There is growing concern that the US is becoming a follower rather than a leader when it comes to artificial intelligence technology.
Ajay Mohan, North American practice lead for AI and analytics at business advisory firm Capgemini Americas, said the relative lack of investment has left the US lagging behind other advanced economies on the AI curve. The general consensus is that it does. “In the current political climate, there is relatively limited funding for STEM, public-private partnerships, and AI-focused education to build an effective workforce pool to deliver AI. investment, especially in people, is somewhat lacking.” Moreover, driven primarily by safety and ethical concerns, the regulatory environment for developing and leveraging AI applications in the United States is dwarfed by other It may have been viewed as much more restrictive than some countries in the United States, he adds.
Top-down knowledge
Becoming an AI leader is not easy. Sabina Stanescu, AI innovation strategist at cnvrg.io, an Intel company that provides a full-stack data science platform, uses not only top-down knowledge of data assets, but also insights based on data analytics to develop critical It states that a business decision needs to be made.
As AI pushes into more areas, many US companies still struggle to find qualified data scientists, Stanescu said. “This area is a recent addition to undergraduate and graduate research, so there is a shortage of experienced data scientists,” she explains.
Organizations whose data stores are currently trapped in siled systems will need ramp-up time to put the right infrastructure in place, Stanescu said. “Even the most sophisticated algorithms cannot draw conclusions without high-quality data,” she observes. “Identifying target data for AI projects and sourcing and integrating data from disparate systems requires analytics and automation.”
To harness the power of AI across the enterprise, Stanescu launched a developer training program focused on AI fundamentals to assess AI opportunities and get immediate positive revenue results. I am proposing. “Companies need to invest in sustainable infrastructure to train, deploy and maintain their data pipelines and models,” she notes. “One of my client companies has a program that teaches business her users and subject matter experts the basics of her Python and data analysis.”
lost land
Anand Rao, Global AI Lead and U.S. Innovation Lead for the Emerging Technologies Group at business consulting firm PwC, said the U.S. has a dynamic ecosystem, filled with entrepreneurs and startups with a risk-taking culture. I’m here. Meanwhile, the US appears to be losing his AI regulatory lead. “The legal system is complex, making it harder to pass regulations and guidelines than in other countries,” he explains.
Corporate executives also lack urgency, says Scott Zoldi, chief analytics officer at credit score giant FICO. He points to a recent FICO-sponsored survey that found that 73% of his global Chief Analytics Officers, Chief Data Officers and Chief AI Officers prioritize AI ethics and responsible AI practices. made it clear that he was having trouble getting support from the management of the company. “Today’s AI applications must meet increasing AI regulations, and many organizations do not have a responsible AI strategy,” he said. “Such a strategy begins with well-documented model development governance practices to ensure models are built responsibly.”
Also hampering AI regulatory initiatives is the fact that the United States, unlike most other major nations, lacks a basic national AI policy. “It is up to each state to implement its own interpretation of what AI regulations should look like,” he says. “This lack of unity leads to disparities between states, putting them in competition with each other.” To move forward and keep innovation thriving, the United States must create consistency at the federal level, he said. I believe that we must. “That way, companies can innovate more stably, which benefits everyone in the long run,” said Rao.
Prospects for AI
There are signs that business and government leaders are beginning to realize that AI regulation needs to be proactive. “We have seen the U.S. adopt regulations similar to those passed in other parts of the world because global companies need to comply with these regulations,” Rao said. “Additionally, there have been efforts by the U.S. government to outline guidelines and concerns, as seen in the announcement of the AI Bill of Rights by the White House; the Algorithm Accountability Act of 2022; and the New York City Bias Auditing Act. .”
While regulatory issues are being resolved, Stanescu believes companies should continue to strive to enable AI across their organizations. “In short, companies need to democratize AI by making it accessible to more developers and business users,” she said.
Stanescu advises companies to reskill their software engineers and create programs to make data accessible across the enterprise. “Today, with online training and readily available tools, a software engineer, even a business user with a background in mathematics, can become a citizen data scientist.”
What to read next:
How to choose the right AI project
An insider’s look at Intuit’s AI and data science business
Special Report: Privacy in the Data-Driven Enterprise
