How I think about AI’s impact on design

Leadership | 7 minute read

OVERVIEW

AI excels at production, helps with exploration, but struggles with problem framing. Design leaders need to invest in strategic capability, not just automation.

A framework for exploring AI in design

Since the first large language models arrived, I’ve wrestled with AI in two ways: first, bringing an AI assistant to market at NetDocuments and second, understanding how AI reshapes design itself.

Every design leader I know is asking how to use AI but suggest we also need to ask what parts of design work AI actually changes and what that means for how we lead.

I’ll use framing from a former Fidelity Investments colleague, Jen Cardello, who broke the design research into three parts:

  • Right Problem: Are we solving the right problem? (intent, framing, business alignment)

  • Right Solution: Are we building the right solution? (exploration, validation)

  • Done Right: Are we executing well? (production, quality, delivery)

My experience show’s that AI impacts differs dramatically at each of these phases. I’ve learned that AI excels during “done right”, helps with “right solution”, but requires human judgement with “right problem”. Understanding this nuance has influenced how I structure teams, develop designers, and decide which capabilities to build or automate.

A NOTE ON TIMING

AI is evolving faster than any technology I've integrated into product design teams. These observations originate from hands-on experience bringing AI products to market at NetDocuments and integrating AI tools into design workflows. Capabilities will continue to rapidly evolve, but I’m betting the strategic questions for design leaders will endure.

Right problem: Where AI requires human judgement

The ‘right problem’ stage is the most difficult and least familiar to many organizations. These are questions to ask at this stage:

  • Are we solving the right problem for the business?

  • How do we balance user needs with business constraints?

  • Which problems move the needle of revenue, retention, or other strategic imperatives?

  • What are we choosing not to solve?

This is where design thinking meets business strategy which requires a careful consideration of business context, stakeholder needs, technical constraints, and market dynamics.

AI optimizes for patterns in your data, then makes predictions. Deciding which predictions matter for you depends on your goals and requires human judgement about strategy, ethics, and risk.

At NetDocuments, I used ChatGPT to analyze a long list of customer ideas. It was fantastic at converging, categorizing, and ranking ideas, but missed the target when asked which idea would deliver the greatest impact. ChatGPT lacked context. It didn’t know that we had a strategic prospect hammering on a nonexistent feature they wanted before signing, or that a past architectural decision made executing an idea extremely difficult, or that a competitor was generating tremendous buzz with a new capability we lacked.

Understanding this distinction between what AI does well and what requires human judgment shapes my approach to building design teams. I look for business curiosity, not just user empathy. I structure teams to give designers direct access to strategic discussions. I develop designers by teaching them to articulate business impact alongside user value. Getting the right problem solved matters more than getting the solution perfect.

Right solution: Where AI helps (and hurts)

Once you've defined the right problem to solve, you need to find the right solution. Traditionally, this phase takes days to weeks depending on problem complexity. AI has compressed this cycle to hours by generating more ideas, faster.

At NetDocuments, we had a complex file-upload UI lacking queuing. Using the AI-assisted design platform Uizard, I generated 30 variations in minutes, including directions I wouldn't have considered. After 30 minutes, I had 70+ options.

If you're already embracing fail fast, learn fast, this generative leap is incredibly powerful: generate options quickly, evaluate against user needs and business goals, choose the best direction to pursue.

But hidden costs exist. In a Wharton study, teams using AI generated far fewer unique ideas compared to human-only groups. Research from the University of Exeter found AI raised usefulness at the cost of variety.

I saw this pattern in my Uizard session. Those 70+ variations collapsed into three distinct approaches. The rest were minor tweaks. Also, evaluating each option against business context and design principles required time, even when I dismissed options at a glance. Volume without discretion leads to the same solutions everyone else ships, just faster.

Design teams need to harness AI's speed without sacrificing originality. I've changed how I develop designers to address this. Before opening AI tools, designers must define what they're testing and what success looks like. During reviews, I ask how many unique directions they explored, not how many total variations they generated. When designers show me dozens of AI options, I push them to collapse those into the three or four meaningful differences worth evaluating. The goal is to train designers to recognize when generating more options overwhelmed their judgement.

