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Best practices for designing AI tools in 2024

Let's look at new AI design trends and learn how to create a more responsible, human-centered, and empathetic design.

3 January, 2024
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AI and design... Two forces intertwining more intricately with each passing year since AI tools are getting more and more popular. They're part of our everyday life now. So, naturally, AI trends are something we need to start considering even more thoroughly.

As 2024 rolls in, let's talk about what's new and what's working. We'll look at new AI trends and learn how we can talk to machines just like we talk to each other. We'll share tips on making AI assistants and peek at the newest design trends. Stay tuned for our own experience making and designing an AI tool.

AI and design: a current overlook

In 2023, the design of AI tools has taken a significant leap forward, transforming AI from a simple tool to an integral partner in various industries. This shift is evident in innovative products like Tempo Move and bionic wearables, which represent a growing trend of integrating AI seamlessly into everyday life.

The evolution isn't just in physical products but also in how AI is designed to interact, learn, and adapt. Designers are now prioritizing user-friendly interfaces and intuitive interactions, making AI more accessible to a wider audience. They are blending traditional design principles with cutting-edge technology to create AI tools that are not only functional but also engaging and easy to use.

The focus is increasingly on creating AI that understands and enhances human experience, marking a move towards more empathetic and adaptable tools. As AI becomes more embedded in our lives, designers are ensuring that these tools respect ethical considerations and enhance our capabilities without overstepping.

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AI design has recently seen notable shifts, particularly with the widespread adoption of generative AI in businesses. Executives are harnessing these tools not just for operational efficiency but also to drive innovation and creativity. Designers and developers are crafting AI applications that are not only functional but also intuitive and user-centric.

The aesthetics of AI interfaces have evolved, focusing on simplicity and ease of use while embedding advanced functionalities. There's a growing emphasis on personalization and adaptability, with AI systems designed to learn and evolve according to user preferences and behaviors.

However, the rapid incorporation of AI in design raises significant challenges, particularly in ethical and responsible deployment. Companies were and still are increasingly aware of the need to develop AI with transparency and accountability, integrating ethical considerations right from the design phase. This involves setting clear guidelines and adopting strategies to mitigate risks, particularly those related to inaccuracies or biases in AI systems.

Leading organizations were setting AI trends by incorporating AI into their design philosophy, extending beyond mere cost-cutting. They've been investing in areas like Machine Learning Operations (MLOps) to ensure that AI and ML products are not only technically robust but also aligned with broader design and business objectives.

In 2024, trends in AI and design will be shaping how we interact with technology, making AI more integrated, ethical, and collaborative. Let’s start with AI development trends.

AI development trends 2024
AI development trends 2024

Generative AI will evolve from experimental to practical, now featuring prominently in everyday tools. It's going to redefine productivity through voice-enabled AI assistants and coding aids, and content creation tools like text and image generators. 

As large language models become more prevalent, the focus will also shift to understanding and mitigating their unpredictable behavior and inherent biases. Through ongoing research and improved control methods, we're moving towards more reliable and unbiased AI interactions.

With AI's expansion, there's going to be an increased global push for regulation as well. Governments and organizations are and will continue working to ensure ethical development, transparency, and safety in AI applications. This means designing AI that is not only effective but also fair and accountable.

Consequently, the demand for transparency in AI's decision-making processes is leading to the rise of explainable AI. This trend will emphasize the importance of making AI's logic understandable and accessible, enabling users to trust and effectively use AI technologies.

Finally, enhancements in AI are going to be focused on augmenting human abilities, not replacing them. Whether it's predicting customer behavior, automating data analysis, or powering research and creativity, AI is increasingly designed to collaborate with humans, enhancing our capabilities and efficiency.

