Confluent Intelligence, launched in 2025, addresses critical challenges in AI-driven customer and employee experiences. It closes the AI context gap by streaming real-time, trustworthy data to AI systems. This enables context-rich, event-driven AI that powers smarter, scalable Customer Experience (CX) and Employee Experience (EX) applications. With enterprise-ready infrastructure based on Apache Kafka and Flink, Confluent Intelligence supports real-time decision-making and automation at scale, a key advance over traditional batch AI models.
Confluent Intelligence: Facing Real-World CX and EX Challenges Today
CX and EX leaders face daunting hurdles. Most AI initiatives targeting CX fail to deliver returns, with up to 95% failing due to lack of real-time context and fragmented workflows. Legacy systems rely on static or delayed data for AI models, causing outdated or irrelevant insights, slower response times, and poor personalization. The growing demand to unify customer and employee data streams—across websites, CRM systems, contact centers, and operational backends—exposes existing infrastructure limitations.
Real-time contextual awareness is now critical. Customers and employees expect immediate, relevant, and proactive engagement that adapts dynamically to their evolving needs and behaviors. However, integrating streaming data pipelines with AI workloads remains complex, leading to fragmented or stalled AI programs. These issues impact retention, satisfaction, and operational efficiency.
Why Real-Time Data Streaming is Transformational
Confluent Intelligence tackles these challenges by embedding continuous data flow into AI systems. Unlike traditional batch processing, it streams historic and live data to AI models, ensuring they always “know” what happened before, what is happening now, and can anticipate what will come next. This improves speed, accuracy, and relevance of AI-driven CX/EX solutions.
Key benefits include:
- Enhanced Personalization: Continuous data feeds enable AI agents to tailor responses and offers in real-time, deeply understanding customer preferences and contexts.
- Proactive Problem Solving: Streaming analytics detect anomalies or friction points early, allowing firms to resolve issues before they affect customer loyalty or employee productivity.
- Scaled AI Orchestration: Teams can build event-driven AI agents that automate workflows and decisions without manual inputs, vastly accelerating deployments.
- Governance and Trust: Real-time context engines deliver governed, secure, and replayable data streams that assure data quality and compliance at scale.
These capabilities bridge the AI context gap, enabling enterprises to move beyond chatbots to truly adaptive, intelligent AI agents embedded in live experiences.
Expert Insights and Case Studies
Jay Kreps, Co-founder and CEO of Confluent, emphasizes that off-the-shelf AI models excel only when coupled with seamless, continuous data flow. “Without the constant stream of data, models can’t deliver timely and uniquely valuable business decisions,” he says.
Atilio Ranzuglia, Head of Data and AI at Palmerston North City Council, credits Confluent’s platform for enabling real-time AI-powered workflows that automate processes vital to their smart city initiatives. Similarly, Nithin Prasad, Senior Engineering Manager at GEP, highlights how Confluent powers AI-driven procurement and supply chain applications by providing reliable real-time data streams.
Research affirms that AI-driven CX platforms with real-time data outperform traditional solutions. For example, Kantar’s ExperienceDiagnostics uses machine learning on streaming customer data to classify customer sentiments comprehensively, moving beyond limited survey feedback. This approach enables businesses to identify at-risk customers earlier and personalize retention strategies effectively, increasing predictive accuracy up to 74 percent.
Practical Applications in CX and EX
Confluent Intelligence’s offerings enable a wide range of impactful CX and EX use cases:
- Real-Time Context Engine: Provides structured, trustworthy streaming data to AI agents without exposing complex backend infrastructure. CX teams can access up-to-the-second context, ensuring AI decisions reflect the latest customer actions and environment changes.
- Streaming AI Agents: Teams can build agents that observe data events, reason on the fly, and act autonomously. This supports intelligent automated support, dynamic personalization, and contextual employee assistance.
- Embedded ML Analytics: Built-in ML functions for anomaly detection, forecasting, and model inference empower teams. Primarily to extract immediate insights and respond faster to operational challenges.
Confluent integrates Anthropic’s Claude large language model as the default on these streaming agents. Thus, it combines cutting-edge AI reasoning with the strongest data foundation. This partnership facilitates advanced capabilities like prioritizing critical anomalies and delivering personalized experiences in real time.
Actionable Takeaways for CX/EX Professionals
- Prioritize Real-Time Data Integration: Move beyond static or batch AI models. y incorporating continuous streaming data to keep AI contextually relevant and timely.
- Invest in Event-Driven AI Architectures: Building AI agents native to streaming pipelines. That reduces complexity and accelerates AI deployment across CX and EX workflows.
- Leverage AI Agents for Automation: Enable intelligent, autonomous agents. Especially to observe, decide, and act to enhance customer personalization and employee productivity without manual intervention.
- Focus on Data Governance: Ensure data streaming platforms provide secure, governed, and replayable data streams to maintain trust and compliance.
- Collaborate with AI Providers Offering Contextual Intelligence: Choose platforms that integrate advanced LLMs with real-time data. For adaptive, context-rich AI capabilities.
- Measure Impact Through Predictive Analytics: Use streaming-enabled AI insights to predict churn, detect anomalies, and optimize operational KPIs dynamically.
Conclusion
In today’s digital economy, successful CX and EX depend on delivering highly personalized, proactive, and seamless experiences. The AI context gap, stemming from outdated data and fragmented workflows, remains the largest barrier for enterprises.
Confluent Intelligence’s unified, cloud-native streaming data platform redefines AI integration. By powering context-rich, real-time AI agents that scale from pilot to production. This allows businesses to unlock AI’s full potential to transform customer and employee journeys with reliability, agility, and measurable ROI. CX and EX professionals need to embrace real-time data streaming with intelligent AI agents. That is the fastest path to future-ready experiences that truly resonate in the moment.
Investing strategically in these technologies and approaches important. Organizations can shift from reactive to proactive, elevate satisfaction, and gain competitive advantage in the experience economy. The era of context-rich AI-powered CX and EX is here. Aligning infrastructure, data, and AI is the key to winning tomorrow’s customer and employee loyalty.
