Two articles published last year caught my eye. One article insisted that artificial intelligence (AI) was losing steam while the other article observed that AI was simply losing hype. In the first article, technologist Christopher Mims wrote, "It sure seems like the AI hype train is just leaving the station, and we should all hop aboard. But significant disappointment may be on the horizon, both in terms of what AI can do, and the returns it will generate for investors. The rate of improvement for AIs is slowing, and there appear to be fewer applications than originally imagined for even the most capable of them. It is wildly expensive to build and run AI. New, competing AI models are popping up constantly, but it takes a long time for them to have a meaningful impact on how most people actually work."[1]

At first, the second article, published in The Economist, seemed to agree with Mims. It stated, "A growing number of observers now question the limitations of large language models, which power services such as ChatGPT. Big tech firms have spent tens of billions of dollars on AI models, with even more extravagant promises of future outlays. Yet according to the latest data from the Census Bureau, only 5.1% of American companies use AI to produce goods and services."[2] At this point, however, the article begins to differ with Mims. "Gently raise these issues with a technologist and they will look at you with a mixture of disappointment and pity. Haven’t you heard of the 'hype cycle'? This is a term popularized by Gartner, a research firm — and one that is common knowledge in the Valley. After an initial period of irrational euphoria and overinvestment, hot new technologies enter the 'trough of disillusionment,' the argument goes, where sentiment sours. Everyone starts to worry that adoption of the technology is proceeding too slowly, and profits are hard to come by. However, as night follows day, the tech makes a comeback. Investment that had accompanied the wave of euphoria enables a huge build-out of infrastructure, in turn pushing the technology towards mainstream adoption." The article noted, "For some, that is proof the tech will in time succeed." The article then asked, "Is the hype cycle a useful guide to the world’s AI future?"

Losing Steam or Losing Hype? Or Both?

Analysts from IDC believe AI will climb out of the "trough of disillusionment" and contribute a great deal in the years ahead. In fact, they insist, "Every dollar spent on AI will generate $4.60 into the global economy." An IDC press release reported, "New research from IDC entitled, The Global Impact of Artificial Intelligence on the Economy and Jobs, predicts that business spending to adopt artificial intelligence, to use AI in existing business operations, and to deliver better products/services to business and consumer customers will have a cumulative global economic impact of $19.9 trillion through 2030 and drive 3.5% of global GDP in 2030. As a result, AI will affect jobs across every region of the world, impacting industries like contact center operations, translation, accounting, and machinery inspection. Helping to trigger this shift are business leaders who almost unanimously, 98%, view AI as a priority for their organizations."[3]

Mims doesn't disagree that AI could have a profound effect on the global economy. He wrote, "None of this is to say that today’s AI won’t, in the long run, transform all sorts of jobs and industries." He stated his argument this way, "The problem is that the current level of investment — in startups and by big companies — seems to be predicated on the idea that AI is going to get so much better, so fast, and be adopted so quickly that its impact on our lives and the economy is hard to comprehend. Mounting evidence suggests that won’t be the case." The Economist article quotes Noah Smith, an economics commentator, who insists, “The future of AI is just going to be like every other technology. There’ll be a giant expensive build-out of infrastructure, followed by a huge bust when people realize they don’t really know how to use AI productively, followed by a slow revival as they figure it out.”

As I have written elsewhere, "Artificial intelligence has emerged as a transformative force in the business world, offering extraordinary potential to unlock new capabilities and competitive advantages across enterprise environments. Yet as organizations invest billions in AI initiatives to improve large-scale operations, many find themselves grappling with a sobering reality: AI projects are not guaranteed successes. Many, in fact, have failed to realize their anticipated business value. ... The complexity of implementation, the rapid pace of technological change and the challenge of aligning AI initiatives with strategic goals can all contribute to AI projects falling short."[4]

Making AI Useful

On balance, I am sanguine about AI's future. I believe that companies focusing on five critical factors can significantly increase their chances of AI success and ensure their projects deliver real, measurable value. These key elements go beyond technical performance, addressing the project’s broader impact on business operations, financial outcomes and organizational culture. Those factors are:

1. Ensuring the AI Project Is Relevant and Sustainable. The long-term viability of any project rests on its ability to remain relevant as circumstances change. A sustainable AI project delivers consistent value over time, adapting to changing business needs and technological advancements. To evaluate sustainability, organizations should consider factors such as the scalability of the AI model, its ability to continually learn and improve over time, and the ongoing costs associated with maintenance and updates.

