Although it may be a cliché at this point, most AI projects fail due to data management challenges. Gartner has found that 63% of organizations either don’t have or are unsure if they have the right data management practices in place for AI.
With respect to using AI for customer experience (CX) applications, the job is arguably even more difficult given the myriad data streams in enterprises today, from social to email to contact centers. Organizations must unify diverse data streams to not only learn from that collective data but also enable those streams to inform and learn from each other.
That may sound like a tall order, but ironically, it’s one that AI is tailor-made to address. The key is to employ a platform that ingests data across CX channels, functions, and formats, and then applies AI-powered continuous learning and feedback loops, says Abhishek Priyam, vice president of product management at Sprinklr, which provides a leading AI-powered platform.
Fragmented CX data presents AI challenges
Today, CX data is highly fragmented, spread across a mix of communication channels that vary by use case and a range of technology platforms. “Email marketing may run on one platform while email-based support runs on another, each capturing only a slice of the customer journey,” Priyam says.
Even when companies succeed in integrating data from disparate systems, it’s often inconsistent, duplicated, or incomplete. What’s more, it’s challenging to aggregate and process data coming from multiple channels and touchpoints in a scalable manner without introducing an unacceptable level of latency.
Effective AI requires orchestration
Addressing these issues requires a CX platform that can orchestrate communications across channels in real time while fostering a tight coupling between data and AI tools.
“Every CX workflow, whether powered by AI agents or automated processes, should feed outcomes and signals back into the system, ensuring the underlying context becomes smarter and more accurate over time,” Priyam says. “The same platform that manages the semantic layer should also power AI agents and workflow orchestration, eliminating disconnects and enabling truly context-aware actions.”
Accomplishing these tasks requires a platform that ingests and reconciles data from myriad channels and functions in any format, including structured, unstructured, solicited, or unsolicited, he says.
Finally, robust governance and control are a must. “CIOs need strong oversight of how data and context are created, accessed, and used across both human users and AI agents to ensure security, compliance, and responsible AI usage,” Priyam says.
Customers provide evidence of success
The Sprinklr platform addresses each of these requirements, bringing together data from voice, digital, and social channels, as well as from service, marketing, advertising, and insights. It’s all funneled into a single system that preserves the context of each communication.
Customers employ these capabilities in several ways. A large global retailer, for example, connects customer service signals with its insights and research teams. The retailer then uses AI to analyze patterns in service interactions, helping to identify issues early and recommend corrective actions before issues escalate, Priyam says.
Similarly, a large electronics manufacturer combines social listening, customer support data, and device telemetry to create a comprehensive customer view that enables it to detect potential device issues before customers are even aware of them. Service teams can then proactively reach out with solutions.
Another Sprinklr client is turning feedback from customer surveys into immediate action by routing reports of poor experiences to support teams for proactive outreach – even though the customer has not explicitly raised a complaint.
“Those are results you can get when AI is embedded across your CX platform to capture and act on contextual signals,” Priyam says. “Because both human users and AI agents operate on the same platform, every interaction generates feedback that continuously refines the underlying data and context.”