AI is raising expectations for customer experience, pushing it toward a model where decisions unfold in real time and journeys shift moment by moment. But behind the scenes, many enterprises are still relying on fragmented systems that struggle to unify customer identities and maintain a reliable view of each interaction.
Decisions that appear instantaneous are often built on stale or incomplete data.
The modern tech stack is a patchwork of customer data platforms (CDPs), customer relationship management systems (CRMs), messaging orchestration tools, contact center platforms and data warehouses, each introduced at different points in time, each with its own latency profile. When a customer moves from app to website to chat agent to phone call, the journey crosses five or six systems before anyone makes a "real-time" decision. At every handoff, data ages, and the decisions downstream age with it.
And yet, budgets continue to flow into decisioning engines, AI layers, and orchestration platforms, frequently without a hard look at whether the underlying data systems can support true real-time execution.
"The biggest challenge is not the data volume, it's the data readiness," Hardik Parikh, Chief Revenue Officer and Co-Founder of Shaip, told CX Today in an interview.
"Most customer interaction data in today's world is fragmented across systems, unstructured, in silos, [which is] very difficult to operationalize for AI in real time."
The real gap, he argues, is in "turning raw interaction data into structured, compliant, AI-ready data sets that can actually support faster decision-making.”
How Real-Time Is Your CX Data, Really?
One of the core issues is the definition. “Real-time” has become a catch-all term that spans different technical realities.
At one end there’s event streaming, data captured and processed as it happens, often within milliseconds. This is what powers use cases like fraud detection or dynamic pricing in high-frequency environments.
Micro-batch processing, which runs every few seconds or minutes, is more common. Many customer data platforms (CDPs) and analytics platforms operate here, even if they’re marketed as real-time.
And then there’s near-real-time dashboards or systems that update every few minutes, sometimes longer. That’s often sufficient for reporting and monitoring, but it can introduce meaningful lag to decision making.
These approaches are often conflated. A vendor might stream events into a platform in real time, but if identity resolution runs every five minutes and activation pipelines update every 10 minutes, the end-to-end system is anything but instantaneous.
Where Latency Creeps In
Even in modern tech stacks, latency isn’t caused by a single bottleneck but builds across multiple layers, each introducing its own delays.
It often starts with data collection. Web and app tagging relies on software development kits (SDKs) that buffer events, retry failed sends, and sometimes drop data altogether. Network conditions, device constraints, and ad blockers all play a role.
Enterprise analytics systems typically capture roughly 85–95% of expected events, implying that 5–15% of interactions may be lost, particularly under complex conditions or peak traffic. That data is simply gone and no downstream processing can recover it.
From there, data moves into pipelines that transform and load it into warehouses or lakes. Scheduled batch jobs might run continuously, or on schedules measured in minutes. Either way, they introduce a gap between event capture and availability.
Identity stitching adds another layer. Matching anonymous and known users, resolving duplicates, and linking devices requires computation and, often, batch processing. Even advanced systems rarely perform this perfectly in real time. Match rates for cross-device identity resolution range from around 40% to 70%, depending on the richness of consented identifiers available, data shows.
Then pushing enriched data back out to engagement platforms, whether email, push notifications, contact center routing, or ad systems, adds further lag as each has its own ingestion limits and refresh cycles.
By the time a “real-time” decision is executed, the underlying data may already be several minutes old, at least.
What’s Broken in Customer Identity Resolution?
Identity is the other, arguably more complex, side of the coin. Modern customer experience depends on stitching together interactions across devices, channels, and contexts. A single customer might browse anonymously on mobile, log in on desktop, call a contact center, and respond to a marketing message, all within a short window.
Connecting those touchpoints into a unified profile is hard enough. Doing it in real time is harder still.
Industry benchmarks put cross-device identity match rates at 40–70%, and they fall further in environments where consent signals are fragmented and third-party cookies are gone.