2. Ensuring the AI Project Drives Financial Gains. Investments are only valuable if they provide a return on that investment. An AI project that demonstrably contributes to the company’s bottom line through increased revenue, cost savings or improved efficiency provides clear and quantifiable value. Financial gains can manifest in various ways, from direct revenue increases through improved customer targeting or pricing optimization, to cost reductions via automated processes or predictive maintenance.

3. Ensuring the AI Project Is Expandable Across Multiple Use Cases. Great technologies find multiple ways to be used beyond the original scope of their creators. The ability to adapt and apply the AI solution to multiple scenarios or departments within an organization significantly amplifies its overall value. An AI solution that demonstrates this kind of versatility and scalability is likely to deliver substantially more value than one confined to a single, narrow use case.

4. Ensuring the AI Project Is Adoptable by Nontechnical Employees. Mims observed, "Changing people’s mindsets and habits will be among the biggest barriers to swift adoption of AI. That is a remarkably consistent pattern across the rollout of all new technologies." An AI solution that can be effectively leveraged by nontechnical staff has the potential to create widespread impact and drive significant value. High adoptability leads to increased usage, which in turn generates more data and insights, creating a virtuous cycle of improvement and value creation.

5. Ensuring the AI Project Unlocks Human Efficiency. Increased employee productivity is a goal of every business. When evaluating the business value of an AI project, it’s essential to measure both the quantity and quality of work automated, as well as the resultant impact on employee productivity and job satisfaction.

Concluding Thoughts

Arguments insisting that AI is losing steam focus primarily on the fact that AI systems are beginning to plateau and converge. Currently, that may be true. What matters more is how the technology is going to be used. The Economist observed, "AI could still revolutionize the world. One of the big tech firms might make a breakthrough. Businesses could wake up to the benefits that the technology offers them. But for now the challenge for big tech is to prove that AI has something to offer the real economy. There is no guarantee of success." Companies need not wait for "big tech" to prove the worth of AI. In today's business world, it's not the large language models that are making the difference but small language models applied to specific industries and circumstances. AI-enhanced robotic process automation is also making a significant contribution. Freelance writer Mary K. Pratt explains, "IT’s embrace of AI is nearly ubiquitous, with 89% of IT decision-makers surveyed for Foundry’s 2024 CIO Tech Priorities study saying they’re researching, piloting, or currently using AI-enabled technologies — up from 72% in 2023. Moreover, 64% of those IT decision-makers expect AI and machine learning to significantly alter the way their business operates over the next three to five years, up from 39% who said the same in 2023. More specifically, IT leaders believe AI will transform their organizations primarily through process automation and efficiency gains, as well as enhanced customer experiences."[5] When it comes to business success using AI, I would say forget the hype, look for real use cases, and then go full steam ahead.

Footnotes

[1] Christopher Mims, "The AI Revolution Is Already Losing Steam," The Wall Street Journal, 31 May 2024.

[2] Staff, "Artificial intelligence is losing hype," The Economist, 19 August 2024.

[3] Michael Shirer, "IDC: Artificial Intelligence Will Contribute $19.9 Trillion to the Global Economy through 2030 and Drive 3.5% of Global GDP in 2030," IDC, 17 September 2024.

[4] Stephen DeAngelis, "For AI Adoption Success, Focus On These Five Critical Value Drivers," Forbes, 13 September 2024.

[5] Mary K. Pratt, "How AI is transforming business today," CIO, 30 September 2024.