Many organizations have successfully launched their first AI agents in customer service to answer FAQs, deflect requests, or assist agents with routine tasks. However, as these projects or pilots move into wide-scale production, they typically encounter the “fog” of customer interactions and succumb to unpredictability and scalability issues.
The problem is that customer journeys span multiple systems, channels, and constantly evolving knowledge sources, causing AI agents to become brittle. Lacking sufficient context and adaptability, agents often break down at the first unexpected turn – such as when a customer calls to inquire about a delivery delay, then shifts to asking for a refund.
As organizations seek to modernize the customer experience (CX), they often encounter these challenges when moving from experimental bots to production-grade AI agents.
“Pilot environments are highly controlled, with agents tested on narrow use cases using clean, curated data,” says Abhishek Priyam, VP of product management, with Sprinklr, which has built an AI-powered platform to help enterprises manage customer-facing functions. “In production environments conversations are messy and dynamic, because users may have multiple purposes behind any given interaction.”
Under such situations, several issues come to the fore, Priyam says:
- Lack of context: Agents cannot access past conversations, customer history, or cross-channel signals, so they don’t have all the information required to make sound decisions.
- Static knowledge: Knowledge bases are outdated and not continuously refined from real customer interactions.
- Disconnected systems: Workflows, analytics, and feedback loops sit in silos in various customer systems and databases.
- No learning loop: Failures are not typically captured and fed back into the system.
Overcome shadow AI and provide excellent CX
It’s a thorny problem because, as we’ve seen with shadow IT solutions in the past, if the business doesn’t offer a solution, employees may try to find their own. It’s not difficult to find chatbots, large language models, and low-code agents to perform all sorts of tasks. When users start adopting these tools on their own, the enterprise and IT teams risk a lack of governance over AI solutions.
An effective solution is a platform that orchestrates AI agent activity, provides context to agent conversations, and embeds learning into the system. For example, the Sprinklr platform captures conversations that occur across various CX channels, including voice, social media, email, and chat. It analyzes conversations as they happen to detect shifts in intent or sentiment, as well as churn or upsell signals.
All this collected data provides context that Sprinklr surfaces dynamically during customer interactions. Importantly, the same context is available to both human and AI agents whenever they need it –– mitigating the risk of shadow AI.
Every interaction also feeds a closed-loop learning system, leading to continual improvement. The need to escalate a customer problem is a signal of a knowledge gap, for example, while emerging patterns can indicate the need for workflow and policy updates. By analyzing large volumes of conversations, companies can identify where human or AI agents struggle, detect workflow or policy execution breakdowns, and identify missing or outdated knowledge resources.
“Conversations, knowledge, workflows, and analytics are not discrete. They operate as a single unified layer,” Priyam says. “Sprinklr eliminates the fragmentation that causes agentic systems to fail.”
Learn more about how Sprinklr can help your CX teams benefit from production AI workflows.