The case describes the development of artificial intelligence (AI) enabled recruitment automation software Talkpush and how it leveraged the technology of open application programming interface (API) to address changes in consumer’s communication preferences, and create a software as a service (SaaS) business. In 2021, Talkpush was handling 12 million hiring conversations per annum, forming one of the largest conversational databases in recruitment. It applied speech recognition to convert audio to text, and recognized over 45,000 “intents” through natural language processing (NLP). Recognizing the vast amount of data—voice, text, and images—that could be mined for business insights or used as training data for machine learning, Talkpush’s founder Max Ambruster was eager to look for a strategy to extract value from the company’s growing data assets.
Students will learn to
- Identify the various structured and unstructured data involved in a business process and understand how such data can be mined for insights and value creation.
- Explain how supervised and unsupervised machine learning work and provide examples of their applications.
- Develop AI and deep learning ideas by mining the text, audio and video data generated in business processes.
- Recognize the limitations and pitfalls of AI, e.g., algorithmic bias.
- Describe the connection between big data and AI and explain their similarities and differences.