Which lesson does this scenario illustrate?

B. Intelligent segmentation allows proactive customer retention.

From a social science lens, which philosophical concern is most relevant in this scenario?

B. Epistemology source and interpretation of knowledge become questionable.

What critical insight does this provide?

D. AI boosts logistics efficiency, but human-AI coexistence

What does this case reflect about business intelligence adoption?

A.Successful BI requires both technological and human change management.

Which methodological flaw does this scenario expose?

D. Use of non-representative data leads to flawed generalizations.

What key advantage did the Physical Internet model offer in this disruption?

C. AI-driven, decentralized, and dynamic logistics optimization

Which of the following best reflects the critical lesson from Siemens’ experience?

C. cybersecurity becomes both a vulnerability and a competitive differentia

What dual advantage did Walmart achieve through this approach?

B. Real-time risk reduction and post-event traceability

What is a core lesson from using AI in this context?

C. AI cannot fully replace, economic reasoning

What does Tesla’s IoT strategy demonstrate?

D. IoT enables continuous improvement and proactive service delivery.

What insight does this provide?

A. Community-driven intelli. complement systems with agility and scale.

Which strategic takeaway applies to AI-powered business models?

D. Businesses must balance AI efficiency with ethical, auditable practices.

What is the axiological issue raised in this automation case?

B. Valuing productivity over people raises ethical concerns.

What does this reveal about Big Data in planning?

B. Big Data planning balance insight with transparent, ethical data usage.

What leadership insight does this case highlight?

A. AI must enhance—not erode—human trust and workplace culture.

What is the value of data-driven assortment in retail?

A.Personalization increases stock turnover and relevance.

What business insight does P2P sharing with AI offer?

A. AI enables scalable, trust-based, and efficient sharing economies.

What’s a core takeaway from this digital service strategy?

C. Personalization boosts engagement, but data ethics must be considered.

What strategic insight does this transformation offer?

C. sustainable transformation- tech upgrades paired with human upskilling.

Which academic concern is most relevant when using powerful AI assistants in research?

A. Epistemological integrityis challengedAI mediates in knowledge

What service design principle does this highlight?

C. AI improves service efficiency, but adoption requires inclusive design.

What does this suggest about the future of MIS?

D.Big Data makes MIS proactive, cross-functional, and strategic.

Which principle is best illustrated here?

B. Mining blogs helps detect weak signals and emerging trends early.

What strategic insight emerges from this?

D. Network-based proximity can reveal unexpected strategic alignments.

What is demonstrated here?

C. Early modeling of supply risks enables proactive, competitive responses.

Which key supply chain advantage is demonstrated by Big Data here?

A.Big Data allows agile, localized supply decisions in real-time.

What does this case reveal about national tech strategies like MiC2025?

C. sovereignty hightech industries limited by upstream global dependencies

Which axiological debate is central to this case?

C. Valuing human presence over machine empathy is critical ethical dilemm

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Artificial Intelligence and Business Applications bba

