During your next artificial intelligence discussion and its impact on the workforce, consider taking a “reflective moment” on what occurred with other technical changes in the past. It may provide a different perspective.
While reflecting, ask yourself, is AI, or agentic AI, anything new? Well, yes, it is, but while you are taking that moment, think about the adaptation effort.
Consider the manufacturing sector, which has seen plenty of changes over the years. When robots were adopted, the workforce adapted — but jobs were impacted. AI is already in this sector helping to optimize production, improve quality control, etc. Adopting agentic AI will add value by autonomously optimizing complex processes in real time (e.g., sensor data analysis). The company can help those directly affected to adapt with additional training to address the company’s unmet needs.
Consider the business side of any industry vertical. The adoption of AI would likely follow a similar path. Mundane workflow tasks would likely be the initial targets. As agentic AI is adopted, business teams will likely adapt to having the ability to make better decisions, potentially increasing productivity and resolving problems more quickly. With agentic AI adoption, business teams will adapt over time.
OK, back to our reflective moment. Within IT, technological changes are not uncommon, and with each change, the impact on the workforce is unknown. Reflecting on the mainframe workforce, one could expect they likely wondered how the introduction of 16-bit minicomputers would have on them. Following that, the minicomputer workforce was likely wondering about the impact of personal computers. Did each workforce adapt? In a broad sense, yes, but not everyone transitioned.
Maybe the technical sector isn’t the best scenario to illustrate the point, but it should be acknowledged that each impact was unknown. Yet, the technology was adopted and the workforce adapted.
AI is a little different in that in an AI scenario, the AI agent can not only learn from human interaction, but the human can learn from the AI interaction. In the tech sector, this reciprocal learning scenario is certainly plausible. In the manufacturing scenario, maybe less plausible. Given the potential for reciprocal learning, the underlying question is this scenario mutually beneficial? It may be a little too early to provide an accurate response.
So, are we technology adopters or technology adapters? Well, sort of both. When the decision is made to bring in AI, we are adopting the technology. Adapting to AI is more difficult than adopting AI, as people are typically loath to change (any major change, actually) due to fear of the unknown. Change is not easy, and the workforce impact should not be taken lightly. Adapting to AI requires as much (or more) effort than adopting AI. Yet, different from the above scenarios, we can learn from this technology and AI can learn from us. Again, is this mutually beneficial? Perhaps, perhaps not. Yet as we adapt to AI in the workforce there are many things we can initially learn and possibly more importantly, continually learn.
Dan Kempton is the Sr. IT Advisor at North Carolina Department of Information Technology. An accomplished IT executive with over 35 years of experience, Dan has worked nearly equally in the private sector, including startups and mid-to-large scale companies, and the public sector. His Bachelor’s and Master’s degrees in Computer Science fuel his curiosity about adopting and incorporating technology to reach business goals. His experience spans various technical areas including system architecture and applications. He has served on multiple technology advisory boards, ANSI committees, and he is currently an Adjunct Professor at the Industrial & Systems Engineering school at NC State University. He reports directly to the CIO for North Carolina, providing technical insight and guidance on how emerging technologies could address the state’s challenges.
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