032 AI, Originality, and the Value of Expertise

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For those of us working in product management, UX design, and other creative professions, we have spent years developing our skills, learning from mistakes, understanding users, and figuring out how to translate ambiguous problems into thoughtful solutions.

Now we have tools capable of producing outputs that look remarkably similar to what professionals create.

It's natural to wonder what happens to the value of our work when the act of producing it appears easier.

In our latest Pixels & Priorities conversation, J and I welcomed A.J. Wood, Principal Solutions Consultant at Adobe, to explore what he calls a reckoning with AI in an era of counterfeit creativity.

Our conversation moved beyond whether AI is good or bad. We talked about originality, protecting creative work, and how professionals can adapt without surrendering the expertise that makes their contributions meaningful.

When Replication Becomes Easier Than Originality

Generative AI has changed the economics of content creation. Producing an image, generating copy, or recreating an existing visual style no longer requires the same time or technical knowledge.

A.J. described watching visual trends appear and disappear at remarkable speed because anyone can quickly reproduce something they've seen.

The result is an abundance of content that may look different at first glance but increasingly follows the same patterns.

"But the people who are duplicating it are not creating things. They are replicating things." — A.J. Wood

We can generate dozens of interface concepts, user flows, or product descriptions. The existence of those outputs doesn't mean we've identified the right problem, understood the people experiencing it, or made a thoughtful product decision.

Replication produces another version. Creative expertise helps determine whether that version should exist.

For practitioners, the opportunity is to spend less time proving we can generate an artifact and more time demonstrating the reasoning that makes it valuable.

The Difference Between Using a Tool and Knowing Your Craft

J brought up a provocative question: Can someone call themselves a musician if they can only create music through AI and can't produce it without that technology?

His point centered on the value of foundational knowledge.

"I believe that higher level of artistry requires those foundational understandings and skills." — J Schuh

A.J. challenged the idea that we should define who qualifies as creative based on whether they use AI. We can value craftsmanship without using a particular tool as the entrance requirement for creativity.

For UX professionals, foundational skills include understanding human behavior, conducting research, recognizing interaction patterns, evaluating accessibility, and making tradeoffs.

For product managers, they include problem framing, prioritization, communicating across disciplines, and understanding the consequences of decisions.

These skills don't disappear when AI enters the workflow. Instead, they become particularly important when a tool produces an answer that looks convincing but doesn't solve the actual problem.

AI Can Increase Output Without Replacing Expertise

One of A.J.'s most important observations concerned the difference between having more output and having more capability.

He discussed the risk of organizations replacing experienced professionals with technology while assuming the technology also replaces their knowledge.

I've seen how much invisible work goes into good product decisions.

The final deliverable might be a roadmap, research report, prototype, or recommendation. What isn't always visible is the judgment required to decide what belongs there.

That expertise comes from experience, context, questioning assumptions, and understanding how decisions affect people.

AI can accelerate portions of the process. It cannot automatically supply the organizational knowledge and professional judgment lost when experienced people leave.

For product and design leaders, this is worth remembering when evaluating productivity.

More deliverables do not necessarily mean better decisions or better customer experiences.

Instead of measuring only how much faster people can produce work, we should examine whether technology is improving the quality of the decisions behind it.

Creative Protection Is a Separate Problem From AI Adoption

A.J. was equally clear that legitimate concerns about AI shouldn't be dismissed.

He shared an example involving stunt performer Michelle C. Smith, whose video choreography was reportedly reproduced using AI while replacing her appearance with someone else's.

The concern isn't merely that technology can imitate a creative style. It's that someone's actual work can be appropriated and repackaged.

These are questions of ownership, attribution, permission, and accountability.

A.J. also emphasized copyright registration as an important consideration for creative professionals.

While the specifics of copyright protection depend on the work and applicable law, the larger lesson is relevant to anyone developing digital products: adoption and responsible use need to be considered together.

Product teams should be asking where training material, reference assets, and generated outputs come from, and what safeguards exist around their use.

We don't have to choose between acknowledging AI's potential and advocating for protections against misuse.

What AI Makes Possible for Small Teams

Another part of our conversation challenged the assumption that AI always eliminates an existing creative job.

A.J. reflected on producing his own videos before working at Adobe. He handled scripting, recording, editing, and marketing himself.

Later, working with a production team allowed him to focus on fewer tasks and accomplish considerably more.

AI may give independent creators something similar: access to capabilities they previously couldn't afford.

That doesn't erase the real concerns about displacement though. A.J. acknowledged instances where technology replaces paid work.

It does suggest that the impact varies depending on who is using the technology and what resources they had before.

Many of us working in small product teams or independent businesses recognize this tension. We have ideas we cannot pursue because we lack sufficient time, specialized support, or budget. Used thoughtfully, AI could reduce some of those constraints.

The more useful question may be: Where could these AI tools extend our capabilities without sacrificing the expertise or responsibility the work requires?

The Next Shift Is About Workflows, Not Just Prompts

A.J. described an evolution from application-based creative production toward prompting, APIs, automation, and visual node-based workflows.

A node-based workflow connects individual functions into a repeatable process. For example, one step uploads an image, another removes its background, another generates variations, and subsequent steps prepare multiple output formats.

Instead of manually performing every operation, a professional designs the sequence.

A.J. connected this to the evolving role of the creative technologist: someone who understands both creative practice and the technology that supports it.

As repetitive production work becomes easier to automate, professionals can contribute by designing the workflows, defining quality expectations, reviewing outcomes, and ensuring the process serves real users.

We still need to know what good looks like.

The technology changes how we execute. Our expertise determines what we're trying to accomplish and whether we've succeeded.

Questions for Reflection

1. Which parts of your work demonstrate professional judgment, and which are primarily production tasks that technology could accelerate?
2. How is your team distinguishing meaningful originality from rapid replication of existing patterns?
3. What safeguards should your organization establish before scaling AI-generated content or automated design workflows?

Key Takeaways

• Originality is more than production. The ability to generate an artifact isn't the same as developing the ideas and judgment behind it.
• Foundational skills still matter. Product and design professionals need to understand why a solution works, not just how to produce it.
• Responsible adoption requires safeguards. Copyright, attribution, and creative ownership deserve serious attention.
• AI can expand individual capability. Small teams may gain access to workflows and production capacity that previously required more resources.
• Workflow design is becoming a valuable skill. Understanding how to orchestrate tools may become as important as knowing how to operate them individually.

Final Thoughts

As we wrapped up our conversation, I kept coming back to the feeling that everything is changing around us and the expertise we have developed through our own experiences.

"There are no quick and easy answers, but a focus on your expertise, what you bring to your work, what you uniquely have to bring into this world is always a good place to start." — Metsy Rose

We don't have to pretend the risks aren't real. We also don't have to assume that faster technology makes our experience irrelevant.

For me, the most interesting question isn't whether AI can produce what we produce.

It's whether we are continuing to develop the judgment, curiosity, and creativity that make our work worth producing.

Listen to Episode 032 of Pixels & Priorities, featuring A.J. Wood, for our full conversation about generative AI, creative ownership, professional expertise, and emerging workflows.

I'd love to hear what you're seeing in your own work. Where is AI genuinely extending your team's capabilities, and where is it creating new challenges?

– Metsy
Co-host, Pixels & Priorities

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Connect on LinkedIn: Metsy Rose | J Schuh | Pixels & Priorities