We’re joined today by Dominic Jainy, a distinguished IT professional whose work at the intersection of artificial intelligence and business operations is providing a new lens on customer engagement. A recent industry survey revealed that while speed is a major benefit of AI in customer support, the real transformation lies in how teams are reinvesting that saved time. We’ll explore how businesses can measure the true return on this investment, transition support agents into revenue-generating roles, and strike the critical balance between powerful automation and the indispensable human touch.
Over half of support teams cite faster resolution times as a top benefit of AI. Beyond speed, how should leaders measure the ROI of this saved time, and what specific metrics can prove that AI is freeing up agents to handle more valuable customer interactions?
That’s the core question leaders should be asking. The 53% who point to speed are seeing the most obvious benefit, but it’s just the tip of the iceberg. True ROI isn’t just about closing tickets faster; it’s about what that reclaimed time enables. We see a clear maturity curve here. The survey showed that while 59% of teams just starting with AI measure ROI by time saved, that number jumps to nearly three-quarters for teams with mature deployments. They’ve moved past the initial “wow” of speed. To prove AI is working, you need to track metrics like first-contact resolution on complex issues, agent-driven upsell or cross-sell contributions, and even agent satisfaction scores. When your best people are no longer bogged down, they stick around, and that retention is a massive, tangible return.
As teams gain experience with AI, they increasingly use the time saved to focus on revenue-generating activities. What practical steps can a team take to transition agents from handling simple tickets to performing complex tasks that directly contribute to customer retention and growth? Please share an example.
The transition has to be intentional and structured. You can’t just flip a switch. The first step is identifying what “high-value” means for your business. Is it walking a hesitant customer through a complex purchase? Is it proactive outreach to a user who might be at risk of churning? Once you define those tasks, you build training paths. For example, a team can start by having the AI handle all password resets and basic “how-to” questions. The agents who used to handle those are now trained on identifying expansion opportunities. When a customer calls with a complex problem, the agent solves it and then, equipped with insights from the AI about the customer’s usage, can say, “I see you’re using this feature heavily. Did you know our premium plan could automate that process for you?” This shift is happening; 56% of mature AI teams are already focusing on revenue generation, compared to just a third of teams in initial deployment. It’s a deliberate evolution from problem-solver to trusted advisor.
Scaling support without growing headcount is a major advantage of AI. What is the right balance between automation and human agents, and how can teams design their workflows to ensure that AI handles mundane tasks while preserving the essential human touch for complex issues?
The right balance is a moving target, but the guiding principle is this: automate the predictable, elevate the human. You start by mapping out your customer journey and identifying the friction points that are repetitive and don’t require empathy or complex problem-solving. Those are prime candidates for AI. A great workflow design uses AI as a triage expert. The AI can handle the initial contact, gather context, and resolve the simple issues 24/7. But it should be programmed to recognize signs of frustration, complexity, or high-value opportunities and seamlessly escalate to a human agent with all the context gathered. This ensures the customer doesn’t have to repeat themselves and the agent can dive right into the heart of the matter. This preserves the agent’s energy for the interactions where empathy and critical thinking truly make a difference, which is crucial because customers still deeply value the ability to connect with a person.
AI is highly effective at stripping away administrative work like call summarization and knowledge retrieval. Can you describe how this changes an agent’s day-to-day role and what new skills they might need to develop to thrive in an AI-assisted support environment?
This is probably the most profound change to the agent experience. The impact of stripping away that administrative drag is immense. Think about it: an agent is no longer a data entry clerk. They don’t have to spend precious minutes after a call manually summarizing, tagging tickets, or entering data. Instead of digging through internal wikis while a customer waits, the AI surfaces the exact technical spec instantly. Their day-to-day role shifts from being a reactive processor of information to a proactive relationship builder. The new essential skills are less about technical recall and more about emotional intelligence, strategic thinking, and consultative communication. Agents need to become experts at interpreting complex customer needs, showing empathy, and identifying moments to deepen the customer relationship or drive business growth. They are now the human face of the brand for the most critical moments, not just the fastest typists.
What is your forecast for the evolution of AI in customer support over the next five years?
Over the next five years, I foresee AI in customer support moving from a tool for efficiency to a core driver of the entire customer experience. We’ll see a shift from reactive AI—bots that answer questions—to proactive AI that anticipates customer needs before they even arise, initiating helpful engagement based on user behavior. The technology will become so integrated that the line between human and AI support will blur, not to deceive the customer, but to create a seamless, intelligent support system. Agents will become “AI coaches,” training their digital counterparts and managing a suite of automated tools to deliver hyper-personalized service. Ultimately, the metric of success will no longer be “cost to serve” but “customer lifetime value,” a figure that AI will be instrumental in not just measuring, but actively growing.