Focused on your synopsis about the partnership between Novo Nordisk and OpenAI, while preparing you for broader questions across the full Information Economics & Business curriculum.
Digital strategy, platforms, AI governance
Novo Nordisk x OpenAI
Theory depth and case synthesis
How do digital operating models, data, and AI change competitive advantage?
Explains why Novo uses AI to lower prediction costs in drug discovery and build faster data-driven learning loops.
How do network effects, platforms, and positive feedback reshape digital competition?
Useful for assessing whether OpenAI's ecosystem creates platform dependency and switching costs for Novo.
How should firms capture value from digital products, services, and platforms?
Useful for discussing how AI vendors monetize enterprise services and how lock-in can create future pricing power.
How do firms reconfigure resources under AI-driven uncertainty?
Central lens for explaining Novo's OpenAI partnership as sensing, seizing, and transforming under deep uncertainty.
How does digital transformation challenge managerial cognition?
Explains how managers search, interpret AI disruption, and redistribute decisions between humans and algorithms.
How does competition change with connected products?
Useful for discussing AI-enabled R&D, smart data, and how digital operating models transform the pharma value chain.
How do platforms coordinate work, ecosystems, and market participants?
Useful for analyzing OpenAI as part of an ecosystem and for discussing algorithmic management in digital work.
How does digitalization change make-or-buy decisions and transaction costs?
Explains why Novo may partner with OpenAI rather than fully internalize AI development, while still facing lock-in and monitoring costs.
How do organizations evaluate algorithmic alternatives?
Highly relevant for evaluating AI vendors, validating model outputs, and governing black-box systems in pharma.
What responsibilities arise from digital scale, AI bias, labor effects, and environmental costs?
Important for discussing patient data, biased clinical evidence, hallucinated scientific claims, labor effects, and AI's resource footprint.
The paper discusses many organizational implications of AI without narrowing down to a sharply defined strategic problem.
You mention several themes. The examiner may ask how the theories connect rather than simply coexist.
Your case relies heavily on announcements and strategic expectations rather than observable outcomes.
Be careful claiming AI will necessarily improve efficiency or innovation. The examiner may push you on evidence.
Because the case concerns how Novo Nordisk adapts to technological disruption under conditions of uncertainty. The issue is not only owning resources, but the ability to sense AI opportunities, seize them through partnerships, and transform organizational processes.
Transaction cost theory and capability gaps both matter. OpenAI already possesses specialized AI competences, infrastructure, and learning capabilities that would be extremely costly and slow for Novo to replicate internally.
Dependency on external AI providers, data governance risks, algorithmic opacity, regulatory concerns, and potential erosion of internal capabilities if too much knowledge creation is outsourced.
Your synopsis currently spends substantial space describing AI trends and the partnership itself. During the oral exam, you must continuously explain WHY these developments matter strategically.
Avoid generic statements like "AI improves efficiency." Instead say: "From a dynamic capabilities perspective, AI enables faster sensing and resource reconfiguration."
The examiner will likely move beyond your exact synopsis and ask broader questions about platforms, transaction costs, data strategy, algorithmic trust, and ethics.
High grades usually require discussing trade-offs and limitations. You should be able to explain both the strategic opportunities and the risks of AI adoption.