You’re accountable for 5M EUR in ARR. You can’t see the retention dashboard. The BI team says your request is “in the queue”, estimated at 3–4 weeks. Welcome to revenue-accountable Product Ownership.
A few days ago, I wrote about how 92% of product leaders now own revenue outcomes — more than double from just a few years ago. Organizations restructured accountability without restructuring access. They gave Product Owners CEO-level responsibility for business results while maintaining coordinator-level access to business data.
That gap — between accountability for revenue and access to revenue data — has been one of the sharpest contradictions in modern product operating models. Until now.
What Changed in 2025
Generative AI is accidentally solving the data access problem. Not because organizations suddenly care about Product Owners having the right instruments to measure what they’re accountable for. But because Gen AI architectures require something traditional BI models fought against for decades: data democratization.
65% of organizations are now adopting AI-powered analytics, and the infrastructure they’re building to support conversational queries and embedded intelligence is breaking down the gatekeeping models that kept Product Owners locked out of their own metrics.
The Brazilian retailer Grupo Casas Bahia cut analysis time from 5–6 hours to 2 minutes using AI-powered self-service analytics. That’s not a marginal improvement. That’s the difference between “submit a ticket and wait” and “ask a question and get an answer.”
When you can query retention rates, activation metrics, or revenue trends in natural language and get real-time responses, you’re no longer dependent on the BI team’s sprint schedule. You’re operating with the instruments that match your accountability.
The Irony Nobody’s Talking About
Companies aren’t doing this for Product Owners. They’re doing it because Gen AI requires distributed data access, real-time dashboards, and conversational interfaces to function. Product Owners are benefiting from infrastructure built for AI initiatives, not organizational empathy.
90% of enterprise decision makers identify predictive analytics as central to strategic objectives. 41% of large enterprisesare using AI to improve collaboration across teams. The driver isn’t “let’s fix the Product Owner accountability gap.” It’s “let’s make AI work.”
The result is the same: Product Owners finally have access to Current Value metrics — revenue, retention, customer satisfaction — without filing tickets or waiting weeks. But the path to get here was accidental.
Thanks, I guess?
What This Actually Enables
Evidence-Based Management defines four key value areas that organizations need to measure: Current Value, Unrealized Value, Ability to Innovate, and Time to Market. For years, Product Owners have been accountable for improving these metrics without the ability to see them in real time.
AI-powered analytics changes that equation:
Current Value: Real-time access to revenue, retention, activation rates. No more “we’ll have those numbers next sprint.”
Unrealized Value: Direct access to market data, competitive intelligence, customer feedback patterns. The ability to spot gaps and opportunities without intermediaries.
Ability to Innovate: Measurement of experiment velocity, learning cycles, feature adoption. The capacity to run tests and see results without BI gatekeepers.
Time to Market: Tracking cycle time from concept to customer value. Visibility into where delays actually occur.
Product professionals are saving an average of 33 hours with AI across core functions. That’s not about efficiency. It’s about access. Access to insights that used to require specialized teams, SQL knowledge, or weeks of waiting.
When Clari reports that AI will guide decisions at every level, they’re describing distributed decision-making infrastructure. That infrastructure requires data access at every level. Including Product Owners who’ve been accountable for revenue without being able to measure it.
The Reality Check
Before we get too optimistic: only 1% of companies consider themselves “AI mature”. 42% of companies abandoned most AI projects in 2025 — up from 17% the year before. Enterprise AI initiatives achieved an average of just 5.9% ROI.
Data access ≠ decision rights
Data access ≠ funding models
Data access ≠ organizational authority
You can see the retention dashboard now. That doesn’t mean you can act on what it shows. If your organization still operates with project-based funding, output-focused governance, and hierarchical decision rights, having access to revenue data just makes the contradiction more visible.
The expectation is still mini-CEO. The reality is still feature coordinator — now with better dashboards.
The Organizational Architecture Question
In my previous piece, I identified four structural gaps between revenue accountability and organizational reality: decision rights, governance models, funding approaches, and measurement systems.
AI is solving the measurement problem. The other three remain untouched.
The real question isn’t whether Product Owners can access data. It’s whether organizations are restructuring decision authority and governance models to match. Are you allowed to kill a feature based on what the retention data shows? Can you reallocate resources when the experiment results come back negative? Do you have the autonomy to act on the evidence you can now see?
Or are you just producing prettier reports for stakeholders who still make the actual decisions?
Data democratization makes evidence-based Product Ownership possible. Whether organizations allow Product Owners to practice evidence-based Product Ownership is the remaining gap.
What Comes Next
For the first time, revenue-accountable Product Owners have the instruments to measure what they’re accountable for. That’s progress. Real progress.
But infrastructure change doesn’t fix organizational architecture. Access to data doesn’t grant authority to act on data. Self-service analytics doesn’t restructure governance models or funding mechanisms.
AI solved the data access problem by accident — a side effect of building conversational interfaces and embedded intelligence. The harder problems — decision rights, governance, funding — require intentional organizational design.
The good news: you can finally see the metrics. The bad news: seeing them makes it obvious when you’re not allowed to act on them.
The path forward is clear. Build organizational capability that matches the accountability you’ve assigned. If Product Owners own revenue outcomes, give them revenue authority, not just revenue dashboards.
The infrastructure is ready. The question is whether the organization is.
Ralph Jocham is Europe’s first Professional Scrum Trainer, co-author of “Professional Product Owner,” and contributor to the Scrum Guide Expansion Pack. As an ICF ACC certified coach, works with organizations to build Product Operating Models where strategic clarity, operational excellence, and adaptive learning create measurable competitive advantage. Learn more at effective agile.