I'll be honest: one number from the latest embedded analytics research stopped the entire planning conversation for this webinar. 57% of teams with embedded analytics report no measurable business impact, and that means not low impact or underwhelming impact, but no measurable impact at all.
That stat set the stage for a candid, wide-ranging conversation between Ivan Seow from ThoughtSpot and Jeff Rubinson, VP of Product at Endpoint Clinical, during the June 25 webinar "Bridging the Embedded Analytics Revenue Gap."
What followed was part research briefing, part real-world case study, and part honest advice for anyone building analytics into a product. Here’s what stood out.
The Revenue Gap Is Real, and It’s Not About Adoption
If embedded analytics was struggling because nobody used it, the fix would be straightforward: improve the product, drive adoption, measure results. But the Product Led Alliance Embedded Analytics Opportunity 2026 Report featured in the webinar tells a different story.
71% of product leaders are already building or using embedded analytics. 50% report increased engagement and stickiness. People are using the dashboards, clicking around, spending time.
So why does more than half the market still see no measurable ROI?
Most product teams haven't calculated the real cost of their embedded analytics approach.
As Parul Jain, Principal PM at Walmart, put it: "The gap exists because many teams measure usage, not business impact."
Ivan echoed this during the webinar: "ROI doesn't come from just usage, but it comes from impact."
The distinction matters. Tracking page views, session times, and click rates tells you whether someone opened a dashboard; it tells you nothing about whether that dashboard changed a decision, a behavior, or a business outcome.
Three Problems Hiding Behind One Gap
Ivan walked through the research and identified three root causes that explain why teams with healthy usage numbers still can't prove ROI.
1. A Problem of Focus
Too many teams treat analytics as a feature checkbox rather than a product strategy. When analytics is positioned as "nice to have" rather than a competitive differentiator, it gets the attention (and the engineering investment) of a nice-to-have.
2. A Problem of Tools
Static dashboards can show you what happened, but they can't tell you whether anyone acted on what they saw. If your analytics experience ends at a chart, you have no way to measure whether the insight influenced a downstream decision.
3. A Problem of Instrumentation
Teams are measuring the wrong things. When your success metrics are clicks, views, and time on page, you are optimizing for engagement theater rather than business outcomes.
Did the analytics change a decision? Did it alter a behavior? Did it produce a measurable result?
Those are harder questions to answer, and most instrumentation isn't set up to answer them.
Endpoint Clinical's Story: 15 Years of Building, Three Months of Shipping
This is where the conversation shifted from research to lived experience. Jeff Rubinson has spent 15 years at Endpoint Clinical, which has operated roughly 2,500 clinical trials across 90+ countries.
Clinical trial supply typically accounts for 10-30% or more of the total trial budget, so getting the analytics right has real financial consequences.
Jeff didn't sugarcoat the history. "As a product leader, I always say there's a couple of things you don't want to do internally," he said. "One is analytics."
Over those 15 years, Endpoint tried building analytics in-house multiple times. Each attempt ran into the same compounding problem: building analytics is not a one-time investment.
Every data model evolution, every new customer expectation, every AI capability that your users start asking about adds another layer of maintenance. And every month your team spends maintaining that infrastructure is a month they are not spending on the product that makes your company unique.
Jeff framed the real cost clearly: "It's not just the cost that you save for the implementation and the maintenance and the continuous support. But it's also the value that is lost by not being able to focus on your core competency."
The Decision to Buy, and The Speed That Followed
Endpoint signed with ThoughtSpot in December 2025 and launched Elo AI, their new analytics experience, in March 2026. Three months. With approximately 1.5 full-time resources dedicated to the implementation.
What does Elo AI include? Conversational AI that lets clinical trial managers ask questions of their supply chain data in plain language, deep analysis mode for more complex investigations, and actionable recommendations that connect insights directly to decisions.
Jeff described the customer reaction: "When we demo this solution, it's almost like magic to some of our customers."
That three-month timeline reframes what's possible for any product leader who has been told, or has assumed, that embedded analytics is a two-year build cycle.
It also underscores a pattern the research confirms: teams on the right side of the ROI gap have done four things differently:
They replaced static dashboards with interactive, user-driven analytics
They delivered intelligent analytics experiences in months rather than years
They focused engineering resources on their core product
And they closed the loop from insight to action inside the product itself
The AI Dimension: Planning Versus Production
The webinar surfaced a tension that many product leaders will recognize. 79% of product leaders say AI has fundamentally changed how they think about building, buying, and deploying analytics. 91% plan to invest in AI in the next 12-18 months. But only 14% have actually shipped AI-powered analytics to customers.
Why is the gap between planning and production so wide? Ivan pointed to what he called the "two impulses" AI creates. The first impulse is to build everything yourself because AI tools make it seem possible.
The second, less obvious realization is that AI actually raises the complexity bar. Building a reliable natural language interface, an embedded agentic experience, proper security and governance, performance at scale: these are not weekend projects, and they compound fast.
Jeff's advice to other product leaders was direct: "Do what you do best. Focus on your data, focus on your core competencies, and then leverage the experts to do what they do best. Because it's not a one-time implementation. It's a lifestyle."
What Users Actually Want
The research gave a clear signal on what end users are asking for. 74% want real-time data access. 71% want to intuitively find answers for themselves rather than waiting for a report. And 29% want to take action directly from analytics into the other tools they use.
That last number is worth sitting with. Nearly a third of users are already asking for analytics that connects to action, not just information. If your analytics experience ends at a chart with no path to a decision or a workflow, you are building for a shrinking portion of user expectations.
Three Takeaways Worth Carrying Forward
If you are evaluating your embedded analytics strategy or deciding whether to build or buy, this webinar surfaced three ideas worth remembering.
1.Measure What Matters
Usage metrics are not ROI metrics. If you cannot trace a line from your analytics to a decision, a behavior change, or a business outcome, you are measuring the wrong things.
2. Speed Is a Strategic Advantage
Endpoint went from contract to launch in three months. That timeline didn't just get their product to market faster; it freed their engineering team to focus on the clinical trial supply chain problems that only Endpoint can solve.
3. The Build-versus-buy Math Has Changed
AI capabilities have raised the bar for what users expect from embedded analytics. Building and maintaining a conversational AI layer, an agentic experience, and a governed data pipeline on your own is a compounding commitment.
For most teams, the smarter path is to focus your engineering on what differentiates your product and let a purpose-built platform handle the analytics layer.
As Jeff put it: "It's a lifestyle." The question is whether you want that lifestyle to be maintaining analytics infrastructure, or building the product that sets your company apart.
Read the Endpoint Clinical case study here or watch the webinar on-demand.
