Behind the AI hype of power and speed, a messier reality is taking shape as organizations chase the technology's potential without the operational readiness to realize it. This creates a costly gap between ambition and execution. When companies treat AI like a product to be bought instead of a new way to operate, it can waste money and damage customer loyalty by making flawed processes even worse. Realizing AI's promise requires fundamentally redesigning the workflows, training, and accountability that define the customer experience.
Andrew Hill offers a practical understanding of how to execute this. As the Founder and Chief Operating Officer of the AI-driven customer experience platform FiveLumens, he has a track record of driving massive growth by focusing on operational fundamentals. Hill believes the industry must move past technological enthusiasm and embrace strategic discipline.
"Unless you change the underlying workflow or architecture, AI is just an expensive tool that can make the customer experience even worse. It’s the operational change that makes it valuable," he says. The allure of a quick fix is where he sees many initiatives go wrong. When organizations attempt to simply bolt AI onto fragmented data, siloed departments, and legacy processes, they often find the technology magnifies these problems.
An eternity on hold: The journey from AI hype to reality shows that a poorly planned deployment can quietly break customer loyalty. Hill illustrates with an experience that, for most of us, feels all too familiar. "There's an airline I always use, and I used to be able to call in and talk to somebody and get great answers. Now, I get stuck in an IVR where the voice bot doesn't understand what I'm asking for. By the time I get to somebody, I’m so riled up from going through an eternal voice agent loop."
The agent's burden: The friction then lands on agents, and the result is a snowball effect where customer frustration leads to burnout for the very agents the tools were meant to help. Automating simple tasks without retraining agents for more emotionally charged interactions only compounds the impact. "As agents churn, you bring in new people who are not trained, which creates an even worse customer experience," he says.
According to Hill, the antidote is a framework built on people, process, and technology. Success requires a delicate balance of all three, blending AI with human expertise. "It's not all technology, it's not all people, and it's not all process. Great operations are always built on all three pillars." He advises leaders to begin with the deceptively simple question of 'What is the problem we are trying to solve?' before ever considering a specific tool.
Letting go of legacy: In order to work backward from a business outcome, Hill explains that organizations need to overcome their own inertia, which can be a significant obstacle. "There is often a hesitation to admit that longstanding processes need to change." He recognizes it can be difficult for a leader to scrap processes they had a hand in creating, but says the focus must be on the bigger picture. "It's not about an individual or a single process. It's about what needs to change operationally, technologically, and with people to get to the solution.”
For leaders who have already invested in AI without a clear roadmap, the path forward often calls for the discipline to pause and build a proper generative AI strategy. To prove value, Hill recommends a controlled, experimental approach. He describes how, in a previous role, his team did just that to test an AI simulation tool designed to get BPO agents to proficiency faster. "We took two individual groups of agents and we trained them in exactly the same way. 100% the same material, same trainer, same quizzes, same tests, except one used AI-powered simulation for role-play and one didn't." The class with the AI outperformed, reaching proficiency faster within the first two weeks on six out of nine metrics. "By week eight, they outperformed in nine out of nine metrics," he shares. It's a clear example of using AI to augment, not replace, human agents.
Reset required: This kind of success, Hill notes, isn't guaranteed. Even the best plans can collapse without genuine buy-in across departments. "I've had projects fail when I thought we were on the same page. We went to implement, but operations didn't support it. We had to do a complete reset." He explains that projects fail when alignment is assumed rather than earned, sometimes requiring leaders to make a tough call. "Rather than pointing fingers, we focused on getting full agreement on what everyone was going to do to move forward."
Ultimately, Hill believes success depends on bridging the gap between the operational mindset, which can be resistant to change, and the technology mindset, which often ignores operational reality. This requires either working backward from the problem to the technology or aggressively pressure-testing the technology against operational problems. "When technology is handed to you, you need to flip your mentality. Ask questions to understand what it does and doesn't do. Then you can take that information back to determine if the tool actually fits with any of the business problems you are currently trying to solve."
But the most important factor, he says, is ownership. "I've always believed that clear is kind. If you don't give clear definition to who owns it, what their role is, and how they're going to be accountable for it, it's going to fail. What's important is that the person that owns it knows they own it, everybody knows they own it, and they have clear direction."