Intelligent experiences depend on predicting what help or information a user will want before they ask and then act on that prediction.
Intelligent experiences (IX) are continuously learning, context-aware interactions that use real-time data and AI to anticipate needs and orchestrate outcomes across every employee and customer touchpoint.
Unlike traditional customer experience (CX), which tends to be reactive (optimize a journey step, respond to a ticket, run a rules-based campaign), intelligent experiences are predictive and adaptive. In particular, an IX infers intent, personalizes content or assistance for the moment, and automates the next best action across channels and processes.
Adoption and market signals driving the intelligent experiences
Multiple markets related to the needed building blocks for IX are all pointing upward.
Real-time analytics is scaling quickly. The global spending was about $0.89B in 2024 and is projected to reach $5.26B by 2032, a 25% CAGR, according to Fortune Business Insights. That growth is attributed to the shift from batch reporting to streaming insights that power in-the-moment decisions.
On the experience platform side, the digital experience platform (DXP) market is expected to grow from roughly $16.1B in 2025 to $26.5B by 2030, according to a market research report by Mordor Intelligence. That indicates sustained investment in the tooling that unifies content, data, and decisioning, which are all needed for IX.
Additionally, consumer expectations are high. McKinsey reports that 71% of consumers expect personalized interactions and 76% get frustrated when they don’t. McKinsey further notes that companies that excel at personalization generate about 40% more revenue from those efforts than their peers.
See also: Solving Industry-Wide Customer Experience Challenges with Event-Led Integration
What’s required to deliver intelligent experiences
A variety of underlying core technologies are required to support intelligent experiences. They include:
1) Event-centric data architecture: IX starts with capturing signals as they happen, such as clicks, transactions, sensor events, service interactions, and making them queryable with low latency. Practically, that means event streaming (e.g., Kafka-class pipelines), stateful stream processing, and a feature store that exposes “fresh” features (recency, sequence patterns, propensity scores) to decision engines. Without an event backbone, “personalization” degrades to yesterday’s averages rather than this moment’s intent. The rapid growth of real-time analytics spending noted above reflects this foundational pivot.
2) A decisioning brain that blends ML + rules: IX needs a policy-aware brain that can pick the next best action per context. Those actions might include content, price, offer, workflow step, recommended knowledge, or agent assist. What is needed is a combination of predictive models (likelihood to churn, purchase, escalate); recommendation models; LLMs for retrieval-augmented generation (RAG) and natural interaction; and business rules for compliance and brand constraints.
3) A unifying experience platform: Delivering IX requires consistent content and journey orchestration across web, mobile, service, and internal tools. Modern DXPs integrate content management, journey orchestration, and API-first delivery with decision engines so that the “what” (decision) and the “how/where” (experience rendering) are decoupled but synchronized.
4) Trust, governance, and controls: IX must be responsible by design. That includes consented data usage, transparent model behavior, feedback capture, and kill switches for automated actions. Governance should codify which signals can be used for which decisions, how long data persists, and how to measure bias or drift. Firms that pair data governance with real-time decisioning are over-represented among top performers in conversion and spend lift, suggesting maturity isn’t just about algorithms; it’s controls plus capability.
5) Employee-facing intelligence, not just customer-facing: Intelligent experiences should elevate employees, too. In service and operations, LLM-powered agent assist can summarize context, propose responses, and fetch next steps; in knowledge work, copilots can draft, reason over documentation via RAG, and surface anomalies from streaming telemetry.
Why real-time analytics and AI are required
Intelligent experiences depend on predicting what help or information a user will want before they ask and then act on that prediction. That requires:
- Low-latency sensing to detect micro-moments (hesitation on checkout, sudden device error, a policy change affecting a claim).
- On-the-fly inference to score intent and value (recommendation, deflection, escalation).
- Immediate actuation in the channel (content swap, proactive outreach, route to specialist, self-healing action).
When done well, these capabilities move key metrics such as conversion, CSAT, handle time, and revenue because they collapse the distance between signal and outcome.
The bottom line is that Intelligent experiences are not a rebrand of CX. Such experiences make use of real-time data and AI in every interaction. That allows organizations to anticipate intent and deliver outcomes.
