Chapter 1 Exercises: The AI-Powered Organization

Section A: Recall and Comprehension

Exercise 1.1 Define the following terms in your own words, using no more than two sentences each: (a) artificial intelligence, (b) machine learning, (c) deep learning, (d) generative AI, (e) large language model.

Exercise 1.2 Describe the nested relationship among AI, ML, deep learning, and generative AI. Why does the distinction matter for a business leader evaluating an AI vendor's claims?

Exercise 1.3 List the five stages of the AI maturity model presented in this chapter. For each stage, identify one characteristic and one associated business risk.

Exercise 1.4 What are the five recurring themes of this textbook? Write one sentence explaining each theme's relevance to a business leader.

Exercise 1.5 Summarize the key findings from Ravi Mehta's initial assessment of Athena Retail Group's AI readiness. Organize your summary into four categories: data infrastructure, talent, culture, and governance.

Exercise 1.6 Explain the difference between AI adoption and AI value creation. What evidence from the chapter supports the claim that most organizations have achieved the former without the latter?

Exercise 1.7 What were the two "AI winters" described in the chapter? For each, identify the primary cause of the funding collapse and one lesson for contemporary business leaders.

Section B: Application

Exercise 1.8: AI Maturity Assessment Select an organization you are familiar with (a current or former employer, a company you have studied, or a well-known public company). Using the five-stage AI maturity model: - (a) Place the organization at a specific stage. Provide at least three pieces of evidence supporting your assessment. - (b) Identify the two most significant barriers preventing the organization from advancing to the next stage. - (c) Propose two specific actions the organization's leadership could take within six months to begin addressing those barriers.

Exercise 1.9: The Hype-Reality Gap in Practice Find a recent press release (2024 or later) in which a company announces an AI initiative. Analyze the announcement using the hype-reality gap framework: - (a) What specific AI capabilities are claimed? - (b) What evidence is provided for these claims? - (c) What questions would you ask the company's leadership to assess whether the claims are realistic? - (d) Based on your analysis, where does this announcement fall on the hype-reality spectrum?

Exercise 1.10: Defining AI for Your Industry Choose a specific industry (e.g., healthcare, financial services, manufacturing, education, entertainment). Write a one-page briefing for a non-technical executive in that industry explaining: - (a) The three most impactful current AI applications in that industry - (b) The two most significant barriers to AI adoption in that industry - (c) One AI application that is currently overhyped in that industry and why - (d) One AI application that is currently underappreciated in that industry and why

Exercise 1.11: Athena's Priorities Imagine you are Ravi Mehta in his first month at Athena Retail Group. Based on his assessment findings described in the chapter, prioritize the following initiatives from most urgent to least urgent. Justify your ranking: - (a) Hire a team of data scientists to begin building ML models - (b) Unify customer data across the four siloed systems - (c) Deploy an enterprise AI chatbot for customer service - (d) Establish a data governance framework with data owners for key datasets - (e) Launch AI literacy training for the executive team - (f) Replace the legacy POS system

Exercise 1.12: Build vs. Buy — First Instincts For each of the following AI capabilities, indicate whether Athena should likely build, buy, or adopt a hybrid approach. Explain your reasoning in two to three sentences for each. (Note: We will revisit this exercise with more sophisticated frameworks in Chapter 13.) - (a) Demand forecasting for inventory management - (b) AI-powered customer service chatbot - (c) Personalized email marketing recommendations - (d) Product catalog data cleaning and deduplication - (e) In-store computer vision for foot traffic analysis

Section C: Analysis and Evaluation

Exercise 1.13: The Expert Systems Analogy The chapter describes the rise and fall of expert systems in the 1980s. Some critics argue that today's generative AI boom shares important similarities with the expert systems bubble. Others argue the situations are fundamentally different. - (a) Identify three similarities between the expert systems era and the current generative AI era. - (b) Identify three fundamental differences. - (c) Based on your analysis, do you believe generative AI is likely to experience a "winter" similar to the expert systems collapse? Why or why not? Support your argument with evidence.

Exercise 1.14: The Cost of Ignorance vs. the Cost of Expertise The chapter argues that AI literacy is a leadership imperative and that the "cost of ignorance" exceeds the "cost of expertise." - (a) Identify a real-world example (not from the chapter) where a company's lack of AI literacy led to a costly mistake. - (b) Identify a real-world example where a leader's AI literacy enabled a valuable strategic decision. - (c) What are the limits of this argument? Under what circumstances might a leader be justified in choosing not to invest in AI literacy?

Exercise 1.15: Evaluating AI Maturity Claims A technology consulting firm publishes a report claiming that 65 percent of Fortune 500 companies have reached Stage 3 (Systematic) or higher on the AI maturity model. The chapter suggests only about 45 percent of large enterprises are at Stage 3 or above. - (a) What methodological factors might explain this discrepancy? - (b) How would you design a study to more accurately measure organizational AI maturity? - (c) Why might organizations have an incentive to overstate their AI maturity? What are the risks of doing so?

Exercise 1.16: Professor Okonkwo's European Retailer At the end of the chapter, Professor Okonkwo tells the story of a European retailer whose CEO dismissed AI as irrelevant — only to see competitors use AI to erode his margins. - (a) What specific cognitive biases might have contributed to the CEO's dismissal of AI? - (b) What early warning signs should the CEO's leadership team have flagged? - (c) Is it possible that the CEO was partially right — that customers didn't want algorithms — and still made a strategic error? Explain.

