Previously there was lots of potential, but many AI tools weren’t commercially viable, even if some were put out into the world. Time will tell, but I think we can at least describe it as a commercial threshold moment. To me that suggests significantly increased growth of AI over the next three to five years. Many of these users of AI will come up with new ideas and creative applications of AI. With millions of people exploring the possibilities of generative AI technologies and products for example, that definitely expands the top of the innovation funnel. To get to a stellar breakthrough idea often requires starting with a broad set of possibilities that are narrowed down to the truly transformative one. A concept that has stuck with me is the innovation funnel. People have suggested we’re now at a point with AI where the adjacent possibilities can expand rapidly.Īt Yale SOM, I loved the Innovator class. With the advent of electricity, an extraordinary number of adjacent possibilities were opened up for businesses, homes-all of society. The democratization and increased accessibility of AI capabilities has led to a step change in awareness and excitement around the opportunities and also the risks associated with AI. Recently, we’ve seen an explosion of public recognition of AI through the generative AI capabilities. More and more AI capabilities are designed to involve low code or no code. And many AI capabilities still require a large team of PhD data scientists, but cloud-based, SaaS AI solutions are expanding quickly and are commercially viable in a broader set of contexts. To date, AI tools have been most naturally suited for an enterprise setting-a large corporation with a large data set and the resources to develop, to train, and fine-tune AI models. Q: I’m guessing airlines and mines aren’t using off-the-shelf AI. These are incredibly capital-intensive operations, so making them more efficient and more environmentally friendly, also can mean hundreds of millions in savings. This has enabled mines to increase throughput and yield by more than 10%. For a mining company, an AI engine drawing on IoT (Internet of Things) sensors can deliver guidance on precise adjustments to crushers or chemical baths based on the characteristics of the ore being processed. For an airline, that might be what passenger routes to fly, improvements to maintenance operations, or how to maximize cargo yield. It can take in vast data sets and a vast number of variables and deliver recommendations. In all of these, the AI is embedded in products used for things like customer service analytics, customer segmentation, lead generation, new customer acquisition, or marketing.ĪI is great with multi-variable optimization challenges. Product or service development is consistently near the top. The top use cases of AI overall are for service operations optimization. Q: What are the typical uses of AI today? One of the core challenges companies face is developing processes to integrate their people and AI tools and insights. We see the best results when people can supplement their expertise with the rich insights AI can deliver. An important reason for that is that adopting AI isn’t simply investing in a new technology. However, we have seen a leveling off around that 50% level over the past few years. Today, that number is two and a half times larger. In 2017, about 20% of companies responding to McKinsey Global’s AI survey reported adopting AI in at least one of their business areas. Q: How quickly are companies adopting AI?
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