You may not have gotten a promotion, but now you’ve got a whole team to support you, including assistant designer, strategist, editor, and creative partner in the form of AI tools. Historically, designers moved from hands-on design to a lead designer role, giving direction and feedback. Now, we all need to do it.
AI is changing the work we do as designers and how we work. This series investigates the potential transformative impact of artificial intelligence on product design.
- Trends and AI impact on design – Exploring AI-driven trends and their impact on design
- The evolution of design practice – What is this means for design and designers
- Future-proofing designers – The essential skills and mindset designers need in the age of AI
What AI Means for Designers
According to Open AI Ambassador Abran Maldonado, “Someone who knows AI will take your job before AI does.” Every time a new technology revolutionizes an industry, there will be winners and losers. Designers who learn to work with AI to improve their efficiency, creativity, and deliver great products are most likely to be the winners.
Designers will need to rethink their value within AI-enhanced teams. Sketching, conducting interviews, creating wireframes, and mockups are tasks that might not be as important to a designer’s job in the next decade. We may spend a whole lot more time in conversation with AI giving instructions and feedback.
We will have a team of AI to support us in roles such as strategist, assistant, coach, board member, or creative partner.
- Strategist: Perplexity can help you run a SWOT analysis on your product.
- Creative partner: Autodesk’s Dreamcatcher can be a creative partner, generating design alternatives based on specific goals and constraints.
- ChatGPT can coach you on preparing a presentation to your leadership team.
The inevitable surge of AI-generated designs could saturate the market with generic outputs. It underscores the importance of maintaining a distinct personal and professional identity in an AI-driven design landscape to stand out. Leveraging AI for rapid prototyping and research assistance can make it easier for us to learn faster and then take that learning into Figma to create something meaningful and valuable.
Designers must adopt continuous skepticism towards what we’re learning and seeing from AI. Before reacting, it’s wise to verify sources and ensure the output meets our standards. Understanding how to manage risks, including bias, ethics, sourcing, and explainability, is imperative to utilizing AI responsibly.
We need to learn how to design iterative improvements that take advantage of the dynamic nature of AI systems. This requires a new approach to iteration. AI-enabled products should be designed with mechanisms for continuous monitoring and improvement, similar to how Netflix uses AI to enhance its recommendation engine continuously, thereby constantly improving the user experience.
It can be scary to look at a way of working and thinking that feels so different from what we’ve done in the past or what you were taught at school. Change is an opportunity, and the opportunity here is to move beyond being a “pixel pusher” making things pretty and become a deep and creative thinker able to create new things with wildly powerful tools while ensuring those things serve society and don’t further fray it.
How the Products We Design Need to Evolve
Initially, users will require extensive guidance and support as they begin utilizing AI tools. Setting clear expectations and providing comprehensive tutorials, as seen with tools like Adobe Photoshop’s AI features, can significantly enhance user competence and confidence.
For a long time, we’ve relied on classic principles or heuristics to guide and evaluate our work. We expanded them to consider the mobile context and include concerns like data privacy. As I thought about what to add for AI-enabled products, it became clear I needed to step back and rethink the entire set. Here is what I propose, and I hope I don’t get flamed too much.
- Useful: Effectively meet users’ needs and enable them to accomplish their goals efficiently, providing relevant and valuable functionality.
- Supportive: Support and empower users by giving them decision-making authority and the ability to provide feedback. Reduce workload, provide feedback, structure, sequencing, help, prevent errors, and make it easy to correct any errors that occur. The product should prioritize supporting the user rather than dictating actions.
- Inclusive and Accessible: Be simple to use for a diverse range of users. Proactively detect and mitigate unfair biases in data and algorithms, align with ethical principles and societal values, ensure responsible deployment practices, and prevent discrimination against any user groups.
- Transparent and Understandable: The AI system should provide transparency into its data sources and decision-making processes to build trust and accountability with users. It should use visualizations, confidence scores, and plain language explanations to ensure that its operations and outputs are understandable to users, prioritizing user comprehension over technical explainability.
- Trustworthy and Ethical: Include safeguards, fallback mechanisms, and clear boundaries to ensure reliable, safe, and ethical operation, even in unexpected situations or adversarial conditions. It should adhere to ethical principles and prioritize user well-being.
- Adaptable and Continuously Learning: Incorporate mechanisms for continuous learning from user interactions and feedback loops, allowing it to adapt and enhance its performance based on real-world usage data and user input. It should evolve and improve over time to better meet user needs.
- Privacy and Security: The AI product should implement robust data protection measures to ensure user privacy and security. It should give users control and choice over their data collection and usage, adhering to privacy regulations and best practices.
Every organization should develop principles that uniquely speak to their customers and business needs, drawing inspiration from platforms like Design Principles FTW, where various companies have shared their approaches.
The third installment of this series will explore what new skills designers need, which skills should be enhanced, and how we should approach teaching and developing designers for this AI-enhanced future.
Image: I usually make all the images on my site because it’s fun for me but for an AI series I had to go with Dalle