Section D: Research

Exercise 1.17: State of AI Adoption Find the most recent McKinsey "State of AI" report (or a comparable survey from BCG, Deloitte, or Accenture). - (a) Summarize the three most important findings. - (b) Compare the findings to the AI landscape described in this chapter. What has changed? What has remained consistent? - (c) Identify one finding that surprised you and explain why.

Exercise 1.18: AI Maturity in Your Industry Research AI adoption in a specific industry of your choice. Find at least three credible sources (industry reports, academic papers, or quality journalism). - (a) What percentage of companies in this industry have deployed AI in production (not just pilots)? - (b) What are the most common AI applications in this industry? - (c) What are the industry-specific barriers to AI adoption? - (d) Identify one company in this industry that is widely considered a leader in AI adoption. What has it done differently?

Exercise 1.19: The Environmental Cost The chapter mentions that the International Energy Agency estimates data center energy consumption could double by 2030 due to AI workloads. - (a) Research the current energy consumption of major AI data centers. What are the most recent estimates? - (b) How are major AI companies (Google, Microsoft, Meta, Amazon) addressing the environmental impact of their AI operations? - (c) Should environmental cost be a factor in enterprise AI strategy decisions? Defend your position.

Section E: Discussion and Debate

Exercise 1.20: Is AI Overhyped? For classroom debate or written argument. Position A: The current AI boom is fundamentally different from previous hype cycles. Generative AI represents a genuine paradigm shift comparable to the internet, and companies that do not aggressively adopt AI will be left behind. Position B: The current AI boom exhibits classic signs of a technology bubble — inflated expectations, unsustainable investment levels, and a gap between demos and production capabilities. A correction is inevitable. Choose a position and argue it persuasively, using evidence from this chapter and your own research.

Exercise 1.21: Who Needs AI Literacy? For classroom discussion. Professor Okonkwo argues that all business leaders need AI literacy. But how deep should that literacy go? - Should a CMO understand how gradient descent works? - Should a CFO be able to evaluate a model's precision-recall trade-off? - Where is the line between useful literacy and unnecessary technical detail? - How does the answer vary by industry, company size, and role?

Exercise 1.22: The Ethics of Shadow AI For classroom discussion or written analysis. Ravi's assessment of Athena reveals that several employees are using ChatGPT to draft customer communications without any organizational oversight. - (a) What are the potential risks of this practice? - (b) What are the potential benefits? - (c) If you were Ravi, what would you recommend: ban unauthorized AI use immediately, create guidelines and allow continued use, or something else? Justify your recommendation. - (d) How does your answer change if the employees are in marketing? In legal? In customer service? In HR?

Exercise 1.23: Data as Strategic Asset — Who Benefits? For classroom discussion. The chapter asserts that proprietary data is a durable competitive advantage. But this raises questions: - (a) Whose data is it? The company's? The customer's? The employee's? - (b) If data is a strategic asset, does that incentivize companies to collect more data than necessary? What are the implications? - (c) How do data privacy regulations (GDPR, CCPA) affect the "data as strategic asset" thesis? - (d) Is it ethical for a company to build competitive advantages on data that customers didn't know was being collected?

Exercise 1.24: NK's Skepticism For written reflection. NK Adeyemi enters the course skeptical of AI hype and describing herself as "not a coder." She represents a significant population of business professionals who are smart, experienced, and uncertain about how AI relates to their work. - (a) Write a one-paragraph argument that would be most effective in convincing NK that AI literacy is worth her time. What framing would resonate with someone from a marketing background? - (b) Write a one-paragraph argument that NK might make back — a legitimate case for skepticism about the AI hype cycle. - (c) How should an MBA program balance technical skill-building with strategic understanding for students like NK?

Section F: Integrative Application

Exercise 1.25: Your Organization's AI Readiness Memo Write a two-page memo (similar to Ravi Mehta's internal assessment) evaluating the AI readiness of an organization you know well. Cover: - Data infrastructure (quality, accessibility, integration) - Talent (technical skills, AI literacy, gaps) - Culture (data-driven decision-making, resistance to change, innovation appetite) - Governance (data governance, AI use policies, privacy compliance) - Current AI maturity stage (with justification) - Top three recommended actions for the next 12 months

Exercise 1.26: Briefing the Board You are the Chief Digital Officer of a $1.5 billion manufacturing company. The board of directors has asked you to present a 10-minute briefing on "what AI means for our business." Using concepts from this chapter, prepare: - (a) A one-page executive summary - (b) A list of the five key points you would emphasize - (c) The three questions you would anticipate from board members and your prepared responses - (d) One concrete recommendation for the board to approve at this meeting

Exercise 1.27: Timeline Exercise Create a visual timeline of AI's development from 1950 to 2026, marking: - Key technical milestones (Turing test, Dartmouth Conference, AlexNet, GPT-3, ChatGPT, etc.) - Key business milestones (first expert systems, first AI winter, Google search, Netflix Prize, etc.) - Key regulatory milestones (GDPR, EU AI Act, etc.) For each milestone, write one sentence explaining its significance for business leaders.

Answers to selected exercises are available in Appendix B.