Harvard Case - Taiger AI: Unbundling the Business Value of NLP
"Taiger AI: Unbundling the Business Value of NLP" Harvard business case study is written by Seema Chokshi, Lipika Bhattacharya, Wee-Kiat Lim. It deals with the challenges in the field of Strategy. The case study is 12 page(s) long and it was first published on : Apr 27, 2021
At Fern Fort University, we recommend Taiger AI pursue a multi-pronged growth strategy focused on leveraging its core NLP capabilities to expand into new markets, develop innovative solutions, and solidify its position as a leading player in the rapidly evolving AI landscape. This strategy involves a combination of organic growth, strategic partnerships, and targeted acquisitions to capitalize on the increasing demand for AI-powered solutions across various industries.
2. Background
Taiger AI is a Singapore-based company specializing in Natural Language Processing (NLP) solutions. The company provides AI-driven solutions for customer service, document processing, and knowledge management, primarily serving the financial services sector. Taiger faces the challenge of scaling its business while navigating a competitive landscape with larger, established players.
The case study focuses on Taiger's CEO, who is seeking to develop a strategy to unlock the full potential of their NLP technology and achieve sustainable growth.
3. Analysis of the Case Study
To analyze Taiger AI's situation, we employ several strategic frameworks:
a) Porter's Five Forces:
- Threat of New Entrants: High, due to the increasing availability of open-source NLP tools and the ease of entry for startups.
- Bargaining Power of Buyers: Moderate, as clients are increasingly demanding customized solutions and seeking cost-effective options.
- Bargaining Power of Suppliers: Low, as Taiger AI relies on readily available technology and talent.
- Threat of Substitutes: High, as alternative technologies like machine learning and deep learning are emerging as potential substitutes for NLP.
- Competitive Rivalry: High, with several established players like Google, Microsoft, and IBM competing in the AI space.
b) SWOT Analysis:
- Strengths: Strong NLP expertise, proven track record in financial services, agile and innovative culture.
- Weaknesses: Limited resources compared to larger competitors, reliance on a single industry, potential for technology obsolescence.
- Opportunities: Growing demand for AI solutions across industries, potential for partnerships and acquisitions, expansion into emerging markets.
- Threats: Competition from larger players, rapid technological advancements, regulatory uncertainty in the AI space.
c) Value Chain Analysis:
Taiger AI's value chain consists of:
- Research & Development: Developing and refining its NLP technology.
- Product Development: Creating customized solutions for specific industries.
- Sales & Marketing: Reaching out to potential clients and promoting its solutions.
- Implementation & Support: Deploying and maintaining its solutions for clients.
d) Business Model Innovation:
Taiger AI can explore various business model innovations:
- Subscription-based pricing: Offering access to its NLP platform on a subscription basis.
- Value-based pricing: Pricing solutions based on the value delivered to clients.
- Partnerships: Collaborating with other companies to offer integrated AI solutions.
- Vertical integration: Expanding into related areas like data analytics and consulting.
4. Recommendations
Taiger AI should pursue the following recommendations to achieve sustainable growth and unlock the full potential of its NLP technology:
a) Expand Market Reach:
- Market Segmentation: Identify new industries with high demand for AI solutions, such as healthcare, retail, and manufacturing.
- Market Development: Develop targeted marketing campaigns and sales strategies for each identified industry.
- International Expansion: Explore opportunities in emerging markets like Southeast Asia and Latin America, where the adoption of AI is rapidly growing.
b) Develop Innovative Solutions:
- Product Development: Invest in research and development to create new and innovative NLP solutions that address specific industry needs.
- AI-driven Platform: Develop a comprehensive AI platform that integrates NLP with other technologies like machine learning and data analytics.
- Vertical Integration: Expand into related areas like data analytics and consulting to offer a more comprehensive suite of services.
c) Strategic Partnerships and Acquisitions:
- Strategic Alliances: Partner with leading technology companies, industry players, and research institutions to leverage their expertise and expand market reach.
- Targeted Acquisitions: Acquire smaller companies with complementary technologies or expertise to accelerate growth and enhance its portfolio of solutions.
d) Embrace Digital Transformation:
- Digital Marketing: Utilize digital marketing channels like social media, content marketing, and search engine optimization to reach a wider audience.
- Cloud-based Solutions: Offer its solutions as cloud-based services to enhance scalability and accessibility.
- Data Security and Privacy: Implement robust data security measures and comply with relevant regulations to build trust with clients.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies: Taiger AI's core competence lies in its NLP technology, which is the foundation for its growth strategy.
- External Customers: The recommendations focus on expanding into new markets and developing solutions that address the specific needs of different industries.
- Competitors: The recommendations aim to differentiate Taiger AI from its competitors by offering innovative solutions, building strategic partnerships, and expanding into new markets.
- Attractiveness: The recommendations are expected to generate significant return on investment (ROI) through increased revenue, market share, and brand recognition.
6. Conclusion
Taiger AI has the potential to become a leading player in the AI space by leveraging its core NLP capabilities and pursuing a multi-pronged growth strategy. By expanding into new markets, developing innovative solutions, and forming strategic partnerships, Taiger AI can achieve sustainable growth and unlock the full value of its technology.
7. Discussion
Alternative strategies include focusing solely on organic growth or pursuing a more aggressive acquisition strategy. However, these options carry higher risks and may not be as effective in achieving long-term sustainable growth.
Key assumptions include the continued growth of the AI market, the availability of skilled talent, and the ability to secure funding for research and development.
8. Next Steps
Taiger AI should implement its growth strategy in a phased approach:
- Phase 1 (Year 1): Focus on market segmentation, product development, and strategic partnerships.
- Phase 2 (Year 2-3): Expand into new markets, develop innovative solutions, and pursue targeted acquisitions.
- Phase 3 (Year 4+): Solidify its position as a leading AI player, expand its global footprint, and explore new business models.
By following these recommendations and implementing a well-defined strategic plan, Taiger AI can capitalize on the growing demand for AI solutions and achieve its ambitious growth goals.
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Case Description
Set in April 2020, the case talks about TAIGER, a software-as-a-service (SaaS) company providing natural language processing (NLP) solutions in a rapidly growing market where demand and competition for such solutions are high. TAIGER solutions could process and digitise large amounts of physical data, perform search and extract functions on data, and make appropriate recommendations. The algorithms and tools were bundled under three packages, and were customised for every client. Client companies were mainly large organisations and government entities. Despite a growing customer base, Arroyo and his team found it increasingly difficult to service new clients, who demanded more customisation and services. TAIGER's solution packages were bundled together with customisation and post-implementation support services based on contract licenses. The downside of this model was that it used many resources and limited the delivery of the products to a per project basis. The monetisation of the model was also complicated and project costs were difficult to control. Administering customised solutions was time consuming and expensive for both TAIGER and its clients; it also lacked flexibility and quick scalability for large-scale implementation. Arroyo realised that he needed more than just efficient solutions, given the expanding opportunities for NLP in the market and the constraints of TAIGER's existing solution packaging. He wondered if designing a new business model was the right way forward. Would he also need to devise a new pricing strategy and rebundle solution offerings? How could he unbundle the business value of TAIGER's NLP solutions?
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