Question 1: According to McKinsey research, what percentage of AI projects fail to move beyond the pilot stage?
A) 45%
B) 70%
C) 85%
D) 55%
✓ Correct! McKinsey found that 70% of AI projects fail to scale beyond pilots, primarily due to lack of strategy and organizational readiness—not technical issues.
✗ Incorrect. McKinsey research shows 70% of AI projects get stuck in "pilot purgatory," mainly due to organizational challenges rather than technical limitations.
Question 2: When convincing skeptical stakeholders about AI, which approach is MOST effective?
A) Explain the technical superiority of neural networks
B) Compare to competitors without specific data
C) Present industry-specific ROI data from similar companies
D) Emphasize that "AI is the future"
✓ Correct! Industry-specific ROI data from comparable companies provides concrete evidence that resonates with business leaders. Generic promises or technical details rarely persuade skeptics.
✗ Incorrect. Business leaders are persuaded by concrete ROI data from similar companies in their industry, not technical explanations or vague claims about the future.
Question 3: Which statement about AI's business impact is TRUE?
A) AI primarily benefits technology companies
B) AI adoption guarantees competitive advantage
C) Small companies should wait until AI matures
D) AI is transitioning from competitive advantage to competitive necessity
✓ Correct! AI has moved from optional competitive advantage to baseline requirement. Companies not adopting AI risk falling behind as it becomes expected by customers, employees, and investors.
✗ Incorrect. AI is now transitioning from competitive advantage to competitive necessity across all industries. Waiting puts companies at risk of being disrupted by more agile competitors.
Question 4: What is the primary reason companies fail at AI transformation?
A) Insufficient budget allocation
B) Lack of change management and organizational alignment
C) Immature AI technology
D) Shortage of data scientists
✓ Correct! Most AI failures stem from organizational challenges—resistance to change, misaligned incentives, inadequate training—not technology or budget issues. It's 20% technology, 80% change management.
✗ Incorrect. While budget, technology, and talent matter, the #1 reason for AI failure is poor change management. Scaling AI requires organizational transformation, not just technical implementation.
Question 5: When communicating AI initiatives externally, what is the recommended approach?
A) Lead with customer benefits, not technology features
B) Emphasize cutting-edge algorithms to demonstrate innovation
C) Avoid mentioning AI to prevent customer concerns
D) Focus on cost savings achieved through automation
✓ Correct! External AI communication should focus on customer value—faster service, better personalization, improved outcomes—not technical details or internal cost savings.
✗ Incorrect. Customers care about benefits (faster, better, more personalized service), not technical sophistication or your internal cost savings. Lead with value, not features.
Question 6: What is the "AI trust equation" for building customer confidence?
A) Accuracy + Speed + Cost Savings
B) Technology + Data + Algorithms
C) Transparency + Control + Human Oversight
D) Innovation + Efficiency + Scale
✓ Correct! Customer trust in AI comes from: (1) Transparency about how AI is used, (2) Control/opt-out options, (3) Human oversight for important decisions. This formula builds confidence.
✗ Incorrect. Customer trust requires Transparency (how AI works) + Control (ability to opt out) + Human Oversight (people in the loop). Technical performance alone doesn't build trust.
Question 7: According to the 3-Horizon Framework, what is the typical investment range for Horizon 1 (Quick Wins) projects?
A) $10K-$50K
B) $50K-$500K
C) $500K-$5M
D) $1M-$20M
✓ Correct! Horizon 1 projects (0-6 months, proven technology, quick ROI) typically require $50K-$500K investment. These are chatbots, forecasting, basic automation—low risk, fast payback.
✗ Incorrect. Horizon 1 quick wins fall in the $50K-$500K range. Lower investments often fail to deliver meaningful impact; higher investments belong in Horizon 2 (core transformation).
Question 8: In the RICE prioritization framework, how is the priority score calculated?
