We’re standing at the edge of a generational shift in the energy industry. One where GenAI isn’t just helping optimize customer interactions, it’s redefining what “service” even means. The old model of reactive, transactional customer support is giving way to predictive, adaptive engagement.
If we get it right, customers may never need to ask a question again.
But let’s be clear: there’s as much risk here as there is reward. Treat GenAI like a magic wand, and you’ll hallucinate your way into reputational damage. Treat it like a teammate, carefully trained, clearly guided, and respected for what it can’t do, and you unlock real competitive advantage.
The use of generative AI in energy is no longer hypothetical; it’s happening now. Yet success isn’t measured by whether you’ve deployed a chatbot or automated some back-office workflows. The winners in this space will be the companies who strike a careful balance between innovation and control, between automation and humanity. That balanced approach needs to extend to how you set goals for your AI as well. If you drive it with only a single metric, say, minimizing average call time, the AI will find a way to hit that target, often at the expense of quality or customer satisfaction. Instead, define success across multiple dimensions: make sure your GenAI agent is optimizing for efficiency, accuracy, compliance, and customer happiness together, so it can’t game the system by sacrificing one outcome for another.
Start Simple, Stay Smart
Energy providers shouldn’t dive into GenAI with the expectation of solving the hardest problems first. Start small. Use AI to field routine customer queries, book service appointments, or send proactive outage alerts. Keep the complex, nuanced cases, like hardship policies, billing disputes, or support for vulnerable customers, for well-trained humans who can empathise and exercise discretion. Even when you hand certain tasks over to the AI, keep a human in the loop for the big decisions. That means inserting manual checkpoints for high-impact actions: if the AI is about to implement a major account change or send out a sensitive communication, a person should review and approve it first.
Your AI doesn’t need to be all-knowing; it needs to be trained. That means running pilots with friendly internal staff, pressure-testing use cases, and learning from failure before rolling out to production. Let your teams break things in testing, not in front of customers. Just like your digital ecosystems, quality and full feature sets rule.
While you’re piloting, also try breaking tasks into smaller, modular steps with a validation check after each one. This way, a minor mistake can be caught and corrected early rather than compounding into a larger problem. For especially critical processes, you might even deploy two AI models in parallel to double-check each other’s outputs, if their answers differ, that’s a red flag to pause and investigate before any action is taken.
Guardrails, Not Guesswork
Hallucination is a real risk. We’ve all seen the headlines: AI agents confidently delivering inaccurate or misleading information. You can’t afford that in a regulated sector like energy. That’s why industry-specific FAQs, documented flows, and clearly articulated “do and don’t” scopes for your agents are essential. You should also give your AI a firm grounding in real data, connect it to a verified internal knowledge base or document store so it pulls facts from a trusted source instead of making them up. You wouldn’t let a new hire talk to customers without a playbook; your AI deserves the same. Those “do and don’t” guidelines should serve as explicit constraints on what the AI can and cannot do, effectively giving it a built-in ethical compass during decision-making.
Another key guardrail is transparency. Make sure the AI keeps an action log or even a “chain-of-thought” trace of how it reaches its conclusions. This way, if something ever goes sideways, you can retrace the AI’s steps, audit its reasoning, and quickly pinpoint what went wrong (and why).
And privacy? That’s non-negotiable. Keep your LLM instances private, off the public internet. Build in strong security guardrails from the start. For example, restrict the AI’s access to sensitive systems using role-based permissions, and limit the actions it can perform to a predefined allow list of approved commands. On top of that, put filters in place for potentially malicious inputs, a layer to detect and sanitize prompt injection attacks before they cause any harm.
A smart approach also means making the model your own, not just embedding knowledge, but also your company’s tone, processes, and priorities. Embed your internal process documents directly into the model. Train it not just on what you do, but how you do it. However, customizing your AI isn’t only about knowledge, it’s about values too. Be sure to conduct regular bias audits on the data and the AI’s outputs to catch any unintended skew. This ensures your AI doesn’t inadvertently amplify biases and that it treats all customers fairly, in line with your company’s standards.
Teach Your AI the Industry
Don’t expect a general-purpose LLM to know what a tariff code is, or why a customer might be on a demand time-of-use plan. You must build industry awareness into the model; otherwise, you’re starting every conversation with a blank slate. Imagine on boarding a new employee with no knowledge of utilities, regulations, or customer expectations.
That’s your GenAI agent, untrained. Industry-specific context, vocabulary, and workflows must be deeply embedded. It’s the difference between a helpful assistant and a glorified autocomplete engine.
It’s Not Just About Cost
Let’s not pretend money isn’t a major factor. GenAI promises significant cost savings through automation, reduced handle time, fewer errors, and increased scalability. But cost alone isn’t the metric. You’re also buying flexibility, speed, and the ability to continuously adapt.
That’s why understanding your AI vendor’s commercial model is crucial - subscription-based? usage-based? hybrid? It matters.
Get this wrong and your cost-to-serve could spiral; get it right, and you unlock transformative ROI. At the end of the day, customers will choose providers who balance service, value, and trust. GenAI can help you win on all three, but only if you wield it wisely.
The Human Element Isn’t Going Anywhere
The myth that AI will replace human agents en masse is just that: a myth. What we’re really doing is redeploying human empathy and expertise to where it matters most. AI handles the repetitive; people handle the personal.
That might be the most important shift of all, and it needs to be managed carefully. Keep an eye on the ripple effects of this change: retrain and upskill your team to mitigate any job displacement and pay attention to customer sentiment so that trust isn’t eroded as more interactions become automated.
In the coming years, the best-performing energy providers won’t just use GenAI, they’ll partner with it. Train it. Teach it. Trust it with the right guardrails.