Foreword: The Great Analytics Shift
by Yazan Sehwail, Co-Founder & CEO, Userpilot
In 2025, product teams achieved unprecedented visibility. The democratization of analytics, driven by autocapture, session replay, and enhanced visualization, gave us a complete view of the user journey. Yet, this created a paradox: teams found themselves drowning in dashboards but starving for insight. Overwhelmed by volume, many retreated to gut-driven decision-making. The lesson became undeniable: visibility is dead weight without real-time intervention. Why? Because observation doesn't drive growth - action does.This realization paved the way for the current AI transformation. The competitive advantage is no longer just using AI, but harnessing it to go deeper in understanding behavior, act faster on signals, and see clearer paths to growth.As AI evolved from a tool to a teammate, it exposed a hard truth: many data foundations were too shaky to support an automated future, leading to an erosion of trust.In 2026, our mandate is to move beyond passive counting to enabled action. We are shifting to high-velocity control, where AI integrates into the workflow across four phases:
Data and Scope
Surveying our users and interviewing experts across product teams, we noticed that 2025 has brought a curious discrepancy: while product teams declare a need for systemized, AI-powered analytics, they struggle with structural challenges and data bottlenecks making effective implementation impossible.
Key Research Questions
- What are the product teams' most pressing analytics needs and problems?
- How can AI best help them solve these problems?
- How can teams reach sufficient AI adoption maturity to leverage AI to its full potential?
What You'll Find Below
- State of product analytics in 2025: Use cases, top needs, AI readiness
- The product analytics challenges: How to get deeper insights and do more with less?
- The AI-driven analytics transformation: Bridging the gap between data and action
- Predictions and recommendations for 2026 product analytics
All the insights are based on a blend of quantitative data analysis from the Userpilot Customer Survey (Nov 2025), with 194 respondents across 160 companies and 22 countries.
The survey findings are complemented by expert interviews with industry leaders: Deborah Chang (Product Manager, Business Insights, PagerDuty), Ibrahim Bashir (SVP Product, Ontra), Lisa Ballantyne (UX Researcher, Userpilot), Beth Bourg (Director of Product Marketing, Tackle.io), and Dhaval Shah (Senior Director of Product Management, Reversing Labs).
State of Product Analytics in 2025
In 2025, product analytics were widely used across different teams and functions. Our survey shows that professionals in roles spanning product management, UX/UI design, customer success, marketing, and engineering work with Userpilot hands-on. This is a good sign of product analytics becoming more accessible, and product-led companies recognizing its importance in driving long-term growth.
Primary Use Case for Product Analytics
The data on exact use cases of product analytics tools reveals that many companies still haven't implemented data-driven workflows across the whole product lifecycle. Teams use product analytics to understand feature adoption (50%), as well as to improve onboarding or activation (28%). The two remain the primary use cases across all roles and industries.
This means product analytics tools are used to drive initial product value. Deeper metrics, such as retention and churn, or roadmap and stakeholder validation, remain underutilized.
Top Issues with Product Analytics
These issues may stem from the very first step: data collection. Our respondents recognized "inconsistent tracking/missing events" (33%) and "too much data with insufficient insights" (24%) as their top issues.
Teams need to learn how to trust and effectively capture their data before they can move to building fully data-driven pipelines.
Top Product Analytics Needs
When asked about their top product analytics needs, our respondents listed the following:
55%
Automated Reporting
47%
AI Insights
44%
Predictive Analytics
35%
A/B Testing
26%
Data Sharing
While the exact percentages differ across functions, the priorities remain the same. This shows that the raw data is there. The insights, not so much. Users recognize the necessity and urgency of having actionable recommendations readily available.
"If my analytics tool could do one thing automatically, it would be to surface actionable insights without manual digging. I would love a feature that proactively detects anomalies, highlights trends, and recommends actions based on user behavior and performance data... This would help me make faster, data-driven product decisions and focus more on strategy than manual analysis."
