Is generative AI disruptive or enabling?

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One of the hottest topics right now is generative AI. Many may have already experimented with ChatGPT, which is based on generative AI. McKinsey has a good article on what generative AI is and how ChatGPT fits in.

In just the past 48 hours, both Google and Microsoft have made major announcements about their generative AI endeavors. Yesterday, Google announced a new conversational AI service called Bard which is based on LaMDA. Today, Microsoft announced they are reinventing search with new AI-powered Bing and Edge browsers.

Recently, I was asked the question: Is generative AI disruptive or enabling?

Investment in generative AI is growing

Before I get to providing my perspective, let’s look at a few data points. To say investment in generative AI is hot would be an understatement. First, let’s look at where the investments are being made.

Looking at where investment in generative AI is going is only partially insightful to answer the question of disruptive or enabling.

The CB Insights study shows that investments in generative AI have topped $2.5 billion in 2022, up from $1.5 billion the previous year.

The amount of money being invested in generative AI is accelerating. Companies like Microsoft are investing billions of dollars which only adds to the fuel.

In generative AI disruptive or enabling

So, where does generative AI go from here? Is it disruptive or enabling. I believe the short answer is that it will be enabling more than disruptive. That is not to say that it may disrupt some functions.

From a value perspective, generative AI solutions could provide significantly more value as a function to an existing solution over a stand-alone product. Today, much of the experimentation is happening at a functional level too.

There are concerns about how data is sourced and used in generative AI solutions. Companies like Microsoft are publishing their approach to responsible AI which will only help. However, solutions need to go beyond this.

Enterprises are already looking for ways to mitigate the risk from generative AI while capitalizing on the opportunity. One of the ways they are mitigating risk is by limiting sources of data to internally produced data. At a minimum this means that only internal data will make its way into the process.

Building generative AI into products

Looking at the concerns from a broader risk perspective, generative AI solutions could lower the risk burden by focusing on data within an existing solution versus introduction of external data.

Imagine a product like CRM, CX, HCM, ERP, Supply Chain, etc that uses generative AI within the product to develop solutions and insights. This approach significantly reduces the risk from the current wild-wild-west approach to generative AI, while still providing significant value.

One thing is for sure, generative AI is getting a lot of attention and I don’t expect that to wane anytime soon. Much of the demand is driven by the need for increasing efficiency, infusing automation, and gleaning insights.

In the cybersecurity space, generative AI is also being used for more nefarious purposes by adversaries. Hence, there is great pressure to understand, leverage and capitalize on the opportunities while heeding a cautionary note.


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