From Strategy to Action
You've learned what AI can do, assessed risks, and secured stakeholder buy-in. Now comes the hard part: execution. This lesson provides a battle-tested framework for translating AI ambitions into implemented solutions that deliver measurable value.
The difference between companies that succeed with AI and those that fail isn't technology—it's strategy execution. Let's ensure you're in the winner's circle.
💡 The AI Implementation Paradox
McKinsey research found that 70% of AI projects fail to move beyond pilot stage. Why? Not technical challenges—but lack of clear strategy, unrealistic expectations, and poor change management. This lesson solves those problems.
The 3-Horizon AI Roadmap Framework
Successful AI strategies balance three time horizons simultaneously: Quick Wins (0-6 months), Core Transformation (6-18 months), and Future Bets (18+ months). Neglect any horizon and you either lose momentum or miss the future.
0-6 Months | $50K-$500K
Goal: Build credibility and momentum
- Low-risk, high-visibility projects
- Proven technology
- Clear ROI (3-6 month payback)
- Examples: Chatbots, forecasting, RPA
Success Metric: 3-5 pilots deployed, 1-2 in production, positive ROI demonstrated
6-18 Months | $500K-$5M
Goal: Transform core operations
- Medium-risk, high-impact initiatives
- Integration with critical systems
- Measurable business transformation
- Examples: Predictive analytics, personalization, automation
Success Metric: 20-30% efficiency gains, $2M-$10M cost savings, competitive parity achieved
18+ Months | $1M-$20M
Goal: Create competitive moats
- High-risk, transformational potential
- Emerging technologies
- New business models
- Examples: Autonomous systems, generative AI products, AI-native offerings
Success Metric: New revenue streams ($10M+), market leadership, defensible competitive advantages
⚠️ Common Mistake: All Horizons 3, No Horizons 1
Companies that only pursue transformational projects ("Let's reinvent our business model with AI!") run out of patience and budget before seeing results. You need quick wins to fund and justify long-term bets. Recommended allocation: 40% Horizon 1, 40% Horizon 2, 20% Horizon 3.
The RICE Prioritization Framework
You have 50 potential AI projects and resources for 5. How do you choose? Use RICE scoring: Reach × Impact × Confidence / Effort = Priority Score.
RICE Scoring Method
Reach (Scale 1-10): How many people/processes affected?
- 1-3: Single team or department (<50 people)
- 4-6: Multiple departments (50-500 people)
- 7-8: Company-wide (500-5,000 people)
- 9-10: Enterprise + customers (5,000+ people/all customers)
Impact (Scale 1-10): How much improvement for each person/process?
- 1-3: Minimal (5-15% efficiency gain)
- 4-6: Moderate (15-30% efficiency gain)
- 7-8: High (30-50% efficiency gain)
- 9-10: Massive (50%+ efficiency gain or entirely new capability)
Confidence (Scale 10-100%): How certain are you of Reach and Impact estimates?
- 10-30%: Pure speculation, no data
- 40-60%: Some analogous examples, limited data
- 70-80%: Strong analogies, internal pilot data
- 90-100%: Proven at scale, solid ROI data
Effort (Scale 1-10): How much time/money/people required?
- 1-2: 1-2 months, $50K-$100K, 2-3 people
- 3-4: 3-4 months, $100K-$250K, 3-5 people
- 5-6: 6-9 months, $250K-$500K, 5-8 people
- 7-8: 9-12 months, $500K-$1M, 8-12 people
- 9-10: 12+ months, $1M+, 12+ people
Formula: RICE Score = (Reach × Impact × Confidence) / Effort
RICE Scoring Example: Customer Service AI
| Project |
Reach |
Impact |
Confidence |
Effort |
RICE Score |
Priority |
| AI Chatbot (FAQ) |
9 (all customers) |
5 (30% ticket reduction) |
80% (proven tech) |
2 (3 months, $150K) |
18.0 |
P1 - DO FIRST |
| Predictive Customer Churn |
7 (high-value customers) |
8 (40% churn reduction) |
60% (some data) |
5 (6 months, $400K) |
6.7 |
P2 - DO NEXT |
| Voice AI Agent (Full Calls) |
9 (all customers) |
9 (50%+ cost reduction) |
40% (emerging tech) |
8 (12 months, $800K) |
4.1 |
P3 - LATER |
| Sentiment Analysis Alerts |
4 (service reps only) |
4 (20% faster escalation) |
70% (proven) |
3 (4 months, $200K) |
3.7 |
P4 - DEPRIORITIZE |
Decision: Start with AI Chatbot (highest RICE score, quick win). Use savings to fund Predictive Churn. Revisit Voice AI once confidence increases.
