AI Adoption in America: What, Where, and Why
Pam Sornson, JD
April 2, 2024
A recent Burning Glass Institute report (BGI) analyzed which U.S. regions were most prepared to embrace Artificial Intelligence (AI) as an economic development tool, which many consider to be an indicator of future economic growth. Yes, almost every region of the country would benefit from utilizing the more sophisticated technological base, but most have not yet invested in the foundational infrastructure to support that digital evolution.
The BGI analysts compared the occurrence of legacy tech skills versus AI-based skills – what BGI calls ‘Frontier Skills” – in communities across the country to determine which geographical area would see the most AI-driven expansion, both in its workforce and its economy. Their findings were sometimes surprising:
Not all digital skill sets are the same;
Industries evolve differently depending on their location and local resources, and
Not all industries lend themselves to an early or comprehensive adoption of the still unpolished computing opportunity.
While AI resources are advancing across all regions, only a few are truly prepared to maximize the opportunities it presents right now.
What Needs Doing: Legacy Expertise versus AI Frontier Skills
Fundamentally, AI and legacy skills match at the most basic level. Every digital tool – AI or otherwise – needs to be:
programmed for launch and then reprogrammed over time as needs evolve;
continuously managed to ensure full functionality, and
secured to ensure no inappropriate intrusions or actions can threaten its performance.
Companies using technology in any capacity typically have an IT department to manage these functions and maintain their productivity and safety. Further, as digital technology permeates more elements of the industrial complex, there will always be demand for these types of skills.
AI programming, however, requires a different set of skills over and above those fundamental actions. In addition to simply reliably running its program, AI software also adds services not found in non-AI tech:
Machine Learning incorporates neural networks, a foundational database structure modeled on the workings of the human brain, to facilitate ‘deep learning’ programming that can ‘read’ disparate data types like images, audio, and text to discern insights and make predictions. The neural network exchanges data across its nodes to ‘learn’ from other information caches, find and fix mistakes, and improve its functionality without additional human intervention. Over one-third of patent submissions in the past ten years contain a ‘machine learning’ (ML) capacity, indicating its popularity as a digital business tool.
Computer vision is another AI-related technology that requires upskilled tech training. Computer vision facilitates the software’s capacity to ‘see’ the data it’s aimed at and derive and act on the information gleaned from those sources. This AI technology collects images of environmental elements using cameras, sensors, and algorithms. It then identifies factors that indicate locations, threats, and other relevant elements to inform the AI system’s ‘decisions.’ This technology is a critical element of a ‘self-driving’ vehicle.
Natural Language Processing (NLP) is also an AI component. It can be ‘rule-based’ (driven by programming specific to the entity) or ML-based (driven by both rules and the results of countless inquiries and searches). NLP seeks to understand the meaning of text and voice inputs so it can respond to both written and oral data. Its use in ‘chatbots,’ robots programmed to respond to written inquiries, and ‘digital assistants,’ like Alexa and Siri, has revolutionized how many people use their digital devices.
The demand for AI-specific programmers is large and growing, with one in three companies saying they can’t move forward with AI adoption because they lack the technically skilled workforce needed to do so. However, those current programmers with computer science degrees and a mastery of logic, reasoning, and problem-solving can attain AI skills by pursuing degrees within that specific field, assuming they can find a school that offers the training. They’d be well advised to take that path: companies that have already implemented their AI strategy are reporting their intentions to increase their investments – and consequently, their Frontier Skilled workforce – in 2024 and beyond.
Where AI Skills Are Most Concentrated
The BGI report analyzes Frontier Skills capacities in metro areas based on the size of the community, with large ‘metropolitan statistical areas’ (MSAs) comprising cities with 25,000+ tech workers, medium-sized MSAs with tech workforces numbering between 5,000 and 25,000, and small MSAs that are home to 5,000 or fewer tech workers.
Not surprisingly, those regions and urban metropolises that have already invested in tech- and data-enabled economies are leading the country in their AI adoption processes, although in many cases, the capacity for local industries to embrace AI also impacts its adoption rate.
Three large MSAs – Seattle (first), San Jose (second), and San Francisco (third) – lead the country in Frontier Skills concentrations due to their underlying foundations of ‘technology’ as an industry in and of itself. Los Angeles-Long Beach-Anaheim ranks 8th on this list.
Given their histories as ‘tech-heavy’ economies, San Diego, Austin, Boston, and New York are also high on the list of large MSA early adopters.
Notably, while the Washington D.C. MSA is home to one of the largest tech-based workforces in the country, its industries are mainly defense and government contracting, which are typically based on legacy technology. Of the 27 large-sized MSAs identified by the BGI, Washington D.C. ranked 21st, behind less obvious contenders Detroit (18th), Kansas City (19th), and Philadelphia (17th).
Utah’s burgeoning ‘Silicon Slopes’—Provo-Orem (1st), Ogden-Clearfield (10th), and Salt Lake City (3rd)—have propelled the region to the top of the mid-sized MSA category.
Surprisingly, Fayetteville-Springfield-Rogers Arkansas is number 2 on the mid-sized MSA list due mainly to the tech-forward presence of Walmart. Walmart has been investing in advanced technologies for years, so its early adoption of AI is not unexpected.
MSAs actively growing in population are not also enlarging their Frontier Skilled workforce. Miami, Houston, and Dallas lag in the bottom half of the large MSA list, at 14th, 26th, and 22nd, respectively, primarily because they’ve not yet developed a dedicated, tech-focused workforce.
Overall, across the four geographical regions—West, Midwest, Northeast, and South—the West’s workforce dominates the country with its Frontier Skills concentrations, while the South lags behind the rest of the nation.
The BGI document reveals how America is managing the deluge of AI-enabled business opportunities now flooding its databases. Organizations intent on building their AI-fueled “Frontier Skilled” workforce can look to the successes being had in the various regions to ensure their AI adoption strategy is one that promises similar rewards.