Everywhere you look, you’re bombarded with articles, statistics, books, boardroom meetings and more highlighting the imminent, transformative impact that artificial intelligence will have on the world. The excitement and anticipation is palpable, but there’s a discrepancy between the hype and actual rates of AI adoption.
Despite the heavy investment and media coverage, when compared to the rate at which businesses are adopting artificial intelligence, the math doesn’t add up: One plus one doesn’t equal two, it’s closer to 0.01.
U.S.-based companies are investing roughly $290 billion in AI, with optimistic forecasts estimating a total of $1.2 trillion to $3.8 trillion for research, development, and application across a range of applications and industries, including autonomous vehicles, healthcare, commercial banking, and the federal government.
According to Goldman Sachs Research, the United States is the market leader in AI technology, and American companies are likely to be early adopters, with China likely to be smaller and lagging behind in investments. This is not surprising, given that 83% of the 333 million U.S. companies claim to leverage AI in their business strategies.
While the potential of AI is undeniable, according to the U.S. Census Bureau, only 5.4% of U.S. businesses are actively using AI as of February 2024. AI usage is expected to rise from 3.7% in fall 2023 to 5.4%, but the total projected increase is estimated to be just single digits at 6.6% by fall 2024.
In comparison, China has the highest AI adoption rate, with 58% of companies leveraging AI, and while Chinese investments are reported to be smaller than U.S. companies, the effectiveness and utilization of those investments is 974% higher.
The main question now is: why? What are the underlying causes and drivers of the mismatch between investment and adoption?
There’s a lot of speculation, but some things are clear.
Fear: Knowledge workers and employees are resistant to the adoption of new AI technologies, fearing that they will replace their jobs. Employees are right to be concerned about the impact on their lives, and this fear can be a significant barrier to widespread adoption of AI within organizations. Inability to calculate business value: The total cost of ownership associated with implementing and maintaining AI systems often does not provide an attractive return on investment, making it difficult for organizations to justify the significant financial commitment required. The true value of AI in increased productivity, improved decision-making, and the potential to unlock new business opportunities is not always easily quantifiable and is a tough sell to business leaders. Complexity: The technical complexity involved in integrating AI into existing IT ecosystems poses a significant hurdle. Many Fortune 1000 companies are still grappling with the challenge of reducing technical debt and modernizing their overall technology infrastructure. Adding AI technologies on top of this existing infrastructure is akin to building AI on top of a house of cards, creating a shaky and unsteady foundation. Too many strategic priorities: The tension between managing current strategic priorities and exploring transformative technologies like AI can be a barrier to widespread adoption. Organizations are often torn between the need to focus on immediate concerns of driving market growth, expanding profits, and improving operational efficiency, and the desire to experiment and invest in cutting-edge technologies that could provide long-term benefits. This balance is delicate, and limited available resources, both in terms of funding and personnel, can make it difficult for organizations to allocate the attention and resources required for AI initiatives.
There are many logical reasons for the discrepancy between AI investment and adoption in the U.S. market, but its immediate, transformative impact is clear. The technology is here to stay and will likely be integrated into our daily lives without us even realizing it. The question remains not if adoption will catch up with investment, but when.