The 90-Day Sprint Model
Long AI projects lose momentum and adapt slowly to change. Instead, organize work in 90-day sprints with clear objectives, reviews, and go/no-go decisions.
Sprint Structure (Repeating Cycle)
Week 0-2: Planning & Setup
- Define sprint objectives (1-3 AI projects to advance)
- Assemble cross-functional teams (tech + business + data)
- Secure resources and budget
- Define success metrics and evaluation criteria
- Kick-off meeting with executive sponsor
Week 3-10: Execution
- Data acquisition and preparation
- Model development and training
- Weekly standup meetings (30 min, blockers only)
- Biweekly stakeholder updates
- Continuous testing and iteration
Week 11-12: Evaluation & Decision
- Demo to stakeholders (live working system, not slides)
- Compare results vs. success criteria
- Conduct retrospective (what worked, what didn't)
- Go/No-Go decision:
- GO: Proceed to production deployment (next sprint)
- PIVOT: Modify approach based on learnings (next sprint)
- KILL: Terminate project, reallocate resources
Week 13: Planning Next Sprint
- Review overall portfolio progress
- Reprioritize projects (RICE scoring)
- Allocate teams for next sprint
- Update roadmap based on learnings
✅ Why 90-Day Sprints Work
- Maintain momentum: Teams see progress every quarter, not years
- Fail fast: Bad projects are killed after 90 days, not 12 months
- Adapt quickly: Quarterly reviews allow pivoting to new opportunities/threats
- Executive engagement: CEOs can commit to quarterly reviews (not monthly micromanagement)
- Budget flexibility: Resources reallocated every quarter based on performance
Resource Allocation: The 70-20-10 Rule
How should you allocate AI budget and talent across your portfolio? Google's famous innovation framework applies perfectly to AI strategy:
Budget & Talent Allocation
70% - Core Business AI
- Proven use cases applied to your business
- Examples: Chatbots, forecasting, RPA, personalization
- Low risk, predictable returns
- Staffing: Mix of internal + vendors/contractors
- Goal: Efficiency gains, cost reduction, competitive parity
20% - Adjacent Opportunities
- Extending AI into new areas/functions
- Examples: New AI features in existing products, expanding AI to new departments
- Medium risk, high potential upside
- Staffing: Internal teams with external expertise
- Goal: New revenue streams, market expansion
10% - Transformational Bets
- Emerging technologies and entirely new business models
- Examples: AI-native products, autonomous systems, generative AI offerings
- High risk, potentially game-changing
- Staffing: Dedicated innovation teams, partnerships with research labs
- Goal: Competitive moats, category leadership
Example Budget Allocation ($10M Annual AI Budget):
- $7M (70%): Deploy chatbots, improve forecasting, automate processes across 5 departments
- $2M (20%): Launch AI personalization for e-commerce, pilot AI in customer success
- $1M (10%): Explore generative AI for content creation, investigate autonomous agents
⚠️ Don't Violate 70-20-10
- 90-5-5 (Too Conservative): You're not innovating fast enough. Competitors will leapfrog you.
- 40-30-30 (Too Aggressive): You'll burn cash on risky bets while core business suffers.
- 100-0-0 (No Innovation): You'll achieve short-term gains but miss the AI revolution.
Success Metrics That Actually Matter
"We're using AI" is not a success metric. Define concrete, measurable KPIs tied to business outcomes. Use this framework:
3-Level Metrics Hierarchy
Level 1: Technical Metrics (For AI Teams)
- Model accuracy, precision, recall
- Inference latency
- Data quality scores
- System uptime
These matter for engineers but don't report them to executives.
Level 2: Operational Metrics (For Department Heads)
- % of tasks automated
- Time saved per employee
- Error rate reduction
- Throughput increase
These show efficiency gains but don't yet prove business value.
