Jose Tamez is managing general partner at Austin Michael, a retail, wholesale and brand channel executive search firm based in Golden, Colorado.
Artificial intelligence (AI) seems to have the same premonition as Paul Revere's famous cry on an April night in 1775: “The British are coming!” But just as Paul himself knew the British were already on their way, so too does AI.
Its origins date back to 1950, when Alan Turing published his book “Computer Machinery and Intelligence”. Turing testused to measure a computer's intelligence. In 1952, computer scientist Arthur Samuel developed a program to play checkers, which was the first to learn a game independently. John McCarthy, a mathematics PhD at Princeton University, held a workshop on “artificial intelligence” at Dartmouth College in 1955, which was the first known use of the term.
First, let's differentiate between AI and GenAI (Generative AI or Advanced AI). AI uses data analysis to identify patterns, make predictions or interpretations, and perform specific tasks. AI operates based on predefined rules, making the decision-making process more transparent and interpretable. GenAI creates its own data based on human input and data analysis, like a creative content assistant. It can create writing, music composition, new media, image generation, and more, and its ability to create new content is powered by deep learning. For example, you input an idea, and it expands on that idea, fills in gaps, and offers alternatives. So, to be clear, GenAI needs human command. We'll discuss this in more detail later.
As we all know, GenAI is garnering attention as a potential replacement for much of the workforce. Indeed, its widespread adoption will have a variety of impacts across the labor market, specifically those in highly skilled white-collar jobs and roles defined by a knowledge economy rich in data-driven tasks and structured processes. At the very least, nearly all types of these roles will see future transformation, while blue-collar jobs will remain relatively unaffected.
The introduction of GenAI is solely aimed at increasing worker productivity and efficiency by leveraging new technology and new unit economics. Interestingly, the increase in production due to GenAI does not guarantee a reciprocal increase in demand for goods and services. Further challenges for GenAI include: It is not particularly original and may result in copied content, limited critical thinking, low emotional intelligence, limited factual accuracy, data privacy, and security concerns.
Additionally, GenAI has early challenges related to slow adoption. From multi-million dollar investments to building teams, breaking down silos, and setting up methodologies, it's clear that GenAI is not on the verge of sweeping all industries next week. In fact, most companies currently practicing AI or claiming to be “AI-powered” are practicing traditional AI, not full-scale GenAI or large-scale advanced AI. Security, privacy, licensing, and proprietary issues have led many companies, including retail, to ban ChatGPT, a variant of GenAI, from using it company-wide.
The companies that are calling for companies to get in fast are likely to be those benefiting from the growth of AI and what many analysts call AI overvaluation (overvalued in the short term, likely undervalued in the long term). Many who are bullish on AI want to see real results, not hype, and categorize AI as part of the next technology cycle, not a panacea.
Again, AI needs input of commands, most likely from a human. In the short term and fundamentally, AI itself will not take over people's jobs, although people who understand AI might. Think of it like in the past, when technology did not take over jobs, but rather tech-savvy people did.
Enterprise We are trying to take full advantage of AI, but most companies don't have the budget to build the infrastructure, so they end up renting it from other companies. However, AI is the next hard skill in the evolving economy. There are now graduate programs available across the country, but if you have the opportunity to work with AI, it is essential to have at least a working knowledge of it.
Finally, GenAI/Advanced AI As traditional roles are disrupted, human skills such as critical thinking, empathy, and adaptability will come to the forefront. Businesses can achieve optimal outcomes by blending the power of GenAI with human intelligence. The intersection of art and science still produces the greatest results.