A) (Reach + Impact + Confidence) / Effort
B) Reach × Impact × Confidence × Effort
C) (Reach + Impact) / (Confidence + Effort)
D) (Reach × Impact × Confidence) / Effort
✓ Correct! RICE score = (Reach × Impact × Confidence) / Effort. This formula prioritizes projects that affect many people (Reach), create significant improvement (Impact), with high certainty (Confidence), relative to resources required (Effort).
✗ Incorrect. RICE uses multiplication for positive factors and division by Effort: (Reach × Impact × Confidence) / Effort. Higher scores indicate better priority.
Question 9: What is the recommended resource allocation for AI initiatives using Google's 70-20-10 rule?
A) 70% core business AI, 20% adjacent opportunities, 10% transformational bets
B) 70% pilots, 20% scaling, 10% maintenance
C) 70% technology, 20% training, 10% governance
D) 70% internal projects, 20% vendors, 10% R&D
✓ Correct! The 70-20-10 rule allocates: 70% to proven AI for core business (low risk, efficiency gains), 20% to adjacent opportunities (medium risk, growth), 10% to transformational bets (high risk, game-changing potential).
✗ Incorrect. Google's innovation framework suggests: 70% proven core business AI, 20% adjacent opportunities, 10% transformational bets. This balances reliable returns with innovation potential.
Question 10: Which AI application typically delivers the FASTEST ROI for customer-facing businesses?
A) Predictive churn modeling
B) Computer vision for quality control
C) FAQ chatbot for customer service
D) Personalized recommendation engine
✓ Correct! FAQ chatbots deliver fastest ROI (3-6 months) because: (1) Proven technology, (2) High-volume repetitive tasks, (3) Easy to measure (deflection rate), (4) Minimal integration complexity. Perfect Horizon 1 project.
✗ Incorrect. FAQ chatbots typically deliver fastest ROI—3-6 month payback—due to proven technology, high volume of repetitive queries, and straightforward implementation. Classic quick win.
Question 11: According to IBM research, what is the average cost of an AI-related data breach?
A) $2.1M
B) $4.45M
C) $6.8M
D) $8.2M
✓ Correct! IBM's Cost of a Data Breach Report found AI-related breaches cost $4.45M on average—higher than non-AI breaches due to model complexity, data sensitivity, and regulatory scrutiny.
✗ Incorrect. IBM research shows the average cost of an AI-related data breach is $4.45M, including detection, response, notification, legal fees, and reputation damage.
Question 12: In the RAPID risk framework, what does "RAPID" stand for?
A) Risk, Assessment, Prioritization, Investment, Decision
B) Review, Approval, Planning, Implementation, Deployment
C) Risk, Action, Prevention, Investigation, Documentation
D) Response, Analysis, Prediction, Integration, Development
✓ Correct! RAPID = Risk (in business terms), Assessment (likelihood × impact), Prioritization (vs. other risks), Investment (mitigation cost), Decision (Conservative/Moderate/Aggressive). This framework structures executive risk briefings.
✗ Incorrect. RAPID stands for: Risk, Assessment, Prioritization, Investment, Decision—a framework for presenting AI risks to executives in business language they understand.
Question 13: What is the acceptable bias disparity ratio threshold for AI systems according to industry best practices?
A) Less than 1.0x (perfect parity)
B) Less than 1.1x
C) Less than 1.2x
D) Less than 1.5x
✓ Correct! Industry best practice sets <1.2x as the green zone for bias disparity (e.g., AI approval rate for Group A vs. Group B). 1.2-1.5x is yellow (needs attention), >1.5x is red (unacceptable).
✗ Incorrect. The industry standard is <1.2x bias disparity ratio (green zone). Above 1.2x requires investigation and mitigation; above 1.5x is typically unacceptable for deployment.
Question 14: In the 4T risk mitigation framework, what do the 4Ts stand for?
A) Track, Test, Train, Transform
B) Time, Technology, Talent, Training
C) Target, Tactics, Timeline, Testing
D) Terminate, Treat, Transfer, Tolerate
✓ Correct! The 4T framework: Terminate (eliminate risk by not pursuing), Treat (reduce with controls), Transfer (shift to third party/insurance), Tolerate (accept low-priority risks with monitoring). Classic risk management approach applied to AI.