Our Merge designers have also compiled a list of what they think the AI design future will hold based on their extensive research, experience, and Artificial Intelligence market trends. Here it is:

  1. UI/UX focus. Let us tell you how many AI tools we have seen that have horrible designs. Functionality is good, but not at the expense of beauty. Especially not in digital solutions. 2024 will have to bring an elegant AI UI. We have, read on to see what became of that.
  2. Simplicity or the good ol’ minimalism. Minimalism and simplicity will also dominate AI design trends. AI tools are being created now with a focus on uncluttered designs, essential elements, optimizing layouts, and harmonizing color palettes and typography.
  3. Storytelling. Data storytelling will be transformed in 2024, enabling more compelling narratives based on complex data sets. AI assists in extracting meaningful insights and will also have to present them through engaging visualizations and narratives. 
  4. Advanced motion design. In 2024, AI motion design will contain more complex and personalized animations and interactions. Designers will create fluid, responsive motion elements that react to user behavior and environmental variables.
  5. Voice UI. Voice User Interfaces will dominate in 2024 as AI continues to refine voice recognition, understanding, and generation. This trend will see an adoption of voice interactions, providing users with a more natural and hands-free mode of operation.
  6. Conversational AI. Chatbots and conversational interfaces will become more sophisticated in 2024, thanks to natural language processing, providing more natural, efficient, and contextually aware interactions, closely mimicking human conversation. 
  7. Buttonless UI. The rise of buttonless interfaces will lead to cleaner, more fluid interfaces in devices and AI applications, enhancing the overall user experience by making technology feel more natural and less intrusive.
  8. Inclusivity. In 2024, AI will promote universal, inclusive, and accessible design principles. AI technologies will have more adaptive and responsive designs, catering to a wider range of human diversity, including individuals with disabilities.
  9. AR and MR. AI-driven MR and AR will become more accessible and prevalent in various sectors, including entertainment, education, and marketing, as they offer enriched, context-aware digital interactions.
  10. Emotional design. The trend of Emotional Design will focus on analyzing and understanding user emotions, allowing for designs that cater to emotional well-being and deliver more engaging and satisfying experiences.

Feedback-driven AI design process

In AI design services, adopting a feedback-driven process involves a cyclical development routine where each phase - planning, designing, building, testing, and reviewing - is informed by user feedback, ensuring that the final product is not only innovative but also highly attuned to user needs. 

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At the heart of this method is a strong focus on the end user. By regularly gathering insights on their experiences, preferences, and challenges, designers can craft AI tools that are not just powerful but also intuitive and satisfying to use. This user-centric mindset continues even after deployment, with ongoing assessments of the AI's performance against key metrics, ensuring that the tool evolves and improves over time.

A few real-world cases illustrate the approach's effectiveness. For instance, AI practitioners often develop "golden test sets" based on user feedback, adapting AI products like chatbots for precise customer needs​​. In audio transcription, OpenAI's Whisper model's performance was refined through extensive testing across various accents, identifying weaknesses in handling non-native English speakers​​. 

Similarly, a breast cancer classification project employed metadata analysis to pinpoint higher error rates in denser breast tissues, mirroring challenges faced by human radiologists​. These examples underscore the practical impact of continuously integrating user feedback for targeted, ethical, and inclusive AI solutions.

Accessibility in AI design

Accessibility in AI design means aiming to resonate with all users regardless of their abilities. AI enhances this by automating accessibility checks, improving design processes, and personalizing experiences based on user data. This technology not only makes tools more intuitive but also revolutionizes user testing and support through AI-driven diagnostics and virtual assistance.

However, it's important to note the limitations and potential of AI in this space. Technologies like Apple's VoiceOver are integrating AI to better describe pictures and images, making apps more accessible and demonstrating how AI can augment existing assistive technologies. 

Yet, while AI has made significant strides in interpreting and making content more accessible, it requires constant refinement and should not replace the need for human insight, especially when addressing the nuanced needs of diverse users.

Using natural language when designing AI

When it comes to weaving natural language into AI tool design, there are many strategies and considerations you can use to bring out the full flair of language in interacting and guiding AI systems. 

First of all, to make AI tools truly savvy, train them on a wide array of texts. This exposure helps them appreciate various styles, dialects, and the nuanced spectrum of language. Beyond just words, enabling AI to perceive the context, including user intent and the surrounding scenario, makes interactions much more intuitive and meaningful.

Feed your AI a diet of diverse literature, scripts, dialogues, and other creative concoctions. This diversity is not just about volume but variety, including multilingual content, which sows the seeds for cross-cultural creativity and a richer understanding of human expression. 

A big part of today’s product design is personalization. AI is no different. Have users mold the AI's creative style or output to their liking. Whether it's in writing, visual arts, or music, personalization lets the AI resonate more deeply with individual preferences. 

