MIT's Center for Information Systems Research (CISR) recently celebrated its 50th anniversary, and its founder Scott Morton gave a talk.
“CISR just celebrated its 50th anniversary at MIT. In fact, we had the founder just give a talk, Scott Morton.”
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Barb Wixom
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Scott Morton started MIT's Center for Information Systems Research in 1974, before chief information officers existed in large organizations.
“he started the Center for IS research in 1974. This is before we had CIOs and large organizations were struggling with managing tech and succeeding in tech.”
Scott Morton wrote what Barb Wixom describes as the first doctoral dissertation in decision support, after earning a Harvard PhD in the early 1960s.
“he has the honor of being the very first doctoral dissertation in the area of decision support.”
According to Wixom, Scott Morton's first advisor dismissed his proposal to use system data for management information, and he switched advisors.
“he he was trying to optimize laundry machines at the time, and his advisor said that was the stupidest thing he had ever heard.”
CISR was established to help leaders succeed with technology, and Wixom says it is a nonprofit.
“CISR was established to help leaders succeed. It's actually a nonprofit, and we still do that today.”
Barb Wixom's book Data Is Everybody's Business came out in fall 2023 and synthesizes thirty years of her research.
“I have a book called Data is Everybody's Business that just came out fall of 2023, and the book was a synthesis of literally thirty years of my research.”
Wixom began her research in 1994 on data warehousing, which then included advanced analytics now categorized as AI.
“So I started researching in the space of data warehousing, which, again, back then also included advanced analytics, which we now would categorize as AI in 1994.”
Wixom defines data monetization as converting data from an organizational resource into money, rather than selling data to others.
“Data monetization is when you start with data as an organizational resource and you end up with money and economic resource, and there's a conversion that happens in the organization.”
Wixom says the three routes to monetization are changing the nature of work, changing products, and creating new informational solution revenue streams.
“the context for AI should either be changing the nature of work, changing the nature of products, or creating new informational solution revenue streams.”
BBVA did co-creation and social good AI work as it began investing heavily in AI, which Wixom says helped attract talent to the bank.
“BBVA, for instance, when they first were getting really hard into AI, they did a lot of co-creation, social good work.”
Around 2015, leaders working with CISR began asking about data creepiness, which led CISR to expand its data governance capability to cover values and ethics.
“back in about 2015, we started getting questions from the leaders with whom I work very closely about data creepiness.”
Wixom lists governance and oversight as one of five capabilities needed for data monetization.
“one of the five capabilities that you need is you need to have your governance and your oversight.”
Wixom says a company-wide generative AI tool should be managed like a personal productivity tool, unlike gen AI solutions built for parts of the business.
“when we are rolling out a universal tool gen AI to the masses across an organization, we need to be managing that like we would manage a personal productivity kind of tool.”
CISR identified 52 AI projects in production when it began studying how to measure AI success.
“So at the time, we identified fifty two projects that had been put in production.”
CISR found that making machine learning projects deployable required a sixth capability, AI explanation, beyond the five data monetization capabilities.
“it required a six capability beyond my data monetization, five core capabilities, and it was AI explanation.”
CISR defines AI explanation as covering bias remediation, decision tracing, value formulation and boundary setting.
“AI explanation we define as a capability where you can do bias remediation, decision tracing, value formulation, and boundary setting.”
CISR did a large-scale case study on CEMEX about using AI to optimize plants and then recontextualizing models across different geographies.
“We did a large-scale case on SEMEx, where you would learn how to use AI in order to optimize a plant.”
Wixom says machine learning projects that traditionally took one to three years are taking one to three months with generative AI.
“a machine learning project that traditionally would take one to three years with gen AI, we're seeing one to three months.”
Wixom convenes a data board of about a hundred senior executives who discuss a research topic each quarter.
“So imagine I have these hundred CDOs, COOs, global, really busy people, but they patiently participate in conversations every quarter on some topic.”