem statement
• Potential AI solution approach
• Data requirements
• Success metrics
• Key assumptions
• Open questions

Day 11: Deep Dive on Top Opportunities (Part 2)

Time: 2 hours

Exercise: Continue deep dives on remaining opportunities.

Research:

• What vendor solutions exist?
• What have competitors done?
• What case studies are relevant?

Day 12: Data Validation

Time: 2 hours

Exercise: For each top opportunity, validate data:

Meet with data owners:

• Does the data actually exist?
• What format and quality?
• Can you access it?
• What would be needed to prepare it?

Kill opportunities where data is truly unavailable.

Day 13: Build vs. Buy Analysis

Time: 2 hours

Exercise: For remaining opportunities:

• Research vendor landscape
• Assess build vs. buy for each
• Estimate costs (rough order of magnitude)
• Estimate timeline
• Identify key risks

Day 14: Week 2 Review

Time: 1 hour

Exercise: Consolidate opportunity analysis.

Create an opportunity scorecard:

Rank order your opportunities.

Week 3: Planning and Alignment

Day 15: First Initiative Selection

Time: 1 hour

Exercise: Select your first AI initiative.

Criteria:

• High enough impact to matter
• Feasible enough to succeed
• Fast enough to build momentum
• Visible enough to create support

Write a one-page initiative brief:

• Problem being solved
• Proposed solution
• Success metrics
• Resource requirements
• Timeline estimate
• Key risks

Day 16: Pilot Design

Time: 2 hours

Exercise: Design the pilot for your first initiative:

• Scope (narrow enough to be achievable)
• Users (who participates)
• Duration (recommend 4-8 weeks)
• Success criteria (how you'll know it worked)
• Data requirements
• Resource needs
• Risks and mitigations

Day 17: Resource and Budget Planning

Time: 2 hours

Exercise: Develop resource plan:

• Internal resources needed (people, time)
• External resources needed (vendors, consultants)
• Technology requirements
• Budget estimate (pilot, then production)
• Timeline with milestones

Identify gaps and how to fill them.

Day 18: Stakeholder Presentation Draft

Time: 2 hours

Exercise: Create presentation for sponsors/stakeholders:

• AI opportunity landscape
• Recommended first initiative
• Business case
• Resource requirements
• Timeline and milestones
• Risks and mitigations
• Ask (what you need from them)

Keep it concise. Decision-makers don't read long decks.

Day 19: Governance Framework

Time: 2 hours

Exercise: Outline basic AI governance:

• Who approves AI projects?
• What review is required?
• What policies are needed?
• How will risks be monitored?
• Who is accountable?

Draft initial principles and policies.

Day 20: Stakeholder Alignment (Part 1)

Time: 2 hours

Exercise: Begin stakeholder conversations:

• Share your findings
• Preview recommendations
• Get feedback
• Identify objections
• Build support

Start with champions, then move to skeptics.

Day 21: Week 3 Review

Time: 1 hour

Exercise: Assess stakeholder feedback:

• What concerns emerged?
• What adjustments needed?
• Who is aligned vs. not yet?
• What's needed to get to decision?

Adjust your plan based on feedback.

Week 4: Decision and Launch

Day 22: Refine Plan Based on Feedback

Time: 2 hours

Exercise: Update your initiative plan:

• Address concerns raised
• Clarify ambiguities
• Strengthen weak areas
• Refine estimates

Day 23: Final Stakeholder Alignment

Time: 2 hours

Exercise: Complete stakeholder conversations.

Secure commitment from:

• Executive sponsor
• Budget owner
• Resource providers
• Business stakeholders

Day 24: Steering Committee Presentation

Time: 2 hours

Exercise: Present to decision-makers:

• AI strategy summary
• First initiative recommendation
• Business case
• Resource request
• Timeline
• Governance framework
• Ask for approval

Get the decision.

Day 25: Team Assembly

Time: 2 hours

Exercise: If approved, begin assembly:

• Identify team members
• Begin vendor conversations (if applicable)
• Schedule kickoff
• Set up project infrastructure

If not approved, understand why and adjust.

Day 26: 90-Day Roadmap

Time: 2 hours

Exercise: Develop detailed 90-day plan:

Days 1-30: Pilot preparation and kickoff Days 30-60: Pilot execution Days 60-90: Evaluation and scale decision

Include milestones, dependencies, risks.

Day 27: Communication Plan

Time: 1 hour

Exercise: Plan how you'll communicate:

• Who needs to know what when?
• How will you build excitement?
• How will you manage expectations?
• How will you share progress?

Draft initial communications.

Day 28: Capability Building Plan

Time: 1 hour

Exercise: Beyond the first project, plan for capability:

• What skills need to be developed?
• What infrastructure needs investment?
• What vendor relationships to build?
• What learning should happen?

Day 29: Risk Register

Time: 1 hour

Exercise: Document known risks:

Include technical, organizational, and external risks.

Day 30: Sprint Completion

Time: 2 hours

Exercise: Wrap up the sprint:

Document:

• Strategy summary
• Opportunity pipeline
• First initiative plan
• 90-day roadmap
• Governance framework
• Risk register

Communicate:

• Share summary with stakeholders
• Thank participants
• Set expectations for next steps

Celebrate:

• You've moved from talk to action
• Momentum matters
• Recognize the work done

After the Sprint

Month 2-3: Execute First Initiative

Focus entirely on pilot success:

• Launch and monitor
• Capture learning
• Evaluate results
• Decide on scale

Month 4+: Scale and Expand

Based on first results:

• Scale what works
• Start second initiative
• Build capability
• Refine governance

Ongoing

• Regular review of opportunity pipeline
• Continuous learning
• Governance evolution
• Capability building

The sprint gets you started. Discipline keeps you going.

Final Thoughts

AI is a tool. Like any tool, it works when applied thoughtfully to real problems by capable people with appropriate oversight.

The leaders who succeed with AI will not be the ones who talked about it the most or started the earliest. They'll be the ones who:

• Focused on business problems, not technology
• Built on solid data foundations
• Started small and learned fast
• Invested in people and change management
• Maintained realistic expectations
• Persisted through inevitable setbacks

You've read this book. You've done the sprint. Now execute.

The value of AI isn't in knowing about it. It's in making it work.

Go make it work.