AI success is built on learning
Enterprises seeing real success with artificial intelligence have something in common: they are capable of learning quickly from their successes or failures and re-applying those lessons into the mainstream of their businesses.
Of course, there’s nothing new about the ability to rinse, learn and repeat, which has been a fundamental tenet of business success for ages. But because AI is all about real-time, nanosecond responsiveness to a range of things, from machines to markets, the ability to leap and learn at a blinding pace has taken on a new urgency.
At this moment, only 10% of companies are seeing financial benefits from their AI initiatives, a survey of 3,000 executives conducted by Boston Consulting Group and MIT Technology Review finds. There is a lot of AI going around: more than half, 57%, piloting or deploying AI — up from 46% in 2017. In addition, at least 70% understand the business value proposition of AI. But financial results have been elusive.
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So, what are the enlightened 10% doing to finally realize actual, tangible gains from AI? They do all the right things, of course, but there’s an extra piece of the magic thrown in. For instance, scaling AI — seen as the path to enterprise adoption — has only limited value by itself. “Adding the ability to embed AI into processes and solutions improves the likelihood of significant benefits dramatically, but only to 39%,” the survey shows.
Successful AI adopters have figured out how to learn from their AI experiences and apply them in forward-looking ways to their businesses, the survey report’s authors, led by Sam Ransbotham, conclude. “Our survey analysis demonstrates that leaders share one outstanding feature — they intend to become more adept learners with AI.” This ability to learn and understand the potential and pitfalls of AI enable them to “sense and respond quickly and appropriately to changing conditions, such as a new competitor or a worldwide pandemic, are more likely to take advantage of those disruptions.”
In other words, they give executives and employees the space they need to better understand, adjust and adapt to AI-driven processes — and figure out their roles in making it all work. Automation is not thrust upon them with no preparation or training. “Realizing significant financial benefits with AI requires far more than a foundation in data, infrastructure, and talent,” the researchers state. “Even embedding AI in business processes is not enough.”
Those organizations that lead the way with AI success pursue the following strategies:
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,” Ransbotham and his co-authors state. “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.”
They develop multiple ways for humans and machines to interact. “Deploying the appropriate interaction modes in the appropriate context is critical,” the co-authors state. “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.”
They change to learn, and learn to change. Successful initiatives “don’t just change processes to use AI; they change processes in response to what they learn with AI.”
AI has great potential to expand our visions of where and how businesses can deliver greater service in the months and years ahead. But it requires more than simply installing new systems and processes and waiting to see the results. It’s a continuous process of improvement and innovation,