AI Features That Users Actually Want

Not every workflow benefits from AI. The features worth building are those where: (1) the output quality is meaningfully better than the non-AI alternative, (2) the AI doesn't need to be perfect — near-perfect is sufficient and errors are catchable, (3) the time savings are real — users would pay for this time saving, (4) the cost is proportional to the value delivered. AI features that reliably work in productivity/professional apps: document summarisation, content generation from structured data (reports from form data), classification (routing support tickets, categorising feedback), extraction (pulling specific data from unstructured text), and translation between formats (data → narrative, narrative → structured JSON). AI features that often disappoint: anything requiring perfect accuracy in high-stakes contexts (medical diagnosis, legal advice without review), real-time features where latency exceeds 2 seconds (users stop waiting), and features that require deep understanding of the user's specific context that isn't provided in the prompt. For BathCheck: 'Generate a professional assessment summary from the completed form data' is a perfect AI feature — it saves the OT 15 minutes, produces 80% of the final text, and the OT reviews and edits before sending. 'Automatically determine whether a client is at fall risk' is problematic — AI errors in this context have clinical consequences that professionals can't accept.

For healthcare and accessibility apps, AI features require additional consideration around liability, accuracy, and user trust. The key principle: AI should augment professional judgment, not replace it. Every AI output in a clinical or care context should be framed as a suggestion requiring professional review, not a definitive determination. Implementation patterns for responsible AI in healthcare apps: (1) Always show confidence levels or caveats on AI-generated content ('This summary is AI-generated — please review for accuracy before sending'). (2) Make it easy to edit or reject AI suggestions — the UI should treat AI output as a draft, not a final product. (3) Log AI outputs and user edits — this creates a dataset for quality improvement and demonstrates the human-in-the-loop process for compliance purposes. (4) Provide a 'why' alongside the AI output where possible — 'This area was flagged because...' builds user trust and allows meaningful review. For pitching BathCheck to NDIS providers, the responsible AI framing is actually a selling point: 'Our AI accelerates documentation while keeping the OT in control of every clinical determination.' This directly addresses their compliance and liability concerns.