Harvard Case - Building Watson: Not So Elementary, My Dear!
"Building Watson: Not So Elementary, My Dear!" Harvard business case study is written by Willy Shih. It deals with the challenges in the field of Operations Management. The case study is 19 page(s) long and it was first published on : Sep 6, 2011
At Fern Fort University, we recommend IBM adopt a phased approach to building Watson, focusing on strategic partnerships and iterative development to address the challenges of scaling a complex AI system. This approach will involve leveraging existing capabilities, building a strong foundation for future growth, and fostering a culture of continuous improvement within the organization.
2. Background
IBM, a technology giant, was faced with the challenge of scaling its groundbreaking AI system, Watson, beyond its initial success in Jeopardy!. The case study highlights the complexities of developing and deploying a large-scale AI system, including challenges in operations strategy, supply chain management, product development, and organizational change.
The main protagonists of the case study are:
- David Kenny: Senior Vice President of IBM's Watson group, responsible for leading the development and deployment of Watson.
- John Kelly: Senior Vice President of IBM Research, responsible for overseeing the research and development of Watson's core technology.
- The Watson team: A diverse group of engineers, scientists, and business professionals responsible for building and deploying Watson.
3. Analysis of the Case Study
This case study can be analyzed using the Porter's Five Forces framework to understand the competitive landscape and the challenges faced by IBM in scaling Watson:
- Threat of new entrants: High, as the AI market is rapidly evolving with new players emerging.
- Bargaining power of buyers: High, as customers have a wide range of AI solutions to choose from.
- Bargaining power of suppliers: Moderate, as IBM relies on various suppliers for hardware, software, and data.
- Threat of substitute products: High, as alternative AI technologies and solutions are constantly being developed.
- Competitive rivalry: High, as established players like Google, Microsoft, and Amazon are aggressively investing in AI.
Furthermore, we can analyze the case study through the lens of operations and supply chain management, focusing on the following aspects:
- Operations strategy: IBM needs to define a clear operations strategy for Watson, considering its target markets, competitive landscape, and resource constraints.
- Supply chain management: IBM needs to build a robust supply chain to manage the flow of hardware, software, data, and services required for Watson's development and deployment.
- Manufacturing processes: IBM needs to optimize its manufacturing processes to ensure the efficient production of Watson hardware and software.
- Information systems: IBM needs to develop and implement robust information systems to manage data, track performance, and support decision-making.
- Technology and analytics: IBM needs to leverage its expertise in technology and analytics to continuously improve Watson's capabilities and performance.
4. Recommendations
To successfully scale Watson, IBM should implement the following recommendations:
Phase 1: Focus on Niche Markets and Strategic Partnerships:
- Identify and target specific industries and use cases where Watson can provide significant value, such as healthcare, finance, and education.
- Develop strategic partnerships with leading companies in these industries to co-develop and deploy Watson solutions.
- Leverage existing infrastructure and resources to minimize upfront investments and accelerate time to market.
Phase 2: Iterative Development and Continuous Improvement:
- Adopt an agile development methodology to rapidly iterate and improve Watson's capabilities based on user feedback and market needs.
- Implement a robust quality management system to ensure the reliability, accuracy, and security of Watson's outputs.
- Invest in research and development to continuously enhance Watson's core technology and expand its capabilities.
Phase 3: Build a Scalable Platform and Ecosystem:
- Develop a scalable platform that can handle increasing demand and support the deployment of Watson across various industries and applications.
- Foster a vibrant ecosystem of developers, partners, and users to accelerate the adoption and innovation around Watson.
- Implement a strong marketing and communication strategy to raise awareness and build trust in Watson.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core competencies and consistency with mission: IBM's core competencies in technology and analytics align well with the development and deployment of AI systems. Scaling Watson is consistent with IBM's mission of providing innovative solutions to complex business problems.
- External customers and internal clients: The recommendations prioritize customer needs and address the challenges of scaling Watson for different industries and use cases.
- Competitors: The recommendations acknowledge the competitive landscape and emphasize the need for strategic partnerships, rapid innovation, and a scalable platform.
- Attractiveness ' quantitative measures: Although the case study does not provide specific financial metrics, the recommendations aim to maximize value creation through targeted market entry, strategic partnerships, and efficient resource utilization.
- Assumptions: The recommendations assume that IBM has the necessary technical expertise, financial resources, and organizational commitment to successfully scale Watson.
6. Conclusion
By adopting a phased approach, leveraging strategic partnerships, and fostering a culture of continuous improvement, IBM can successfully scale Watson and establish itself as a leading provider of AI solutions. This approach will require a strong commitment from leadership, a collaborative culture, and a focus on delivering value to customers.
7. Discussion
Other alternatives not selected include:
- Aggressive expansion: This approach involves rapidly expanding into multiple markets with a broad range of solutions, potentially leading to resource overstretching and decreased quality.
- Focus on internal development: This approach involves solely relying on internal resources and expertise, potentially limiting innovation and delaying time to market.
The recommendations presented in this case study solution are based on the assumption that IBM has the necessary resources and commitment to implement the proposed changes. However, there are risks associated with this approach, such as:
- Competition: The AI market is highly competitive, and IBM may face challenges from established players and new entrants.
- Technology evolution: The rapid evolution of AI technology may require IBM to constantly adapt and upgrade Watson.
- Customer adoption: The success of Watson will depend on customer adoption and acceptance of AI solutions.
8. Next Steps
To implement these recommendations, IBM should take the following steps:
- Develop a detailed implementation plan outlining the specific activities, timelines, and resources required for each phase.
- Establish a dedicated team responsible for overseeing the scaling of Watson.
- Communicate the strategy to all stakeholders, including employees, partners, and customers.
- Monitor progress and make adjustments as needed based on market feedback and performance metrics.
By taking these steps, IBM can overcome the challenges of scaling Watson and realize its full potential as a transformative AI technology.
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Case Description
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the championship match. Was the answer a reflection of a strategic weakness, or was it actually consistent with design principles established by the development team? The case seeks to expand students' view of the product development process. Traditional software development projects begin with the gathering of requirements and analysis of the problem, and the writing of a detailed specification. The Jeopardy! problem is different - it requires a probabilistic approach where there is no closed form solution. Instead statistical patterns in the data are important and there is no obvious mapping to the way queries are expressed. Such problems are increasingly common in data mining, optimization problems, or Internet applications where the goal is to find an acceptably good solution in a short amount of time, when a deterministic approach might be less fruitful or impractical. We aspire for students to recognize that product development can take many forms, and that these are enabled by creativity and the right organizational flexibility and mindset. The case highlights the key role of performance metrics in building a flexible system that could be refined through experimentation and testing, steadily improving performance with the incorporation of new algorithmic ideas and new data sources. The case then delves extensively into the analysis of the "Toronto" failure and why the answer that Watson produced was a rational product of a sound strategy. This leaves students to judge the generality of the strategy and its applicability to important business problems.
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