Three principles leaders need to consider when making AI investments

New MIT CISR research offers insights on how to invest in capabilities, involve work staff, and realize value from both predictive and generative AI

Cambridge, Mass., May 16, 2024 (GLOBE NEWSWIRE) -- A new research briefing released today by the MIT Center for Information Systems Research (CISR) establishes three principles supported by data monetization research to guide business leaders when making artificial intelligence (AI) investments.

Titled “AI Is Everybody’s Business,” the research briefing explores managing AI using a data monetization mindset, and was written by Barbara H. Wixom and Cynthia M. Beath, authors of the award-winning book, Data Is Everybody’s Business: The Fundamentals of Data Monetization. Wixom is a principal research scientist at MIT CISR, and Beath is Professor Emerita, University of Texas and an academic research fellow with MIT CISR.

“In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals,” said Wixom. “AI technology’s role is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed.”

Principle 1: Invest in Practices That Build Capabilities Required for AI
MIT CISR research shows that an organization’s progress in AI is dependent on having equally advanced capabilities in data science, data management, data platform, acceptable data use, and customer understanding.

The authors stress that while it is important to invest in AI training, it’s equally important to invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify customer understanding (such as mapping customer journeys).

In particular, adopting policies and processes that will improve an organization’s data governance allows data to be used only in AI initiatives in ways that are consonant with its values and regulatory environment.

“We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep science skills; we do not advise this,” Beath cautioned.

Principle 2: Involve the Entire Organization in Your AI Journey
With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations learn how to build models people trust.

Given the consumerization of generative AI tools, MIT CISR research shows that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects — and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value from AI Projects

AI is costly and needs to pay off, yet some organizations become distracted with endless experimentation. Leaders with data monetization experience make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real world challenges and opportunities.

MIT CISR’s research shows that leaders who realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns.

“When people actively engage in data monetization initiatives using AI they learn and, in turn, help their organization learn,” said Wixom. “Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which leads to new ideas and more engagement, which further improves data and delivers more value, and so on.”

“Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data. This is why AI, like data, is everybody’s business,” Beath concluded.


AI is Everybody's Business

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