AI talent strategy: How Rayburn Electric builds AI-ready teams

The thought of an AI talent strategy can evoke images of Google and Meta competing for top-tier AI researchers. However, this doesn't fit the reality of most organizations.

Most CIOs -- especially those at small or midsize companies -- don't need model builders. Instead, they need people who can effectively apply AI to business operations. Chase Snuffer, CIO at Rayburn Electric Cooperative in Rockwall, Texas, focuses his AI talent strategy on upskilling internal staff, using third-party support and retaining people who understand both IT systems and operational needs. CIOs in similar organizations must balance hiring and training to implement AI safely without disrupting critical infrastructure.

In the following interview, Snuffer shares how a small utility organization approaches its AI talent strategy.

Editor's note: The following transcript was edited for length and clarity.

Do the AI talent wars within big tech reflect what you face at Rayburn Electric?

Chase Snuffer: It's a little different. The Metas and Googles of the world are mostly fighting over talent who can help them build out their AI suites. We, on the other hand, typically use said technology to increase efficiency and automate tasks within the organization. We need people who are AI champions. We're looking for idea people -- people who know how to use those platforms within the organization.

Are you hiring AI specialists, or are you focusing more on upskilling your current team in AI?

Snuffer: We're not hiring dedicated AI specialists right now. Instead, we're using third-party support. We do have about three guys who, on their own initiative, have done extensive research and gained significant knowledge from an in-house perspective. Those guys are using third parties to fill in the gaps.

The other piece is bringing in third parties to support more homegrown training across the organization, so we can uplift overall understanding of AI and how to use it.

How does Rayburn Electric currently apply AI?
Snuffer: We have pockets of AI usage -- things like ChatGPT, Gemini and image-generation tools. On our roadmap, we plan to bring a larger AI platform in-house. We handle a lot of sensitive data, so we can't just put it out there for the world. So, we're investing in a comprehensive AI platform -- software and hardware -- that will let us securely integrate our data and use AI across the organization.

As you roll out this AI platform, will you rely on internal staff to implement it, or do you need specialized AI skills from outside the organization?

Snuffer: This is where we're using the third party heavily right now. We've already purchased the equipment and licensing, but lead times on GPUs, RAM and everything that these big data centers need have gone from a couple of weeks to a few months. We've already placed our order and are just waiting for the equipment and other items to arrive.

This project will bring out the implementers within the organization. Right now, we're having internal meetings to decide how we'll handle it and how we'll pull those implementation pockets out of the organization and bring them to the surface. We don't want AI to be feared within the organization, so we're trying to showcase the wins people are having with it.

How do you think about talent retention?

Snuffer: We're 30 minutes east of Dallas, so we compete with Dallas for talent. We must be strong in our talent retention from a pay, culture and benefits aspect. We've always had to maintain a high standard to retain talent in general. That applies across the board, from an AI perspective as well.

We have a lot of people who live just outside of Dallas who must drive past our office to get into the city, and when you can offer very similar -- if not better -- pay, culture and benefits, it's easy to keep those employees because now they don't have to drive into town and fight traffic.

I would much rather have someone who is a cultural fit and a little less technical versus someone who is extremely technical but not a cultural fit.

You've mentioned in other interviews that a cultural fit is important when considering a new hire. Can you tell me more about that?

Snuffer: When we're looking for talent, we're looking to hire the most technically skilled person we can find. However, we're also looking for a cultural fit. I would much rather have someone who is a cultural fit and a little less technical versus someone who is extremely technical but not a cultural fit.

What does cultural fit look like for your team? Are there specific qualities you look for?

Snuffer: I'm looking for someone who is driven and doesn't need a lot of management -- a high achiever who takes the initiative to get training. We have a very generous training policy, and while you may not necessarily have time in the office to take that training, I'm looking for people who might work through a lunch or stay after an hour. Those people just elevate everybody around them.

Has an AI talent shortage slowed any projects for your team, or changed your roadmap in any way?

Snuffer: AI in general has changed our roadmap, but we're not feeling any pain from the talent shortage. That's not to say that it doesn't happen in the future. What we're doing today may be very different from what we're doing next year because the technology is changing so quickly.

Aside from AI, are there any other areas where you see IT talent shortages or have trouble hiring?

Snuffer: From an IT perspective, I think the biggest issue is twofold. One, we're a smaller shop -- about 30 people -- and a decent portion of our work is driven by compliance obligations. That means a lot of paperwork and overhead, which can slow things down. People might not get to jump into the latest technologies as quickly because of that.

The second part is how we define senior-level roles. For example, someone might be an expert in configuring a system, but they don't understand why that configuration exists or the bigger picture. They may have seen one system repeated a hundred times. Larger organizations often pigeonhole employees into doing one thing over and over, whereas we expect people to see the entire picture.

In our IT group, you get to see everything -- from the firewall where the internet comes in, all the way through the network to the end user. You're not just tied to one switch or task. You see the full environment. That's the biggest gap we notice when hiring -- finding people who can operate at that broader level.

In a few years, that entry-level position will likely require much more expertise.

In two to three years, how do you think your IT team will differ from today?

Snuffer: It will probably look different because some of the entry-level roles we have today may not exist in the same form. Right now, someone can step into a beginner role after a few months of certifications or training. In a few years, that entry-level position will likely require much more expertise, especially in implementing AI tools, to add real value from day one. I've already seen AI improve troubleshooting across networking, cybersecurity and server management.

AI can handle many of the basic checks, and humans step in at a higher level to take over from there. That will significantly change the nature of beginner roles. Humans won't be as involved in lower-level troubleshooting as they are today.

Additionally, cybersecurity will be the biggest piece when it comes to AI. We're already starting to use some AI tools built into our cybersecurity platforms. But from an adversary perspective, threats are only going to increase exponentially.

Hackers can download models and continuously hammer systems. Before, an adversarial team might work during certain hours and take breaks. Now, they can program servers to attack many more targets than a human could manage, and AI can learn from what fails and adjust accordingly. That means attacks will become more efficient and sophisticated over time. If we aren't using AI ourselves to stop those attacks, we'll be at a major disadvantage.

In terms of IT talent, what keeps you up at night?

Snuffer: It's the length of time it takes to get someone up to speed -- from when they walk in the door to when they're providing high-level value to the organization. Before AI, that was typically about six months. A new hire needs to understand how the servers are set up, how to access them, networking, naming conventions, IP addresses and how all the systems connect. It usually takes a very smart person six months to navigate that.

I think it will take even longer as we add AI into the mix. AI is handling some tasks for you, so a new hire must figure out that layer: how the organization uses AI agents, how it schedules tasks and how it automates workflows.

I always hope new hires will bring experience from other organizations -- little nuggets of insight like, 'We did this over there' or 'I've seen this done this way.' These insights can add efficiency right out of the gate.

Tim Murphy is site editor for Informa TechTarget's IT Strategy group.