Theory 01
Dynamic Capabilities Framework
Teece et al. (2016) define Dynamic Capabilities as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. It's the capacity to remain competitive when "inflection points" emerge.
Sensing
Identifying and assessing opportunities (and threats) in the environment. Scanning the horizon for shifts like AI.
Seizing
Mobilizing resources to address the opportunity. Making the strategic "bet" and investing in the new path.
Transforming
Continuous renewal and reconfiguration of assets. Changing organizational structures and culture (Teece et al., 2016).
Theory 02
AI as an Organizing Capability
Stelmaszak et al. (2026) propose an ontological shift: AI is not just an "entity" or a "tool". It is an Organizing Capability that arises from the relations between human and algorithmic actors.
Properties of AI Capability (Stelmaszak et al., 2026)
- Connective: AI links people, data, and processes across traditional silos.
- Codependent: Neither humans nor algorithms can act effectively without the other in complex tasks.
- Emergent: The resulting organizational intelligence is greater than the sum of its parts.
Theory 03
Uncertainty vs. Risk
Teece et al. (2016) distinguish between Risk (known outcomes with probabilities) and Deep Uncertainty (unknown unknowns). Strong dynamic capabilities are essential for addressing the latter.
Risk
Can be managed with traditional tools (insurance, hedges). Probabilities are calibrated (Teece et al., 2016).
Deep Uncertainty
Ubiquitous in innovation economies. No clear probabilities. Requires agility and asset orchestration (Teece et al., 2016).
Case Application
Novo Nordisk — Dynamic Agility
Applying Session 04 concepts to the synopsis:
- Sensing the AI Inflection: Novo's partnership with OpenAI is a clear example of Sensing the disruptive potential of LLMs in biotech.
- Asset Orchestration: By integrating OpenAI's algorithms with their own bio-data, Novo is Seizing the opportunity and reconfiguring its R&D competence.
- Agility vs. Efficiency: The 9,000 layoffs represent a painful Transformation — sacrificing traditional organizational efficiency (human-centric silos) to build the agility required for an AI-led future.
Exam Preparation
Likely Oral Exam Questions
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Core What are the three pillars of Dynamic Capabilities according to Teece? ▶
- Sensing: Scanning for opportunities/threats.
- Seizing: Mobilizing resources to act.
- Transforming: Reconfiguring the organization to sustain the change.
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Core Why does Stelmaszak et al. (2026) argue that AI is an "Organizing Capability" rather than an entity? ▶
- Traditional views see AI as a "tool" or "autonomous agent".
- Stelmaszak argues that intelligence *emerges* from the relation. Without human data, feedback, and context, the algorithm is useless. Without the algorithm, the human cannot process the scale of data.
- Strategic implication: Competitive advantage is in the *quality of the relation*, not just buying the best tech.
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Synthesis How does "Deep Uncertainty" impact the "Make or Buy" decision for AI? ▶
- In conditions of deep uncertainty, building internally (Make) is risky because the tech evolves so fast.
- Partnering (Buy/Partner) provides Agility. It allows a firm like Novo to access cutting-edge tech (OpenAI) without being locked into a soon-to-be-obsolete internal system.
- However, agility comes at a cost of potential dependency (Session 08/TCT).