Redefine Your Design Skills to Prepare for AI

Summary:
Designers must embrace 5 principles as our industry shifts with AI.

Generative AI is impacting job markets. According to researchers at Harvard Business School, the German Institute for Economic Research, and the U.K.’s Imperial College London Business School, demand for automation-prone jobs fell 21% eight months after the release of ChatGPT in late 2022. As a designer, how can you navigate these fast-paced disruptions and stay relevant?

Automation and Augmentation of Designers’ Tasks

Technology has been shifting how we complete tasks within most disciplines for decades. Take the role of an accountant: while the title remains unchanged, the day-to-day tasks of an accountant in 1954 (e.g., calculating sums by hand, on paper) are markedly different from those in 2024 (e.g., autocalculating sums in an advanced spreadsheet).

Design tasks depend on our role, goal, and expertise. They include facilitating workshops, ideating solutions, designing wireframes, implementing design systems, and reviewing requirement documents.

Erik Brynjolfsson identified two primary ways in which AI will affect human tasks: automation and augmentation.

A subset of our existing tasks will be automated, while a new set of tasks, previously outside of our capabilities, will become accessible.

Task Automation

AI will allow us to perform certain tasks more efficiently.

Even today, tools like Miro or Mural can automatically cluster sticky notes, ChatGPT can draft an email. As AI models become more sophisticated, we can expect automation to increase in both scope and intensity.

Automation doesn’t mean that tasks will be completely delegated to AI. Rather, it enables designers to work efficiently by combining AI capabilities with human oversight.

Automation isn’t an overnight process. The pace varies across different tasks, with some changes happening slowly and others rapidly, but the direction toward increased automation remains consistent.

Task Augmentation

AI will enable us to perform tasks that traditionally fell outside our professional roles, while also expanding what’s possible within our domain. For designers, this means being able to interpret complex data patterns without a data analyst or code prototypes without a developer, and shifting from designing screens to defining the rules that generate them, as well as enhancing our existing capabilities in ways we’re just beginning to explore.

Increasing Competition in Design

Both these processes — automation and augmentation — are happening gradually and in parallel. We’ll increase productivity — spending less time on some tasks or accomplishing more in the same amount of time for other tasks — while simultaneously expanding our capabilities into new areas.

As designers take on more tasks outside their expertise, the job market will only become more competitive. An individual contributor will be able to do more, and thus fewer contributors will be needed.

To stay competitive and relevant, designers must be prepared to shift how they think about their skillset.

Principle 1: Own Strategic Thinking While Outsourcing Tactical Tasks

Not all tasks are created equal.

Most designers perform a range of tasks in their every day work — from tactical to strategic. For example, within a single project, a UX designer may work to define the product vision and create user flows (strategic-oriented tasks), while also organizing design assets and creating developer specs (tactical-oriented tasks).

The image titled "The Spectrum of Design Activities" illustrates a continuum from tactical to strategic design tasks. Tactical activities, such as organizing design assets, maintaining design systems, and writing spec docs, transition toward strategic activities like facilitating stakeholder alignment, creating user flows, and defining product vision. — *UX designers balance tactical execution, like organizing assets and writing specifications, with strategic activities such as defining product vision and facilitating stakeholder alignment.*

Similarly, a service designer may develop a roadmap and define high-level metrics (strategic-oriented tasks) in parallel to creating handoff documentation and making presentation decks (tactical-oriented tasks).

The image titled "The Spectrum of Service Design Activities" illustrates the progression from tactical to strategic tasks in service design. Tactical tasks include creating handoff documentation and making presentation decks, while strategic tasks involve defining service metrics and developing roadmaps, with activities like journey mapping and prototyping concepts bridging the spectrum. — *Service designers combine tactical work like handoff documentation and presentation decks with strategic activities like service metrics and roadmap development.*

Choosing the right tasks to automate is critical to maintaining the integrity and value of UX work.

Tactical tasks, like organizing raw data or generating quick mockups, are good candidates for automation because they follow predictable patterns. Offloading these tasks to AI (with proper oversight) can save hours of effort, freeing up time and mental energy for designers to focus on high-impact activities.

In contrast, automating strategic tasks — like crafting a design vision — is risky. These activities require nuanced judgment, contextual awareness, crossdisciplinary collaboration, and an understanding of human behavior that AI, as it stands, cannot replicate.

Embracing automation for tactical efforts while intentionally cultivating strategic thinking ensures that designers maintain their thought-leader and decision-maker roles, while no longer acting as mere executors of predefined processes.

Principle 2: Balance Trust in AI with Scrutiny

As AI becomes a trusted collaborator in our workflows, we must assess biases that it may introduce. AI systems are only as good as the data they’re trained on and the algorithms behind them, which often reflect the limitations and biases existing in the world.

