Generative AI: Is it being overhyped?

Here in home furnishings land, I believe we all intuitively know that artificial intelligence is the key that will somehow, someway, someday unlock the door to new levels of efficiency, productivity and profitability.

Truth be told, if my life depended on providing you the specifics, it would probably have the value of yesterday’s newspaper.

While I am not proud to admit that, even minds far sharper than mine are still on the fence about AI.

So, I was relieved when I read a recent article quoting Adam Selipsky, Amazon’s CEO of web services, who cautioned that, instead of being the newest game changer that everyone seems to think it will be, AI-generated solutions and all the related hype could end up being the next dotcom bubble.

While Selipsky is clearly a fan of generative artificial intelligence, he is concerned that some AI companies may be overhyping generative AI.

Since we are talking about AI, I just asked ChatGPT to define generative AI for me. Here is what I got back:

Generative AI refers to a class of artificial intelligence techniques and models that can generate new content, such as images, text, audio, and even videos, that is similar to the data it has been trained on. Generative AI models operate by learning the underlying patterns and structures present in the training data and then using that knowledge to produce new examples that resemble the training data.

Generative AI has a wide range of applications, including image synthesis, text generation, creative artwork generation, music composition and more. It has sparked significant interest and innovation in the field of artificial intelligence and has the potential to revolutionize various industries by enabling machines to create new and original content.

Now that we have a working definition of generative AI, let’s get back to Selipsky’s concerns.

Speaking at a recent conference at Harvard Business School, Selipsky said he is concerned that companies may be overhyping AI much the way the internet was overhyped back in 1997.

He went on to say that as companies search for ways to integrate generative AI to their own businesses and industries, they need to be careful not to be misled by the hype.

With all the AI options being brought to market, many companies are struggling to understand which ones are best for them. Factoring in the expenses of integrating the technology, it seems that Selipsky worries that many of these initiatives may be short lived.

Caveats aside, a recent report from McKinsey & Co. clearly underscores the financial impact AI will have on not only the global economy, but on retail as well.

According to the McKinsey & Co. report, Generative AI’s impact on productivity could add trillions of dollars in value to the global economy.

The company reported: “Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed — by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15% to 40%. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.”

About 75% of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering and R&D. Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks.

Generative AI will have a significant impact across all industry sectors. Banking, high tech and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year.

Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities. Current generative AI and other technologies have the potential to automate work activities that absorb 60% to 70% of employees’ time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25% of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.

The more I learn about AI, the more I see it as a two-edged sword. 

Used properly, it can cut through workflow log jams and redundancies and allow companies to run leaner and more profitably.

Used incorrectly, it can quickly cause a healthy company to die a death of a thousand cuts.

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