AI for IA: Enabling Artificial Intelligence for Intelligent Augmentation of Business
Inspired by his interest in applying optimization to the intersection of technology and business, R. Ravi launched the Tepper School of Business’ multidisciplinary Center for Intelligent Business (CIB) in April 2023.
“I started the Center for Intelligent Business to bring in more cutting-edge problems involving modern data from industry to the Tepper School, and to create opportunities for faculty and students to study them,” said Ravi, the Andris A. Zoltners Professor of Business, professor of operations research and computer science, and director of analytics strategy at the Tepper School.
His conversations with Tepper School alumni, as well as with company CEOs, established a pipeline of interesting projects focused on the strengths and shortcomings of technology proposed for business applications.
“ The deep technical work going on at the Tepper School is unique,” Ravi continued. “Most business schools don’t have such a depth of technical expertise in areas related to data analysis and AI — for example, in operations, finance, and marketing where people are developing and applying machine learning techniques.”
- Professor R. Ravi
IT'S ALL ABOUT THE PROCESS
CIB Coffee Chats are held on Friday mornings to informally explore potential projects.
“ We identify companies we want to collaborate with from alumni, faculty, or company requests,” Ravi said. After an initial conversation, a company rep who is sufficiently entrenched in the mechanics and market segment of the problem and empowered to make the next set of business decisions is invited to a Chat.
Faculty from the Tepper School and multidisciplinary CMU departments with a focal interest in the problem attend. A couple dozen people receive a brief introduction to the context of the problem, and faculty and students probe into aspects worth modeling. “We try to see if we can convert their aspects of concern into an interesting business problem to deeply investigate,” Ravi said.
“We recently had a Chat with a person from a local company that makes train control systems for railway companies across the world. There was clearly interest from engineering and operations, so we invited the right set of faculty and Ph.D. students,” Ravi explained.
Ravi said in the best-case scenarios, they walk away with a rough new problem statement, and a follow-up meeting with a unit head who can share data and more context. As that pipeline grows, it matures into a project involving Ph.D. students and faculty members.
In addition to the opportunity to develop a research paper or thesis, this process focuses on developing a model of immediate value to the company as well as learnings that could be prototyped and internalized at the company for full scale testing and production.
“The sweet spot for us seems to be companies that are about 100 to 500 strong: They’re nimble, have lots of organically forming and reorganizing units, and they’re innovating all the time,” Ravi said. “They are not quite startups and have enough infrastructure to engage fully with us.”
“We are actively talking with about a dozen companies, although I’ve talked with four or five dozen by now,” he added.
“Things come and go, and it’s a pretty exciting business. It’s intellectually stimulating, which is always a nice thing.”
WHAT COMES NEXT? MANAGEMENT SCIENCE 2.0 AND BEYOND
What will the next set of tools be for managing business, given the increase in data and the new AI tools? What scientific problems need to be solved?
“From the student’s perspective, the excitement is in thinking about the impact of these technologies on business decisions,” said Ravi.
“When we are not teaching students, we’re building and validating these models as part of our research so they will become Management Science 2.0 or the next generation of widely deployed models: Our goal is to build models that will become codified in business tools of the future. We’re researching tools they will be exposed to five or 10 years from now.”
“A lot of the core AI technology is being developed right here across the street: We have strong ties and collaborations in our Ph.D. program with faculty in computer science. These ties allow us to stay a little bit ahead of the curve because knowing the extent of the technology allows you to better see its applicability or inapplicability in business surroundings,” he added.
THE SILVER BULLET MENTALITY
People are fascinated by what machines and AI can do — enabling chatbots, painting pictures, writing songs — but often transfer that understanding mistakenly into thinking AI or a chatbot tool will completely replace a manager or cause immediate waves of layoffs. They think of AI as a “silver bullet” that will solve all their problems and transform the future of work.
“There is a lot of mundane work people are doing now in sifting through data and extracting what’s valuable before they apply business decision making and logic to it. AI is going to make that mundane work go away completely or happen much faster, but AI is not going to take over the entire management team anytime soon,” Ravi countered.
“You need human judgment to analyze things along with all the numbers. The biggest issue is accountability. When you run a business, you’re accountable to your customers. You’re legally liable for what you deliver, and it’s dangerous, if not downright rash, for machines to make business decisions because you can’t hold them accountable.”
Roadblocks faced by companies center around talent and engagement.
“There’s little or no bandwidth in many companies today to engage in these next generation tools and problems,” he conceded. “Companies are sometimes unrealistic, and more education about ‘the art of the possible’ can help. We encourage them to engage in an executive education program with us, and then we are better able to explain why some goals are still far away.”
TEPPER MBA STUDENTS WILL HELP DEFINE THE FUTURE OF AI
Ravi said the biggest distinguishing ability that the Tepper School offers its MBA students is to prepare them to serve as the liaison between business oriented, high-level strategic decision making and tactical analytical quantitative modeling. “We try to give our students a healthy understanding of both, so they serve as a translator back and forth.”
The Tepper Data Analytics Club hosted the Generative AI Innovation Challenge in March 2024, sponsored by Google and open to all CMU students. In this case competition event, mixed student teams used the new genAI tools to answer a challenge. “Between that and the Coffee Chats, we are creating a unique environment where students not only collaborate using cutting-edge tools, but also see where new problems are being defined for the future,” Ravi said. In addition to competitive events, weekend workshops on the analytical capabilities of working with data and conducting analysis will soon be deployed.
“The reason we chose the name, Center for Intelligent Business, is that we are always forward looking. If you want to be intelligent, you’ve got to investigate the opportunities in the future. Looking ahead five years, the goal of the CIB is to stay abreast of new technologies that are available for corporations and the general empowerment of business,” Ravi said.
This year, for example, the CIB is beginning another initiative in distributed ledger technologies, which is the foundation for blockchains and cryptocurrencies. “We are trying to formulate the Management Science 2.0 problems and in five years, it will be 3.0,” Ravi said. Our goal is to engage intellectually in this intersection between technology and business.”
The most forward-thinking companies are investing heavily in improved data analytics tools that allow them to take raw data and use it to bypass earlier, mundane steps. “I’m seeing a lot of leapfrogging, particularly using genAI, and that is very interesting,” Ravi said.
“I believe that ability will separate the winners from the losers as people adopt this technology.”