About the Center for Intelligent Business
AI for IA: Unleash Potential in The Intelligent Future
What are the benefits of the increased availability of data and AI in business? AI and related technologies aid human capabilities in a variety of spheres such as robotic-interaction, vision, speech, and language processing. CMU is the incubation ground for many of these technologies; we draw on the expertise of colleagues in all other schools to understand and map these advances to a business context.
Business executives are tasked with making managerial decisions and executing them at strategic and tactical levels. Intelligence Augmentation (IA) is the use of AI and related technologies to aid human decision-making in the following ways.
- Processing the large volume of data that is becoming available for making the relevant decisions and presenting it in a succinct and reliable way
- Extract information in novel ways from the vast data that aid in decision making using relevant models
- Suggesting entirely new decision-making pathways that were not possible before based on scientific modeling
The Tepper School (formerly GSIA) consistently stands out for its role in introducing Management Science, the first approach to integrate scientific methods into complex problem-solving. With new technologies in the mix now, the time has come for Management Science 2.0.
Inventing Management Science 2.0
To take this approach, we will re-examine the traditional problems that have been researched and taught in business schools.
- How has the change in the content, volume, and speed of available information affected the decision-making horizon and scope for managers?
- What are the commonly available reliable AI tools related to the managerial task and how can they be used to augment the information for decision making?
- Should traditional business problem formulations be recast considering these changes?
- How can deployment of automated or intelligent agents in business be sufficiently de-risked?
- How can this deployment be carried out in an ethical and socially beneficial manner?
Leveraging Our Methodology
Our traditional approach in devising Management Science models has been to examine business problems under the lens of improvement, intent and incentive. I.e. quantitative methods, behavioral methods and incentive-based methods. This allowed us to use foundational techniques from Optimization, Organizational Behavior, and Economics to frame and address important managerial problems. We will use the same approach for examining the infusion of new data and analytical capabilities in defining Management Science 2.0. However, this calls for a much expanded toolkit including modern techniques in AI and Machine Learning, as well as causality modeling, and human-computer interaction design, that involves all of our partners across campus.
Example
An ongoing collaboration with Glance, an innovative lock screen content provider in India, illustrates how a research project could evolve. By sharing their problem context and large data with faculty at the school, the business and data science managers helped formulate new questions related to recommending short-lived high-volume content, user production of quality content, and platform monetization. Working with doctoral students, the team of managers, data scientists, and faculty designed sound formulations of these new business concerns, developed new algorithms, validated them on past data, and convinced the business units to run field experiments based on them. The success of such experiments has deepened the trust as well as the bandwidth of collaboration. At the same time, Glance has sponsored capstone course experiences for masters’ students anchored on the shared data. This helped train these students and provide them a deeper understanding of the business context of the research problems.
Leadership
Professor R. Ravi, the Andris A. Zoltners Professor of Business and the Director of Analytics Strategy for the Tepper School, is the founding director of the center. His research involves the application of discrete optimization and computer science to the intersection of business and technology in areas ranging from omnichannel retail to digital advertising. He has worked extensively with analytics firms on research and consulting projects, and developed new training and courses in the area for a wide audience ranging from undergraduates, MBAs, specialized masters and executives.
Please contact Jen Cadman for administrative enquiries.
