Avoid implementation paralysis by focusing on the majority of use cases rather than rare edge cases. The fear that an automated system might mishandle a single unique request shouldn't prevent you from launching tools that will benefit 99% of your customer interactions and drive significant efficiency.

To drive adoption, changing the default from opt-in to opt-out is far more effective than simply reducing friction. When a company automatically enrolled new employees into a 401(k) plan, participation jumped from 50% to 90%, demonstrating the immense power of status quo bias.

Before implementing a chatbot or complex tech to drive user action, first analyze the user flow. A simple change, like reordering a dashboard to present a single, clear next step instead of five options, can dramatically increase conversion with minimal engineering effort.

The most significant opportunity for AI in healthcare lies not in optimizing existing software, but in automating 'net new' areas that once required human judgment. Functions like patient engagement, scheduling, and symptom triage are seeing explosive growth as AI steps into roles previously held only by staff.

The friction of navigating insurance and pharmacies is so high that chronic disease patients often give up, skipping tests or medications and directly worsening their health. AI can automate these tedious tasks, removing the barriers that lead to non-compliance and poor health outcomes.

By analyzing real-world data with machine learning, Walgreens can identify patients at risk of non-adherence before a clinical issue arises. This allows for early, personalized interventions, moving beyond simply reacting to missed doses or therapy drop-offs.

To drive adoption of automation tools, you must remove the user's trade-off calculation. The core insight is to make the process of automating a task forever fundamentally faster and easier than performing that same task manually just once. This eliminates friction and makes automation the default choice.

An attempt to use AI to assist human customer service agents backfired, as agents mistrusted the AI's recommendations and did double the work. The solution was to give AI full control over low-stakes issues, allowing it to learn and improve without creating inefficiency for human counterparts.

Counterintuitively, Uber's AI customer service systems produced better results when given general guidance like "treat your customers well" instead of a rigid, rules-based framework. This suggests that for complex, human-centric tasks, empowering models with common-sense objectives is more effective than micromanagement.