Inside Chicago C-Suite Talks on AI in Healthcare: Successes and Challenges

AI In Healthcare: What C-Suite Leaders Are Actually Discussing

At a new C-suite-focused event in Chicago earlier this month, executives compared notes on the real work of bringing AI into healthcare. The conversations centered on where AI is delivering wins, where it stalls, and how to build momentum without adding risk.

Where AI Is Working Today

• Operational efficiency: automating repetitive back-office tasks, accelerating prior auth reviews, and reducing manual entries.
• Clinical support: summarizing notes and routing information so clinicians can move faster with fewer clicks.
• Patient access: smarter triage and scheduling to reduce wait times and no-shows.
• Imaging and diagnostics support: prioritizing cases and flagging anomalies for faster follow-up.

Where It Gets Hard

• Data quality and access: siloed systems, inconsistent coding, and limited feedback loops.
• Integration: EHR workflows, API gaps, and change requests that pile up in IT queues.
• Risk and compliance: audit trails, explainability, and validation for safety and bias.
• Adoption: clinician trust, role clarity, and incentives that don't match new workflows.
• Procurement: vague vendor claims, unclear total cost, and long security reviews.

A 90-Day Executive Playbook

• Set a one-page intent: three priority outcomes (e.g., reduce denials, shorten LOS, cut admin time) with owners and deadlines.
• Pick two high-signal use cases with clean data and clear ROI; avoid moonshots.
• Stand up lightweight governance: intake, risk scoring, human oversight, and rollback plans.
• Demand vendor evidence: baselines, controlled pilots, QA process, model update cadence, and total cost.
• Instrument metrics from day one: time saved, error rates, throughput, exception volume.
• Train the front line: short, role-based playbooks and escalation rules.

Metrics That Matter

• Value: minutes saved per task, throughput per FTE, denial reduction, readmission reduction, patient wait-time improvement.
• Safety and quality: false positives/negatives, bias checks by cohort, override rates, and escalation-to-human frequency.
• Reliability: model drift indicators, downtime, and revalidation cycle time.

Governance That Scales

Create a simple intake and review process that any department can use. Require documentation for data sources, validation method, monitoring plan, and who is accountable for outcomes.

Anchor your approach to recognized frameworks to speed audits and align language across teams. See the NIST AI Risk Management Framework here and FDA guidance on AI/ML in Software as a Medical Device here.

Build vs. Buy: Make It Boring

• Default to buy for common workflows; build only where your data advantage is real.
• Require API-first tools, PHI-safe architectures, and clear model update policies.
• Plan for human-in-the-loop checkpoints where risk is non-trivial.
• Budget for integration and change management-often bigger than license fees.

People And Process

AI shifts work: some tasks shrink, others emerge. Define who reviews outputs, who can override, and how exceptions flow back into training and process improvement.

• Create role-specific SOPs and quick reference guides.
• Run brief simulations before go-live to build trust and catch edge cases.
• Reward teams for measured outcomes, not tool adoption.

Quarterly Action Checklist

• Confirm your top three outcomes and the two use cases that map cleanly.
• Sign one vendor with proven results and a clear pilot plan.
• Instrument metrics and dashboards before deployment.
• Launch with a small cohort, weekly reviews, and a defined rollback path.
• Publish a one-page governance standard and intake form.
• Upskill leaders and line staff with targeted training and office hours.

If you need structured upskilling for cross-functional teams, browse role-based programs at Complete AI Training.