AI can accelerate a strong operating model, but when decision rights, incentives, or data are already unclear, it can make the mess spread faster.
In this episode, I sit down with Denise Tilles, a leading voice in product operations, to unpack how her career moved from editorial work at Condé Nast into product management, commercial leadership, and eventually product operations. Denise shares how learning to work with revenue data, product analysts, and operating models changed the way she thought about product leadership and led to her work helping enterprise organizations make faster, better-quality decisions.
We explore what product operations actually does, why an operating model needs to define how decisions get made, and where incentives can quietly undermine even a well-designed process. We also dig into what happens when AI makes producing documents, specifications, and analysis nearly effortless: generating more output doesn't remove the work of judgment. In many cases, it makes clarity about inputs, outputs, ownership, and what “good” looks like even more important.
Key Takeaways
- Product ops should improve decision-making: Denise defines it around business and data insights, customer and market insights, and the operating model.
- Commercial context changes product thinking: At Cision, learning P&L, ACV, and recognized revenue helped Denise connect product decisions directly to business outcomes.
- Good analysis reveals hidden opportunities: A product analyst uncovered an add-on opportunity that generated roughly $1 million within a year.
- Operating models need clear decision rights: Teams need to know what gets worked on, who decides, and how work actually gets done.
- AI amplifies the system already in place: If ownership, data, or processes are unclear, AI can make those weaknesses spread faster.
Additional Insights
- Informal decisions can override formal processes: Denise learned that hallway conversations and executive requests often mattered more than the documented workflow.
- Proof does not create authority: Better analysis can earn credibility, but changing a system still requires ownership, sponsorship, and permission.
- Incentives shape behavior: Product and sales can both act rationally while optimizing toward conflicting measures of success.
- AI can shift work downstream: Faster artifact creation still leaves someone responsible for checking the reasoning, evidence, and assumptions.
- Operations may become more connected: Denise sees product ops, design ops, sales ops, and other functions moving toward a more unified “Omni Ops” model.
Episode Highlights
00:00 - Episode Recap
Denise explains why the starting point for operating-model and AI work should be the pain a company is actually experiencing, from PRD structure to data quality, rather than adopting AI simply because the technology is available.
02:01 - Guest Introduction: Denise Tilles
I introduce Denise Tilles and her work in product operations and operating models, setting up our discussion about data, decision-making, incentives, and how product organizations can operate more effectively.
03:13 - From Editor to Product Manager
Denise traces her move from media and content strategy at Condé Nast into product management, a role she initially had to define for herself because the discipline was still relatively young.
04:50 - Learning the Economics of Product
Moving to Cision gave Denise access to commercial data she had never had before, pushing her to learn from the CFO and understand product through revenue, P&L, ACV, and business outcomes.
08:41 - The Analyst Who Changed the Team
Denise explains how hiring a product analyst gave her team more objective insight into customer and product data, including an overlooked opportunity that generated roughly $1 million in its first year.
12:55 - Finding Revenue Hidden in Behavior
I share how an analyst at Lastminute.com identified a collapse in same-day booking conversion after 7 p.m., giving the team a specific customer behavior to investigate and improve through experiments.
15:14 - The Three Pillars of Product Ops
Denise defines product operations through business and data insights, customer and market insights, and the operating model, all designed to help product managers make faster and better-quality decisions.
17:01 - The Process You Don't See
Denise describes discovering that documented processes were often competing with informal conversations and executive requests, revealing why operating models need to account for how decisions really get made.
19:59 - Who Actually Gets to Decide?
At its core, Denise says an operating model defines what a company works on, who has the right to decide, and how the work gets done within the organization's real constraints.
24:41 - Operating Models Need Owners
Denise warns against treating an operating model as something you publish once and forget, because without clear ownership, maintenance, onboarding, and reinforcement, the system quickly falls out of use.
25:48 - Incentives Beat Better Process
A mismatch between how product and sales were measured taught Denise that people naturally optimize around their incentives, even when that produces conflicting outcomes for the wider business.
29:09 - AI Makes Drive-By Requests Harder to Reject
Denise explains how a senior leader's opinion can now arrive with an AI-generated specification, data, and outcomes attached, making an untested idea look more rigorous without necessarily improving the underlying thinking.
33:22 - Define What Good Looks Like
As AI increases the volume of work teams can produce, Denise argues that operating models need clearer standards for what should be created, what evidence belongs in it, and how colleagues are expected to consume it.
35:33 - Your Output Is Someone Else's Input
We explore the value of looking at work end to end, because one team's output often becomes another team's input and localized optimization can create problems elsewhere in the system.
38:56 - Use AI Where the Pain Justifies It
Denise is increasingly advising companies to use less AI than they initially expect, starting instead with the problem, the value AI might add, and the human judgment and context that still need to remain in the loop.
41:10 - From Product Ops to Omni Ops
Denise looks ahead to a more connected model where operational disciplines work across functional boundaries, allowing companies to design the engine of operations as one system rather than a collection of independent silos.
42:09 - Closing Reflections
I close by encouraging listeners to explore Denise and Melissa's Product Ops and to think of their own organizations as systems that can be deliberately redesigned and experimented on.
FAQs
What is product operations?
Denise describes product operations as helping product managers make faster and better-quality decisions. Her model has three pillars: business and data insights, customer and market insights, and the operating model or ways of working that support product teams.
What is a product operating model?
At its simplest, Denise says an operating model determines what a company decides to work on, who gets to decide, and how the work gets done. Every organization has one in practice, but many have accumulated theirs informally rather than designing and communicating it intentionally.
How does AI affect product operations?
AI can accelerate activities across product operations, but Denise argues that judgment and context remain essential. When organizations apply AI to an unclear operating model, poor data, or unresolved decision rights, the technology can amplify those existing weaknesses rather than solve them.
Why do incentives matter when designing an operating model?
People tend to optimize around what they are measured on. Denise experienced this when product was focused on recognized revenue while sales celebrated closed contracts, creating different definitions of success even though both teams were acting rationally according to their incentives.
How should a company decide where to use AI in its operating model?
Denise starts with the pain points rather than the technology. She looks at issues such as PRD structure, data analysis, source quality, ownership, and decision-making first, then asks whether AI genuinely improves that part of the system and where human judgment still needs to remain.
Useful Resources
- Product Operations — the book Denise co-authored with Melissa on building the systems and capabilities that help product managers make faster and better decisions.
