User-centric design is one of the hottest trends in product design, and AI is another. But how do these two ideas go together? Engaging in user-centric AI design means creating products leveraging AI tech that reflect users' needs and pain points.
With 64% of business owners believing AI-powered solutions have the potential to improve customer relationships, there's clear positive momentum behind designing user-centric AI experiences. Now, it's up to designers to internalize the best practices of this emerging field.
There are a few key differences between AI-enabled technology design and creating traditional software. The AI algorithms at the core of the solutions are complex to work with. They:
Design for AI user experiences must account for those differences. AI UX needs to keep the complexity of the algorithms under the surface, presenting an intuitive, user-friendly interface. The AI under the hood is what makes these applications powerful, but the UX ensures they serve users' needs and values.
This combination of powerful functionality and easy usability is the essence of AI UX design. The goal is to create systems that are intelligent — but also intuitive, ethical and empathetic.
Creating effective UX for AI isn't just a matter of navigating the divide between complex algorithms and simple UI. There are specific pain points for designers to address. These include:
Together with addressing the challenges outlined above, designers should focus on creating solutions that suit their end users' needs. This is where simple AI design gives way to user-centricity. Specific focus areas include:
User-centric technology leveraging AI should focus on users' preferences and requirements. The systems should focus on simplifying the tasks users perform in their day-to-day lives and enhancing their experiences. The UI should also make it clear what the app's capabilities and limitations are, as well as its policies around data use.
Designers should consider:
One possibly overlooked facet of usability is avoiding features that don't have practical value. Adding AI for its own sake, and not because it represents a real upgrade, is merely adding to the glut of products capitalizing on AI's hype and name recognition.
Creating AI-enabled technology is very much like any other piece of software. That means following best-practice design principles, such as a focus on accessibility to as large a swath of potential users as possible.
The presence of AI features allows designers to go further than before when creating accessibility tools. This can mean enabling multiple ways to interact with the algorithm, including voice and written text. The responses should be clear, whichever format users choose.
A user-centric UX design for AI shouldn't make the user feel forced into any actions. There should be a clear, easy way to correct a wrong answer or to opt out of a recommendation. Users should be aware of every action the AI takes, especially ones that directly affect them and their work or input.
The key is for users to remain in control, using the AI as an aid rather than something replacing their efforts. The AI is making suggestions, not anything more definitive, and users should be able to edit and change their decisions. Straightforward settings and preferences should allow users to tailor the AI's behavior and set conditions for overriding its suggestions.
Designing for transparency means making sure the AI's decisions are explicable to its users. This is a challenging feature to design, but it is an essential part of building trust.
There's a two-stage process for achieving an acceptable level of transparency:
Designers who have internalized the challenges of AI design, as well as the major functional considerations, can move on to the specific principles and priorities of designing user-centric AI experiences. These include:
Designing predictive user experiences as part of an AI UX means creating interfaces that turn past user interactions into data points. For example, a weather app could study past user input to guess which forecasts that individual wants to see first.
This is a simple and functional use case for AI, customizing the app UI on the fly to be more immediately useful. Our guide to building machine-learning interfaces can guide designers through the process.
Designers should always have error handling in mind when engaging in AI user interface design work. Rather than assuming the AI will always be right — or glossing over its mistakes — the solution should admit its errors and provide alternatives.
Users should also have self-driven tools that allow them to report issues and make corrections. In addition to obvious mistakes, these reporting features can also log odd or unexpected behavior by the algorithm.
Positive user experiences with AI-enabled technology come in part from individual knowledge and familiarity with the technology. To ensure users' expectations align with the technology's features, it's valuable to create a clear onboarding tutorial explaining how the algorithm uses data and makes decisions, along with its limitations.
When technology leveraging AI doesnn't incorporate educational materials, users may come in with inflated expectations, leading to a disconnect when they actually use the system. A user who assumes an AI-enabled tech tool will effortlessly or immediately deliver personalized experiences may be disappointed, leading to a general distrust of AI that hurts overall uptake.
The methods used to collect feedback are key parts of AI interface design. There should be formalized input loops wherein users can tell the AI how accurate its responses, calculations or recommendations were. The responses then become part of the training data set, helping the AI shape its future output.
Feedback systems can be as simple as a thumbs up/thumbs down interface or a star rating system. Asking users to rate the AI's performance frequently creates an engine for continuous refinement.
Since AI is such a fast-moving and rapidly evolving technology area, the future of user-centric design will involve constant change to suit the latest capabilities. Perhaps the most important area of focus through this process will be data privacy ethics. Users' information is the fuel for AI algorithms, but collecting more data than necessary can erode user trust, as can the irresponsible use or storage of that data.
It's important to note that best practices may involve implementing more stringent data use policies than legally required. In periods when the law falls behind technological capabilities, it's worth treating legal compliance as a starting point and protecting data further.
When you're ready to start applying design and development projects with best UX design practices for AI, it can pay to work with an expert partner that brings experience with cutting-edge technologies. You can fill in knowledge gaps and add hard-to-find expertise, helping you make the most of AI and other evolving tech areas.
Whether you're interested in a design consulting engagement, hands-on full-cycle development or anything in between, Transcenda has the experience to help your team thrive. Our experts can help you integrate AI and other cutting-edge technologies in helpful, user-centric ways, delivering real value instead of hype.
