Shift Center

Why “shift left” no longer captures where research is headed.

For the better part of a decade, the UX anthem has been shift left. Meaning, involve research earlier so that teams learn before they build. It’s a philosophy I championed for years, one I invested in deeply because it put people first, oriented products to solve real problems, reduced waste, and saved organizations millions.

Today, shifting left no longer reflects our reality because development has fundamentally changed. Without the semblance of a process, with the fringing of responsibilities and ownership, the concept of multiple directions, of a left or right has become archaic. Product development no longer has deliberate phases, the punctuation that once created space for reflection, iteration, and alignment is bygone. Building is now a compressed continuum, one that promotes simultaneous decision-making. To sustain the work we do, we need to find a new entry point for UX.

While this may be unsettling for some, adaptation has always been part of our ethos, a muscle we have flexed so gracefully through every industry tide. Design is not dead: as always it is evolving, and that is why the shift left metaphor no longer fits.

Over the past month, I listened to a series of panels hosted by the Design Executive Council where research leaders from organizations including Pinterest, Expedia, Amazon, Cisco, TD Bank, Taxwell, and DScout discussed how their organizations are transforming. What surprised me wasn’t what they were talking about, but the topics they quietly left out.

Very little of the conversation centered on the classic refrains: getting a seat at the table, proving the value of research, becoming more strategic, or moving from reactive to proactive. Those challenges haven’t vanished, but they have become secondary, because our foundation has collapsed and without that infrastructure, business-as-usual won’t help us achieve our OKRs.

Instead, today’s urgent strategies center on repositories that make insights accessible, governance models that preserve quality, AI agents grounded in prior research, shared taxonomies, and evaluation frameworks.

Tuning in, I was initially caught off guard by how tactical and operational these leadership priorities appeared. The longer I listened, the more I understood the conversation isn’t about tooling, it’s about how customer understanding moves through an organization.

When product development is driven by raw momentum, which is inherently anti-process, our old strategies no longer create the same leverage. If we want to preserve our human and strategic ambitions in an environment optimized for speed, our entry point isn’t another phase of development, it’s how organizations learn.

I’ve started thinking about this paradigm as a Shift Center.

Building Organizational Memory

Research has traditionally been anchored around product areas, business priorities, and social capital. For many individual contributors, that can feel like moving from one isolated project to another. We complete a study, deliver recommendations, and move to the next question.

The dream, has always been for research to become continuous, additive, and cumulative. The challenge wasn’t that we lacked the vision, we lacked the infrastructure. Until recently, building and maintaining that infrastructure required an investment few organizations were willing to make. What’s changed isn’t the ambition, it’s what’s now possible.

Research Operations has been laying this foundation for years, but today’s tooling now enables those investments to be dynamic.

• Static repositories become interactive.

• Insights breathe and gain new life as studies begin to correlate.

• Standards become embedded.

• Governance becomes scalable.

• Customer understanding can surface in the moment decisions are being made.

None of this diminishes the value of research, if anything, it expands the reach in a durable way.

A Different Role for Research

One theme continues to transpire in conversations with research leaders: democratization is no longer the primary question.

Whether democratization succeeded almost feels beside the point because AI has changed the landscape entirely. Access to information is becoming easier, access to understanding is not.

AI can synthesize thousands of pages of feedback in seconds, but it cannot do so with contextual judgment and nuance. When any product manager or marketer can generate a beautifully formatted, authoritative-looking summary with a single prompt, our risk is no longer a lack of data, it is the widespread hallucination of confidence.

Because of this, organizations aren’t simply asking how more people can access research. They’re asking how to ensure what an AI retrieves is trustworthy, contextual, and appropriate for the high-stakes decision they’re making.

This is precisely why we are seeing more UX professionals get directly involved in model design and model evaluation.

These are different kinds of questions than we were asking five years ago. The work is changing, it is less about executing research well and more about architecting the systems that create the conditions for good judgment at scale.

The profound irony of our current moment is that the most strategic move a research leader can make right now is a deeply operational one. In a world driven by raw momentum, infrastructure is strategy. Instead of fighting for a seat at the table, researchers are designing the knowledge systems that shape the table itself.

The Skills We Need to Shift

Since UX Skills for Business Strategy is now out in the world, I’ve found myself revisiting some of the ideas Kim, Torrey, and I wrote about. Looking at the 99 skills through the lens of Shift Center, the most critical capabilities aren’t methods that produce artifacts, they are the business skills that build the structural capacity for an organization to learn. They form the literal blueprint for a Shift Center:

• Extrapolate Stakeholder Needs (S06)

• Cultivate Relationships (S66)

• Align on Success (S74)

• Understand Organizational Perspectives (S80)

• Establish a System for Governance (S87)

• Centralize Data and Insight (S89)

• Establish Research Operations (S97).

When we wrote about these business skills, it was because they create leverage far beyond any single deliverable. As AI lowers the cost of producing artifacts, organizations don’t become less dependent on research. They become more dependent on the systems that help them connect information, exercise judgment, and continuously learn from customers.

Many of us have always viewed research as this kind of connective tissue, but historically, executing it at scale was functionally impossible. We couldn’t be in every room at the right moment, and we lacked the infrastructure to centralize organizational data and support our (human) cross-project synthesis.

Today, we can embed our standards, our insights, and our governance into the very fabric of the tools and systems that drive daily product decisions. We can scale our perspective to be part of the decision-making loop, even when we aren’t physically in the room.

This doesn’t diminish the need for human researchers, (when I feel hopeful) I believe it can elevate us. AI can scale the information, but humans must still anchor the judgment. For years, we’ve focused on finding the right sequential moment to insert ourselves into the product development process. The opportunity ahead of us is much larger: to architect the continuous, living engine through which customer understanding flows across the entire organization.

P.S. If you are interested in my new book UX Skills for Business Strategy, it is currently on sale via Amazon. If you aren’t ready to add a new book to the library, Kim, Torrey and I have created a new Substack where we share resources, career reflections, deep-dives, individual UX skills, attitudes, and business impacts.