Artificial Intelligence: Bridging the rift between Aspiration & Activity

I was reading an article on Artificial Intelligence at MIT Sloan Review regarding a study that they and The Boston Consulting Group conducted. Some of the interesting results of that study are (you can read it here):

• Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage
• Only about one in five companies has incorporated AI in some offerings or processes
• Only one in 20 companies has extensively incorporated AI in offerings or processes
• Less than 39% of all companies have an AI strategy in place
• The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one

These numbers show that though there is a lot of hype about AI & automation, the adoption is still very low. Though it is in-line with any new disruption- expectations exceeding reality & then finally realigning as initial requirements are exceeded, just met, or even unmet, it is logical to assume that innovators & early adopters are less in this area.

The biggest challenge is the lack of proper sanitized data to train the Bots. At an enterprise, for any AI solution to make returns requires rich & diverse sets of data to train the algorithms. In addition, with AI algorithms, the fundamental logic of Garbage-In-Garbage-Out holds, so the quality of data is also very important. Moreover, talent & resource shortages along with regulatory compliance adds to the complexity in adoption of AI.

Though these challenges and governance mechanisms will get sorted out, there are other challenges which needs to be overcome to realize the full benefits of AI. Thinking of AI use case as point solution, rather than an end-to-end solution is one of them. Automating just single task may not help enterprises improve the overall effectiveness & efficiency of their processes. Until the time, business processes operate in silos, as a use-case of AI, the overall potential will not be achieved.

The solution here is to re-engineer the enterprise operations, looking at them from new angles and trying to leverage the organizational cognition. The way to start this journey is to find a common ground between humans and machines, leveraging the advantages of both. I wrote in my previous article (read here) regarding this - humans can take decisions when factors are abstract and have a sense of empathy, rather than through modelling and systematic training; designing the right interfaces between the two entities is very important.

AI can transform the business operations, but first, we must understand what do we want from it, how do we align it with our current structure, and how to best leverage the organizational cognition with it. With this focus, it is easier to start thinking in terms of getting AI adopted in your enterprises.