Why isn't AI Transforming your Customer Service into a Competitive Advantage with Rolf Gordon Adamson

Host John Lee Dumas opens by asking Rolf Gordon Adamson to share a belief about becoming successful that most people disagree with. Rolf says he struggled to identify one because it is difficult to pinpoint a single contrarian belief, but he ultimately focused on people’s attitude toward hard work. Many people view success as the enjoyable part and the earlier work as an unpleasant price to pay. Rolf disagrees: he finds the process of becoming successful fun, not merely the state of being successful. That perspective leads into his professional history. Rolf has worked in customer operations, including contact centers and retail organizations, and has consulted businesses on customer experience and customer-journey design. While working for a telecom company, he became determined to challenge the assumption that improving customer experience necessarily costs more. The problem he wanted to solve was structural: he believed businesses could actually spend less when they give customers a good experience than when they provide a poor one. This led him toward automation and AI, particularly because keeping the right people available, properly trained, and equipped with the right information at the exact moment a customer needs help is extremely difficult.

Rolf describes a familiar support failure: a customer calls an internet service provider, waits in a queue, identifies themselves through an IVR, and then has to repeat the same information to the agent. The agent asks for the customer’s name and account details, types while talking, and may eventually announce that the customer reached the wrong department. The call is transferred, starts over, or disconnects after 15, 30, or 40 minutes. Rolf and John treat this as an example of a broken customer journey rather than simply an underperforming employee. From visiting hundreds of contact centers and consulting for roughly 10 years, Rolf says he repeatedly saw organizations focus on making the human agent better. He does not reject agent training, but argues that it addresses only part of the problem. According to his analysis, 75% of the issues that lead to customer dissatisfaction have nothing to do with the agent. Customers do not call because the service is working perfectly; they call because their internet is down, an invoice is confusing, or another business process has failed. Rolf therefore says companies should treat customer contacts as feedback about defective journeys and processes. The goal should be to understand why customers need support in the first place and fix those causes. AI may politely deflect contacts, but it will not solve failures outside the contact center.

After the break, John asks why companies are not seeing the expected results from generative AI despite its potential to transform customer interactions and internal workflows. Rolf says businesses must treat generative AI like “a new kid on the block.” Before it can represent a company effectively, it needs context: who the company is, how it sounds, how it talks to customers, what its processes are, and what matters to its customers. He says many organizations provide only a rulebook—what the AI can and cannot do—without teaching it how the company actually operates. Rolf compares this to teaching someone to drive by explaining traffic laws but not how to drive on a road or respond to everyone else in traffic. Rules alone do not create situational judgment. Human employees learn not only from formal instructions but also by watching how their colleagues behave; generative AI does not have that experience unless the organization deliberately supplies it. Rolf recommends training AI on historical data and examples of what was right and wrong, while also defining the desired tone and behavior. The better the system understands the company’s identity, practices, and customer context, the more likely it is to act as the organization would in a particular situation rather than mechanically following isolated instructions.

John asks Rolf to address the fear that AI will replace workers. Rolf argues that the most effective systems work alongside people, especially because good customer service depends on empathy and attention. An agent should be focused on understanding the customer’s situation, not typing, searching multiple systems, or trying to discover the correct process while the conversation is happening. He gives a concrete example involving a service interruption. An AI-supported system could recognize the caller, retrieve the relevant account information, identify an outage in the customer’s area, and show the expected duration. The agent could then greet the customer by name and say that the interruption is expected to last about three hours, apologize, explain what will happen next, and offer to send a text update if the problem lasts longer. This is materially different from making the agent ask who the customer is, where they live, and how long the disruption will last while the customer waits. For Rolf, AI’s value is not simply reducing the number of human contacts. It is supplying context and removing administrative work so the agent can concentrate on empathy, explanation, and solving the customer’s actual problem. The resulting experience is better because the human is able to be present rather than distracted by systems.

When John asks about deploying AI across large organizations, Rolf’s first recommendation is to start somewhere rather than wait for one system that will revolutionize everything. Companies should choose an area where they already deliver value and use AI to replicate that success. If a business has achieved a repeatable result, Rolf says it knows what it is doing; it is not merely benefiting from luck. Starting with a successful area also helps employees accept the technology. Rolf says workers need to understand that AI is being introduced as a tool to make them better and improve the customer experience, not simply as a replacement for them. Once employees see that the technology produces an improvement, the organization can expand into more difficult areas that require additional adaptation. His recommended sequence is therefore to identify an existing strength, apply AI there, demonstrate that it improves performance or service, and then scale gradually. This gives the business a proven model and gives employees evidence that the change is useful. Rolf presents gradual adoption as more practical than trying to impose a company-wide AI transformation before the organization understands what works.

Looking toward the next decade, Rolf says customer experience will be shaped by where consumers move and by the technology that follows them. His strongest prediction is that voice will remain and grow. People already talk to their phones and other devices, while many are tired of downloading and managing an app for every service. He expects businesses to pursue simpler ways for customers to interact with AI rather than forcing them into increasingly fragmented digital channels. Rolf says this shift is especially important among younger generations, whom he describes as more likely to talk than type. He notes that voice is already growing across the channels his company supports and expects that growth to continue for at least the coming decades. In his view, voice-powered AI for customer service will become fundamental for businesses because customers will increasingly expect to communicate in the way that feels easiest to them. He adds that customers are becoming more selective and less willing to accept whatever level of service a business decides it can afford. Companies therefore need to prepare for both automation and changing expectations, while keeping the interaction simple. Whether the underlying system is labeled AI or ordinary automation matters less than whether it makes service easier and more responsive.

Rolf invites listeners who want to explore AI-powered customer experience to visit NexcomGlobal.com, where Nexcom publishes blogs and articles about developments in the field. He also warns listeners to choose information sources carefully. When evaluating advice about customer experience or AI, he recommends looking for people who have actually delivered these systems and accumulated what he calls the “scars” of doing the work themselves. Rolf distinguishes that experience from a technology company whose only reference point is what its customers supposedly do. A business should examine whether the people offering advice have tried the approach, learned from mistakes, and can explain what worked and what did not. He summarizes the principle with the analogy, “We don’t go to the baker and ask about meat. We go to the baker and ask about bread.” In other words, credibility depends on relevant practical experience. The interview closes with John directing listeners to the episode’s show notes at eofire.com and thanking Rolf. The final promotional material again mentions The Foundation and HighLevel, while a separate callout invites entrepreneurs with high-ticket offers but insufficient quality leads to email John at john@eofire.com about Nexus Install.