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Questions 29

Answers

1
Which lesson does this scenario illustrate?
D. Churn cannot be predicted using call data.
A. Predictive analytics is too risky for marketing.
B. Intelligent segmentation allows proactive customer retention.
C. Data mining reduces the need for customer service.
2
From a social science lens, which philosophical concern is most relevant in this scenario?
B. Epistemology source and interpretation of knowledge become questionable.
A. Ontology, because AI changes the structure of knowledge itself.
C. Axiology, since AI has no values and therefore is neutral.
D. Methodology, AI only improves quantitative speed, not accuracy.
3
What critical insight does this provide?
B. AI removes the need for any warehouse workers.
D. AI boosts logistics efficiency, but human-AI coexistence
A. Logistics automation reduces strategic capability.
C.Robots are only suitable for large corporations.
4
What does this case reflect about business intelligence adoption?
C. Efficiency gains always ensure stakeholder acceptance.
D. Route optimization undermines logistics strategy.
A.Successful BI requires both technological and human change management.
B. Data models are infallible.
5
Which methodological flaw does this scenario expose?
B. Over-reliance on qualitative data.
A. Insufficient ontology alignment.
D. Use of non-representative data leads to flawed generalizations.
C. Failure to test sentiment at scale.
6
What key advantage did the Physical Internet model offer in this disruption?
B. Centralized transport control
D. Reduced labor costs alone
C. AI-driven, decentralized, and dynamic logistics optimization
A. Predictable traffic schedules
7
Which of the following best reflects the critical lesson from Siemens’ experience?
B. The more intelligent a system is, the less oversight is needed.
A. AI systems inherently improve all security layers.
D. Predictive maintenance always outweighs all other operational concerns.
C. cybersecurity becomes both a vulnerability and a competitive differentia
8
What dual advantage did Walmart achieve through this approach?
A. Complete automation with no oversight
C. AI-generated marketing insights
D. Elimination of supply chain labor
B. Real-time risk reduction and post-event traceability
9
What is a core lesson from using AI in this context?
C. AI cannot fully replace, economic reasoning
A. Social media is an objective economic predictor.
D. Sentiment data removes the need for macroeconomic data.
B. Traditional models are obsolete.
10
What does Tesla’s IoT strategy demonstrate?
B. Real-time data is only for marketing.
A. IoT is limited to GPS tracking.
C.IoT data lacks personalization.
D. IoT enables continuous improvement and proactive service delivery.
11
What insight does this provide?
B. Crowdsourced data is unreliable for emergencies.
D. Social media can't be used for real-time action.
C.Official systems are unnecessary.
A. Community-driven intelli. complement systems with agility and scale.
12
Which strategic takeaway applies to AI-powered business models?
A. Faster service always means better service.
D. Businesses must balance AI efficiency with ethical, auditable practices.
B. Algorithmic decisions are above regulatory scrutiny.
C.Human staff are no longer necessary.
13
What is the axiological issue raised in this automation case?
B. Valuing productivity over people raises ethical concerns.
D. Automation cannot happen in traditional industries.
C. AI cannot function without human judgment.
A. AI increases worker salaries by eliminating errors.
14
What does this reveal about Big Data in planning?
C. Predictive analytics guarantees ethical practice.
A. Big Data renders strategic planning obsolete.
B. Big Data planning balance insight with transparent, ethical data usage.
D. Retailers should avoid predictive models.
15
What leadership insight does this case highlight?
A. AI must enhance—not erode—human trust and workplace culture.
C.AI fully replaces the need for middle managers.
D. Employee privacy is irrelevant if productivity increases.
B. Monitoring metrics always improve performance.
16
Which paradox best explains this inconsistency in performance?
D. Moravec’s Paradox
C. Gilder’s Law
B. Metcalfe’s Law
A. Moore’s Law
17
What is the value of data-driven assortment in retail?
D. Data cannot impact inventory planning.
B. Customer preferences cannot be measured
C.Standardization across stores ensures brand identity.
A.Personalization increases stock turnover and relevance.
18
What business insight does P2P sharing with AI offer?
D. P2P sharing is limited to travel sectors only.
A. AI enables scalable, trust-based, and efficient sharing economies.
B. AI cannot personalize peer-sharing services.
C.People don't trust shared platforms.
19
What’s a core takeaway from this digital service strategy?
C. Personalization boosts engagement, but data ethics must be considered.
D. Apps cannot deliver consistent service.
B. AI should never handle food delivery.
A. Innovation should reduce customer involvement
20
What strategic insight does this transformation offer?
C. sustainable transformation- tech upgrades paired with human upskilling.
A. Industry 4.0 eliminates the need for skilled workers.
B. High-tech systems don’t need human interaction
D. Robots are a complete substitute for human intelligence.
21
Which academic concern is most relevant when using powerful AI assistants in research?
D. AI has no impact on academic practice since it is just a tool.
A. Epistemological integrityis challengedAI mediates in knowledge
B. The issue is ontological since AI is not real intelligence.
C. AI increases plagiarism risk but otherwise supports scholarship
22
What service design principle does this highlight?
A. Virtual assistants cannot replace human service.
C. AI improves service efficiency, but adoption requires inclusive design.
B. AI cannot assist in financial services.
D. Automation reduces customer satisfaction
23
What does this suggest about the future of MIS?
B. MIS should focus only on historical data.
A. Static reports are enough for modern management.
D.Big Data makes MIS proactive, cross-functional, and strategic.
C. MIS systems are too rigid to handle real-time data.
24
Which principle is best illustrated here?
D. BI tools cannot process text-based data.
B. Mining blogs helps detect weak signals and emerging trends early.
A. Traditional marketing data is faster than blogs.
C. Blogs are too unstructured to analyze.
25
What strategic insight emerges from this?
A. Physical proximity is more important than digital.
C.BI ignores social connections.
B. Only large firms benefit from proximity analysis.
D. Network-based proximity can reveal unexpected strategic alignments.
26
What is demonstrated here?
D. EV companies cannot manage their resource risk
C. Early modeling of supply risks enables proactive, competitive responses.
B. Predictive models are too complex for strategic planning.
A. Industry models cannot handle supply chain shocks.
27
Which key supply chain advantage is demonstrated by Big Data here?
D. Forecasting is irrelevant when demand is unpredictable.
C.Static supply planning is superior during crisis.
B. Predictive insights reduce supply chain responsiveness.
A.Big Data allows agile, localized supply decisions in real-time.
28
What does this case reveal about national tech strategies like MiC2025?
C. sovereignty hightech industries limited by upstream global dependencies
D. The future lies in decoupled, autarkic manufacturing.
A. AI eliminates the need for global collaboration.
B. AI accelerates national self-reliance in all areas.
29
Which axiological debate is central to this case?
D. Robotics in care work has no social implication.
B. AI will reduce the emotional burden on humans.
C. Valuing human presence over machine empathy is critical ethical dilemm
A. Robots will soon fully replace caregivers.

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