GenAI is effective in areas where its capabilities align with complex or data-driven tasks. It excels at understanding natural language, making it valuable for conversational interfaces and text analysis. GenAI can produce tailored recommendations that improves user experience. It can also predict future events based on patterns in data, detect categories efficiently, and identify rare or evolving events. Additionally, it shines in automating specific repetitive tasks and delivering dynamic, context-aware content.
Automation is best for tasks that need to be streamlined, made safer, or completed faster. It works well when people lack the skills, resources, or willingness to do something, especially for repetitive, monotonous or risky tasks.
Assistance helps users without replacing them. It’s useful when people enjoy the task, are responsible for its outcome, or when the stakes are high. Assistance is also great for tasks with personal or hard-to-explain preferences. The key question is: “Do I need AI to do this for me, or just help me along?”
Assistance works best when it makes tasks more enjoyable and engaging for users. Success is achieved when users maintain control over the AI, feel responsible and fulfilled, and accomplish more with less effort and feels confident about the results. Effective assistance enhances user creativity, enabling them to achieve better results.
Traditional product design prioritizes consistency, ensuring uniformity in functionality and behavior across the entire solution. In contrast, GenAI operates differently, producing diverse outputs for the same input. This presents opportunities, such as exploring multiple responses to gain insights and possibilities. However, it also introduces a dimension of ambiguity, including invalid outputs, incorrect interpretations, and confusing outcomes.
From a design perspective, this presents a challenge: how to ensure that unexpected elements function and behave as intended? While this may not necessarily be an issue, it is beneficial to approach this aspect of design with the mindset that GenAI should work and behave in a specific manner, although occasional errors may occur. This shift in perspective from consistency to tradeoffs allows for a more nuanced evaluation of design decisions. The primary question to consider is whether the lack of consistency in a particular aspect is acceptable when it aligns with a greater benefit.