The question AI still can’t answer about product management | Cengage Product SVP

Ghazal Badiozamani is the SVP of Product Management at Cengage, one of the world’s largest education companies with roots in publishing that go back over 100 years. Before Cengage, she spent eight years at Elsevier helping legacy businesses understand and adopt emerging technology, starting with machine learning before it was mainstream, eventually building a digital team from scratch and launching a digital authoring system from nothing. In this episode of the CPO Rising series, Products That Count Resident Chief Product Officer Renee Niemi sits down with Ghazal to talk about what it takes to lead product inside a company that has existed for over a century, why the squad model may be approaching a fork in the road, and her take on the question AI still can’t answer about product management: what does product even mean now?

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Show Notes:

1. Ghazal fell into product management from a strategy role at Elsevier, where she was tasked with solving problems across business units. She kept gravitating toward the technology group, began teaching executive teams about machine learning before it was mainstream, turned those conversations into funded business cases, and after the third one, her CEO told her to stop asking for money and go build it herself.
2. What drew her to Cengage was the chance to work again with a former colleague who had become chief digital officer there, and the opportunity to drive mission-critical transformation at global scale inside an institution that has existed for over 100 years. She describes legacy companies as an intellectual challenge: figuring out how to make something ancient relevant for the next 20 to 50 years.
3. Leading product inside a legacy company is fundamentally different from working at a startup because you are not starting with a blank slate. You are moving a massive ship from one way of doing things to a completely new one, and doing it with people who have spent years or decades doing it the old way. That requires a growth mindset from everyone, and the day-to-day work of a product leader becomes as much about coaching as about building.
4. Ghazal’s experience is that what starts as a product problem almost always ends as a people opportunity. The technical transition is rarely the hard part. The hard part is helping tenured employees who are good at their jobs understand why and how those jobs need to change, and creating conditions where they want to make that change rather than resist it.
5. When someone on her team cannot or will not adapt to a new way of working, Ghazal frames their departure as a positive outcome rather than a failure. The moments she finds most rewarding are when someone she has been coaching for a long time becomes a high performer and moves on to a bigger opportunity she could not provide.
6. The most valuable thing a product leader can do is bring the outside in: continuously scanning what other companies are doing, what products outside the industry are gaining traction, and what can be learned and applied internally. AI has intensified that obligation because now product leaders have to learn and translate a rapidly evolving technology on top of everything else.
7. AI has moved through three distinct phases in how product organizations think about it. First, efficiency: using AI to summarize customer feedback and product workflows. Second, the product development process itself: do you even need a PRD, or can you just build a prototype? Third, the deepest question: what does product actually mean when users no longer navigate deterministic step-by-step flows?
8. The mental model shift required of product teams is significant. Instead of designing menus and pathways that guide users linearly through a system, teams are now orchestrating networks of agents that create experiences that feel free-flowing to the user even though they are structured underneath. Building for nonlinear human thinking, rather than imposing linear structure on it, is the new core design challenge.
9. On team structure, Ghazal’s team still runs on the Spotify squad model with triads and outcome-based metrics. The underlying principles of discovery and agile delivery have not changed. What has changed is the pace: work that used to take three to five months now takes two, and expectations of how quickly value should be delivered have shifted accordingly.
10. She is actively thinking about whether the squad model is still the right model going forward and sees two plausible futures. In one, the product manager and designer work together to build a front end using tools like Codex and hand it to an engineer to wire to a backend. In the other, a fully equipped engineer builds everything while the product manager operates as a purely strategic role, focused entirely on understanding the market and directing without ever touching a keyboard.
11. She pushes back hard on the idea of removing design from the product triad, even in leaner or turnaround contexts where the pressure is to cut. Her argument is about cognitive styles: the product manager brings commercial acumen, ownership, and the ability to align stakeholders; the engineer brings structured analytical thinking and risk discipline; the designer brings empathy and the sensitivity to notice the gap between what customers say and what they actually do. Removing any of those creates a product that is visibly missing something.
12. The analogy she finds most useful for AI’s impact on product is the shift from screwdriver to electric drill. The core principles of what you are trying to build have not changed. The tool is dramatically more powerful and enables things that were previously unimaginable. The job is to learn how to use the new tool without losing sight of the principles that were already working.
13. Development speed is no longer the primary constraint. A startup founder she advises recently suggested that sprints should be one day long, and she thinks the field is heading in that direction. But she is equally focused on a different constraint: how much change users can actually absorb. As product updates become easier to produce, the risk of overwhelming users grows faster than the benefit of building faster.
14. She sees customer adoption as the emerging bottleneck that will reshape how product teams think about release cadence. Just as the explosion of content has not led to more consumption because people cannot process it all, an acceleration in product delivery does not automatically translate into more user value. Building for humans means respecting human limits on change absorption.
15. The CPO alpha effect, the idea that companies with excellent product leadership consistently outperform their peers on investor returns, resonates directly with why she moved into product. She believes the reason is that great product leadership requires two capabilities almost never found in the same person: the ability to think deeply about where the industry is going and articulate a compelling vision, and the ability to execute with high standards and drive fast, innovative delivery.
16. Her own edge as a leader comes from starting in strategy, which gave her a foundation in structured analytical thinking. She pairs that with being demanding of her teams in a way she describes as ambitious but supportive: she expects a lot from herself and asks her teams to expect a lot from themselves. The combination of strategic clarity and execution discipline is what she believes drives results.
17. Ghazal’s favorite tool is product analytics software, which she uses almost every day. The specific approach she adapted from a marketing colleague is to map user movement through the platform as a conversion funnel, tracking not just standard metrics like monthly active users but the percentage of users who move from one defined point of value to the next. That kind of dashboard surfaces opportunities and flags problems faster than any other method she has found.
18. The funnel dashboard has served two distinct purposes on different teams: it reveals where to invest next when conversion rates look wrong, and it functions as a health monitor that catches problems early. On one team, a sudden influx of users from a single region caused success rates to drop visibly on the dashboard, allowing the team to identify and respond before it became a serious problem.
19. What makes a great product leader, in her view, is the combination of conviction and deep listening. She jokes that product managers do not actually do anything since engineers build and designers map. But that is also why the ability to carry a strong sense of ownership across a wide range of stakeholders, and to herd everyone toward a shared goal without direct authority, is such a rare and valuable skill.
20. When she asks her AI agent how to use AI, it gives excellent step-by-step procedural answers but struggles with the human dimension: how agents change the way people think, how to help teams adopt AI tools while staying curious and still doing real thinking themselves, and how to avoid turning people into managers of agents rather than independent thinkers. Those questions, she finds, are better answered by conversations with peers than by any AI tool currently available.