The 3 capabilities designers need to build for the AI era | by Alex Klein

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The reverse reckoning of UX design.

3 key design capabilities of the AI era

“These goofy little AI models need our help!”

…this was one of my biggest insights from a user test last week.

From the outside, it looked just like a traditional usability test, featuring an upbeat researcher (that’s me), an unknowing participant, and a series of tasks to be completed using a digital tool.

This test, however, stood out because we were assessing a novel AI feature.

I encountered a scenario I believe will become increasingly familiar in our field: I found myself analyzing the model’s behaviors and perceptions as closely as the participant’s. In fact, it felt like there were two participants in the test, both deserving my careful attention as they tried to complete a task together.

And, just like any good user test, it revealed many ‘aha’ moments — obvious opportunities for improvement — but across two dimensions: the human and the model.

There seems to be a hidden assumption that you can strap an API to any product and drive immediate value, bypassing the need for design. Yet, last week’s user test illuminated the opposite: not only do we have to study the expectations, behaviors and peculiarities of the human user; we also have to do the same for these goofy little models.

The success of future AI experiences depends on Design.

It’s been a doomy, gloomy year for the design discipline. We’ve been forced through a reckoning that resulted in harsh layoffs in favor of, well, anything that will pump profit and drive short-term growth. What’s worse, in the corporate environment, there seems to be an even bigger cloud surrounding Design: that the Apple-motivated hype of the 2000’s and 2010’s didn’t deliver value.

Now the next big era of technology is here. AI software companies are projected to grow 63% faster in 2024 than non-AI software companies.

Yet, instead of leading confidently with our unique skills, demoralized designers are stepping back, waiting for engineers to take the lead. If designers are crucial to the success of AI, and AI is essential for the success of businesses, it’s time to confidently reverse reckon our discipline. Just like digital transformation, it will be designers that enable organizations to overcome the monumental task of reinventing experiences and processes from the ground up.

Last week, I wrote about how AI will drive the reinvention of every CX/UX, which I’m calling Experience 3.0. This will also transform the object and output of Design. To summarize…

  • From digital interfaces → AI agents. Just like a product is synonymous today with a mobile app or digital interface. Soon, it will become synonymous with the AI agent. Instead of users being left to navigate an app or interface solo, they will be continuously supported by an AI partner. And the value of every product will be evaluated by the quality of its built-in AI agent.
  • From software features/flow → AI “skills.” In Experience 3.0, if a company wants to create differentiated value for customers, it will have one option: increase the functionality, or ‘skills’, of an AI agent to solve more customer needs. Future product roadmaps will prioritize these ‘skills’ over traditional features. These new ‘skills’ will offer more value without complicating navigation or requiring a user to learn new functionality.
  • From wireframes/mockups/prototypes → scenario maps. Designers will still need to communicate what an amazing interaction feels like. However, the nature of the deliverable will change. Moving away from crafting wireframes that outline fixed paths, designers will now develop scenario maps. These maps will articulate criteria for success across a spectrum of open-ended scenarios and adaptable user journeys, fundamentally redefining what it means to define an interaction.

As we navigate this new paradigm in CX/UX, it’s clear that the role of the designer will also undergo significant evolution.

In Experience 3.0, for designers to remain valuable, they must develop three essential capabilities.

Capability 1: AI Strategy

The term “strategy” seems to intimidate many designers, as if it’s a complex concept they’ve never been taught. However, product strategy is just an action plan to solve customer problems.

This is crucial for companies, especially now, as the AI “arms race” has led them to run frenzied proof of concepts and ship random features that, well, don’t solve real customer needs.

AI is merely a lever to deliver differentiated value to users, and companies need help figuring out which levers they will pull.

Furthermore, companies ought to strategically reinvent their UX/CX with AI, not just tack it on as an afterthought. I wrote about this a few weeks ago.

Prerequisites:

  • A deep understanding of your customer/user’s needs
  • A foundational understanding of GenAI (see Google’s free GenAI learning path)
  • An ability to pinpoint which needs can be solved with the unique capabilities of machine learning. (see Jess Holbrook’s primer)
  • An ability to envision the future AI experience, bringing to life the power of a 3.0 CX/UX. (The best example I’ve seen of this was Dr. Tina Monahan’s talk at the Autonomous Innovation Summit. The recording is available here.)

Capability 2: AI Interaction Design

In the AI era, interaction design looks entirely different. We’re used to designing along linear pathways with predictable outcomes, but now, the introduction of open-ended interactions and flexible paths brings limitless possibilities.

We’ve moved from designing “waterslides,” where we focused on minimizing friction and ensuring fluid flow — to “wave pools,” where there is no clear path and every user engages in a unique way.

In this new landscape, like a wave pool, our objective is to establish the conditions that will lead to a safe and valuable experience for users.

Interaction design will become far more abstract than applying UI components based on standard patterns. It will demand a deep understanding of user expectations as they begin the experience, the ability to pinpoint what success and failure look like, and a theoretical eye for potential risks or vulnerabilities.

Google’s People+AI initiative highlights four critical areas for consideration in designing an AI interaction:

  1. acceptable actions
  2. unacceptable actions
  3. thresholds of uncertainty
  4. vulnerabilities

This framework offers a valuable guide for designing effective AI interactions.

Prerequisites:

  • An ability to evolve user testing methods for AI functionality
  • An ability to define the criteria surrounding a successful interaction (see Google’s approach to Interaction Policies)
  • An understanding of emerging best practices for interactions (see IBM’s GenAI design principles)

Capability 3: Model Design

Traditionally, there’s been a clear division between the land of engineering and design, with the best designers and engineers occasionally crossing over for productive discussions before retreating to their respective kingdoms.

Now, however, natural language processing allows for direct interaction with large language models, drastically reducing the divide between the two disciplines.

Instead of writing a line of code, you can clearly instruct the model on its mission by writing a prompt. And the strange thing about training a model on the entire corpus of human text is that it takes on many of the peculiarities of humans. (I mean, even using a smiley face emoji can also increase performance.)

This presents an opportunity for designers to apply their skills in understanding and empathizing with users directly to AI models.

In fact, I’m convinced that the average designer’s prompt writing ability might just outshine that of the average engineer. 😲 Because designers have extensive experience in distilling complex user requirements and clearly communicating needs.

To do this, designers need to move beyond ‘wizard of oz’ experiments and get comfortable informing the technical execution of an ideal experience.

Prerequisites:

  • An understanding of design’s value in model development process (see Paz Perez’s illustration of the ‘model designer’)
  • An expertise in writing effective prompts (see Open AI prompt engineering guide)
  • A foundational understanding of how an LLM works (see Stephen Wolfram’s summary)

Let’s face it: no one’s going to sit around waiting for designers to become valuable in the AI world.

But here’s the thing — companies really need what designers have got in their toolkit, especially their knack for solving hard problems without obvious answers.

When imposter syndrome strikes, just remember, nobody has all the answers. This whole AI thing is new for pretty much everyone, and we’re all trying to figure it out as we go, together.

This article was originally published in Empathy & AI, follow for more human-centered AI content or reach out on Linkedin.

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