Why AI gets stuck in pilot mode (and how to fix it)

It's a reaction we've heard more than once, and it reflects a deeper truth about how the energy sector approaches innovation.

Many support teams operate under constant pressure, with fragmented tools, missing documentation, and technical and service questions coming in faster than they can be answered. In that context, AI can feel like a luxury, something to explore later, when things are quieter. But of course, things rarely quiet down. And here lies the fundamental paradox: the very operational pressure that seems to delay AI adoption is precisely the reason to consider it in the first place.

The "too busy" paradox

‍Energy manufacturers, OEMs, and distributors in the energy sector often think they need to solve their operational problems before implementing AI. But the operational problems are precisely what AI is designed to solve. When companies report 40% fewer first-line support tickets and 30% reduction in telephone support calls, they're not reporting improvements to efficient systems, they're reporting escapes from overwhelming daily pressure. The mistake is treating AI as an additional burden rather than as relief for existing burdens. Your support team isn't too busy for AI; they're too busy without it.

The hidden cost of constant searching

‍The average information retrieval time in energy companies is 12 minutes per query. That's not problem-solving time, that's searching time. Your support team is functioning as human search engines, burning through their capacity on tasks that should be instant. When colleagues spend hours searching internal databases for information that someone else already found, you're paying multiple times for the same solution. Consider what this operational pressure actually costs: A team handling 100 queries daily at 12 minutes each represents 20 hours of searching time — €260,000 annually at €50/hour. Well-targeted AI implementations can reduce this to around 9 hours daily, saving €130,000 annually in search time alone. The cost of not implementing AI is often invisible, but significant.

AI as operational relief

‍The most effective approach treats AI as a response to operational pain rather than a technology initiative. In organizations that have embedded AI into support workflows, teams report fewer repetitive requests, not just reducing workload, but making the day-to-day feel more manageable.

This requires a fundamental shift in perspective: from "let's try AI" to "let's solve our installer support crisis." Purpose should drive technology choices, not the other way around.

Why most AI projects fail to scale

‍Most AI initiatives in the energy sector follow a familiar pattern. They start with energy and ambition, deliver promising results in the pilot phase, and then quietly fade as operational pressure takes over. The challenge is rarely technical. It’s organizational.

Many projects begin as open-ended experiments, without a clear operational problem to solve. As a result, the technology remains disconnected from day-to-day work. When AI is introduced as a standalone system rather than integrated into existing workflows, it becomes just another thing to manage. Teams that are already stretched simply don’t have the capacity to adopt tools that require additional effort or process change.

Another reason projects stall—or never get off the ground—is the assumption that the data isn’t good enough. When companies discover that their information is incomplete, unstructured or spread across multiple systems, they pause. But this isn’t a reason to delay. It’s the very reason to begin. Data has been called the new gold for years, and this is where scattered gold dust can finally be turned into something of value. Waiting for perfect conditions only prolongs the status quo.

It helps to stop thinking of AI as a tool and start treating it as a new team member. Most AI systems take on tasks that used to fall to human colleagues: simple, repetitive, but essential. And like any new colleague, AI needs time to get up to speed. With the right approach, that onboarding can happen in weeks—not months—and the result is a reliable contributor that scales across teams without adding to the workload.

The psychology of seamless adoption

‍Real transformation happens when technology feels invisible. The most successful AI implementations don't announce themselves, they simply make existing tasks faster and more accurate. Support agents don't think "I'm using AI now," they think "finding information is finally easy." This invisibility requires deep integration with existing systems and workflows. When AI appears within familiar interfaces, adoption becomes natural rather than forced. The energy sector's technical complexity actually favors this approach, teams are already skilled at using sophisticated tools, they just need those tools to work better.

From pilot to sustainable practice

‍Moving from pilot to practice requires a shift in focus, away from efficiency metrics toward genuine relief for the team. Rather than expanding features, the path forward involves perfecting a single use case. A support system that handles installer queries seamlessly is more valuable than one that handles multiple query types poorly. Once that relief is felt, momentum tends to spread organically. Teams who experience tangible improvements will seek out similar solutions elsewhere. This internal momentum drives sustainable scaling better than executive mandates.

The key insight is measuring what matters to overwhelmed teams: reduced stress, fewer interruptions, faster issue resolution. When support agents can find answers quickly, they experience immediate relief. This emotional impact drives adoption more effectively than technical metrics.

The competitive reality

‍Companies that master AI-supported operations aren't just more efficient, they're setting new standards for what's possible in customer service and technical support. This creates a widening gap between early adopters and traditionalists. While some organizations debate AI strategy, others are already delivering support experiences that would have been impossible five years ago. The question isn't whether this technology will become standard, but whether your organization will help define that standard or struggle to meet it.

The operational pressure that makes AI feel impossible today is the same pressure that will make it essential tomorrow.