- Athira Nair, Product Manager at Symtrain
AI Adoption and Readiness
AI-powered tools carry a promise of mitigating most of those issues. They can introduce the following improvements:
Clearer outlook on high-quality data
Enablement for fast actions
Actionable recommendations and predictions
Autonomous acting
However, AI adoption across product teams is still lagging as of November 2025. On average, only 39% of our respondents declared that they were already using AI capabilities, while 36% claimed they were exploring, but hadn't implemented yet. At the same time, only a small minority, 8%, said AI wasn't their current priority. These responses indicate strong readiness and appetite for AI-driven analytics.
Unsurprisingly, we found the biggest number of AI adopters in the IT industry (closely followed by industrials), where 43% of our respondents declared already using AI. Healthcare, consumer discretionary, and financial still haven't adopted AI into their analytics workflows.
Ultimately, product teams in 2025 want to be AI-ready starting now, but few have actually reached that maturity yet.
The Product Analytics Challenges
How to Get Deeper Insights and Do More with Less?
Despite the growing capabilities of product analytics tools, professionals still struggle to keep up with the growing pace and demands of the market. Through our research, we have identified three core limitations stemming from the architecture of traditional analytics tools: The Depth Ceiling, The Speed Trap, and The Clarity Crisis.
The Depth Ceiling
Why Teams Struggle to Go Deeper
33% of our survey respondents named inconsistent tracking/missing events as their top issue. But beyond tracking, teams hit a "depth ceiling" where tools show what happened, but obscure why.
- Obscured but Crucial Behaviors: Teams struggle because low-frequency or unexpected user behaviors are often collapsed into generic "Other" nodes. These valuable edge cases and potentially important user path patterns may represent missed opportunities for growth.
- Slowing Analysis with Exports: Because many tools lack native controls for ratio-based analysis or property-level aggregations, teams are forced to export data to spreadsheets. This manual process slows down analysis and introduces human error.
"Interpreting data and finding the signal in the noise is still a high-value skill(...). The hope is that tools can evolve to a point where even novice data explorers can find meaningful insights."
– Ibrahim Bashir, SVP Product at Ontra
The Speed Trap
Why Teams Can't Act Faster
The time from asking a question to getting an actionable insight involves too many steps and data friction.
- Distorted Behavior Reporting: Legacy funnels often use calendar windows (day/week) to measure conversion, but user behavior happens in sessions. This mismatch leads to approximate answers regarding how long activation truly takes.
- Inflated Active User Metrics (MAU/WAU): Counting system or background events can artificially inflate active user metrics. When teams don't trust their "actives," decision-making around retention and adoption grinds to a halt.
- Slow Segmentation: Interviewees emphasized that creating multiple segments to answer targeted questions (e.g., "How do enterprise users behave?") slows down the iteration process. It can take hours to answer basic product questions due to manual filtering requirements.
"Metrics that drive action are fundamentally easier to understand, easier to debate, and easier to track(...). If a decision maker needs to wait on a data analyst/scientist to corroborate what they think the metric is telling them, that slows down action, all the way to inaction."
– Ibrahim Bashir, SVP Product at Ontra
The Clarity Crisis
Why Product Teams Can't See Clearer
Clarity is lost when analytics are cluttered, inconsistent, or scattered.
- Dashboard and Tool Sprawl: Tool sprawl is the norm, with teams using over 10 tools on average for product, analytics, and engagement. This leads to fragmented storytelling where no single source of truth exists.
- Fragmented Insights: 41% of PMs say their insights are spread across too many views (funnel, trend, retention, path), making it difficult to create a single narrative.
- Cognitive Overload: Complex dashboards reduce decision confidence. Executives struggle to interpret cluttered analytics environments, leading organizations to shift away from static dashboards because they often hide insight behind complexity.
"Many PMs still lack a streamlined way to connect data with business impact(...). Building confidence in data means not just knowing what the numbers are but understanding what they mean for the product's value and direction."
– Deborah Chang, Product Manager at PagerDuty
How can Userpilot help with Analytics Depth, Speed and Clarity?
The AI-driven Analytics Transformation
Bridging the Gap Between Data and Action
All the experts we interviewed agreed that skillfully used AI can help solve the problems of Depth, Speed, and Clarity.
