Personalisation & Adaptation

AI systems that learn your preferences and adapt to your working style can be remarkably effective. An assistant that remembers you prefer concise responses, a recommendation engine that understands your actual taste rather than demographic averages, a tool that adapts its level of explanation to your expertise - these create genuinely better experiences. But personalisation introduces its own set of challenges. Filter bubbles, where the system shows you only what it thinks you want, can narrow your perspective without you realising it. Over-personalisation can feel invasive, especially when a system demonstrates knowledge of your habits that you didn't consciously share. And adaptation can create lock-in: once a tool has learned your preferences, switching to an alternative means starting from scratch. Good personalisation is transparent (you can see what the system has learned about you), controllable (you can adjust or reset preferences), and bounded (it enhances rather than replaces your choices). The goal is a tool that gets better at helping you over time without quietly constraining what you're exposed to or making you dependent on its particular way of working.