Key takeaways:
- Generative artificial intelligence (AI) has the potential to replace menial work in content creation, customer service, human resources (HR) and recruitment, tourism and travel, and other industries.
- Generative AI has limited potential to disrupt primary sector industries, industries that rely on interpersonal trust, and those that are highly regulated, as well as industries that rely on fresh creative content.
- There are further artificial intelligence implications for socially conscious procurement and diversity, equity, and inclusion (DEI) initiatives within organizations.
Recently, AI language models have emerged as powerful tools transforming a wide variety of industries. These models, such as OpenAI’s ChatGPT, Google’s Bard, and others, have the ability to generate, analyze, and understand large volumes of text-based data. However, the impact of these language models will vary across different sectors. Which industries will AI language models affect the most, and which ones will AI affect the least?
Which industries could generative AI disrupt?
Industries often cited as targets for AI disruption include content creation, customer service, HR and recruitment, and tourism and travel. However, these discussions often fail to mention a key caveat: AI can replace basic and menial tasks, but it struggles to accomplish complex jobs and advanced creative work at its current stage of sophistication.
To illustrate the distinction between basic and advanced work, let’s look at artistic vocations such as graphic design, copywriting, and creative direction. While AI’s impact on these careers is hotly debated, there is one thing that is known for sure: current language models operate based on existing bodies of knowledge and the data sets they’re trained on, which is to say they lack originality in their inputs. Language models can reproduce content based on what already exists, but it cannot innovate or create new and original content, so its impact on this field all depends on how creative you need the AI to get.
Looking to generate simple, generic copy for product descriptions on a website? ChatGPT has you covered. Looking for clever prose for an advertisement campaign to guide the artistic direction of a brand? You’re better off with human ingenuity.
On one hand, businesses with relatively simple marketing needs stand to benefit from virtually free content generation thanks to the large language models (LLMs). However, businesses planning a sophisticated marketing campaign that requires the touch of human ingenuity might find that the prices for the remaining experts are higher than before.
For example, Adobe recently introduced Firefly, a generative AI model that can quickly produce images and text for commercial campaigns. This technology was also able to identify a recent increase in solo travelers, after which it generated content for a campaign that highlighted the value of solo travel with specific deals. This illustrates AI’s potential to perform simple reasoning based on existing information. However, when it comes to highly sophisticated marketing campaigns, there are limits to AI’s ability to understand cultural nuances, tell compelling stories, and make predictions about the future.
While much of the public discourse on AI asks which industries will be affected, a better question might be “Which industries won't be affected?”
Which industries are less affected by generative AI?
Industries that rely on interpersonal trust
There are many reasons why face-to-face human interaction is valuable, but underpinning that value is one important human commodity: trust. After all, the ability to empathize with and trust each other is part of what makes us human. Eye contact, handshakes, and shared experiences are all elements that establish a connection between human beings. AI is incapable of replicating that.
Trust is especially useful and necessary in negotiations that involve high-stakes purchases, long-term contracts, and unclear stakeholder incentives. When transaction volume is low and the market isn’t efficient, trust is what fills in the gaps. Trust is something that gets one party to agree to a deal when they don’t have all the information necessary to fully analyze risk. Industries that rely heavily on the commodity of trust within human interactions include financial services and legal services.
Why is trust necessary? This could be boiled down to one economic concept: market efficiency. Efficient markets are markets where information is readily available to all parties, competition between suppliers is strong, and market prices react quickly to new information. In inefficient markets, however, trust helps bridge the gap between buyer and seller. Real estate, a market often considered inefficient, can serve as an example here. Real estate transactions can involve a long courting process, where the seller has to first build trust in order to communicate how the intangible aspects of a property justify the price. Market inefficiencies heighten the scrutiny of buyers, thereby making the sales role—and its ability to build relationships and trust—imperative.
There is, however, an important distinction to make between interpersonal reactions that require trust and those that don’t. Some menial customer service work do not require the skill and connection of human worker. AI language models can swiftly assume the role of conversational customer support through chatbots and other programs that help customers from point A to point B. This development already has momentum, with one report noting that 80% of internet consumers have interacted with a chatbot. The same report found that chatbots are capable of executing full customer conversations about 70% of the time.
Industries in the primary sector
Industries that rely on moving objects through physical space are less likely to be disrupted by AI advancement. This includes almost all industries in the primary sector of the economy, which are industries that extract materials from natural resources: agriculture, mining, oil and gas extraction, and others.
Because of their demand stemming back to biological needs like food and warmth, these industries are insular to AI language models.
Industries that are heavily-regulated
Highly regulated industries, such as finance, healthcare, and legal sectors, face significant barriers to adopting AI language models due to compliance and privacy concerns. These sectors prioritize data security, confidentiality, and the interpretation of complex regulations, often requiring human judgment.
What are the risks in AI driving procurement initiatives?
Although the jury is still out on the overall ethicality of generative artificial intelligence, AI algorithms may perpetuate biases that socially conscious initiatives aim to reverse. AI is trained on historical data, which may contain biases and discrimination that have existed throughout the past. Without human decision-making, algorithms might learn to replicate these historical biases in their decisions. For example, AI-driven candidate screening and hiring may be counterproductive to diversity, equity, and inclusion (DEI) initiatives. In general, a strictly AI-driven procurement strategy could steer an organization away from socially conscious procurement initiatives.
Recent concerns about LLMs changing their behavior over time have also surfaced. According to a recent study, it’s “unclear how each [software] update affects the behavior of these LLMs,” which “makes it challenging to stably integrate LLMs into larger workflows.” Updates to data sets or other aspects of the AI may affect its “thought process” and how it goes about producing content, resulting in an answer to a question or prompt that’s different from what it would’ve produced months ago.
Final Thoughts
As the new paradigm progresses, human attributes like emotional intelligence will become more valuable in some industries. Ensuring that your human-facing workforce is distinguishable from AI can give businesses an edge in areas like sales, customer service, and internal leadership. Businesses may want to consider placing an emphasis on emotional intelligence traits when hiring new employees.
When considering utilizing AI, you should ask yourself the following questions:
- Could this product or service, as applied to our organization, benefit from human originality?
- To what degree do business transactions in this market require interpersonal trust?
- Does this industry primarily operate in the primary sector?
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