AI for Improving and Streamlining Data Analysis Workflow
Our survey respondents named automated reporting and AI-generated insights as the most pressing product analytics needs. AI is expected to automate the data loop, leaving product managers with more confidence and time to act.
We see AI integrating into the product workflow across four crucial phases, around which we designed Userpilot's Product Growth Agent:
Identifying events
AI automatically defines and tracks relevant user events, streamlining the data foundation.
Drawing insights
AI draws sophisticated correlations and synthesizes complex insights, reducing uncertainty.
Suggesting actions
AI moves from reporting to recommending specific, high-leverage actions a team should take within the product experience.
Actioning autonomously
AI begins to execute certain prescriptive actions, such as triggering an in-app guide or a workflow, without human intervention.
"AI shines in surfacing the next best insight—without requiring teams to dig through dashboards or pivot tables. It accelerates speed to action by identifying trends, anomalies, or opportunities proactively."
– Deborah Chang, Product Manager at PagerDuty
AI as an Efficiency Multiplier
AI ensures scalability that helps teams achieve results better and faster. By identifying the biggest revenue drivers automatically, AI increases work speed and accuracy.
AI as a Lever for Non-Technical Teams
AI democratizes access to data. As Bashir states: "You shouldn't have to be a SQL or chart expert to ask questions. The hope is that tools can evolve to a point where even novice data explorers can find meaningful insights."
"Increasingly, design and engineering teams are looking at the raw data to contribute to those insights... This shift has made collaboration in product development much more efficient as there's a shared understanding of user behavior."
– Lisa Ballantyne, UX Researcher at Userpilot
Predictions and Recommendations for 2026 Product Analytics
Incorporating AI into product analytics is inevitable to keep up with growing professional and market demands. The data in this report validates that the time to adopt AI is yesterday.
Here are some recommendations for 2026.
Here are some recommendations for 2026.
Introduce Mature AI Tools into Analytics workflows
Investing in a mature AI-powered product analytics platform that can support the whole analytics platform from the get-go proves to be the shortest path to success. Userpilot recommends adding AI via three autonomy levels:
- Observe (surfacing insights)
- Copilot (drafting solutions)
- Autonomous (running the loop)
Establish Product Analytics Rituals
For the AI engine to run smoothly, you have to make data-driven decisions part of regular planning. Ibrahim Bashir underlines the necessity of data rituals: "The biggest delta between teams that do analytics theater vs. live the mindset is rituals. (…)The first and most important step is to define a clear North Star metric, and from there get into a cadence of reviewing how the metric moves and debating the drivers."
Prioritize Deep Research
AI enables quick development, but that poses a threat of shipping without purpose. Relying on human judgment remains the number one differentiator. As Deborah Chang states, "Teams must treat AI as a decision-support system, not a decision-maker—(…)layering in human judgment to avoid blind spots or bias.”
Measure Success by Impact
Teams need to acquire business fluency. Winning organizations will master cross-functional alignment, connecting product strategy seamlessly to GTM execution with clearly defined metrics.
Eradicate Organizational Misalignment
Organizations need cross-functional alignment where all teams understand the meaning behind the collected data. No-code, AI-enhanced tools can make insights accessible to less data-savvy users.
Success in 2026
Product teams in 2025 still struggle with gaps in data collection, insufficient insights, and growing expectations. The primary limitation product teams face is not a lack of skill or data, but the architecture of the tools they rely on.
In 2026, the solution lies in building analytics capabilities that strengthen the foundations of insight-driven work. This will begin with:
Depth
Inline formulas, property-level aggregations, and multi-breakdown support to understand the "why" behind the data.
Speed
Session-based analysis, refined MAU definitions, and streamlined filtering to shorten the question-to-answer cycle.
Clarity
More intuitive visual patterns and clear report organizations to drive alignment.
The future of product analytics focuses on connecting insight to action directly, with AI as a decision-support system (not a decision-maker) that accelerates speed by proactively identifying trends and anomalies. Successful product teams will operate with genuine confidence, backed by a foundation that is transparent, session-aware, and deeply analytical.