Faculty
Rama Ramakrishnan is a Professor of the Practice in AI/ML. His teaching, research, and advisory interests center on the practical business application of Predictive and Generative AI techniques and in the creation of intelligent products and services.
Rama is a passionate educator and was awarded Sloan's most prestigious teaching award, the Jamieson Prize for Excellence in Teaching, in 2025 and MIT's Teaching with Digital Technology Award in 2024 (one of his MIT Sloan courses, Hands-on Deep Learning, is now freely available on MIT OpenCourseWare).
Rama believes that AI knowledge should be accessible to everyone and shares his expository work at https://ai.ramakrishnan.com. He is also an AI columnist for the MIT Sloan Management Review and a member of its editorial advisory board.
Prior to joining MIT Sloan, Rama was a tech entrepreneur and executive for over 20 years. He has founded or has been a senior executive in four software companies that have exited to technology titans: Oracle, Salesforce, and Demandware. He is active in the startup ecosystem as an advisor and angel investor.
Most recently, Rama was senior vice president at Salesforce (NYSE: CRM) and chief data scientist for Salesforce Commerce Cloud. In this role, he led Salesforce Einstein for Commerce—the analytics/machine-learning platform that powers Salesforce Commerce Cloud—and was responsible for product management, engineering, data science, and cloud production operations. The Einstein platform uses analytics techniques to predict and influence the shopping behavior of hundreds of millions of unique shoppers monthly.
The path that led Rama to Salesforce started in July 2010 when he founded a startup, CQuotient, to build a data-science-based personalization platform for retail and e-commerce. Backed by funding from Bain Capital Ventures, Rama built and grew the company to a successful exit to Demandware (NYSE: DWRE) in October 2014. As a member of the Demandware executive team, Rama was involved in the successful sale of Demandware to Salesforce in July 2016 for $2.8 billion. CQuotient technology, now known as Salesforce Einstein for Commerce, is one of the top B2C recommendation engines in the world and influences the shopping behavior of billions of consumers annually.
Prior to founding CQuotient, Rama taught analytics at MIT Sloan, was chief scientist and vp of R&D at ProfitLogic, was chief analytics officer and vp of R&D for the retail business of Oracle, founded two analytics companies, and was a consultant at McKinsey & Company.
Rama has a BTech degree from the Indian Institute of Technology, Chennai, and MS and PhD degrees from MIT.
As organizations begin deploying agentic systems that can execute entire workflows, a consequential question is emerging: How can leaders turn agentic AI productivity gains into business growth?
Experts in artificial intelligence from MIT Sloan are keeping an eye on the human-LLM accuracy gap, AI guardrails, and other trends.
Professor of the practice Rama Ramakrishnan recommended breaking tasks up into small, discrete parts, and then considering the potential costs of using AI to do them. "Some tasks, like writing ad copy, product descriptions, or a movie plotline, have slightly more room for error. Uses that require logical reasoning or factual knowledge; encompass cause-and-effect relationships; or have high stakes, like medical care, demand more accuracy," he explained.
Professor of the practice Rama Ramakrishnan wrote: "Many executives are aware of agentic AI coding assistants like Claude Code but assume that these tools are relevant only to software developers. This assumption is rapidly being proved wrong. It turns out that the features that make AI coding tools useful for programmers can be equally valuable for knowledge work that involves no programming whatsoever."
The unpredictability of AI's strengths and weaknesses means that it takes time to develop intuition for how to use the technology. Painful lessons are learned in the initial rush to adopt AI. Avoiding these painful lessons means solving a variety of technical issues. Yet the hidden costs of doing so are easily overlooked, says professor of the practice Rama Ramakrishnan.
Generative AI uses should be taken on more carefully because they carry greater risks, said professor of the practice Rama Ramakrishnan. "The first mistake is being overly aggressive and essentially deploying the LLM for a task without enough scrutiny," he said. On the other extreme, Ramakrishnan said, are people "who are so apprehensive about the hallucinations and the error rates, they literally are paralyzed. They don't want to do anything."
This online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) challenges common misconceptions surrounding AI and will equip and encourage you to embrace AI as part of a transformative toolkit. With a focus on the organizational and managerial implications of these technologies, rather than on their technical aspects, you’ll leave this course armed with the knowledge and confidence you need to pioneer its successful integration in business.
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