As I have described before, and perhaps you’ve picked up on this even without my own self-descriptions, I think of myself as a (somewhat) techno-optimist, a design leader, an entrepreneur, and a design educator (for future design leaders). And… while wearing all of those hats, I am witnessing a paradigm shift in design leadership in real time.

The way I see it is that as AI collapses execution time and makes creation cheap (for ANYONE), the real bottlenecks associated with launching and maintaining great products/services have moved upstream. What seems to matter now isn’t how fast teams can produce, but how well they decide what to produce. We might call this slop vs. value. But…whatever the case, many design leaders feel this shift instinctively: influence no longer feels sufficient, process no longer guarantees trust, and accountability keeps expanding without matching authority.

So, rather than just think about these shifts, I thought I would write about what I am seeing. In this article, I try to lay out how design leadership is moving from influence to responsibility, from craft to judgment, and from shaping artifacts to governing systems. I hope that it offers a simple way to locate yourself in the shift, without hype or posturing.

You tell me…

— Justin Lokitz

For decades, design leadership was framed as influence. In fact, even as I write this, I look back on some of my previous articles where I framed design leadership as influence and cringe just a little bit.

That way of thinking goes something like this: If you had strong taste, clear rationale, and the ability to persuade cross-functional partners, design could shape decisions upstream. In this, the design leader’s job was to advocate, align, and elevate quality.

Alas...it would seem that with the advent (and onslaught) of AI, that model is breaking, mostly due to the nature of decisions having changed. AI has compressed execution. Prototyping is cheap. Generative systems surface plausible options instantly. What’s scarce now isn’t ideas or artifacts. It’s judgment, writ large.

And judgment doesn’t sit comfortably in an advisory role. In many respects, it’s binary (just like our AI counterparts ;-).

Across the design community, I see the same tension keeps surfacing. Designers debate whether the double diamond still holds. Leaders question whether the design process still earns its keep. Teams are asked to move faster while remaining accountable for outcomes they don’t fully control.

What’s emerging underneath all of this is a shift in the currency of leadership.

Expertise used to differentiate leaders. Today, it’s judgment: deciding what should exist, what should not, and who bears responsibility when systems act on our behalf.

Uday Gajendar names this directly in The Translator’s Burden:

That translation becomes unavoidable when AI accelerates execution. When systems can generate screens, flows, and copy in minutes, leadership is no longer about producing more. It’s precisely about deciding what’s worth producing at all, and under what constraints.

In his most recent article, AI Upskilling for Product Designers, John Maeda makes a parallel point in his recent essay on AI upskilling. He warns against confusing tool sophistication with maturity, noting:

Depth of judgment beats breadth of tooling. That’s a leadership claim, not a skills one.

Another pattern shows up repeatedly in recent conversations: design teams are still responsible for outcomes, but they rarely own the systems that produce them.

Design systems quietly decay. Identity systems get overridden by marketing. UX reports into product, but without authority over tradeoffs. When something breaks, design gets blamed without having held decision rights. To be clear, I am guilty of this, too.

This is why many design leaders are stepping into new roles: founders, principals, operators, or agency owners with real authority. Not because they’ve abandoned design, but because influence without ownership has become untenable.

Sid Vanchinathan has described this collapse of traditional handoffs clearly. As AI compresses the distance between idea and execution, the historical separation between product, design, and engineering erodes. The work converges around people who can both decide and act.

Leadership follows responsibility, not title.

In almost every respect that I can think of, governance sounds abstract. Yet…it’s already part of the job. It shows up whenever leaders decide…

These are structural decisions!

In his Design Bootcamp article The New Skills of Design Leaders, Suresh John Senegarapu makes this explicit, arguing that the industry is shifting toward “strategy, ethics, and systems thinking” as core leadership competencies. He points out that AI is now embedded in how teams “research, ideate, and make decisions,” which forces leaders to take responsibility for system behavior, not just outputs.