Level 3: Business Impact Metrics (For Executives)
- Revenue: Incremental sales, new revenue streams, customer lifetime value
- Cost: Actual $ saved (not theoretical), headcount avoidance, operational cost reduction
- Customer: NPS improvement, churn reduction, acquisition cost decrease
- Strategic: Time-to-market reduction, competitive position, market share
Report these to the board. Everything else is noise.
Example: AI Chatbot Success Metrics
| Metric |
Baseline |
Target (6 months) |
Actual |
Business Impact |
| Tickets Deflected by AI |
0% |
30% |
35% |
✅ 2,000 hrs/month agent time saved |
| First Response Time |
45 min |
5 min |
3 min |
✅ CSAT improved 4.2 → 4.6 |
| Cost Per Ticket |
$15 |
$10 |
$9.50 |
✅ $550K annual savings |
| Agent Turnover |
35%/year |
25%/year |
22%/year |
✅ $220K recruiting/training savings |
Executive Summary: AI chatbot delivered $770K annual savings (ROI: 5.1x in year 1) and improved customer satisfaction by 0.4 points. This is how you report success.
The First 12 Months: Your Action Plan
Putting it all together: Here's a realistic timeline for getting AI from strategy to production impact.
Year 1 Implementation Roadmap
Month 1-2: Foundation
- Appoint Chief AI Officer (or equivalent)
- Establish AI steering committee (CEO + functional heads)
- Conduct AI opportunity assessment across all functions
- Develop 3-year AI strategy and 12-month roadmap
- Secure budget and resources
- Set up data infrastructure (if not already done)
Month 3-5: Quick Wins (Sprint 1)
- Launch 3-5 pilot projects (Horizon 1 quick wins)
- Deploy proven technologies (chatbots, RPA, basic ML)
- Target 3-6 month payback
- Build internal capability and confidence
- Goal: Demonstrate value, build momentum
Month 6-8: Scale & Transform (Sprint 2)
- Scale successful pilots to production
- Launch 2-3 Horizon 2 transformation projects
- Begin organizational change management
- Implement governance and risk management frameworks
- Goal: Move from pilots to production impact
Month 9-11: Enterprise Rollout (Sprint 3)
- Deploy AI solutions across multiple departments
- Measure and communicate business impact
- Launch employee AI training programs
- Begin exploration of Horizon 3 opportunities
- Goal: Enterprise-wide AI adoption, measurable ROI
Month 12: Review & Plan Year 2
- Conduct comprehensive review with executive team
- Measure against KPIs: Revenue, cost, customer, strategic
- Celebrate successes, document failures and learnings
- Develop Year 2 strategy based on Year 1 results
- Increase budget allocation for successful initiatives
Key Takeaways
✅ Execute Your AI Strategy
- Balance 3 horizons: Quick wins (40%), core transformation (40%), future bets (20%)
- Prioritize with RICE: Reach × Impact × Confidence / Effort = Priority Score
- Work in 90-day sprints: Maintain momentum, fail fast, adapt quickly
- Follow 70-20-10 allocation: Core business (70%), adjacent opportunities (20%), transformational bets (10%)
- Measure business impact: Report revenue, cost, customer, strategic metrics—not technical metrics
- Execute the 12-month plan: Foundation → Quick wins → Scale → Enterprise rollout
- Kill projects ruthlessly: 30-40% of AI projects should be terminated after evaluation—that's success, not failure
📝 Knowledge Check
Test your understanding of moving forward with AI strategy!
1. What is the key to successfully moving forward with AI?
A) Waiting until technology is perfect
B) Starting with clear goals and iterating based on results
C) Implementing everything at once
D) Copying competitors exactly
2. How should organizations prioritize AI initiatives?
A) Randomly select projects
B) Focus only on the most complex problems
C) Based on business impact and feasibility
D) By alphabetical order
3. What is an essential component of a long-term AI strategy?
A) Continuous learning and adaptation
B) Never changing the initial plan
C) Avoiding all risks
D) Focusing only on short-term gains
4. How should leaders approach AI transformation?
A) Delegate entirely and stay uninvolved
B) Resist organizational change
C) Focus only on technology
D) Champion change and invest in people and processes
5. What role does experimentation play in AI strategy?
A) Experimentation wastes resources
B) Controlled experiments help identify what works best
C) Only large companies can experiment
D) Experimentation should be avoided