Also, why stop at text? Incorporate various inputs like images and sounds to provide a more layered and inspiring context. Sensory data can also be a goldmine, enriching the AI's responses with emotional intelligence and a better grasp of the physical world.

Here’s another advice - design AI systems that don't just learn but evolve. Encouraging continual learning and a flair for experimentation ensures the AI remains at the cutting edge of creativity, surprising and delighting users with unconventional ideas and outputs.

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Designing AI assistants

To begin with, it's crucial to differentiate advanced AI assistants from their rudimentary chatbot cousins. AI assistants use AI, NLP, and machine learning to navigate complex human interactions and personalize content, stepping far beyond predefined Q&A patterns to truly understand user context​​.

Current practices are pretty much grounded in user research and a systems-thinking approach. Merge would suggest that you make the interface of your AI assistant as clear as possible. Examples like ChatGPT demonstrate the importance of guiding users through their AI interactions. 

These systems are designed to handle multi-intent conversations and generate responses that are relevant, ethical, and contextual. The user interface thus becomes not just a medium of interaction but also an educational tool, showcasing the system's capabilities and limitations​​.

For example, this company recommends seven best practices for designing AI assistants: 

  1. Broadcast the use of AI. Clearly communicate the assistant is AI to manage expectations about its capabilities and limitations, differentiating it from human agents and ensuring users understand they're interacting with a machine.
  2. Use goal-based UI. Utilize call-to-action buttons to guide users towards their objectives efficiently, facilitating quicker, goal-oriented interactions and decisions while embedding user confirmations for legal compliance and user control.
  3. Use source links as trust markers. Enhance trust by visibly indicating the sources of information, distinguishing between assistant-generated content and factual data pulled from reliable external sources, thereby making the assistant's responses more credible.
  4. Follow up as a default response. Proactively ask clarifying questions to understand user queries better, avoiding dead-ends by guiding users closer to resolution and offering alternatives when the assistant reaches its limits, demonstrating comprehension and adaptability.
  5. Design a clutter-free interface. Design with minimalism to focus user interaction on conversation, using self-dismissing banners and universal icons to maintain a clean and straightforward user interface that enhances engagement without overwhelming users.
  6. Always aim for transparency. Be upfront about the assistant's capabilities and limitations, particularly around data security and privacy concerns, using disclaimers and transparent communication to build user trust and set realistic expectations.
  7. Encourage quick and consistent feedback collection. Encourage and make it easy for users to provide immediate feedback on responses, facilitating rapid improvements and adaptations in the assistant's performance and aligning the assistant's learning with user satisfaction and needs​​.

Our experience with designing AI

At Merge, we created Promtify, an AI-powered chat tool designed to make work conversations easier and more effective. Our journey with Promtify is not just about building an app, but making an easy and enjoyable experience for everyone.

Our own AI tool Promtify
Our own AI tool Promtify

Our journey started with a basic prototype to test if our idea could work. We used OpenAI's API to create a user-friendly design. We quickly realized that to make Promtify stand out, we needed to make it not just functional but also enjoyable to use. So, we added a better design, more features, and a system to support these changes.

Choosing the right technology was key. We used Next.js for our app's front end, Supabase for managing user data, Vercel for hosting our app, and Stripe for handling payments. We also used tools like Google Tag Manager and Hotjar to understand how people use Promtify. Over two months, we worked on everything from setting up the basic technology to adding features and getting the app ready for users.

We also faced some big challenges, like creating an easy-to-use template editor. We chose Slate.js for this because it was flexible and had good documentation. We worked hard to make the editor more user-friendly based on feedback from people using it. 

Promtify is all about making daily tasks simpler and quicker. Features like saving templates and sharing them make routine work more efficient. We also focused on making Promtify look good and work well, which makes it different from other similar apps. This journey was always about creating a tool that's helpful and enjoyable for all.

Conclusion

As we look at 2024, the focus on creating robust, ethical AI is more pronounced. As AI becomes an integral part of the business toolkit, its design is set to become more human-centered, reflecting a broader shift towards intelligent, responsive, and responsible technology solutions.

The design of AI tools is also all about balancing innovation with practicality. It's about creating tools that solve real-world problems while also being accessible, ethical, and enhancing human creativity. The future promises AI tools that are more intuitive, empathetic, and capable of working alongside humans to unlock new possibilities in every field.

author

CEO and Founder of Merge

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

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