Chapter 36 Quiz: Industry Applications of AI

Question 1

Which of the following best describes the relationship between AI capabilities and industry applications?

A) Each industry requires unique AI algorithms that cannot be applied elsewhere B) A small set of universal AI capabilities — prediction, optimization, NLP, computer vision, recommendation, and anomaly detection — appear across virtually every industry, though the data, regulation, and organizational context differ C) AI capabilities transfer directly across industries with no modification required D) Industries adopt AI in a fixed sequence, with each industry waiting for the previous one to succeed before beginning its own adoption

Question 2

NK says, "Churn prediction works the same whether you're predicting customer churn or patient attrition." This statement is:

A) Completely accurate — the models, features, and deployment contexts are identical B) Partially accurate — the underlying classification technique is similar, but the features, data sources, regulatory constraints, ethical considerations, and consequences of error differ significantly across industries C) Inaccurate — churn prediction and patient attrition prediction use fundamentally different algorithms D) Accurate only if both industries use the same software vendor

Question 3

According to the industry maturity framework discussed in this chapter, which tier includes financial services and technology?

A) Early Stage — these industries are just beginning AI adoption B) Emerging Adopters — they have high potential but are constrained by regulation C) Fast Followers — they have moderate data readiness and growing investment D) Leaders — they have high data readiness, strong technical talent, significant investment, and competitive pressure driving adoption

Question 4

What is the primary reason that financial services leads AI adoption across industries?

A) Financial services has the least regulatory burden of any industry B) Financial services generates vast quantities of structured digital data, has deep investment budgets, faces intense competitive pressure, and can measure AI value directly in financial terms C) Financial services was the first industry to invent AI D) Financial regulators require all financial institutions to deploy AI

Question 5

Anti-money laundering (AML) systems that use ML instead of traditional rule-based approaches have:

A) Eliminated all false positives from AML screening B) Reduced false positive rates by 40-60% while maintaining detection accuracy — but face regulatory challenges because ML outputs are harder to explain and audit than rule-based alerts C) Been banned by financial regulators who require rule-based systems D) Increased false positive rates but improved detection of true money laundering

Question 6

Which statement best characterizes the current state of AI in healthcare?

A) Healthcare leads all industries in AI adoption due to the life-saving potential of the technology B) Healthcare has the most transformative AI potential but faces formidable barriers including data fragmentation, heavy regulation, unstructured data, and the high-stakes consequences of error C) Healthcare has rejected AI due to physician resistance D) Healthcare AI is limited exclusively to administrative automation

Question 7

The FDA has approved over 700 AI-enabled medical devices as of 2025. Which of the following best describes how these devices are regulated?

A) AI medical devices require no regulatory approval B) All AI medical devices are classified as the highest risk category and require clinical trials C) AI medical devices are classified along a risk spectrum, and no AI system has been approved to make autonomous clinical decisions without physician oversight D) AI medical devices are regulated by technology companies, not government agencies

Question 8

In manufacturing, predictive maintenance uses AI to:

A) Replace all maintenance staff with autonomous robots B) Analyze sensor data to predict when equipment will fail, enabling maintenance to be scheduled at the optimal moment — late enough to avoid unnecessary servicing, early enough to avoid unplanned failure C) Eliminate the need for any maintenance by making equipment indestructible D) Perform maintenance on a fixed calendar schedule

Question 9

Regarding NovaMart's AI strategy compared to Athena's, Ravi Mehta argues:

A) "NovaMart's approach is superior in every way and we should copy it immediately" B) "NovaMart moves faster, but they have no governance, no ethics review, and three pending lawsuits — speed without responsibility is a liability" C) "NovaMart and Athena have identical AI strategies" D) "Athena should abandon AI entirely to differentiate from NovaMart"

Question 10

Predictive policing has been criticized and abandoned by several cities primarily because:

A) The technology was too expensive for municipal budgets B) Police officers refused to follow the model's recommendations C) The models are trained on arrest data (which reflects policing practices) rather than actual crime data, creating feedback loops that disproportionately target communities that are already over-policed D) Predictive policing was found to violate the Fourth Amendment in all cases

Question 11

Michigan's MiDAS automated unemployment fraud detection system is cited in this chapter as an example of:

A) Successful AI deployment in the public sector B) A cautionary tale — the system flagged over 40,000 claimants for fraud, and 93% of the fraud determinations were later found to be wrong, devastating innocent people's finances C) A system that was never deployed D) An AI system that caught all legitimate fraud cases with no false positives

Question 12

In the legal profession, the most significant risk of using generative AI for legal research is:

A) The cost of AI tools is prohibitive for law firms B) AI-generated legal text is always grammatically incorrect C) LLMs can fabricate case citations and legal authorities (hallucination), and if these are submitted to courts without verification, attorneys face sanctions — as demonstrated by the 2023 New York federal court case D) Judges have banned all use of AI in legal proceedings

Question 13

The "crawl-walk-run" approach to AI deployment in manufacturing means:

A) Starting with simple operational use cases that have clear ROI (predictive maintenance, quality inspection) before expanding into more complex strategic applications (digital twins, supply chain optimization) B) Deploying all AI capabilities simultaneously across all manufacturing operations C) Beginning with the most complex AI application and working backward to simpler ones D) Running AI models at increasingly faster speeds

Question 14

Tom observes a paradox about AI maturity and regulation. Which of the following best captures this paradox?