Done Right: Where AI shines

Once you’ve framed the right problem and solution, you need to produce. This is where AI deliverers on the productivity gains that make headlines.

McKinsey reports up to 70% faster cycle times for production tasks similar to this phase. At NetDocuments, using AI-assisted design using tools like Lovable and Bolt in combination with our design system got us to 60% good very fast with the designers delivering the critical 40% of detail and refinement in Figma. With production work compressing, more space for strategic work expands.

The question for design leaders is how do you reinvest productivity gains? This shift has changed how I structure teams and develop designers.

I now develop designers to spend more time on problem framing and less on production. Junior designers used to advance by improving craft. Now they progress by demonstrating business understanding. I evaluate them on whether they grasp how their work connects to revenue, retention, or strategic goals. Senior designers get promoted based on their ability to influence which problems the organization solves.

I've established new ways of working. Pairing a designer with an engineer when using AI-assisted coding tools improved both velocity and quality of what we shipped. I've also built safeguards against AI risks like hallucinations by requiring human review of all AI-generated outputs before they reach production.

Understanding where AI accelerates production and where human judgment stays essential has changed which skills I hire for, how I measure performance, and where I focus development efforts. Designers who thrive in this environment spend less time perfecting pixels and more time shaping strategy. Design leaders who fail to redefine success leave their teams unclear about their value.

What this means for design leaders

The decisions you make now about AI will define your design organization for years. Once you restructure teams, shift hiring criteria, and automate work, reversing course becomes expensive and slow. I've watched teams chase efficiency gains only to realize they optimized for the wrong work.

Understanding where AI helps and where human judgment remains essential has changed how I operate. The changes fall into three categories: how designers spend time, who I hire, and how I measure success.

Protect strategic time

I estimate my teams now spend 40% of their time on problem framing and business context, up from about 15% two years ago. Recalibrating expectations led to restructuring calendars to support:

  • I insist each designer attend 1 cross-functional meeting weekly: revenue planning, customer advisory sessions, roadmap prioritization. This builds the context they need to apply judgment to the right problem phase of work

  • I rebalanced critique to focus on problem framing over pixel refinement. The group pressure-tests business assumptions, not just design execution.

Hire for judgement

Portfolio reviews now focus on problem selection, as well as visual craft.

  • I look for cross-functional experience. Candidates who've worked closely with sales, finance, or executives understand business context better than those who stayed in design silos. I ask about their relationships with non-design stakeholders and listen for curiosity about organizational strategy

  • I show candidates AI-generated design options and ask "what's missing here?" The best answers identify sameness and explain how they'd push beyond obvious patterns. Poor answers focus on polish and refinement.

Measure differently

Performance reviews changed.

  • I removed "quality of visual execution" as a standalone metric. I added "influence on problem selection" and "alignment of work with business outcomes."

  • Junior designers progress by articulating how their work affects retention, revenue, or competitive position. Senior designers get promoted based on influencing organizational strategy

  • I track how designers allocate time across the three phases: right problem, right solution, done right, looking for a shift left

Why this matters now

The decisions organizations make now will shape their design teams for the foreseeable future. Team structure, process, capability investment, and what to automate will be hard to reverse as AI continues to accelerate. I’ve witnessed teams make quick efficiency gains only to struggle months later when they realize they’ve addressed the wrong problems. Thinking through how you want to apply AI through the right problem, right solution, done right framework can help organizations weigh how to optimize.

If you’re hiring design leadership, ask where they see AI helping and where they see risks across these three phases. Ask how they’re reimagining teams and developing talent. Ask whether they advocate investing in strategic capability or optimizing for execution efficiency. Their answer will give you insight as to if they see AI as a strategic shift or an exercise in selecting the newest tools. I think the difference matters.

Takeaway

I’ve spent over 15 years building design teams that drive positive business outcome and much of my success can be attributed to learning early on that design activity must be connected to those outcomes. AI represents a tremendous opportunity for entire design teams to operate in a more business savvy manner, not just design leadership. Organizations that recognize this now, using AI as a way to expand design’s business impact, will see durable success.

If you’re building that kind of organization and looking for design leadership that thinks this way, I’d welcome a conversation.

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