Pendo’s AI Pivot: From Product Analytics to Enterprise Intelligence
Pendo’s evolution reflects a broader shift in enterprise software strategy: moving beyond traditional product adoption metrics to deliver actionable intelligence that spans product, revenue, and customer experience. This goes beyond simply tracking whether a user clicked on something or logged in to tracking how product usage affects real business outcomes.
Historically, product teams have often relied on their own gut assumptions about user behavior, often adding features without clear evidence of value. This frequently results in a bloated product roadmap (often referred to as the “everyone has a voice model”) and missed opportunities for value, because it is almost impossible to see what users do inside software without a product analytics platform.
Pendo initially disrupted this model by providing behavioral insights such as clicks, swipes, and sentiment analysis that informed design and adoption strategies. Today, the company is extending that foundation into predictive analytics and AI-driven workflows to better support organizations’ revenue operations and customer success.
What’s Changing
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Gain True Visibility Across AI and UI Journeys:
Pendo reports that it can now track user journeys across both traditional UIs and AI interfaces. This hybrid approach acknowledges that generative systems will coexist with legacy workflows for years, requiring unified visibility into adoption and friction points. -
Measure AI Agent Performance at Scale:
Pendo claims to now be able to monitor AI agent performance using metrics like “rage prompts” to capture user frustration signals (such as repeated failed attempts or profanity). This feature enables organizations to quantify agent effectiveness, prioritize improvements, and validate AI ROI. -
Turn Your Usage Data Into Predictive Business Signals:
Through its Pendo Predict solution, the company offers churn, expansion and modeling that ingests behavioral and third-party data (CRM, Snowflake, Mixpanel). Insights are delivered directly into systems like Salesforce and HubSpot, which reduces manual effort and accelerates response. -
Automate Discovery and Support With Product Intelligence:
Agent Mode and MCP integrations reportedly allow teams to automate workflows such as customer discovery and support triage. These tools leverage Pendo’s data layer to recommend power users for interviews, enrich support tickets with session replay, and surface insights in conversational interfaces.
Why It Matters
The tech landscape is quickly evolving, and AI is becoming a core part of modern products. As this shift continues, Pendo plans to support both its classic UI usage visibility and emerging agent behavior insights under one roof. This approach will allow Pendo to be a system of record for experiences because it will understand what users do in traditional interfaces, what users say to AI agents via prompts, and what AI agents do in return. It will also understand whether these interactions fail or succeed. As enterprises continue to sink massive investments in AI development, they need a way to track how much these AI interactions are actually helping users. Pendo aims to provide that layer of truth.
Competitive Landscape
Pendo’s evolution moves it beyond the traditional digital adoption platform (DAP) category, where competitors like WalkMe and Appcues focus on in-app guidance and onboarding. In predictive analytics, Pendo challenges spreadsheet-based churn models and stand-alone tools by combining behavioral depth with rapid deployment. Observability vendors such as Datadog remain focused on technical health (e.g. token usage, latency) rather than user adoption or revenue correlation, leaving a gap that Pendo aims to fill.
Our Take
Pendo has long been known for its ability to deliver product insights, but it now extends that capability into the data that can drive operational decisions. Their pivot signals a strategic shift from product analytics to enterprise intelligence, aligning usage data with financial outcomes and operational workflows. For CIOs, CTOs, and revenue leaders, the platform offers a path to quantify AI performance, reduce user friction, and directly link product usage to business metrics.
The company is building a system to help businesses understand how their software is used and how that usage connects to revenue. As AI companion agents become more common, Pendo provides a clearer way to measure their impact and tie usage data to meaningful results.
Pendo offers a way to bring traditional UI and AI product efforts together in one platform. The challenge for buyers will be validating integration depth and predictive accuracy against existing RevOps and analytics tools. In a market where AI adoption is accelerating but fragmented, Pendo’s hybrid approach positions it as a strong option for organizations seeking unified visibility and actionable insights.
Questions for Pendo:
- What position should an organization be in before considering a Pendo purchase?
- Are there any considerations/gotchas in implementation?
- Have you sorted out A2A with other vendors that Pendo would need to interact with in an organization's ecosystem?