Is Judgment the Most Important Skill for Product and Engineering Leaders in the Age of AI?

Find Out What Jose Gonzalez's Path from Six Sigma to AI Reveals About Leading Products as the Rules Keep Changing

Product and engineering leadership today runs on a handful of famous rules and truisms. Streamline everything, keep cutting until something breaks, then add back only what matters and keep the bar high, no matter the cost to speed. These rules make for great advice on a slide, but Jose Gonzalez has spent twenty years learning the harder skill hiding beneath them: knowing exactly when each one applies, when it works against you, and when the moment calls for something else entirely.

Jose is currently VP of Product Management for Acquisition and Discovery at Ticketmaster, and his path there ran through the dot-com bubble as a web developer, quality and production engineering at a Johnson & Johnson company where he earned a Six Sigma green belt, additional product roles at Yahoo, Vevo, EA, Amazon, and even included a Techstars-backed AI startup he co-founded. 

He recently joined EverOps' CEO, Stephen Koza, on Episode 8 of TechPod Talks to discuss what his diverse career opportunities have taught him about leading products as the ground shifts beneath him. His insights throughout the conversation point to the same idea every time, confirming that the most durable skill today is judgment, which is something no framework fully captures, and no truism can replace. 

Read on as we take a closer look at the journey that took him from a Six Sigma flowchart to leading AI strategy at Ticketmaster and the logic that will change how you think about friction, hiring, and the AI moment we are all standing in right now.

The Leaky Bucket Problem Every Product Team Eventually Hits

Six Sigma has a reputation for stripping waste from a system, but Jose took a different lesson from it. Studying the flow charts of a paper-based approval process at a Johnson & Johnson company taught him to ask a simple question instead. Where does a system want faster throughput, and where does it actually benefit from friction built in on purpose? That question followed him straight into product work, and it shaped one of the clearest lessons of his career.

The proof came from an onboarding flow his team built at Vevo. Sign-ups were healthy, but a large share of new users disappeared shortly after joining, revealing a pattern the team classified as a ‘leaky bucket.’ Their first move was to strip the onboarding flow down to the essentials, cutting anything that slowed a new user's path to getting started. Sign-ups climbed as a result, but new users kept leaving just as fast as they arrived, so more sign-ups alone were not fixing the real problem. The team found that adding one small step back into onboarding, something simple the user built or personalized for themselves, gave people a reason to stay. That single piece of friction lifted both engagement and retention, and it became the one step in the flow the team protected instead of trying to remove.

The same judgment applies to the engineering-culture truism Stephen raised, citing the Elon Musk principle of deleting aggressively until something breaks, then adding the critical pieces back. Jose agrees there is real truth in it. The actual skill, he says, sits one level below the rule itself, in knowing when to apply it. While a concept like "don't repeat yourself" holds up most of the time, it has genuine exceptions, and leaders who treat such rules as absolutes lose the nuance that makes them useful in the first place.

"So much of what we do as leaders in product and engineering is knowing when to apply these things and what the right time is."

What Jose is describing is the skill underlying every rule of thumb: judgment. That word ends up anchoring the rest of the conversation, from hiring to AI adoption, every time a clean rule meets a messy situation.

Why Even the Best Hitmakers Can't Manufacture a Hit on Demand

Every media platform Jose has worked on obeys the same distribution. A small set of events, titles, or features drives most of the consumption, and laying out the numbers surfaces a power curve every time. The strategic problem that follows is real because a single hit can carry an entire year, and manufacturing one to order sits beyond anyone's control.

Jose puts the point in music terms that land immediately, observing that a repeatable process for producing hits would turn every studio into a hit factory and every writer into the next chart-topping producer. Hits arrive unpredictably, and a platform that organizes itself purely around its current winners paints itself into a corner, building the entire product around content that the next big hit then fails to fit.

"If it was really a repeatable process to create hits, we'd all be Max Martin, like punching out Taylor Swift hits every year, right? It's just not a thing that you can continue to do."

The fix is building products, capabilities, and systems that support the “hits” when they arrive and actively help new ones climb toward the top, what Jose describes as a wheel that different content rides at different moments. He learned that this framing plays out differently depending on the kind of content involved. Uploading a high-quality video to a distribution platform is straightforward, since most platforms can handle the same file. Porting a video game from one platform to another is far more complex, since each one requires its own version built specifically for it. Live events, however, add a different constraint entirely. A video catalog offers effectively unlimited consumption, but a band on tour can only play so many shows, which puts finite supply at the center of the discovery problem Jose now works on daily at Ticketmaster. That same wheel logic applies to a market where the winners cannot simply be replayed on demand.

