The Accelerated Growth of AI in the U.S.: Drivers, Adoption, and Challenges in the New Era of Innovation

Artificial intelligence (AI) in the United States is experiencing unprecedented expansion, with a growth rate that is redefining entire industries and raising challenges around integration, talent, and regulation.

According to Khadine Fisher, founder, president, and CEO of KM SuperIntelligent AI, “we are witnessing a technological revolution that is happening faster than most organizations had anticipated; AI has gone from being an experiment to becoming a critical infrastructure for productivity and competitiveness.”

The phenomenon has several roots. First, spending on computing and infrastructure has skyrocketed. Frontier model training has grown four to five times per year since 2010, driven by specialized chips, data centers, and massive capital investments.

“The availability of advanced computing not only accelerates the development of more powerful models, it also reduces the time between research and real market value,” Fisher emphasized in an exclusive interview with The Daily Pulse.

This has been coupled with advances in foundational and generative models. These systems, such as large language models and vision-language models, have become tools that can be easily adapted to multiple tasks, according to the executive.

From customer support to programming, data analysis, and design, the transition from pilot tests to productive deployments is now a reality, she highlighted.

“The ability of a single model to adapt to different contexts multiplies the opportunities for application. This is the first time we’ve seen such a high level of cross-functionality in the history of AI,” she explained.

The virtuous cycle is completed with a double engine: industry-led innovation and business pressure for results. Most of today’s most notable models come from the private sector, backed by multi-billion-dollar investments and a revenue ecosystem spanning from chips to cloud services.

At the same time, both executives and investors demand concrete gains in productivity and profitability. According to Fisher, “experimenting with AI is no longer enough.” As she sees it, business impact on metrics is now expected.

“This is forcing companies to accelerate adoption at a scale we have never seen before,” she said, explaining that in terms of sectors most quickly incorporating AI in the U.S., technology and software top the list, along with advanced industries such as semiconductors, automotive, and aerospace.

There, she argued, technical talent, data abundance, and clear returns in research, engineering, and developer productivity converge. Next come financial services, insurance, and fintech, where the data-intensive nature and need for efficiency allow AI to deliver immediate value in risk modeling, fraud detection, and process automation.

Khadine Fisher, founder, president, and CEO of KM SuperIntelligent AI. Source: LinkedIn.

Other Sectors Also Embracing AI

According to Fisher, other sectors accelerating adoption include consumer, retail, and logistics, with use cases in demand forecasting, personalized experiences, and supply chain optimization.

Even healthcare and life sciences, traditionally more cautious, are rapidly incorporating AI for clinical documentation, image triage, and drug discovery, she noted.

“The fact that medicine is advancing in this area, even with its high regulatory standards, is a sign that the change is profound and sustainable,” Fisher affirmed.

The broader context reflects an exponential leap: by mid-2024, 65% of organizations in the U.S. reported using generative AI regularly, nearly double just ten months earlier.

However, the challenges are significant. One of the biggest is the difficulty of scaling from isolated pilots to measurable, sustained value, which requires overcoming barriers around data, processes, and change management. Data quality and governance remain a critical bottleneck, as information is not always labeled, secure, or with clear rights for use.

Added to this are risks related to security, privacy, and compliance, in an environment where federal agencies are demanding increasingly strict controls. “Companies must manage risks throughout the entire AI lifecycle, from privacy to resilience against incidents. There is no sustainable adoption without trust,” the spokesperson warned.

The shortage of specialized talent is another obstacle. AI/ML profiles, MLOps, prompt engineering, and product leadership are in high demand, slowing integration capacity.

Fisher concluded: “It is not enough to hire data scientists. AI requires a redesign of operating models and a new organizational mindset.”