The Digital Data Design Institute at Harvard invites individuals to embark on a journey into the future of business across industries and sectors. This collaborative effort bridges the intellectual chasm between academic research and industry practitioners to scrutinize innovative operating models and business models that are redefining traditional business paradigms that succeed in the age of AI and beyond. We delve into the intricate dynamics of digitalization, ecosystem-driven strategies, and platform-centric approaches that are shaping the contemporary business landscape. Together, we explore strategies and insights poised to empower organizations to excel in the ever-evolving world of commerce.
In today’s competitive corporate landscape, the workplace can be a battleground of ambition and performance. While healthy competition can fuel innovation and productivity, research (“Determinants of Top-Down Sabotage”) by Hashim Zaman, Post-Doctoral Fellow at the Laboratory for Innovation Science at Harvard (LISH) and Karim R. Lakhani, Professor of Business Administration at Harvard Business School, founder […]
As businesses grapple with an ever-growing volume of ideas, products, and solutions to evaluate, decision-making processes are being reshaped by artificial intelligence (AI). Generative AI, in particular, has emerged as a game-changer in creative problem-solving and evaluation, as demonstrated by a recent field experiment described in the working paper “The Narrative AI Advantage? A Field […]
As large language model (LLM) systems grow in complexity, the challenge of ensuring their outputs align with human intentions has become critical. Interpretability—the ability to explain how models reach their decisions—and control—the ability to steer them toward desired outcomes—are two sides of the same coin. “Towards Unifying Interpretability and Control: Evaluation via Intervention”—research by Usha […]
In today’s data-driven world, businesses face the dual challenge of leveraging vast datasets to gain insights while ensuring compliance with stringent data privacy regulations. The concept of machine unlearning, a method for efficiently removing the influence of specific data points from machine learning models, represents a paradigm shift in managing data responsibly. Recent research explores […]
