The purpose of this article is to help you begin designing with artificial intelligence (AI). This isn’t just a battle cry that “you too can become an AI designer!” but rather a reflection on our experiences exploring the intersection of AI and design —creating what we at Rangle call Smart Experiences. Think of Smart Experiences as experiences that feel “smart” and that help you avoid tedious or repetitive work. A Smart Experience can be as simple as an auto-complete suggestion and as complex as a virtual agent capable of answering your every question.

We’ve identified two key requirements for successful collaboration between AI and Design: strong communication and a few modifications to the typical project workflow.

This series of practical tips is informed by what we’ve learned so far to help you and your team begin creating Smart Experiences of your own. We’ve written this guide to empower AI enthusiasts from all disciplines at any stage in their careers.

How AI and Design teams can improve communication

Do your homework

When it comes to designing with new tech, it’s easy to get stuck in your head. AI, like any new technology or framework, can feel overwhelming at first. The best way to tackle this fear of the unknown is to not overthink it, just start with some research. Our approach to research consisted of a bit of theory and a bit of practicality.

In order to understand the basics and speak confidently with AI developers, we familiarized ourselves with key AI and ML concepts. We explored numerous resources that helped our team build a solid foundation of knowledge around this emerging topic. Google’s People + AI Guidebook and Awwward’s AI-Driven Design piqued our curiosity, provided digestible information in relatable terms, and helped us realize that as designers we can have an impact in this new field. We recommend both of these resources as a great place to start.

To ground this theory with practical applications, we acquainted ourselves with existing design solutions that use AI. Quickly, these vast and initially intimidating concepts gave way to reveal that machine learning was simply a way to personalize recommendations on Netflix, or help people find inspiration using Pinterest’s visual search. By acquainting ourselves with real-world examples we could see how other designers were using AI to solve for human needs and it helped us understand what kind of experiences could potentially be enhanced with AI.

Talk to each other

We dedicated time to figuring out how our design and AI teams could communicate more effectively. We worked towards finding a common language with the intention of understanding the fundamentals of each other’s practices.

We ran introductory workshops to get to know each other. We explored the basics of AI and ML and invited our AI team to learn more about design thinking and human-centered design. These workshops highlighted the unique skills we bring to the table and helped us better understand the current landscape of our respective domains.

We created a space where people felt confident exploring outside of their comfort zones. We kicked off one of our first AI and design workshops with Atlassian’s “Exorcise the demons” icebreaker. AI developers and designers were asked to brainstorm the worst applications of AI and ML they could imagine in 5 minutes and then present their terrible ideas back to the group. A lighthearted activity like this one helps participants resist the urge to self-censor when tasked with ideation, especially in situations they may not be used to. There’s nothing like a few laughs to help everyone feel at ease.

We make an effort to meet for coffee or walks to speak candidly about the projects each team is working on. More often than not, these conversations offer unexpected perspectives and exciting opportunities to work together.

How to work on a project together

Start with a human problem

The first time you work on a project that leverages AI or ML, you may feel like you have no idea what you’re doing - and that’s okay. As human-centered designers we’re equipped to tackle all kinds of ambiguous challenges. According to IDEO: “The designer's mindset embraces empathy, optimism, iteration, creativity, and ambiguity. […] A human-centered designer knows that as long as you stay focused on the people you're designing for—and listen to them directly—you can arrive at optimal solutions that meet their needs.”

You don’t need to know everything about AI to begin designing for it. In fact, you could even consider a lack of experience as a quality that may encourage curiosity and an open mind while learning the ropes.

Stop worrying about the unknowns and try leveraging your expertise with design thinking when collaborating with AI developers to keep everyone focused on the people you’re designing for. Google’s Josh Lovejoy and Jess Holbrook said it best: “If you aren’t aligned with a human need, you’re just going to build a very powerful system to address a very small   –or perhaps nonexistent–  problem.”

By getting to know our AI team we learned that designers and AI developers share a common goal. AI developers are creative, natural problem solvers and use many of the same approaches as designers to make sure that they are creating solutions to actual, human problems.

Our AI team created a list of common keywords and flags to help identify pain points that could be mitigated through AI or ML.

Determine the right level of fidelity

We like to start conversations between AI and Design during the discovery phase of a new project when creative, uninhibited thinking is often encouraged. As designers we’ve always been comfortable ideating and asking “how might we?”, but AI developers have the technical know-how to help us validate ideas and bring them to life.

We’ve found journey mapping to be an excellent catalyst for user-centered collaboration between AI and Design.

Journey maps are great because they highlight user pains and goals without jumping to concretized solutions. Having a solid understanding of your user’s journey will help your entire team stay focused on what matters most. A higher level of fidelity, such as wireframes, may constrain or bias thinking. Journey maps provide the right level of fidelity for developers to understand user needs and identify which aspects of the experiences could be solvable through AI.

This level of fidelity allows AI developers and designers to ideate on solutions together. Working together at this early stage ensures that the tech will be seamlessly integrated into the design. It is important to avoid trying to shoehorn AI into a higher fidelity solution later on down the road. Collaboration during discovery helps us avoid AI for AI’s sake and keeps us focused on our users.

By using a journey map as an artefact to facilitate collaboration, we were able to identify that a moment when users felt frustrated by a high number of repetitive, manual administrative tasks could actually be alleviated through automated Intelligent Content Extraction (ICE).

Try inviting AI developers to join you when ideating on a new build or feature, especially when your team needs to get creative designing new solutions. Collaboration between AI and Design is the key to uncovering desirable, viable and technically feasible solutions for your users.

Conclusion

By following these simple steps, we've been able to successfully integrate AI and design on multiple engagements. Although we now have a few projects under our belts, this process is still a work in progress - and we keep learning more every day. We’d love to hear from you: Have you worked on any Smart Experiences? What did you find helpful? Let’s keep the conversation going.

Learn More

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