Maeda reinforces this framing by borrowing from the automotive industry’s SAE levels of automation. His core insight isn’t about moving faster toward autonomy. It’s about clarity of responsibility.

At higher levels of automation, the leader’s role becomes setting goals, constraints, and approval gates, while reviewing outcomes and exceptions. In Maeda’s words:

Fuzzy or not, this sounds like governance to me, which begs the question: what do we do now?

One thing that we ALL must consider is UNLEARNING what we (believe) we know, especially from a design leadership standpoint.

Design leaders are questioning assumptions that once felt foundational. More speed doesn’t always mean progress. More artifacts don’t necessarily mean rigor. More research doesn’t automatically create confidence.

Before you narrow your eyes, readying them to shoot daggers at me, I’ll qualify this statement: this is a rejection of process as performance rather than a wholesale rejection of the design process.

AI makes it tempting to skip visible steps. A model can generate something that looks finished without months of discovery. But as many designers have noted, experienced practitioners don’t skip reasoning; they internalize it.

Leadership now requires making that reasoning explicit again through shared criteria, clear tradeoffs, and reviewable decisions. In many respects, this parallels how we’re asking to interact with the AI tools (especially LLMs). We want to see the thought chain and reasoning they’re making in order to fully trust that they’re doing their job truthfully. And…so it goes for design leaders too!

Maeda emphasizes this distinction when he writes that maturity within a level matters more than the level itself. A leader who sets standards, mentors others, and governs usage is more valuable than someone experimenting with advanced tools without accountability.

Unlearning here means letting go of tool-driven legitimacy and replacing it with accountable judgment.

Gajendar’s idea of translation deserves more attention, because it explains why many senior designers feel stretched thin right now. That is…we are increasingly responsible for translating:

As he notes, the hardest problems leaders face aren’t design problems at all. They’re organizational ones: unclear strategy, fuzzy ownership, and misaligned incentives.

Design leadership becomes the work of designing conditions. Defining decision rights, clarifying what success means, and…making tradeoffs visible before they surface as broken experiences.

This is why simply “speaking the language of business” often feels insufficient in the current context. The real is helping organizations see how decisions ripple across systems, customers, and teams, rather than simply swapping user quotes for revenue metrics.

I believe this also helps explain a trend that’s easy to miss if you only look at job titles. Many design leaders aren’t just changing roles. They’re changing modes of operation.

Maya Elise Joseph-Goteiner’s move from Google’s Area 120 to founding Velocity Ave is a clear example. Inside large organizations, she built influence by translating research into action across dozens of products. As a founder, she took on something different: full accountability.

In conversation, Maya has described how leaving big tech forced her to rethink leadership, built on a foundation of responsibility rather than the typical pomp and circumstance of polish that often comes with the title of design leader. When you own the work end-to-end, there’s no hiding behind process or resourcing. Decisions land differently when you’re accountable to clients, teams, and outcomes simultaneously.

Beneath all of these shifts is a quieter emotional current. Many designers are asking whether they’re still legitimate in a world where AI can “do the work.”

The answer, surprisingly (or maybe not so surprisingly), is to own what machines can’t do, such as…

Sure, AI can generate it. It can optimize it. Yet, it cannot be accountable! In fact, every time I am frustrated by the output of AI after it continues to hallucinate, I turn the gaze back to me: I am accountable.

And…that accountability is becoming the defining feature of design leadership.

Design leadership is no longer primarily about influence or craft, as it once was. Today, it’s about judgment, ownership, and governance. It’s about designing systems that make good decisions possible, even when speed, automation, and ambiguity are unavoidable.

Honestly, this feels like it’s a MUCH harder role that comes with more responsibility and less applause. And…it’s also where design has always belonged: at the intersection of human values, complex systems, and consequential choices.

The leaders who thrive in this next phase won’t just make things. They’ll decide what deserves to exist…and stand behind those decisions when it counts.

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