Blackbox Chatbot: Designing natural language conversations with Data

• Main Authors: MAKANY, Tamas, BHATTACHARYA, Lipika
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Innovation; Conversational AI; Human Centred Design; Design Thinking; Entrepreneurship; Technology Innovation; Entrepreneurial and Small Business Operations; Programming Languages and Compilers; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/cases_coll_all/423; https://smu.sharepoint.com/sites/admin/CMP/cases/SMU-22-BATCH%20%5BPDF-Pic%5D/SMU-22-0008%20%5BBlackbox%5D/SMU-22-0008%20%5BBlackbox%5D.pdf?CT=1654481292191&OR=ItemsView
• Institution: Singapore Management University

The case is set in May 2021 and talks about a Singapore-based small-medium enterprise decision science company called Blackbox (BB) and how it used conversational AI, human-centered design, and a technology innovation partner to design and develop a chatbot that helped its clients understand data insights with the language of everyday conversations. BB generated vast data through opinion surveys and complex market analyses for its clients. While dashboards and reports provided a glimpse into that data, these tools did not necessarily help clients make immediate data-driven business decisions. Analysts would spend time on the phone answering simple and often repetitive questions to explain the key findings. BB decided to build a chatbot that could handle natural language queries independently to reduce the time of live support calls and allow even non-specialized users to access data insights directly. The CEO, David Black, and his team, led by Chief Operating Officer, Saurabh Sardana, embarked on designing a conversational data platform to provide improved services to clients at scale. However, how could BB execute this idea efficiently without an internal technical team specializing in building chatbots? How could the company avoid the pitfalls of typical commercial chatbots that fail to engage users? How could Black and his team design a conversational data platform that efficiently imitated human-to-human conversations, which clients could use independently? Could this new platform help the company become a differentiator in the market? This case helps demonstrate (1) small enterprise decision-making and collaboration factors for innovation with AI development in a non-tech company, (2) the pitfalls of developing a chatbot for a business, (3) the advantages of using conversational AI design, and (4) the significance of agile, iterative, design thinking and human-centric approach to building chatbots.