In a recent CX Today video interview, Karthik Suri, Chief Product and Corporate Strategy Officer at Sprinklr, framed customer service around resolution rather than automation alone. If resolution is the goal, customer context becomes the operational layer that helps AI and human agents reach it.
“Resolution depends on understanding, and understanding depends on context. AI can only make effective decisions when it has access to the customer's history, intent, previous interactions, and current circumstances.”
For CX leaders, the practical consequence is clear. A bot can deflect an interaction, and a dashboard can mark a case as closed, while the customer still feels the company did not understand the issue.
Historical and Real-Time Signals Shape Service
Suri identified two categories of context that contact centers need to bring together.
Historical context includes prior interactions, purchases, preferences, service cases, previous resolutions, and outcomes. If a customer has contacted a company several times about the same issue, that history should be available before the next answer begins.
Real-time journey context often creates the sharper signal. It includes the page a customer visited, the action they attempted, the channel they used, and the language or sentiment they bring into the current interaction. Suri outlined the distinction in operational terms:
“Sometimes understanding why a customer is contacting you now is just as important as understanding what happened six months ago.”
Channel context also changes how service teams should respond. A frustrated post on social, a live-chat question, and a voice call may describe the same issue, but each carries different urgency, visibility, and emotional tone.
Knowledge context matters too. Agents and AI systems need access to accurate policies, product information, troubleshooting steps, and previous service outcomes. Missing or outdated knowledge can turn a straightforward issue into a repeat contact.
Fragmented Context Raises Customer Effort
Customer context usually fails where systems, teams, or channels split the journey into separate records.
A customer may start in self-service, move to chat, escalate to voice, and then post publicly on social. If each step sits in a different system, the next agent or AI assistant sees only part of the problem. Suri warned that customers notice the breakdown quickly:
