AI Can Do the Work. Experts Know If It’s Right.

Over the years, innovation has brought many benefits: from companies investing in on-prem data centers to outsourcing those needs to Cloud Service Providers (CSPs), from paper-based processes and manual record keeping to automated ERP and workflow systems, from manual meter reads to AMI-driven data collection. Each shift has made organizations more agile, freeing them to grow and adapt faster than before.

The workforce has had to evolve alongside these shifts, building new skills over the years. And since COVID, the workforce has faced perhaps its most disruptive change yet: the rise of AI, particularly generative AI. However, the application of AI capabilities is not new to most industries. For years, the oil & gas industry has relied on machine learning and predictive analytics to proactively monitor key assets and prevent costly disruption to operations. In the financial industry, machine learning has played a big role in fraud monitoring and prevention, analysing millions of customer transactions and flagging fraudulent transactions. And closer to home, utilities have long used machine learning for exception handling in metering and billing operations, flagging usage anomalies, estimation errors, and billing discrepancies before they ever reach a customer. Generative AI, however, has emerged to be revolutionary, and by a big measure has changed how we work.

Meanwhile, the pressure on businesses has only grown. Trade wars, energy costs, geopolitical tension, and shrinking margins now shape every major decision, including how to use AI to do more with the same resources, or even less.

This is where the real shift is happening, not just in how work gets done, but also in the resources to do it. To be clear, this isn’t about domain mastery mattering less—deep expertise still carries real value, especially for the complex or high-stakes tasks that only experience can navigate. But for a growing set of tasks, this shift is already playing out in hiring patterns: a moderately skilled professional with working knowledge in a domain area, paired with strong AI skills, can now achieve a similar output as a specialist with limited AI fluency possibly quicker and more efficiently.

Take a utility’s outage or billing operations. For the hardest edge cases, the specialist who has spent a career mastering one OMS or AMI platform remains vital. For the volume of day-to-day tasks, someone with solid experience who can navigate multiple systems, using AI to fill the gaps, may be the more practical choice. Either way, AI output is only as good as the person judging it. The more work shifts to AI, the more it matters that someone with real domain depth sets the guardrails, validates the results, and knows when the AI has it wrong.

Some may counter that AI costs are rising to the point where they offset the savings. That won’t hold for long. As AI edges closer to maturity, costs will come down while capability keeps climbing, and that shift will happen fast, not slow. Look at how far we’ve come from the early ChatGPT models to today. Remember the “Will Smith spaghetti” video? That’s not ancient history, that’s just a couple of years ago.

So, the question isn’t only will AI replace my job? It’s how will AI change the way my job gets done? And possibly, who could get hired to do it? What will their day look like? That person’s day may not start with reading through emails, preparing documents and reports, reviewing AMI exception reports etc., but with signing into their agent environment, deploying autonomous agents for daily tasks, and monitoring their performance and results before funnelling the process into the next workflow. Monitoring those agents still depends on knowing what “right” looks like.

Whether you’re the one making the hiring decisions or the one adapting to stay relevant in your current role, the point still stands: it’s not just about AI replacing jobs but it’s also about changing how works gets done and possibly who gets hired. It’s no longer good enough to be a domain expert, we should also be ready to adopt AI and evolve the way our work gets done. Domain expertise alone is no longer enough, and neither is AI fluency alone. The organizations that win will pair the two, using AI to move faster and deep expertise to make sure it’s moving in the right direction.

Are you ready for that future?