Kalakrit’s dilemma: scaling up business through human expertise or AI

Learning outcomes By analyzing and discussing the case, students should be able to: Case overview/synopsis The case explores Kalakrit’s journey as a translation and localization startup. It highlights the challenges faced by its founder, Sehaj, including client acquisition, cost efficiency and competition from AI-driven translation tools. The case examines how Kalakrit leveraged human expertise, innovative cost-cutting and grassroots marketing to scale its business. The discussion links Kalakrit’s model to the scalability of business model theory. It shows how the company built a distributed workforce of linguists and voice-over artists, focusing on Kalakrit’s unconventional strategies, such as home-based voice-over studios and decentralized audiobook production. Parallels are also drawn to business expansion through diversification. Students will assess the sustainability of these strategies. They will analyze the long-term value of human-driven localization in an AI-driven industry. The case encourages discussion on startup scaling business models in the face of disruptive innovation. It also raises a key question: Should Kalakrit adopt AI-driven translation to achieve the scalability of business operations or position itself as a premium human-led service provider? Complexity academic level This case is designed for graduate and executive education courses. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.