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In our Department of Revenue, we’ve been experimenting with using AI to help catch fraudulent tax returns. Before this, most of the work was a mix of manual checks and a handful of rule-based filters which the customer support tax agents needed to go through. It wasn’t terrible, but it was slow and easy to overwhelm, so a lot of the more subtle erroneous cases slipped right past the tax agents. Once we brought in AI models, the biggest change was speed which suddenly allowed the agency to run through huge volumes of filings almost immediately and pick out odd patterns that would’ve taken forever to notice manually. What caught us off guard, though, was that the bottleneck didn’t disappear; it just moved. The main challenge became keeping all the data that feeds the models clean, consistent, and on time. These systems depend on information from payroll, banks, older tax records, and a handful of other sources. If any of that data shows up late or isn’t formatted the way the models expect, the whole workflow slows down, even though the analysis itself runs quickly. Another sticking point has been getting people to trust the results. Auditors and legal teams don’t want a mystery “flagged” label since they want to understand the logic behind it so they can defend their decisions if challenged. When the system can’t clearly explain itself, the human side of the process grinds to a halt. There are a few things which tipped us off that these new constraints were forming: -We started seeing queues pile up even though computing power was not the issue, which pointed to delays before the data even reached the model. -Auditors pushed back more often, asking for overrides because they weren’t comfortable relying on decisions they couldn’t trace. -Even with faster detection, cases weren’t closing quicker, which made it obvious that review and governance had become the new slow points. If anything, the whole experience has made it clear that speeding up one step just exposes whatever the next constraint is. AI can make detection faster, but that doesn’t matter unless all the ecosystem surrounding processes such as data handling, oversight, and human judgment are able to keep up the pace.

In the power generation sector creativity as far as AI integrations are concerned, outage training approaches experiences for the plant maintenance and operations personnel stand out. Traditionally, the training modules for Operations and Maintenance teams on our Enterprise Learning Management System were built by senior engineers who combined technical knowledge with lessons learned from past incidents throughout their experiences which are well documented. We have just recently experimented with using AI to draft training scenarios for turbine maintenance crews who are involved with various repairs the units are facing. The AI pulled together data from manuals, incident logs, and prior outage reports, then suggested a scenario that could be used as a training exercise, where a simulated wiring fault during a hot‑day peak load in the summer which are normally brutal experiences in Arizona and peak energy demands occur during that time as well. From a quick overview of the AI picked scenario, it did appear like a clever thought-provoking idea. After a deeper analysis of the scenario, we identified that it was a blend of two existing patterns: one from a past wiring issue we had experienced and another from a high‑temperature stress case. AI in this case did not invent a new scenario but blended two different issues to come up with a training scenario. This in my view can be considered as creativity to some extent, basically because of the wide array of issues that the operations and maintenance teams face as far as outage repairs on the turbines to keep the units online. The AI did not just imagine something beyond its inputs, but the way it recombined those inputs gave us a training exercise we had not considered before. The real creativity was when our operational trainers adapted the AI’s draft into a hands‑on drill, adding context about communication protocols and safety culture. For this power generation domain context, AI acts more like a catalyst than a creator. It does not replace human imagination, but it accelerates the process by coming up with combinations we might overlook that provide us with a better training track for the operations and maintenance teams. The creativity portion is mostly how these personnel interpret and apply those outputs to the occurring challenges they experience during the process of keeping the turbines operating at optimal levels to meet energy demands from the market.

In the power generation, distribution and transmission sector, one of the most critical customer-facing processes is complaint handling during service disruptions. When customers lose power because of issues on the power grid infrastructure, their frustration isn’t just about inconvenience but most likely tied to safety, comfort, or even business continuity. AI can be used to strengthen this relationship by acting as an empathetic first responder to the circumstances to the end users. For example: • Personalized communication: An AI system could immediately acknowledge the outage, reference the customer’s location, and provide tailored updates on restoration timelines. Instead of generic “we’re working on it” messages, customers would receive contextual awareness responses like, “We see your neighborhood is affected, crews are already dispatched, and estimated restoration is 2 hours.” • Predictive reassurance: By analyzing grid data and weather forecasts, AI could proactively inform customers of potential risks before they occur, offering guidance on preparation. This shifts the tone from reactive to caring and preventative. • Consistency at scale: AI ensures that every customer despite of their geographical location within the utility provider coverage area, be it in a rural area or a city, they all receive the same level of timely, empathetic communication, reducing feelings of neglect. But there are risks if AI oversteps which may create bad blood/jeopardize client-company relationships which might include the following: a. False empathy: If the AI misreads intent for example, offering cheerful language when a customer is distressed, it can feel tone-deaf and erode trust due to no show of feelings. b. Over-automation: Customers may feel dismissed if they can’t easily escalate and communicate their complaints/grievances to a human representative when emotions run high. c. Data sensitivity: Predictive messaging that seems “too personal” (for instance referencing medical equipment in a home without explicit consent) could cross boundaries and feel intrusive as customer feels their privacy is being invaded. For this case ultimately, AI’s role in complaint handling isn’t just about speed; it’s about creating a connection with them, making customers feel seen and supported during moments of vulnerability. When designed with empathy, it can transform a negative experience into one that builds trust in the utility provider being able to male sure clean, reliable and safe energy services are delivered.

In the power generation utility sector, one leadership scenario where AI is starting to reshape decisions is grid resource allocation and maintenance planning. Traditionally, leaders had to rely on historical demand curves, scheduled inspections during turnarounds for any discovery work identification, and a lot of gut judgment about when to take a unit offline for maintenance or how to balance generation across plants. Now, AI can crunch real‑time sensor data, weather forecasts, and market signals to recommend when to dispatch certain units, predict equipment failures, or even simulate the impact of shifting load between renewable and conventional sources. The upside is obvious where leaders can make faster, more data‑driven choices that reduce downtime and improve reliability. For example, predictive analytics might flag that a turbine is trending toward failure weeks before a human operator in the field would notice, allowing leaders to schedule maintenance without risking an outage. But there’s a downside to it, AI doesn’t always capture the political, regulatory, or community pressures that utility leaders face. A model might say “shut down Plant A for efficiency,” but the leader knows Plant A is in a region where reliability is critical for hospitals or where public trust is fragile. To keep AI as a partner rather than a replacement, leaders in utilities need some new habits which include some of the following: • Cross‑discipline validation. Don’t just accept the algorithm’s recommendation but conduct an exhaustive peer check of it against engineering judgment, regulatory requirements, and community impact. • Scenario stress‑testing. Use AI to simulate outcomes but always ask “what if the assumptions are wrong?” before committing to a plan. • Transparency with teams. Operators and engineers should understand how AI insights are being used, so they don’t feel sidelined by a black box. Offering training to all these frontline employees will appreciate the introduction of smart tools like AI that way they are not having thoughts of their support work being automated. • Balance efficiency with trust. Leaders should weigh AI’s efficiency gains against the human factors—like public confidence and employee expertise—that keep utilities resilient. In short, AI can sharpen the operational lens, but leadership in power generation still requires judgment about people, politics, and timing. The best leaders will treat AI like a highly skilled analyst which is as a valuable addition to decision making, but not the one making the final call.