Scaling Teams Fast Means Hiring for Horsepower and Knowing When to Move the Bar

Building a team under time pressure is a different discipline from building one at a deliberate pace, and Jose is precise about the difference between the two. A team scaling to meet a new product line or a fresh burst of product-market fit has to develop the muscle of making fast decisions with little data, staying comfortable with force-correcting the inevitable mistakes, which is the scale-time posture a wartime leader operates in.

The hiring judgment that enables speed is a bias toward horsepower rather than an exact match of domain and title. Jose looks for the rough set of traits a role actually needs, and for a person who can step into an ambiguous space and redefine it. A genuinely capable, fast-thinking hire can take entire problems off the team's plate and grow into one of its future leaders. Stephen has watched the same pattern play out from the outside. He notes that weaker companies often chase a competitor's exact job title and pay a premium for it, while the teams that win hire for raw capability and trust the person to figure out the domain as they go.

Jose draws one clear exception to this rule. A role that genuinely requires deep, specialized expertise, such as a senior AI leader, calls for exactly that expertise, with no substitute. Stephen adds a close companion to the horsepower bet, stating that the rising star is a candidate who has been promoted repeatedly and is ready for a stretch they have not yet formally done. Both leaders have watched bets like these pay off, growing into VPs at public technology companies years later.

AI Adoption Mirrors the Flickr and TikTok Pattern, and the Leaders Who Stop Resisting Will Define What Comes Next

Jose points to a pattern he watched up close at Yahoo, when Flickr set the terms for what counted as real photography at the time. Pros and prosumers policed that line hard, hazing casual shooters over their gear, their camera settings, and any hint of a Photoshop edit, with a parallel divide running between digital photographers and film loyalists. The iPhone flooded Flickr with new users, and the old guard largely dug in, dismissing the influx as illegitimate rather than teaching the craft to the people arriving there. 

A decade later, Flickr sits as a niche site, Instagram absorbed the community, and everyone with a phone camera counts as a photographer, even if the job title changed to influencer along the way. Video followed the same arc through phones and TikTok, producing more working creators outside film school than inside it, with both populations coexisting in the market today. Jose interprets AI's arrival in coding as the same cycle, playing out on the same timeline, and the leaders holding the gate closed on what counts as real development are on track to miss what the craft becomes as it moves forward without their input.

"Let's stop trying to put the genie back in the bottle, and start figuring out what that's going to be like."

The conversation lands on genuine empathy for the disruption. Losing a job to this shift is painful, and no reassurance changes that. History offers a longer view, pointing to when telephone switchboard operators gave way to automated systems decades ago, and ride-share driving became a livelihood that did not exist ten years back. That pattern extends naturally to platforms like Airbnb, where anyone can become a host, and hotels have found their place alongside an entirely new category built around the same idea. 

In this case, coding and AI look set to follow the same arc, with new roles forming around the technology as it matures. The posture that serves people best is to hop on the wave early and help define its shape. That fluency becomes real leverage for anyone entering the workforce now, since millions of businesses have no AI strategy in place and no clear starting point, and the gap runs especially wide in industries outside the tech bubble that are still catching up on adoption.

Discovery Is the Hard Problem AI Is Finally Positioned to Solve

The connecting thread across every media product Jose has built is the work of getting the right content to the right person at the right moment. He is candid in acknowledging that it remains genuinely hard even with years of accumulated tooling. Open any storefront and a stack of competing algorithms pushes content forward in the hope that something lands, which is a long way from the experience he still measures against.

His benchmark comes from a past life as a DJ, when the staff at his record store knew him well enough to set aside a stack worth checking out and to point him toward a track that broke his usual pattern in a way they suspected he would love anyway. That blend of knowing someone's taste and deliberately stretching it is what recommendation systems have struggled to replicate, since even a strong algorithm leaves most of a deep catalog buried while the genuine hits still demand their moment in the spotlight. Jose sees AI as the first mechanism in a long time capable of solving this in a genuinely new way, taking the flood of available signals and getting materially smarter about them, which points toward interfaces that change in response. That is the work he is most energized by at Ticketmaster, connecting fans to the event worth their attention at the moment it matters.

