On June 16, we’re hosting our summer release event where we’ll talk through what informed AI can do—RSVP here.
Every time a user clicks, hesitates, rage-clicks their way through a broken flow, or quietly closes the tab and moves on, they leave behind a record. Enterprise digital teams have been collecting those records for years. In most organizations, the data isn’t the problem. What to do with it is.
Your team probably knows where users are dropping off. They can tell you which funnel step is losing people, which page has the highest exit rate, which campaign drove traffic that didn’t convert. What they usually can’t tell you, at least not quickly and not without a painful amount of manual correlation work, is why. The specific interaction sequence that preceded the drop. The error state the user hit twice before giving up. The exact moment friction appeared, and what it looked like from where they were sitting.
That gap has a name. We’ve been calling it the context chasm. And when your AI systems are operating without that context, they’re not giving you answers. They’re giving you best guesses dressed up in confident language.
The data exists. The problem is that it’s scattered.
Most digital teams aren’t working from one coherent data source. They’re working from several that were never designed to talk to each other. Product analytics in one system. Session data in another. Customer support history somewhere else. Every one of those systems captures a slice of what happened. None of them, on their own, can tell you the full story.
The result is that answering a straightforward question, why did checkout conversion fall Tuesday, requires someone to go pull data from multiple places, manually assemble it, build a theory, and hope the theory is right. Meanwhile the friction sits in the product, costing conversions and eroding trust, for days or weeks before anything changes.
The same problem shows up in a different form for operations and workforce teams. AI-assisted workflows are getting deployed at speed across support, operations, and back-office functions. Leadership wants to know: is it working? Handle times changed. Ticket volume moved. But connecting those outcomes to specific workflow changes, proving that the AI did it and understanding where it helped versus where it introduced new friction, requires exactly the kind of behavioral visibility that most teams don’t have. The investment is real. The evidence isn’t. Teams end up making expensive decisions in a fog.
When context is fragmented, AI makes confident mistakes
What makes this particularly acute right now is that AI raises the stakes. Agentic systems don’t just surface insights, they take action. An AI that has incomplete context doesn’t produce an incomplete answer. It produces a confident, fluent, completely wrong one. And in a world where those systems are operating workflows, personalizing experiences, and making decisions at the speed of software, wrong is expensive.
The promise of AI in digital experience is real: close the gap between something going wrong and something being fixed, from weeks to minutes. Detect friction before users complain about it. Resolve issues in the session, not in the postmortem. But none of that works if the agentic system is working from fragmented data. AI is fast. Without behavioral context, without the full record of what users actually did, in sequence, including every hesitation and error, it’s fast at the wrong thing.
This is the problem Fullstory MCP was built to address. External AI systems, whether that’s your customer support AI, your coding agents, or any model you’ve wired into your stack, can’t access the behavioral record on their own. MCP gives them a way in. Instead of working from vague inputs and producing plausible-sounding responses, your AI gets the session-level context it needs to trace what actually happened and act on it.
Digital sight is what changes the equation. When an AI system has access to the actual behavioral record, not just aggregated metrics but the interaction-level sequence across a session, it can do something qualitatively different. It can trace a conversion problem back to a specific friction pattern. It can surface an issue for a specific combination of device, flow, and user state, not because someone instrumented for that scenario, but because the data was captured. It can take action on what it sees, rather than inferring from what it was told.
The same gap exists inside your workforce, and it’s just as costly
The behavioral data that tells you what’s happening to your users is also, if it’s structured the right way, the data that tells you what’s happening to your workforce.
Operations teams face a version of the context chasm that’s less talked about but just as costly. They know what comes out of a workflow: resolution rates, handle times, CSAT scores. What they don’t have is clear visibility into how work actually moves through the system, where time is being spent, where the AI tools they’ve deployed are helping versus creating new friction, where the highest-value improvement opportunities are. You can’t optimize what you can’t see. And in most organizations today, the work itself is invisible.
The investment in AI assistance, the copilots, the routing tools, the summarization features, is significant enough that teams need to be able to account for it. Not just “we think it’s helping” but “here’s where it moved the metric and here’s what it changed.” Without process-level visibility, that kind of accountability isn’t possible.
What closing the gap looks like
What closing the context chasm looks like is behavioral data that functions as shared infrastructure, not a separate analytics silo, but a layer that flows into the systems that need it. Session-level context streaming into your customer support tools so your agents and your AI are working from the same picture. Behavioral data moving into your warehouse so it can be combined with transactional records and LTV models without someone spending two days on the export.
Fullstory MCP is already making this possible for teams in beta. Builders are connecting their external AI systems directly to Fullstory’s behavioral data, so the models they’ve deployed can pull the context they need to produce answers that are traceable to what actually happened, not just statistically likely. The difference in output quality is significant, and the difference in trust is more so.
When that’s in place, the question shifts. Teams stop spending time building context and start spending it acting on what the context tells them. Problems get surfaced in the session, not in the review meeting three weeks later. Agentic workflows earn the trust to operate with more autonomy because the work is verifiable. You can see what the AI saw, trace what it did, and confirm the outcome.
On June 16, we’re hosting our summer release event where we’ll talk through what informed AI can do—RSVP here.
