“Hype is a crucial component to introducing any emerging technology into the marketplace,” writes journalist Peter Fretty. “It draws attention and, in many instances, entices organizations to come out onto the bleeding edge. However, at some point, manufacturers need to move beyond the hype and realize the return on their investment.”[1] In this case, Fretty is writing about the hype surrounding artificial intelligence (AI). Part of the hype has been that AI will dramatically increase a company’s productivity. Too often in the past, reality fell short of the hype. However, as AI has matured, the story is beginning to change. Cole McCollum, founder of Incisively AI and a former analyst at Lux Research, told Fretty, “We’re seeing AI substantially improve manufacturers’ operational efficiency and quality control. AI provides both the capability to automate simple, routine tasks, and to extract insights from datasets too complex for human understanding.” The hype surrounding and interest in AI only increased when generative AI became a global phenomenon last year. McKinsey & Company analysts note, “If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using — and deriving business value from — this new technology.”[2]

Seeing Beyond the Hype

Fretty reports that a study by Lux Research highlighted four major factors companies should consider before making AI investments so that they buy value rather than hype. Those factors are:

1. Clearly understand the outcomes implementing AI will provide for their business. “Start with the end in mind by determining the problem and outcome to solve for. Then, work backwards to determine the level of AI needed to solve that problem and whether it is feasible with today’s tools using the AI framework.”

2. Focus on an AI product’s capabilities instead of flashy marketing. You need to make a sound business case for investing. The study notes, “Opportunities exist to leverage this capability, whether for scaling basic human pattern recognition capabilities, emulating expert pattern recognition, or uncovering patterns in data too complex for a human to recognize.”

3. Know when the technology is mature enough to mitigate risk. Great strides have been made since the study was published. Nevertheless, the study concluded, “[The] more complex the environment, the more immature the application will be with today’s technologies. Applications that require longer-term planning or reasoning capabilities from a human are likely many years away from full, successful implementations.” McKinsey analysts more recently reported, “Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology.”

4. Identify practical challenges to both implementation and maintenance of the technology once it is in place. A tool that is not used, is little better than having no tool at all. That’s why the Lux study concludes, “Seek to get buy-in from the end users of the application as it is being developed to ensure that the end users will both trust and use its predictions.”

Analysts from Morningstar add, “Artificial intelligence (AI) techniques that can significantly improve operations and enhance customer engagement have become necessary simply to remain competitive with peers.”[3]

AI and Productivity

As I noted earlier, one of the claims made by proponents of artificial intelligence is that AI can help increase productivity. Business executives pay attention to such claims because increased productivity has always been important. Journalists Jordyn Holman and Jeanna Smialek explain, “Rapid productivity improvement is the dream for both companies and economic policymakers. If output per hour holds steady, firms must either sacrifice profits or raise prices to pay for wage increases or investment projects. But when firms figure out how to produce more per working hour, it means that they can maintain or expand profits even as they pay or invest more. Economies experiencing productivity booms can experience rapid wage gains and quick growth without as much risk of rapid inflation.”[4] They add, “Many economists and officials seem dubious that A.I. — especially generative A.I., which is still in its infancy — has spread enough to show up in productivity data already.” According to McKinsey analysts, “The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development — two functions in which previous research determined that gen AI adoption could generate the most value — as well as in IT.”

McKinsey analysts ask, “Where are those investments paying off?” Their findings conclude, “The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management.” As might be expected, Julian Jacobs, a senior economist at OMFIF, reports that jobs involving writing have seen an increase in productivity thanks to large language models (LLMs) like ChatGPT. He reports, “[There is] empirical evidence of the productivity-boosting effects of AI for many professional tasks including writing, coding, administrative tasks, text summarization, and research.”[5] However, not all economists are bullish. Holman and Smialek report that Robert Gordon, a Northwestern University economist, doesn’t believe AI has “been transformative enough to give a lasting lift to productivity growth.” In an interview, he said, “The enthusiasm about large language models and ChatGPT has gone a bit overboard.” On the other hand, Holman and Smialek report, “Erik Brynjolfsson at Stanford University has bet Mr. Gordon $400 that productivity will take off this decade. … Many companies seem to be in Mr. Brynjolfsson’s camp, hopeful that the shiny new tool will revolutionize their workplaces.”

Concluding Thoughts

Jacobs concludes, “Policymakers should read preliminary evidence of LLM productivity-boosting effects as a promising sign and a reason to continue to support AI advancement and adoption. Collectively, the emerging field that studies LLM productivity-boosting effects offers increasingly auspicious evidence that AI is boosting productivity, particularly for less productive workers. Labor productivity growth is the best vehicle to boost standards of living, and AI’s potential to trigger such growth would be a welcomed development.” Most analysts seem to agree that it’s too early to judge the impact generative AI will have on productivity. Nevertheless, the optimists seem to be in the majority. Holman and Smialek report, “Analysts at Vanguard think that A.I. could be ‘transformative’ to the U.S. economy in the second half of the 2020s, said Joseph Davis, the financial firm’s global chief economist. He said the technology could save workers meaningful time — perhaps 20 percent — in about 80 percent of occupations. ‘We’re not seeing it in the data yet,’ he said, explaining that he thinks that a recent pickup in productivity has been more of a snapback from a steep drop-off during the pandemic. ‘The good news is that there’s another wave coming.’” Let’s hope he’s correct.

Footnotes
[1] Peter Fretty, “Can You See Beyond the Hype?” Industry Week, 31 January 2020.
[2] Alex Singla, Alexander Sukharevsky, Lareina Yee, Michael Chui, and Bryce Hall, “The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey & Company, 30 May 2024.
[3] Staff, “Artificial Intelligence: Empowering Firms like Never Before (Part 1 of 5),” Morningstar, 4 October 2023.
[4] Jordyn Holman and Jeanna Smialek, “Will A.I. Boost Productivity? Companies Sure Hope So.” The New York Times, 1 April 2024.
[5] Julian Jacobs, “Evidence Shows Productivity Benefits of AI,” Center for Data Innovation, 11 June 2024.