Digital Transformation
What is the difference between digitization, digitalization, and digital transformation?
Digitization = converting analog to digital. Digitalization = using digital tools to improve processes. Digital Transformation = reimagining business models, culture, and customer value with digital.
What’s the difference between data and information?
Data = raw facts; Information = data with context and meaning.
What is the key difference between IaaS and SaaS?
IaaS = infrastructure (servers, storage). SaaS = software applications delivered via the cloud.
What is the difference between Artificial Intelligence and Machine Learning?
AI = broader concept of machines simulating intelligence; ML = subset where systems learn patterns from data.
Generative AI models like GPT are primarily trained to do what task?
Predict the next token in a sequence (next-word prediction).
What is the biggest leadership challenge in digital transformation?
Driving organizational culture and mindset change (not technology itself).
Of the 4Vs of Big Data (Volume, Velocity, Variety, Veracity), which is often the hardest to manage strategically, and why?
Veracity — ensuring data accuracy, trust, and reliability is the hardest.
In cloud migration, which factor often proves more challenging—technology complexity or legacy governance and mindset?
Legacy governance and mindset.
What’s the fundamental difference between supervised and unsupervised machine learning?
Supervised = learns from labeled data; Unsupervised = finds patterns in unlabeled data.
Name one major risk of using Generative AI in enterprise decision-making.
Hallucinations, data privacy/security concerns, or bias in outputs.
Many digital transformation programs fail despite significant investment. Name and explain two major reasons why organizations struggle to achieve real impact.
Common reasons: lack of strategic clarity, resistance to culture/mindset change, over-focus on technology instead of customer value, weak leadership sponsorship, poor execution discipline.
Predictive analytics and prescriptive analytics differ in one crucial way. What is it?
Predictive = forecasts what will happen; Prescriptive = recommends what actions to take.
What is “cloud lock-in,” and why is it a strategic risk?
Dependence on one provider’s ecosystem, making switching costly and limiting flexibility.
Why did neural networks remain underutilized until the 2010s?
Insufficient computational power, limited datasets, and lack of scalable algorithms like deep learning backpropagation.
Why is Retrieval-Augmented Generation (RAG) important for business use of Generative AI?
It grounds AI models in enterprise/domain-specific data, improving accuracy, trust, and relevance.
Give one example of a company that successfully redefined its business model through digital transformation and explain what made it successful.
Examples: Netflix (from DVDs to streaming), Adobe (to SaaS), DBS Bank (digital-first culture).
Why is data governance critical for AI adoption?
It ensures data quality, security, compliance, and ethical use, without which AI outputs are unreliable or risky.
Explain why a hybrid or multi-cloud strategy may be superior to single-cloud adoption.
Reduces dependency, improves resilience, regulatory compliance, and flexibility.
Explain one real-world application of traditional AI (non-Generative) that still dominates today.
Examples: fraud detection, credit scoring, supply-chain optimization, recommendation engines.
What makes Agentic AI different from Generative AI?
Agentic AI can autonomously plan, decide, and execute multi-step tasks — not just generate content.
Looking 5–10 years ahead, what will matter more for digital transformation success: technology innovation or ecosystem orchestration? Why?
Ecosystem orchestration — transformation increasingly depends on partnerships, platforms, and value networks, not standalone tech.
In a data-driven enterprise, should competitive advantage come from owning data, or from how data is used? Explain.
From how data is used (insights, decisions, speed, innovation) rather than ownership alone, since raw data is often commoditized.
How will edge computing reshape the role of cloud in the next decade?
By moving processing closer to devices/users, reducing latency, and enabling new AI/IoT use cases; cloud becomes more distributed.
What is the key limitation of traditional AI approaches compared to Generative and Agentic AI?
They rely on predefined models and structured data, lack adaptability, creativity, and autonomous decision-making.
How could Agentic AI change the future of work for managers?
Managers may shift from task supervision to orchestrating humans + AI agents, focusing on judgment, ethics, and strategy.