A) The most regulated industries have no AI adoption B) Financial services leads AI adoption despite heavy regulation — suggesting that regulation does not prevent adoption and may even promote it through governance requirements C) Industries with no regulation have the best AI governance D) Regulation and AI adoption are completely unrelated

Question 15

In precision agriculture, John Deere's "See & Spray" technology uses computer vision to:

A) Identify the most aesthetically pleasing crops for display at farmers' markets B) Distinguish crops from weeds in real time and spray herbicide only on the weeds, reducing herbicide use by up to 80% C) Count the number of crops in a field from satellite imagery D) Monitor weather conditions to determine optimal planting times

Question 16

Which of the following best describes the cross-industry lesson about what separates AI leaders from laggards?

A) Leaders deploy the most sophisticated algorithms, while laggards use simpler models B) Leaders start with business problems (not technology), invest in data infrastructure before model sophistication, embed AI in workflows (not demos), balance speed with governance, and manage the human dimension of change C) Leaders hire the largest number of data scientists, while laggards hire too few D) Leaders operate in industries where AI is easier to deploy

Question 17

The "pilot purgatory" failure mode describes:

A) Running too many AI pilots simultaneously B) AI projects that succeed in controlled environments but never scale to production — remaining perpetual experiments without generating business value C) Deploying AI without any pilot testing D) A pilot project that fails and is abandoned

Question 18

AI in education raises unique concerns about the tension between:

A) Cost and quality — AI is too expensive for educational institutions B) Personalization and surveillance — the same systems that adapt learning to individual student needs also collect extensive data on student behavior and performance, creating potential for privacy violations and algorithmic tracking C) Teachers and students — AI always favors one group over the other D) Public and private schools — AI is only available in private schools

Question 19

Netflix estimates that its recommendation system influences what percentage of content watched on the platform?

A) Less than 10% B) Approximately 25% C) Over 80%, driving an estimated $1 billion per year in retained revenue by reducing subscriber churn D) Exactly 50%

Question 20

Tom writes in his notebook: "In every industry, the technology is the easy part. Data, organization, regulation, and ethics are the hard parts." This observation reflects which recurring theme from this textbook?

A) The hype-reality gap — AI capabilities are always overstated B) The build-vs-buy decision — companies should always buy rather than build C) Human-in-the-loop — AI should never operate without human oversight D) Data as a strategic asset and human-in-the-loop — technical AI capability is insufficient without quality data, organizational readiness, appropriate regulation, and ethical governance

Question 21

Lena Park's observation that "highly regulated industries develop more AI governance — not because they chose to, but because they were required to" implies:

A) Regulation is the only driver of AI governance B) Industries with lighter regulation may move faster but also carry greater reputational and ethical risk if AI deployments cause harm without accountability — voluntary governance is important even without regulatory mandates C) All industries should be equally regulated D) AI governance is unnecessary in lightly regulated industries

Question 22

The common failure mode of "governance theater" refers to:

A) Using AI to govern theatrical productions B) AI ethics committees that exist on paper but lack the authority, resources, or independence to meaningfully influence AI deployment decisions C) Over-governing AI to the point where no projects can be deployed D) Governance processes that are too rigorous and slow

Question 23

Professor Okonkwo says, "The frameworks transfer. The details don't." In the context of this chapter, "frameworks" refers to:

A) Specific software frameworks like TensorFlow and PyTorch B) The six universal AI capabilities (prediction, optimization, NLP, vision, recommendation, anomaly detection) and the strategic, ethical, and organizational principles that apply across industries C) Industry-specific regulatory frameworks D) The hardware infrastructure required for AI deployment

Question 24

Which industry-specific characteristic makes agricultural AI adoption uniquely challenging compared to financial services or retail?

A) Agriculture generates no data that AI can use B) Rural connectivity limitations, high capital costs for AI-equipped equipment, and the risk that AI-powered agriculture widens the gap between large industrial farms and smallholder farmers C) Farmers are philosophically opposed to all technology D) Agricultural data is too simple for AI to provide value

Question 25

The chapter's closing lesson — that every AI leader will eventually move industries and must develop cross-industry pattern recognition — supports which key competency for MBA graduates?

A) Deep specialization in a single industry's AI applications B) The ability to see AI possibilities beyond one's own domain by recognizing that common AI capabilities apply across industries, while the data, regulation, organizational dynamics, and ethical considerations are industry-specific and require domain expertise C) Memorizing the specific AI applications used in each industry D) Avoiding any industry other than the one where you began your career

Answer Key Guidance

This quiz covers material from Chapter 36 only. Questions 1-4 test understanding of cross-industry AI patterns and maturity frameworks. Questions 5-8 test knowledge of industry-specific AI applications in financial services, healthcare, and manufacturing. Questions 9-12 test understanding of strategic tensions, public sector controversies, and professional services risks. Questions 13-16 test deployment strategies and cross-industry lessons. Questions 17-22 test failure modes, regulatory dynamics, and governance concepts. Questions 23-25 test synthesis and the chapter's central thesis about transferable frameworks and industry-specific details. Detailed answer explanations are available in the appendix.