Theory 01
Digitalization: A Strategic Transformation
Iansiti & Lakhani (2020) differentiate between digitization (converting analog info to digital) and digitalization (re-architecting the firm around digital logic). Digitalization isn't about adding IT to a traditional firm; it's about ending the reliance on human labor at the core of the operating model.
Digitization
Converting analog information into digital bits. (e.g., scanning a paper invoice). This is a technical process, not a strategic one.
Digitalization
The transformation of business processes and organizational structures. Redesigning value creation around digital scalability.
Operating Model
The set of processes, systems, and people that deliver value. Digital firms replace human bottlenecks with algorithmic "factories".
Datafication
Turning activities into data. If you can't measure it, you can't optimize it. Every interaction becomes a learning signal.
Theory 02
The Strategic Collision
A Collision occurs when a firm with a digital operating model enters a space traditionally served by conventional firms. Because digital models exhibit increasing returns to scale, scope, and learning, they eventually overwhelm traditional players.
Why Digital Firms Win
According to Iansiti & Lakhani, the advantage comes from three interconnected dynamics:
- Scale: Software can be replicated at near-zero marginal cost.
- Scope: Digital data can be reused to enter new categories (e.g., Uber moving from rides to food).
- Learning: Algorithms improve automatically with more usage data.
Theory 03
AI as Prediction
The "Economics of AI" (Agrawal et al., 2018) argues that AI is essentially a drop in the cost of prediction. When prediction becomes cheap, it is used more frequently, and the value of human judgment and data increases.
Prediction
Filling in missing information. AI uses data to predict outcomes, lowering the cost of uncertainty.
Judgment
Human role of assigning value to outcomes. AI says what will happen; humans decide if we want it.
Automation
When the cost of prediction drops enough, it replaces manual decision-making tasks entirely.
Feedback
The output of one decision becomes the input (data) for the next prediction, creating a learning loop.
Case Application
Novo Nordisk — From Pharma to AI Factory?
Applying Session 01 logic to your synopsis on Novo Nordisk and OpenAI:
| Concept | Application |
|---|---|
| Collision | Novo is preempting a collision by partnering with OpenAI. They are moving before an "AI-first" biotech disintermediates them. |
| Prediction | Drug discovery is a massive prediction task. AI lowers the cost of identifying which molecules will be successful. |
| Learning Loop | Clinical trial data is Novo's "proprietary moat". By feeding this into OpenAI's models, they create a faster learning loop than rivals. |
Exam Preparation
Likely Oral Exam Questions
-
Core What is the difference between digitization and digitalization according to the course? ▶
- Digitization: Purely technical. Converting analog to digital. Does not change the business logic.
- Digitalization: Strategic. Re-architecting the organization. Changing how value is created and captured using digital logic.
- Use the Airbnb vs. Marriott example: Marriott digitized (online booking); Airbnb digitalized (platform-based operating model).
-
Core Explain the "Collision" framework and why digital firms eventually win. ▶
- Collision = Digital model vs. Traditional model.
- Traditional firms have diminishing returns (it gets harder to scale human-heavy ops).
- Digital firms have increasing returns (scale, scope, and learning effects).
- The digital firm might have lower performance initially, but its growth trajectory is exponential.
-
Synthesis How does the "AI as Prediction" theory relate to Novo Nordisk's strategy? ▶
- Drug discovery is essentially predicting biological success.
- By using OpenAI, Novo lowers the cost of these predictions.
- This shifts the value to Novo's Judgment (deciding which diseases to target) and their Data (proprietary clinical trial results).