The most consequential design decision is rarely about visual hierarchy. It’s about where a capability lives inside someone’s workflow.
Consider how a technical facility administrator handles a vulnerability alert. The product promises a continuous path: start from a CVE, trace it through affected software and devices, assess lateral movement risk, create a remediation task. If the design fractures that into disconnected steps, forces them to reconstruct context every time they return, and relies on platform knowledge most operators don’t have, they don’t think “this is poorly designed.” They just stop trusting the alerts or question why they have to spend time learning a new tool. In a product where trust determines whether an operator acts on a CVE or ignores it, that break has consequences you can measure: response times, deferred patches, compliance gaps.
This is the practical end of the March argument that started in Design Leadership Has Evolved. In industrial cybersecurity, that workflow isn’t a metaphor. It’s the product: graph data, risk, vulnerabilities, and an operational decision a person has to make. Most of my design work there has been translating that chain into something an asset owner, an IT/OT engineer, a CISO, or a SOC operator can actually finish.
The same pattern shows up far from industrial sites. GitHub Copilot chose inline suggestions, completing the next step inside the developer’s workflow, and saw explosive adoption. Microsoft 365 Copilot chose a sidebar, pulling the user out of their workflow, and by early 2026 had converted only about 3% of its Microsoft 365 base to paid seats despite massive investment. Same underlying AI capability, completely different outcomes, because of where and how it surfaced inside the workflow. Inline help stays in the job. A sidebar asks the person to leave it.
In both cases, the pattern that best fit the user’s workflow was the product. It wasn’t the technology or the engineering quality. It was the decision about where a capability surfaces. These decisions happen before Figma opens, and they require enough depth about the user’s mental model to choose how AI shows up in their world.
The job now
Design leadership’s highest-order job is setting direction. Clear enough that both human teams and AI agents can execute with precision, and honest enough to change course when the direction is wrong.
That shift is quieter than most leadership advice suggests. It shows up in fewer debates about design budgets, sharper calls before sprints begin, and products operators actually use on the days it matters most.
These beliefs hold under pressure. Especially now.
Also in March: Design Leadership Has Evolved · Clarity Before Agent Action · Ownership Beats Advocacy
Continues in April with The Disappearing Middle of Software Work.
Daniel Arevalo is Product Experience Lead at Omny Security. He builds AI-native tools and writes about design, product leadership, and what it means to direct systems that execute at a speed humans never had before.