If we over-rely on AI without questioning its outputs, we risk perpetuating these biases. The solutions we design may be disconnected from the needs of our diverse user base. To mitigate this danger, designers and researchers should reflect on potential blind spots, scrutinize AI-generated insights, and validate them against real-world user research.

AI should enhance our ability to think critically, not replace it. By staying grounded in critical thinking, we ensure that AI augments our work without compromising our responsibility to create equitable, inclusive, and meaningful user experiences.

Principle 3: Design for Users and AI Agents

AI agents (or assistants) will increasingly become active participants in the ecosystems we design. These agents will engage with systems on our behalf, make decisions, and execute tasks, fundamentally reshaping how we deliver our products and services. This shift requires designers to rethink not just user interactions but also the broader dynamics of our field.

To prepare for this future, we must embrace the complexities of designing for a dual audience: human users and their AI representatives. For human users, the design might prioritize clarity, accessibility, and ease of navigation. AI agents might access the system both programmatically and through the same interfaces as humans. However, for them, the design may need to prioritize different aspects of the experience — from structured data to predictable interaction patterns.

For example, imagine you’re designing a healthcare appointment-scheduling system. An AI assistant scheduling an appointment on behalf of a user would need to query the system for available times, understand service details, and confirm the booking without manual intervention. By considering how these AI actors will evaluate, interact with, and optimize services on behalf of users, we ensure that our systems serve human needs while acknowledging the growing role of AI intermediaries.

Principle 4: Embrace Team Augmentation

The augmentation and automation concepts apply to teams, not just tasks. AI-powered tools will extend teams’ capabilities beyond traditional boundaries. Designers will do data analysis, developers will craft UIs, and analysts will write code.

Imagine a world where crossfunctional teammates could engage with your domain in ways that seem impossible today, while you simultaneously gain the ability to contribute meaningfully to theirs. This increased functional overlap could unlock numerous, unpredictable possibilities.

I anticipate two key implications of functional overlap: organizational boundaries will be redrawn and radical interdisciplinary collaboration will be enabled.

Redrawing Organizational Boundaries

Team augmentation might lead to a restructuring of departmental limits within organizations. The nature of these changes will depend on both technological factors (like the evolution of AI tools) and cultural aspects (such as the organizational legitimacy of each team). Restructuring the organizational boundaries might initially seem like a zero-sum game, with departments competing for expanded territories. Be ready to navigate organizational changes and play the political game.

Enabling Radical Interdisciplinary Collaboration

On a more optimistic note, picture the possibilities for unprecedented crossdisciplinary collaboration. AI tools, particularly those based on large language models (LLMs), could help us communicate complex ideas in terms that nonexperts can readily understand. A designer might use AI to perform initial data analysis and then consult with data scientists to validate the approach and results. Similarly, nondesigners might use AI-powered tools to create interface mockups and interactions while designers shift into expert curator roles, validating and refining the outcomes. In an industry where domain-specific jargon and tools have often hindered collaboration, AI could serve as a universal translator, breaking down these barriers.

The image titled "How AI Transforms Team Collaboration" highlights two concepts: "Evolving Boundaries," shown as overlapping dashed-line shapes around circles, representing flexible roles and responsibilities, and "Cross-Domain Exchange," depicted as circles connected by arrows, symbolizing collaboration and knowledge sharing across different domains. — *AI enables two fundamental shifts in professional collaboration: the evolution of traditional team boundaries (left) and the emergence of fluid knowledge exchange across domains (right).*

Principle 5: Address Unequal Effects to Prioritize Well-Being

UX designers and researchers have long worked to understand how factors like class, race, gender, nationality, and culture intersect with the experiences they design. Our role is to ensure that the systems we design respect and enhance the well-being of everyone they touch — not of just the most visible or powerful groups or of the organizations we work for.

Significant technological shifts, such as AI, risk exacerbating existing inequalities, disproportionately impacting those with less power or representation. It becomes essential that UX professionals embed equity and inclusivity into design and research practices from the start. By engaging diverse perspectives, identifying potential harms early, and rigorously testing our assumptions, we can create AI-powered systems that empower rather than marginalize. As stewards of user-centered design, it is our responsibility to ensure that AI serves as a force of equity and good.

References

Erik Brynjolfsson. 2022. The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence. Daedalus 151, 2 (2022), 272-287. https://doi.org/10.1162/daed_a_01915

Ozge Demirci, Jonas Hannane, and Xinrong Zhu. 2023. Who is AI replacing? The impact of ChatGPT on online freelancing platforms. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4602944