From the dawn of human civilisation to today, every tool we have used has thrived on the crucial attribute of discoverability. Tools succeed when their presence is obvious, their function easily understood, and their purpose clear. These guiding principles that started with the first embers of fire still underpin the design of today’s most advanced AI tools.
According to Corlytics, achieving discoverability requires users to navigate three key levels.
The first level, the visceral stage, asks whether the tool is visually appealing. Next, the behavioural stage considers whether users can easily understand what the tool does and how to use it, and whether clear instructions or feedback are provided if needed.
Finally, the reflective stage evaluates the overall impact: can users gauge the tool’s effectiveness, and was the experience enjoyable? Together, these stages determine whether a tool leaves a lasting, positive impression.
The reflective stage is arguably the most important, as it shapes how users remember the experience rather than the experience itself.
Memory often emphasises positive elements and downplays negatives, much like how fond recollections of family Christmases often surpass the mundane reality recorded in a journal.
This principle is critical when evaluating AI tools: users’ lasting impressions of these tools often outweigh the day-to-day interactions.
Currently, AI tools face unique challenges in discoverability. Many promise solutions to users’ goals but fail to guide them clearly through the process.
Feedforward mechanisms are often inconsistent, in-app cues are sparse, and no standard mental mappings exist for interacting with AI.
Users are experimenting alongside creators, and feedback loops can be chaotic. The result is confusion, frustration, and a perception that using AI is cumbersome, despite the tool’s potential.
Minor delays or small errors in AI output are often overlooked, but uncertainty—such as not knowing if the tool worked or how to correct a problem—provokes a strong negative response.
This has elevated usability as the defining factor in adoption. Users may abandon technically superior AI solutions for ones that feel easier to navigate, even when performance differences are minimal.
Designers play a pivotal role in shaping these interactions. Breaking complex processes into manageable steps and gradually revealing functionality prevents users from feeling overwhelmed.
Beginners should experience a different interface than power users, and transparency is critical as AI tools must communicate reasoning as clearly as results. Visual cues, summaries, and notes help users understand the tool’s decisions, while proactive feedback—guiding users through errors and suggesting next steps—enhances confidence and satisfaction.
The “acceleration effect” is the outcome of well-designed AI. Adoption is no longer a question of whether but how quickly users embrace a tool, and speed of adoption is dictated by usability.
Clear design principles flatten the learning curve, improve reflective memory, and transform early users into advocates. Positive experiences encourage experimentation, deeper engagement, and the development of mental mappings that are currently absent, highlighting the essential role of designers in AI’s success.
Read the full blog from Corlytics here.
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