✗ Incorrect. The 4T risk mitigation strategies are: Terminate (eliminate), Treat (reduce), Transfer (shift to others), Tolerate (accept with monitoring). Choose based on risk severity and mitigation cost.
Question 15: What is a realistic target for AI chatbot ticket deflection in the first year of deployment?
A) 10-15%
B) 25-35%
C) 50-60%
D) 70-80%
✓ Correct! Conservative, achievable target is 25-35% ticket deflection in Year 1. Vendors may promise 50%+, but real-world results typically land in the 30-40% range after optimization. Set realistic expectations.
✗ Incorrect. Realistic Year 1 deflection is 25-35%. Lower targets suggest poor implementation; higher targets (50%+) are vendor promises that rarely materialize. Use conservative estimates for business cases.
Question 16: In building an AI business case, what is the recommended minimum ROI to justify investment?
A) There is no universal minimum—it depends on company's hurdle rate and alternative investments
B) At least 50% ROI
C) At least 100% ROI (2x return)
D) At least 200% ROI (3x return)
✓ Correct! There's no magic ROI number. Companies have different hurdle rates (10-30%) based on industry, risk tolerance, and alternative investments. A 50% AI ROI might be great for manufacturing, insufficient for high-growth tech companies. Context matters.
✗ Incorrect. ROI requirements vary by company. Conservative firms may accept 30% ROI; aggressive growth companies may require 200%+. Compare AI ROI to: (1) company's hurdle rate, (2) alternative investments, (3) strategic value beyond financials.
Question 17: According to Kotter's change management model, which step comes FIRST?
A) Form a strategic vision
B) Build a guiding coalition
C) Create urgency
D) Communicate the vision
✓ Correct! Step 1 is Create Urgency—make the status quo feel riskier than change. Without urgency, people won't prioritize transformation. Show competitive threats, customer feedback, and cost of inaction BEFORE building vision.
✗ Incorrect. Kotter's 8-step model begins with Create Urgency. You must establish why change is necessary BEFORE forming vision or coalition. No urgency = no motivation to change.
Question 18: What is the recommended size of an AI Center of Excellence (CoE) for a mid-size company (2,000-10,000 employees)?
A) 3-5 FTEs
B) 11-16 FTEs
C) 25-30 FTEs
D) 50+ FTEs
✓ Correct! For mid-size companies, an effective AI CoE requires 11-16 FTEs: CAO (1), Product Managers (2-3), Engineers (4-6), Governance (1), Change Management (1-2), MLOps (2-3). Budget: $2M-$4M annually.
✗ Incorrect. A functional AI CoE for mid-size companies needs 11-16 FTEs covering strategy, engineering, governance, change management, and operations. Smaller teams lack capacity; larger teams create bureaucracy.
Question 19: What is the primary cause of "AI fatigue" in organizations?
A) Technical complexity of AI systems
B) Insufficient budget allocation
C) Poor model accuracy
D) Over-communication without tangible results or evolving narrative
✓ Correct! AI fatigue occurs when organizations over-hype AI without delivering results, or when the narrative becomes stale. Prevent with: (1) Regular quick wins, (2) Rotating spotlight across departments, (3) Quantified impact reports, (4) Evolving messaging.
✗ Incorrect. AI fatigue stems from too much talk with too little action, or repetitive messaging. Combat with visible wins, rotating focus areas, celebrating milestones (not just launches), and refreshing the "why" quarterly.
Question 20: According to Microsoft's AI transformation case study, what was their employee AI adoption rate after 18 months?
A) 82%
B) 65%
C) 55%
D) 90%
✓ Correct! Microsoft achieved 82% weekly AI usage across 220,000 employees after 18 months through: executive commitment, democratized access, role-specific training, champions network, gamification, and public celebration of wins.
✗ Incorrect. Microsoft's AI transformation reached 82% adoption in 18 months—remarkable for enterprise-wide change. Key drivers: CEO championing, ubiquitous tool access, targeted training, and cultural shift to "AI-first" mindset.