Adapting to AI is a shift in how work gets done and how customer value is created. I’ve learned that the fastest way to lose trust during moments like this is to sound either euphoric (“AI will change everything!”) or evasive (“Nothing will change!”). Trust is built when leaders are clear about what is changing, honest about what isn’t, and disciplined in how new capabilities are introduced.
That’s the posture of responsible optimism. It means recognizing AI’s real potential without overselling it, being specific about where it improves work and customer outcomes, and intentional about how people benefit. Responsible optimism doesn’t promise instant transformation; it focuses on thoughtful execution.
Customer support as the real-world test case
Customer Support is where this approach is most visibly tested. Support operates at scale and is highly measurable, which makes AI’s impact easy to observe. At the same time, it is deeply human. That combination makes Customer Support an ideal case study for understanding how AI reshapes both work and customer value.
The question, then, is not whether AI can draft a response or summarize a case. We already know it can. The real question is what we do with that capability. Do we use AI to focus on volume, or do we use it to build a support experience customers trust and a work environment where expertise can thrive?
AI that lifts everyone: Reducing variance and raising customer trust
One of the most cited real-world studies on generative AI in Customer Service examined a staged rollout of an AI assistant to more than 5,000 support agents at a Fortune 500 company. The findings were operational:
- Access to the AI tool increased productivity (issues resolved per hour) by roughly 14% on average.
- The gains were significantly larger for newer agents, about 34% improvement, suggesting AI can accelerate onboarding and raise baseline performance, not just amplify top performers.
This evidence suggests a nuanced reality: AI doesn’t just make us work faster. It raises the floor for teams. AI helps newer or less experienced employees deliver more consistent results sooner. That consistency matters because customers often experience support through its weakest moments. Reducing variance, the “it depends on who you get” effect, improves the customer experience, while also giving employees clearer guidance and more confidence in their work.
At Schneider Electric, our Customer Care Centers (CCC) receive millions of tickets a year. Having an internal knowledge bot for our CCC employees has played a key role in improving consistency and supporting their confidence. Importantly, our models operate within clear governance, with humans accountable for final decisions in customer‑impacting moments.
Clearly, AI doesn’t just extract value, it distributes expertise. And when expertise becomes more available, human-added value becomes even more important, not less.
Human value becomes the differentiator
AI can summarize a customer’s last five interactions, but only a human can sense the frustration between the lines, pause, and rebuild trust. AI allows us to give more time and attention to the moments customers actually remember.
- Handling ambiguity, exceptions, and edge cases
- De-escalating emotional situations
- Making judgment calls when policy and reality collide
- Coordinating across teams to connect the dots for the customer
- Protecting customers in high-stakes moments (billing disputes, safety, fraud, privacy)
If AI handles the first pass, human teams can become what customers actually experience. That outcome has to be intentionally designed, and its success depends on how the change is managed.
Redesigning work and managing change for better outcomes
If AI is going to improve both work and customer value, roles can’t stay static. Work needs to be redesigned at the task level, not treated as an abstract role change. Tasks are concrete, observable, and easier for people to understand and adapt to. One practical way to do this is to group work into four task categories:
- Automate: Tasks AI or software can fully take over (e.g., customer query triage).
- Augment: Tasks humans still perform, with AI providing assistance (e.g., drafting a first version of a response).
- Elevate: Higher-value work humans can focus on more because routine tasks are automated (e.g., complex problem solving, relationship management).
- Protect: Tasks that should remain strictly human for ethical, relational, or risk-sensitive reasons (e.g., sensitive customer conversations).
The picture becomes clearer which tasks can be automated. This framing does more than organize work, it creates clarity. People can see exactly what is changing and what is not. They understand where they can be supported by AI, where they can free up their time and focus, and how their skills contribute to customer outcomes.
The standard we should hold ourselves to
AI is a positive force in Customer Support when it augments the work people do and the value customers experience. At Schneider Electric, in Customer Care, we approach AI as a system, combining technology, process, and human capability to create efficient, consistent, and human-centered customer interactions. If you’re navigating similar changes, I encourage you to start by asking two questions: how is AI changing the work your teams do, and is it improving the experience your customers actually feel?
About the author
Irina Zubova VP, Customer Support
With over 25 years of experience, Irina Zubova currently serves as Vice President of Customer Support at Schneider Electric. She oversees the global customer support strategy and operations, driving service excellence across the organization. She is responsible for ensuring high-quality, responsive, and scalable support services that enhance customer satisfaction and strengthen long-term customer relationships.
Irina leads large, cross-functional teams focused on delivering consistent service standards while integrating digital tools that improve efficiency and visibility. Under her leadership, customer support operations have evolved to incorporate automation, data-driven performance management, and streamlined service processes. Her approach balances operational rigor with a strong commitment to customer-centricity, ensuring that technology enhances, rather than replaces the human experience in service delivery.
Originally from Moscow, she studied at Moscow State University of Pedagogy, establishing the academic foundation for her career. Irina is recognized for combining strategic vision with operational execution, positioning customer support as a key driver of organizational performance and customer loyalty.
Beyond her executive responsibilities, she advocates for the development of diverse, high-performing teams and her secret superpower is unlocking talent potential.
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