Why the US Risks Falling Behind in AI Leadership

When it comes to artificial intelligence technology, there's a growing concern that the US is becoming a follower rather than a leader.

By broad consensus, the US is falling behind the AI curve when compared to other economically advanced nations, due to a relative dearth of investments, says Ajay Mohan, AI and analytics North America practice lead at business advisory firm Capgemini Americas. “In the current political climate, US investment, especially on the people side, is somewhat lacking, with comparatively limited funding for STEM, public-private partnerships, and AI-focused education to build an effective labor pool for delivering AI.” Additionally, largely driven by concerns for safety and ethics, the regulatory environment for developing and leveraging AI applications in the US might been seen as far more restrictive than some other nations, he adds.

Top-Down Knowledge

Becoming an AI-leading enterprise isn't easy. It requires a top-down knowledge of data assets, as well as using data analysis-driven insights to make key business decisions, says Sabina Stanescu, AI innovation strategist at cnvrg.io, an Intel company offering a full-stack data science platform.

As AI winds its way into more areas, many US enterprises are still struggling to find qualified data scientists, Stanescu notes. “There's a shortage of experienced data scientists, since the discipline was just recently added to undergraduate and graduate studies,” she explains.

Organizations with data stores currently locked into siloed systems will require ramp-up time to get the appropriate infrastructure in place, Stanescu says. “The most sophisticated algorithm can’t reach any conclusions without high-quality data,” she observes. “Identifying the target data for an AI project, and sourcing and integrating the data from disparate systems, requires analysis and automation.”

To leverage AI's power enterprise-wide, Stanescu suggests launching a developer training program focusing on AI basics, as well as evaluating AI opportunities with the goal of obtaining immediate positive bottom-line results. “Companies need to invest in a sustainable infrastructure to train, deploy, and maintain data pipelines and models,” she notes. “One of my client companies has a program to teach their business users and subject matter experts Python and the basics of data analysis.”

Losing Ground

The US has a dynamic ecosystem, full of startups that are rife with entrepreneurs and a risk-taking culture, says Anand Rao, global AI lead and US innovation lead, in the emerging technology group at business consulting firm PwC. On the other hand, the US appears to be losing ground in AI regulation leadership. “Due to the complex legal system ... it's more difficult to pass regulations and guidelines when compared to other countries,” he explains.

There's also a lack of urgency from corporate leadership, says Scott Zoldi, chief analytics officer at credit score giant FICO. He points to a recent FICO-sponsored study, which revealed that 73% of global chief analytics, chief data, and chief AI officers have struggled to get executive support for prioritizing AI ethics and responsible AI practices. “Today's AI applications need to respond to increasing AI regulation, and many organizations do not have a responsible AI strategy,” Zoldi states. “Such a strategy starts with a well-documented model development governance practice to ensure models are built responsibly.”

Also hampering AI regulation leadership is the fact that, unlike most other major nations, the US lacks a basic national AI policy. “It's left to each state to implement their own interpretation of what AI regulation should look like,” Rao says. “This lack of unification leads to disparity among the states, having them competing with each other.” He believes that in order to move forward and keep innovation thriving, the US must create consistency at the federal level. “By doing so, companies will have more stability to innovate, which benefits everyone in the long run,” Rao notes.

AI Outlook

There are signs that enterprise and government leaders are beginning to recognize they need to aggressively address AI regulation. “We have seen the US adopt regulations similar to those passed in other parts of the world, due to global companies being required to comply with those regulations,” Rao says. “Additionally, there has been some effort from the US government to outline guidelines and concerns, as seen by the release of the AI Bill of Rights by the White House; the Algorithmic Accountability Act of 2022; and NYC’s Bias Audit Law.”

While regulatory issues are being sorted out, Stanescu believes that enterprises should continue striving to make AI attainable across their organizations. “In short, companies should democratize AI by making it accessible to more developers and business users,” she states.

Stanescu advises enterprises to create programs that reskill their software engineers and make data accessible across the enterprise. “Today, with online training and readily-available tools, any software engineer, or even a business user with a math background, can become a citizen data scientist.”

What to Read Next:

How to Select the Right AI Projects

An Insider's Look at Intuit's AI and Data Science Operation

Special Report: Privacy in the Data-Driven Enterprise