Artificial intelligence is expected to affect nearly every industry, though some industries should benefit more, in terms of affect on output or productivity. Generative AI alone could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases analyzed by McKinsey consultants.
About 75 percent of that value would be added by GenAI application to customer operations including marketing and sales, software engineering, and research and development, McKinsey says.
But it is not clear that the industries which benefited most from prior waves of information and communications technology will reap the biggest rewards from AI.
According to job site Indeed, generative AI, for example, is going to supplant more human activity in software development than in driving; more replacement in information technology help desks and less for beauty or wellness jobs.
“Unlike prior advances in robotics and computing that largely impacted manual labor, roles filled by knowledge workers are potentially the most exposed to change from generative AI,” an Indeed study says.
The other methodological issue is that it is difficult to impossible to measure the productivity of office or knowledge work, as “output” is hard to measure.
Whatever we may prefer to think, the introduction of several waves of information and communications technology, from personal computing to the internet to mobile service to cloud computing has had a disparate impact on various industries, if we can extrapolate from productivity changes, and assume that information technology has played a significant role.
Though estimates obviously vary, a reasonable assessment of information and technology impact across industries might show a pattern that shows disparate impact, in terms of productivity outcomes.
Generally, observers would tend to cite agriculture, manufacturing and finance as among those industries which saw the most change in productivity since 1980, with government and education routinely seen as benefiting the least, if at all. Obviously, various studies and estimates vary in the precise degrees of industry impact and in some cases other changes might be more important than computing or communications changes in explaining changes.
In agriculture, for example. Most analyses would credit genetically modified crops, precision farming techniques, and advanced machinery for the productivity gains. Obviously, precision farming is the only innovation directly provided by computing, information and communications technology.
Improved crop breeding, better land management practices, Expanded access to inputs, improved seeds, fertilizers, and pesticides, globalization and trade, government policies,
increased demand for food as incomes have risen, global economic growth, Infrastructure development and even education and training have played important roles in boosting agricultural productivity, many would argue.
Industries expected to benefit most from AI include those with lots of routine, repetitive tasks, large amounts of data, and complex decision-making processes. For example, AI is expected to have a major impact on the transportation, manufacturing and healthcare industries, retailing and finance.
The industries that are expected to be least affected by AI are those that are characterized by human creativity and interaction.
The point is that some industries might benefit from AI more than they have from prior waves of information technology innovation. Healthcare is among those industries that might benefit quite a bit more than in the past.
AI should facilitate more accurate diagnoses, personalized treatment plans, and therefore improved patient outcomes, based not only on a single patient’s history and data, but on all accumulated patient data.
AI also should allow better proactive and preventive care, again using the accumulated data from “all” patient histories. AI can facilitate the development of personalized treatment plans tailored to individual patients' genetic makeup, medical history, and lifestyle factors.
AI is expected to play a big role in accelerating the drug discovery process by analyzing vast datasets of molecular interactions and identifying potential drug candidates. Automated procedures can streamline workflows, reduce errors, and improve efficiency.
Perhaps the important observation is that generative AI alone should have value across most industries in reshaping sales, marketing, software engineering, research and development and customer operations.
