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Business Transformation through Disruptive Technologies (AI/ML)

Module: Module 1 — Management FoundationsCode: BTDTAIML (SSA)Faculty: Prof. Shekhar ShuklaSessions: 2Status: ✅ Drafted

Big idea​

AI is not a new technology — it is a technology that finally had its enabling ecosystem arrive. Prof. Shekhar Shukla's storyline frames why: AI was conceived in the 1950s, predicted at the 1965 MIT summit to reach human-level by 1980, then slept for 32 years (the AI Winter) because the three ingredients it needed — digital data, computing, storage — were not democratised. IBM Deep Blue beating Kasparov (1997), Web 2.0 (2004, the read-and-write web), and ChatGPT (November 2022) were the inflection points. Wrap this in Clayton Christensen's Disruptive Innovation framework: incumbents (Digital, Hilton, Kodak, Nokia, Blockbuster) optimise linearly on present customers; disruptors (Apple, Airbnb, Netflix, OpenAI) target future needs with exponential improvement curves and democratised access. That is the lens managers need.

Key concepts​

  • The AI storyline. WWII cryptography origin → 1965 MIT summit prediction (human-level AI by 1980) → 32-year AI Winter → 1997 Deep Blue beats Kasparov → Web 2.0 (2004) sharing-economy data explosion → November 2022 ChatGPT first-mover democratisation. The maths was always there; the ecosystem wasn't.
  • Why the AI Winter happened. No democratised digital data, mainframe-only computing (not PCs), inadequate storage, weak ecosystems and funding, narrow use cases. The breakthrough required data + compute + storage to become consumer-priced, not better algorithms.
  • AI vs ML vs DL. AI = umbrella term (machines exhibiting human-like behaviour). ML = subset that learns patterns from data (supervised, unsupervised, reinforcement). DL = subset of ML using deep neural nets for unstructured inputs (images, audio, text).
  • Christensen's Disruptive Innovation framework. Sustaining innovation serves current customers on dimensions they already value. Disruptive innovation starts in low-end or new-market niches with worse performance on those dimensions but better on new ones, and improves exponentially. Incumbents rationally ignore — until they can't.
  • The AI factory virtuous cycle. Data → better algorithms → better product → more usage → more data. Netflix's 33 million personalised versions and Amazon's recommendation engine are the textbook examples — each loop deepens the moat.
  • Responsible AI. Hallucinations, training-data bias, lack of emotional intelligence, copyright and IP exposure. Algorithmic transparency, diverse training data and fairness are governance concerns — not optional add-ons.

Self-check​

Hilton Hotels has 120+ years of operating history, owns physical properties, and trains staff intensely. Airbnb launched in 2008, owns no rooms, and within five years exceeded Hilton's market valuation. Through Christensen's lens, what is the most accurate diagnosis?

  • A. Airbnb won on marketing spend
  • B. Hilton was managed badly
  • C. Airbnb is a disruptive innovator — it served a niche (price-sensitive travellers wanting local stays) on dimensions incumbents did not value, then improved exponentially while Hilton optimised its existing model linearly
  • D. Asset-light business models are always better
Why did AI sleep for 32 years (1965–1997)?
The mathematics worked, but the enabling ecosystem did not exist — no democratised digital data, mainframe-only computing not PCs, inadequate storage, weak funding and use-case validation. AI needed Web 2.0's data explosion and consumer compute to wake up.

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Continue learning​

📄HBR · ReadWhat Is Disruptive Innovation?Christensen, Raynor & McDonald's clarifying 2015 piece on what the framework actually means — and what it doesn't.📊Stanford · ReportStanford AI Index ReportAnnual data-rich review of AI progress, investment, deployment and risks — the most authoritative free dataset on the AI economy.📖HBR Press · BookCompeting in the Age of AIIansiti & Lakhani on AI-factory operating models and how algorithms reshape strategy — the modern reframing of disruption.📊McKinsey · ReadMcKinsey QuantumBlack: AI InsightsWorking case studies on enterprise AI adoption, value capture and responsible-AI governance — the practitioner view.
🪞 Apply it — reflection prompts
  1. Which entrants in your industry look like Christensen-style disruptors today — worse on existing dimensions, better on new ones? What is your firm's response posture?
  2. Where in your business could an AI-factory virtuous cycle (data → algorithm → product → more data) compound an advantage? What single data feed would unlock it?
  3. For one AI deployment you've shipped or plan to ship, walk through the responsible-AI checks — bias, transparency, hallucination, IP. Which one is weakest today?

📝 Going deeper. Clayton Christensen, The Innovator's Dilemma (1997) is still the foundational read. For the modern AI-business framing, Iansiti & Lakhani, Competing in the Age of AI (HBS Press, 2020) is the working reference; also see the Stanford AI Index Report for the data view.