The adoption of Artificial Intelligence (AI) is set to change the way industries operate. However, the pace of this change varies, with certain sectors, such as those with high stakes, heavy regulations, and extensive unionization, likely to lag behind.
Slow adoption of AI in high-stakes sectors
The growing wave of AI technology, particularly chatbots connected to vast language models like ChatGPT, promises significant productivity enhancements. However, certain industries will, out of caution or necessity, take a slow, measured approach to adopting this technology. These sectors, which are often characterized by high stakes, litigiousness, substantial unionization, and stringent regulation, will likely take longer to fully embrace AI. This isn't necessarily a reflection of the technology's potential, but rather an indication of the complexities and potential risks involved in these sectors.
Potential benefits and caution in high-stakes fields
Healthcare, and other high stakes fields like corporate deal-making, stand to gain a lot from AI. In healthcare, AI can handle the vast amounts of patient data, allowing for faster and more accurate diagnoses. Despite these potential benefits, the high-stakes nature of the field - where errors can have catastrophic outcomes - means caution often trumps innovation. Similarly, in corporate deal-making, while AI can aid in tasks like due diligence, ultimately, human review is still deemed essential due to the high stakes involved.
Litigiousness, or the propensity for lawsuits, can also act as a deterrent for AI adoption. This is particularly true in industries where high stakes are involved, such as healthcare. However, even in lower-stakes scenarios, litigation risks exist. For instance, a company could potentially face a class action lawsuit if its AI system inadvertently harms a large number of people. This risk could deter businesses from readily adopting AI technologies.
Government entities and heavily regulated industries are also likely to adopt AI at a slower pace. Legacy systems, red tape, and regulator skepticism often make modernization a slow process. These sectors may also face added hurdles, such as the need to secure approvals for any plans involving AI, further slowing down the adoption process. Meanwhile, some existing players may advocate for more regulation as a defense mechanism against more efficient newcomers.
Unions and the resistance to AI
Unions, particularly in sectors like government, utilities, and construction, may resist AI adoption due to fears of job loss. This resistance is not new, and it has often led to limits on automation being included in union contracts. Therefore, as AI technology develops and becomes more prevalent, we can expect further negotiations and potential pushback from unions.
Corporate culture's role in AI adoption
Beyond regulatory, legal, and labor considerations, a company's culture and tradition can also influence the pace of AI adoption. Leaders content with the status quo may be slow to embrace new technologies like AI. This is particularly true in less competitive sectors, such as regulated utilities. However, in more competitive industries, failing to take advantage of productivity improvements brought about by AI could have swift and significant consequences.
While certain sectors may be slow to adopt AI, others are likely to embrace the technology more quickly. So far, the quickest adopters appear to be computer programmers, freelance writers, and solo entrepreneurs, such as consultants. In the coming years, we can expect to see tailored applications that apply AI to specific tasks becoming more common and more widely used across various sectors of the economy.
Evolving corporate AI policies
The adoption of AI is a gradual process, and we're already seeing signs of change. More and more companies are establishing or developing internal generative AI policies, signalling an increased willingness to incorporate AI into their operations. However, this doesn't mean companies are rushing headlong into AI adoption. Instead, they're taking a more cautious approach, with policies often setting restrictions on AI usage. Looking ahead, these limitations will likely be adjusted as companies become more comfortable with AI and as the technology itself continues to evolve.
