Three Principles for Making AI Investments, Backed by MIT CISR Research

“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,” say the authors. “In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals.”

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.

Invest in AI training—but also 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 your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so data is only used in AI initiatives in ways that are consonant with your organization's 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,” caution the authors.

Principle 2: Involve All Your People 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 believes 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 Your 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 challenges and opportunities.

In MIT CISR’s research, the leaders that 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 they help their organization learn,” say the authors. “Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn 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.”

Read the MIT CISR research briefing, “AI Is Everybody’s Business” for sound AI investment principles and examples of leaders achieving big AI wins and maximizing financial returns.