3. Collaboration
IT can’t do this alone. Some of our best AI outcomes came when compliance officers, frontline users and clinical leads co-designed workflows and challenged assumptions. In one case, a nurse navigator pointed out that the model’s recommendations conflicted with how providers structured patient follow-ups. By bringing her into the design process, we adjusted the algorithm and the workflow together, resulting in faster adoption and more trust in the system. Cross-functional teams are not optional — they’re mission-critical.
4. Continuous learning
Once deployed, AI must evolve. Monitor for model drift, feedback loops and unintended bias. Think of it as a digital organism, not a static tool. To support transparency and auditability, tools like Google’s What-If Tool allow teams to test how changes in input data affect predictions, helping to uncover potential bias before deployment. In practice, this means setting up monitoring dashboards, retraining cycles and governance reviews. On one project, we detected drift within six months as prescribing patterns shifted post-COVID. By retraining quickly, we avoided inaccurate prioritization that could have derailed trust in the system.
If you’re in a CIO or digital leadership role and planning to scale AI across patient engagement or healthcare operations, I’d offer the following guidance based on lessons I’ve learned (sometimes the hard way):
