- Significant financial benefits are likely only when organizations define multiple, effective ways for humans and AI to work and learn together;
- Although rare, these successes aren’t confined to a single industry or available only to digital natives or large companies;
- These most successful organizations distinguish themselves through effort and a commitment to learn with AI;
- They don’t just get good at working with machines;
- They get good at tailoring human and machine roles dynamically as situations change;
- They don’t facilitate machine learning; they facilitate mutual learning; and
- They don’t just use AI; they learn with AI.
Condensed knowledge 10
- Our research — based on a global survey of more than 3,000 managers, as well as interviews with executives and scholars — confirms that a majority of companies are developing AI capabilities but have yet to gain significant financial benefits from their efforts. More than half of all respondents affirm that their companies are piloting or deploying AI (57%), have an AI strategy (59%), and understand how AI can generate business value (70%). These numbers reflect statistically significant increases in adoption, strategy development, and understanding from four years ago. What’s more, a growing number of companies recognize a business imperative to improve their AI competencies. Despite these trends, just 1 in 10 companies generates significant financial benefits with AI.
- We analyzed responses to over 100 survey questions to better understand what really enables companies to generate significant financial benefits with AI. We found that getting the basics right — like having the right data, technology, and talent, organized around a corporate strategy — is far from sufficient. Only 20% of companies achieve significant financial benefits with these fundamentals alone. Getting the basics right and building AI solutions that the business wants and can use improve the odds of obtaining significant financial benefits, but to just 39%.
- Our key finding: Only when organizations add the ability to learn with AI do significant benefits become likely. With organizational learning, the odds of an organization reporting significant financial benefits increase to 73%.
- Organizations that learn with AI have three essential characteristics.
- Characteristic 1. They facilitate systematic and continuous learning between humans and machines. Organizational learning with AI isn’t just machines learning autonomously. Or humans teaching machines. Or machines teaching humans. It’s all three. Organizations that enable humans and machines to continuously learn from each other with all three methods are five times more likely to realize significant financial benefits than organizations that learn with a single method.
- Characteristic 2. They develop multiple ways for humans and machines to interact. Humans and machines can and should interact in different ways depending on the context. Mutual learning with AI stems from these human-machine interactions. Deploying the appropriate interaction mode(s) in the appropriate context is critical. For example, some situations may require an AI system to make a recommendation and humans to decide whether to implement it. Some context-rich environments may require humans to generate solutions and AI to evaluate the quality of those solutions. We consider five ways to structure human-machine interactions. Organizations that effectively use all five modes of interaction are six times as likely to realize significant financial benefits compared with organizations effective at a single mode of interaction.
- Characteristic 3. They change to learn and learn to change. Structuring human and machine interactions to learn through multiple methods requires significant, and sometimes uncomfortable, change. Organizations that make extensive changes to many processes are five times more likely to gain significant financial benefits compared with those that make only some changes to a few processes. These organizations don’t just change processes to use AI; they change processes in response to what they learn with AI.
- Organizational learning with AI demands, builds on, and leads to significant organizational change. This report offers a clear, evidence-based view about how to manage organizational learning with AI.
- Adoption of AI continues to increase, and many organizations now use AI technologies to generate some business value. But significant financial benefits are elusive, and few organizations achieve them. Many organizations struggle to build an AI foundation that rests on the right data, technology, and talent. Or they may have built this foundation, use it to churn out AI solution after solution, and yet wonder why the financial benefits are only incremental. Significant financial benefits are likely only when organizations define multiple, effective ways for humans and AI to work and learn together.
- Although rare, these successes aren’t confined to a single industry or available only to digital natives or large companies. Instead, these most successful organizations distinguish themselves through effort and a commitment to learn with AI. They don’t just get good at working with machines; they get good at tailoring human and machine roles dynamically as situations change. They don’t facilitate machine learning; they facilitate mutual learning. They don’t just use AI; they learn with AI.
Category 1: A new, better world for everyone
[genioux fact extracted from MIT SMR]
Authors of the genioux fact
EXPANDING AI’S IMPACT WITH ORGANIZATIONAL LEARNING, Sam Ransbotham, Shervin Khodabandeh, David Kiron, François Candelon, Michael Chu, And Burt Lafountain, October 19, 2020, MIT Sloan Management Review, Findings from the 2020 Artificial Intelligence Global Executive Study and Research Project, In collaboration with BCG.
ABOUT THE AUTHORS
Sam Ransbotham (@ransbotham) is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Review’s Artificial Intelligence and Business Strategy Big Ideas initiative.
Shervin Khodabandeh is a senior partner and managing director at BCG, and the co-leader of BCG GAMMA (BCG’s AI practice) in North America. He can be contacted at firstname.lastname@example.org.
David Kiron is the editorial director of MIT Sloan Management Review, where he directs the publication’s Big Ideas program. He can be contacted at email@example.com.
François Candelon is a senior partner and managing director at BCG, and the global director of the BCG Henderson Institute. He can be contacted at firstname.lastname@example.org.
Michael Chu is a partner and associate director at BCG, and a core member of BCG GAMMA. He can be reached at email@example.com.
Burt LaFountain is a partner and managing director at BCG, and a core member of BCG GAMMA. He can be reached at firstname.lastname@example.org.