Front pagesponsored · Sprinklr
CX Trust3h · 13:31 GMT · 5 min read
Scale and Trust: The Two Tests for AI-Powered Customer Service
As AI takes on more customer service work, leaders face a harder test than automation alone. Sprinklr’s Karthik Suri explains why governance, quality management, escalation, and continuous learning now determine whether AI can scale safely without losing customer trust
An AI service deployment can look successful in a contained pilot and still fail in live operation. The risk emerges when the same model is extended to new channels, regions, customer groups, or higher-stakes requests without clear controls for what the AI can decide, what requires approval, and when a human must take over.
That is the central operational question for CX leaders: not simply whether AI can automate more work, but whether the organisation can expand automation without weakening quality, accountability, or customer trust.
In a recent CX Today video interview, Karthik Suri, Chief Product and Corporate Strategy Officer at Sprinklr, argued that automation is not the finish line for customer service. Resolution is.
“Trust has become the foundation that enables scale. Without governance, organizations hesitate to expand AI adoption.”
Interaction volume, containment, and speed are incomplete measures of AI performance. Leaders also need evidence that automation is producing accurate outcomes, following policy, escalating appropriately, and improving the customer experience as its remit expands
Trust Has Become the Foundation for Scale
Many organizations still approach AI adoption as a tradeoff between innovation and control. Suri argued that this model no longer fits customer service.
Customers will not accept weaker service because AI was involved, and regulators will not give companies more room because they moved quickly.
That creates a new deployment logic. Trust is not a brake on AI adoption. Trust is the condition that lets AI adoption expand.
The reverse problem is also real. Strong controls without operational scale can limit business impact. A well-governed pilot may reduce risk, but it will not change service economics if it never moves beyond a narrow use case.
For customer service teams, scale and trust now have to grow together. That means building AI programs around customer impact, risk level, channel behavior, escalation needs, and business outcomes, rather than treating governance as a final approval step after the technology is already in place.
AI Service Breaks When Expansion Outruns Discipline
Suri identified a common failure pattern in AI-powered service deployments. Teams scale the technology faster than they scale the surrounding context, governance, and operational discipline.
A deployment may perform well in a tightly defined use case, then produce different outcomes when it is extended across regions, languages, channels, or policy environments. The model may be technically available everywhere, but the organisation’s controls may not be. Suri warned that inconsistency quickly damages the customer experience:
“Customers may receive one experience through chat, another through voice, and a completely different experience through messaging or social channels. That inconsistency weakens trust and creates operational complexity.”
Autonomy must also be calibrated to the decision being made. Updating an address and resolving a disputed payment should not operate under the same thresholds.
When leaders fail to define what AI may resolve independently, what requires human approval, and what must be escalated immediately, automation can move into decisions that demand judgement, empathy, or formal accountability.
The issue now is whether every interaction operates within a clearly defined level of authority.
Leaders Need a Governance Frontier
Suri described the first step as establishing a “governance frontier.” That frontier defines where AI can operate independently, where human approval is required, and where immediate escalation is the right path. It should be based on customer impact, business risk, regulatory exposure, and the emotional context of the interaction.
