What insight does this provide?

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

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

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

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

A.Personalization increases stock turnover and relevance.

Which strategic takeaway applies to AI-powered business models?

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

What does this reveal about Big Data in planning?

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

Which lesson does this scenario illustrate?

B. Intelligent segmentation allows proactive customer retention.

What is demonstrated here?

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

What does this suggest about the future of MIS?

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

What is the axiological issue raised in this automation case?

B. Valuing productivity over people raises ethical concerns.

Which axiological debate is central to this case?

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

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 reflect about business intelligence adoption?

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

What does Tesla’s IoT strategy demonstrate?

D. IoT enables continuous improvement and proactive service delivery.

What strategic insight emerges from this?

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

What strategic insight does this transformation offer?

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

What dual advantage did Walmart achieve through this approach?

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

What service design principle does this highlight?

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

What leadership insight does this case highlight?

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

Which methodological flaw does this scenario expose?

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

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

C. AI cannot fully replace, economic reasoning

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

C. cybersecurity becomes both a vulnerability and a competitive differentia

Which principle is best illustrated here?

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

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

C. sovereignty hightech industries limited by upstream global dependencies

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

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

What critical insight does this provide?

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

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

B. Epistemology source and interpretation of knowledge become questionable.

What business insight does P2P sharing with AI offer?

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

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

A. Epistemological integrityis challengedAI mediates in knowledge

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Artificial Intelligence and Business Applications bba

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Questions 29

Answers

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

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