Jason A. Fligman, Sr. Vice President, Service Strategy & Support, and CEO/President, Canon Information Technology Services, Canon U.S.A., Inc.
Recent industry assessments show that AI has moved decisively from experimentation to operational reality, with the vast majority of organizations now using it in at least one business function, including customer service.
Yet only a small share is capturing sustained enterprise value. The gap is not technological maturity; it is leadership philosophy and execution.
Nowhere is this more evident than in customer service. Leaders face a fundamental choice. One approach treats AI primarily as a headcount reduction tool, designed to lower costs and accelerate throughput. The other treats AI as a capability expansion engine that absorbs repetitive work so service professionals can focus on complex problem solving, judgment and human connection. When an organization like Canon manages more than 2,000 service professionals across customer support, field service and contact centers, handling approximately 150,000 customer interactions each month, that philosophical difference quickly becomes operational reality with measurable consequences.
The most successful AI deployments augment human work rather than replace it. In customer service, hybrid human and AI models outperform both fully manual operations and heavily automated ones when measured by customer satisfaction, resolution quality and employee engagement. This aligns directly with large scale operational experience.
Not all service interactions require human judgment or intervention. Status checks, basic troubleshooting, routine documentation and information gathering are necessary, but they are repetitive. Left unchecked, these tasks consume enormous amounts of human energy while delivering limited value. AI excels at this work. It can triage requests, surface relevant customer and product data, prepopulate case notes, guide customers through structured workflows and resolve truly routine issues end to end.
What remains should remain human. Emotionally charged conversations, ambiguous problems, technical edge cases and situations that require discretion or carry reputational risk are where people add irreplaceable value. AI does not diminish the human role in these moments, it elevates it.
Scale fundamentally changes the impact of this approach. Industry analyses show that applying generative AI to customer care functions can drivee substantial productivity gains compared with traditional processes. At high interaction volumes, these gains translate into more than efficiency. They create capacity. When AI absorbs even 20 to 30 percent of routine work across 150,000 monthly interactions, thousands of human hours are reclaimed each month. That time is reinvested in deeper diagnosis, more thoughtful resolution and proactive problem prevention. Agents are no longer forced to rush complex cases to keep up with volume. First contact resolution improves because problems are actually solved rather than escalated or deferred. Service quality becomes more consistent, even during periods of peak demand.
Organizations that use AI to improve decision quality, rather than simply reduce handle time, see stronger long-term loyalty and lower repeat contact rates. At scale, effectiveness matters more than speed.
AI augmentation also reshapes workforce planning in profound ways. Traditional service models emphasize speed to proficiency, training agents to handle standardized workflows as quickly as possible. Also, burnout and attrition are persistent risks in these environments, particularly where work is highly repetitive.
When AI handles routine interactions, the profile of a successful service professional changes. Critical thinking, system fluency, communication skills and emotional intelligence become more important than memorization. Training shifts away from scripts and toward judgment and problem solving. Career paths become clearer because agents are developing expertise rather than cycling through tasks that offer little growth.
When it comes to the future of work, the advancements in automation can theoretically replace portions of many roles, skills tied to assisting, advising and caring remain essential and grow in value when paired with technology. Organizations that redesign roles alongside AI adoption consistently report higher employee engagement than those that deploy AI without rethinking how people work.
This approach also enables a different strategic positioning. Many organizations respond to cost pressure by offshoring portions of customer support. While this can reduce labor expense, it often introduces communication challenges, quality variability and brand misalignment. AI augmentation offers a viable alternative.
By increasing productivity and consistency, organizations can maintain 100 percent U.S. based service operations while remaining competitive. Customers benefit from cultural alignment, clearer communication and greater trust. Brands benefit when the service experience reflects their values rather than undermining them.
When implemented effectively, AI-driven personalization and support improvements have been shown to materially increase customer satisfaction, boost revenue and reduce cost to serve. Premium service is not about doing everything manually, it is about using technology to ensure that human effort is spent where it matters most.
Measuring success in this model also requires a shift. Organizations that treat AI primarily as a cost lever tend to focus on labor reduction and average handle time. The caveat though is that over optimizing for these metrics often degrades customer experience and employee morale, ultimately eroding value.
In augmentation driven models, success is reflected in higher first contact resolution, improved customer satisfaction and stronger agent retention. These outcomes reinforce each other. Better resolution reduces repeat contacts. More meaningful work improves engagement. Stability replaces volatility in performance.
Sustaining excellence through technological change requires discipline. While AI adoption is widespread, only about one third of organizations have successfully scaled AI across the enterprise. Those that succeed invest in governance, continuous training and leadership alignment. They are clear about where AI should operate and where humans must remain in control.
Organizations that sustain top-tier service performance over many years do not chase technology for its own sake. They adopt tools that reinforce a clear service philosophy and integrate them carefully into existing operations. AI becomes part of an ongoing evolution rather than a disruptive shock.
The defining lesson from managing service operations at scale is simple. For customer service leaders, the real decision is not whether to adopt AI, but whether to use it to shrink their workforce or expand its impact.
When AI is deployed to replace people, organizations may achieve short-term savings but risk long-term fragility. When AI is deployed to free people from low value work, organizations unlock capacity, quality and trust at-scale. AI delivers its greatest value when it expands human capability rather than diminishes it.
The future of customer service will not be decided by how many interactions are automated. It will be decided by how thoughtfully organizations choose which interactions should always remain human.
At scale, AI does not diminish premium service. It makes it possible.
About the Author
Jason A. Fligman is Sr. Vice President, Service Strategy & Support, and CEO/President, Canon Information Technology Services, Canon U.S.A., Inc.