AI shouldn’t be used to deflect customers. It should be used to reveal what’s broken in the business.
What [Rohit] has to say about
How Leaders Should Adopt AI in Customer Support?
The biggest mistake leaders make with AI in customer support is treating it as a containment tool instead of a business system.
If the starting question is “How many contacts can we deflect?” the strategy is already too narrow.
Effective AI adoption begins with visibility, not automation. Before AI is asked to resolve issues, it should be used to reveal where the support model is breaking down - recurring failure paths, policy-driven friction, upstream defects in product, fulfillment, or billing, and moments of rising customer effort that never show up in tickets.
Only after that clarity exists should leaders apply automation.
Even then, containment is table stakes, not the objective.
The real value of AI comes from moving beyond the containment loop to close the outer loop: using support intelligence to drive changes in the business that prevent issues from occurring in the first place. This is where AI shifts from lowering cost per contact to improving system performance across teams.
The strongest AI-enabled support organizations use AI to:
Reduce handling cost and agent load
Improve customer outcomes and effort
Trigger upstream action that removes friction at the source
If AI only optimizes support, it limits impact.
If it informs product, policy, and operations, it compounds value.
Leaders who get this right don’t use AI to scale support.
They use AI to make support less necessary.
