5 industries where AI adoption is lagging

Industries where AI adoption is lagging

A business leader would justify the upfront costs of expensive AI technology if they expected to see long-term benefits. If pricing is too high, or if existing processes and employees are enough to get by, it's unlikely a business wants to invest in this new tech. Specific issues with adoption vary from one industry to another.

Here are five industries that experiment with artificial intelligence but haven't fully embraced it.

Manufacturing

AI-powered robots and software can come with high costs, and global inflation can make those worse. Plus, AI tools are helpful but not ready for mission-critical applications since datasets aren't sufficient to train them. A 2024 survey by ABI Research found 66% of U.S. manufacturers and 62% of German manufacturers have the ability to collect and analyze real-time data. Others lack the ability to produce training data.

Some other barriers are AI systems not integrating well with existing machines and processes used at their plants and businesses being forced to hire new talent that understands AI. Concerns about talent exist in other industries, including financial services, media and the public sector. Just 20% of respondents to Deloitte's "State of Generative AI in the Enterprise" report felt their businesses have the talent in place needed to work with AI systems. This even applies to leaders excited about AI.

Semiconductor plants suffered from global supply chain snags when the COVID-19 pandemic hit. Efforts to fix American supply chains and make chips locally have passed into law, but there are still global challenges. Fixing those problems is a higher priority than a digital transformation.

Mining

Mining requires a lot of physical activity, and it takes place in harsh environments. The AI-fueled machines or collaborative robots (cobots) seen at today's factories are less reliable in rugged terrain and extreme temperatures. In a Goldman Sachs research newsletter published in June 2024, experts believe what workers do in manufacturing or mining is complex and needs human interaction. AI systems won't match or improve on this anytime soon.

Construction

Like other professional services, construction requires human precision that AI can't provide. While it's unlikely AI machinery will be a boon in construction anytime soon, AI software is another matter. AI software can generate designs and schematics easier, while hardware such as drones monitor sites for safety. These are just nice-to-haves, though, and not every construction company has the budget for this software.

Agriculture

Agriculture, like construction, requires human activity when operating tractors or other machines. But AI software fused with industrial IoT (IIoT) sensors collect and analyze data on how well their crops are doing. AI algorithms even predict weather events and risks that might affect them. Still, farmers see IIoT and AI as nice-to-have tools for assistance. Despite the ways AI makes farmers' jobs easier, human activity is still needed to do the heavy lifting.

Hospitality

Human empathy -- a quality not built into today's AI systems -- is key to working in this industry. AI chatbots help workers with customer questions and complaints but are known to present liabilities. They can give customers false hope or promise fixes that don't exist. These are also called AI hallucinations. Often, it takes a hotel concierge or travel agent to navigate customer complaints and complex or nuanced situations.

Industries where AI adoption is expanding

Despite reasons for lagging adoption, a few industries are pushing forward with adopting AI. For example, the food industry is making up for labor shortages with AI robotics. Here, long-term productivity is worth the upfront costs. Automating an assembly line with robots and cobots powered by machine learning algorithms is now common. These algorithms are good at predictive maintenance. This means monitoring machines to prevent failures before they happen.

Life sciences and telemedicine use cases are other areas not holding back on adoption. In life sciences, clinical trials on new medicine can be sped up with AI since a tool analyzes the trial data quickly. This means new medications are rolled out to the public sooner than before. In telemedicine -- where healthcare workers engage with patients virtually -- AI can interact with patients to support staff. For example, automating appointment scheduling with AI can lighten call volumes.

Indications for future trends

Different industries have different reasons for lagging adoption rates. These five examples prove AI tools aren't a silver bullet for every industry. Human precision and empathy are still important, and the imagined final form of AI as seen in science fiction -- known as artificial general intelligence -- that will supposedly outperform humans in every way is unlikely any time soon.

But in industries where labor is tight, long-term benefits of adopting AI for automation might save productivity, making short-term costs worthwhile. Overcoming resistance won't happen overnight. The hope is that once AI tools become easier to produce and are less revolutionary, they won't cost as much. Even when cost is less of an issue and businesses feel their employees are better suited to perform tasks, they could still slowly find new ways to incorporate AI tools that make their workers' jobs easier.

Cameron Hashemi-Pour is a technology writer for WhatIs. Before joining TechTarget, he graduated from the University of Massachusetts Dartmouth and received a master of fine arts degree in professional writing/communications. He then worked at Context Labs BV, a software company based in Cambridge, Mass., as a technical editor.