How to Apply These Frameworks This Quarter

The conversation maps onto decisions product and engineering leaders are making right now. A few concrete places to start:

• Find where your product needs friction: Audit a flow you have optimized purely for speed, starting with onboarding, and check whether stripping steps has created a leaky bucket. A small amount of intentional friction that gives users something of their own to create can lift engagement more than another removed step.
• Map your power curve before you plan: Lay out consumption across your events, content, or features, and confirm the shape of the distribution. Build capabilities that support the hits when they arrive and help new ones climb, so the product does not get locked around a single generation of winners.
• Hire for horsepower in ambiguous roles: For a role that needs someone to define a space, weight raw capability and trajectory over an exact title match, and reserve strict domain-expertise requirements for roles that genuinely demand them. Rising-star candidates who have been promoted repeatedly are often the highest-return bet.
• Build fast-decision frameworks for scale time: If you are scaling under pressure, develop explicit heuristics for hiring and prioritization so the team can move quickly with limited data and stay comfortable with course correction. Speed with a framework beats speed without one.
• Get in the water on AI: Treat this moment as early and adopt the posture of defining what comes next, building and experimenting directly while the shape is still resolving. The fluency compounds, and the businesses that need it vastly outnumber the people who have it.

Where AI Needs Judgment, EverOps Builds the System Around It

Jose's conversation repeatedly returned to the most valuable and most underused skill in product leadership today, judgment. As AI takes over more of the decisions people used to make by instinct, that skill is quietly eroding, and the muscle for knowing where to add friction, which bets on people are worth making, and how to build for hits without getting boxed in by them. Getting AI investment to pay off takes that same judgment, knowing where it creates real advantage and where it becomes a science experiment that sits on the shelf.

Our AI Opportunity Assessment identifies and prioritizes the AI use cases that are actually worth a team's time, evaluating data readiness and technical infrastructure before a single line of code gets written. For organizations ready to move past assessment, our strategy consulting and embedded operations engagements bring the hands-on delivery support that turns a roadmap into production-ready results.

If your team is wrestling with any of the questions Jose raises here, reach out to our team today to start the conversation.

Keep Up With TechPod Talks

Jose Gonzalez joined EverOps CEO Stephen Koza for Episode 8 of TechPod Talks. Subscribe to listen to the full conversation on Apple Podcasts, Spotify, YouTube, or the EverOps Podcast Page now. 

Follow Jose on LinkedIn and the EverOps page for more from the series.

Frequently Asked Questions

What is TechPod Talks?

TechPod Talks is a podcast hosted by EverOps CEO Stephen Koza featuring candid conversations with technology leaders, engineers, and operators. Each episode explores how real teams build, scale, and operate modern systems, with a focus on practical takeaways.

What topics does Episode 8 cover?

Episode 8 features a candid conversation with Jose Gonzalez on where to add friction into a product, why media platforms follow a power law that makes hits alone a fragile strategy, scaling teams fast by hiring for horsepower, why AI adoption mirrors the shift from file sharing to streaming, and how AI is reshaping event discovery at Ticketmaster.

Who is Jose Gonzalez?

Jose Gonzalez is the VP of Product for Acquisition and Discovery at Ticketmaster, where he leads efforts to connect fans with the right event at the right moment. His twenty-year career spans web development during the dot-com years, quality and production engineering at a Johnson & Johnson company, and product roles at Yahoo, Vevo, EA, and Amazon. He also co-founded the Techstars-backed AI startup Naria.

What is the power law of media platforms?

The power law describes how a small set of events, titles, or features drives most of the consumption on a media platform. Because a single hit can carry a business and hits cannot be manufactured on demand, Jose argues that durable platforms build systems that both support current hits and help new ones rise, so the product is not locked around one generation of winners.

Where can I listen to TechPod Talks?

TechPod Talks is available on Apple Podcasts, Spotify, YouTube, and the EverOps website, with episodes released in both audio and video formats.

Can I suggest topics or be a guest on the podcast?

Yes. You can share topic suggestions by reaching out on LinkedIn or through the EverOps website, which includes a guest request form for speakers interested in joining future episodes.

How does this episode connect to EverOps' work?

EverOps helps product and engineering leaders navigate the same questions Jose covers in this episode, including AI adoption strategy, organizational scaling, and building the systems that turn discovery into revenue. Services like AI Opportunity Assessment, strategy consulting, and embedded operations map directly to the work Jose describes.