Magical thinking yet again, this time about AI rather than blockchain?
'Expanding AI’s Impact With Organizational Learning' in (2020) MIT Sloan Management Review asks
Only 10% of companies obtain significant financial benefits with artificial intelligence technologies. Why so few?
Drawing on the 2020 Artificial Intelligence Global Executive Study and Research Project under the auspices of BCG, the authors comment
Our research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. Their strategic focus is organizational learning, not just machine learning. Organizational learning with AI is demanding. It requires humans and machines to not only work together but also learn from each other — over time, in the right way, and in the appropriate contexts. This cycle of mutual learning makes humans and machines smarter, more relevant, and more effective. Mutual learning between human and machine is essential to success with AI. But it’s difficult to achieve at scale.
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:
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.
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.
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.
