CPO Rising Series: IBM CPO on Building Products for a World That’s Five Years Away

How do you make product bets today for a world that will not arrive for another six years? In this episode of the CPO Rising series hosted by Products That Count Resident CPO Renee Niemi, IBM Z and Linux One Chief Product Officer Tina Tarquinio will be speaking on building products for a world that’s five years away, and how she and her team manage hardware, firmware, and software roadmaps across multiple time horizons at once, from quantum-safe encryption to AI inference built directly onto the chip. Tina also shares how IBM’s shift to design thinking transformed its product culture from a checkpoint-driven process into a client-obsessed discipline, why usage data matters more than revenue as a product metric, and what it actually took to grow a flagship product line more than 4x over a decade.

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Show Notes:

1. Tina has spent over 26 years at IBM, starting by writing microcode before moving through benchmarking and operating systems work that gave her early exposure to client feedback. That early taste of understanding what clients actually needed, as opposed to what engineers assumed they needed, sparked a perspective on product that she has carried through her entire career.
2. IBM Z and Linux One are the backbone of the global financial system. Ninety percent of the world’s credit card transactions and seventy percent of all global transactions by volume flow through an IBM mainframe, built for extreme resiliency with roughly one hour of downtime every eleven thousand years and near-unlimited scale with minimal performance impact.
3. The IBM Z team builds end to end, from the processor chip through the firmware, operating system, and application layer, which Tina describes as being responsible from chip to chip. That full-stack ownership creates a product planning challenge unlike almost any other in the industry because decisions made at the hardware level have to anticipate market needs years before anyone else can see them.
4. Hardware planning at IBM Z operates on a five to six year horizon, with new processor generations releasing roughly every three years, which means the team is always working on at least two or three generations simultaneously. A capability introduced in a 2022 product launch required the initial design decisions to be made around 2018, which means the team had to bet on quantum-safe encryption and AI inference before either was an obvious market requirement.
5. Quantum-safe encryption was introduced in the Z16 in 2022 at a time when many observers raised their eyebrows because quantum computers were not yet capable of breaking current encryption. IBM knew from experience with previous clients that changing encryption algorithms is a heavy lift requiring years of preparation, and it correctly anticipated that regulatory mandates requiring quantum-safe encryption by 2030 would follow. Getting that decision right required acting in 2018.
6. AI inference capability was built directly onto the IBM Z processor and introduced in 2022, before ChatGPT made generative AI a mainstream conversation. The use case was not speculative: clients were already sending inferencing scoring off-platform to handle transaction fraud detection, and bringing that on-chip eliminated the latency and security exposure of routing transaction data externally.
7. IBM’s product management function underwent a fundamental cultural shift when it adopted design thinking as its operating model, moving away from a checkpoint-driven process where the job was simply to advance a deliverable from step one to step two. The shift reoriented everything around understanding the outcome clients were trying to achieve and then working backwards to features, which made the role meaningfully more engaging and more rigorous at the same time.
8. The three-in-the-box model, where design, product management, and engineering operate as a unified leadership team rather than sequential handoffs, has been central to how IBM Z builds products. Tina is explicit that this model only works when trust is high enough that design can sit in on pricing conversations and engineering can participate in client interviews, because everyone needs to understand how their work affects the work upstream and downstream from them.
9. Product managers at IBM Z maintain a shared persona database that the entire three-in-the-box team has access to. Named personas representing different experience levels and roles on the client side give everyone on the team a shared language for who they are building for and why certain decisions matter.
10. When Tina and a new group of product leaders took a fresh look at the IBM Z business around 2013, they made the deliberate choice to treat the mainframe as a growth engine rather than a mature product that simply needed to maintain its existing footprint. That decision required saying no to a wide range of legitimate ideas in order to double down on the two or three areas of genuine differentiation where IBM Z could price and sell for real value.
11. Over the decade following that strategic refocus, IBM Z grew its market capacity more than 4x and delivered program-to-program and year-over-year commercial growth. Tina attributes that directly to the discipline of choosing fewer things and executing them deeply, rather than spreading investment across everything the team was technically capable of building.
12. The hardest part of product leadership, in Tina’s view, is prioritization. Saying no to a great idea that someone has invested in is genuinely painful, and it requires being very clear on the value messages and pricing strategy so the team can evaluate every proposal against the same criteria rather than arguing about individual merits in isolation.
13. On product metrics, Tina believes revenue is a necessary measure but an insufficient one. Usage data, understanding which features and capabilities are actually being adopted and at what scale, is what gives product leaders the flashlight to know where to look next and where not to invest further. She points to the Coca-Cola Freestyle machine as an example of a physical product that functions primarily as a data collection engine about actual consumer behavior.
14. AI will change the day-to-day of product management primarily by automating the tasks that are necessary but not high-leverage: KPI tracking, market research aggregation, and collateral creation. That should free product managers to spend more time on the work that actually requires human judgment, which is talking directly to clients, shadowing how they use the product, and synthesizing qualitative signals that no dashboard can capture.
15. The boundary between product management, design, and engineering is becoming less distinct, and Tina sees that as a positive development rather than a threat to any discipline. The goal is not to collapse the roles but to develop enough fluency across the three that everyone can have a meaningful conversation in each other’s domain without someone having to translate.
16. Listening is the first of Tina’s two pieces of advice for anyone on a path to CPO. That means understanding where the voice of the client enters your world, whether directly or indirectly, and building relationships with everyone upstream and downstream so you understand how your work affects theirs and how to make that effect a positive one.
17. Market awareness is the second piece of advice. Tina cautions against being too narrow about what trends feel relevant to your specific product area. She points to containerization as an example of a seemingly adjacent technology that turned out to reshape where every product could run, and she sees AI as a similar force that is expanding the addressable space for nearly every product, not just the ones built explicitly around it.
18. Product management at its best sits at the crossroads of clients, engineering, and the future, a description Tina acknowledges sounds overwhelming to some people. For those who are drawn to that intersection rather than daunted by it, she believes it is the most exciting role in a technology company because the work is simultaneously shaped by real human needs, real technical constraints, and real bets on where the world is going.
19. Tina recently reread Dare to Lead by Brene Brown with fresh eyes, focusing specifically on how to create authentic leadership cultures across globally distributed teams. Her takeaway was not about her own authenticity but about how to create conditions where leaders at every level feel safe enough to lead authentically themselves, which then cascades down through the organization.
20. She also reread Who Says Elephants Can’t Dance by Lou Gerstner following his recent passing, and found it landed differently after years of leading an IBM business than it did when she first read it as a newer employee. Returning to foundational books with new experience is a practice she recommends, because the same text reveals different things depending on what questions you are currently living with.