When McKinsey published its latest research on agentic AI, I nearly laughed out loud. They described six lessons that separate success from failure—and every one matched what we learned the hard way building AI for healthcare since 2012.
Back then, “agentic AI” wasn’t a phrase. We were just trying to get machines to handle appointment requests without breaking patient trust. Most organizations still debated whether automation could do anything safely without human supervision.
McKinsey’s findings aren’t just validation—they’re a roadmap for avoiding the mistakes we made so you don’t have to.
We learned these lessons through thousands of patient conversations, dozens of failed experiments, and the humbling experience of watching AI projects that looked brilliant on paper struggle in the real world. When McKinsey notes that some organizations are “rehiring people where agents have failed,” we know that pain. But we also know the turnaround.
Let’s talk about what actually works.
Agentic AI refers to generative systems that don’t just suggest but act.
They execute multi-step processes autonomously—scheduling appointments, handling insurance verification, managing triage protocols—without waiting for human approval at every step.
The potential? Massive productivity gains and dramatically better patient experiences.
The challenge? Most projects stall or create technical debt because organizations focus on the wrong things.
McKinsey found these patterns across industries. We’ve seen them play out for 13 years in healthcare — where the stakes are higher and the workflows are messier.
McKinsey’s finding: Organizations that focus on building impressive agents instead of reimagining workflows end up with great demos and underwhelming results.
Our lesson: You can’t optimize a broken process with AI. You have to redesign it.
In 2012, practices would ask: “Can you automate our scheduling process?”
Technically yes. Strategically wrong. Because if your current process requires three transfers and two callbacks, automating it just means your AI inherits your dysfunction.
Virginia Women’s Center rethought scheduling and triage when implementing CareDesk. They designed a system where AI handles the routine complexity and staff focus on judgment. The result? New agents master workflows in weeks, not months.
McKinsey’s finding: Sometimes simpler tools—rules-based systems or analytics—work better than agents.
Our lesson: The goal isn’t to use AI everywhere. It’s to use the right tool for each job.
Healthcare workflows are layered. Insurance verification may need rules-based logic; appointment booking needs conversation handling; triage demands human expertise. Forcing everything through a single AI layer leads to brittleness.
EmergeOrtho didn’t deploy one massive AI to handle everything. They used rules for verification, automation for scheduling, humans for complex care coordination—and an AI orchestrator to keep them in sync.
McKinsey’s finding: Agents that look great in demos often frustrate users in production. Once trust erodes, adoption collapses.
Our lesson: Onboarding AI is like hiring employees, not installing software.
Early prototypes impressed us—until real patients exposed hidden dependencies and data gaps. The fix wasn’t better models. It was better evaluation infrastructure.
Virginia Women’s Center built evaluation sets mirroring human QA metrics—accuracy, appropriateness, patient satisfaction. They used deviations from expert judgment as learning feedback. That’s why their AI maintains high trust and consistency.
McKinsey’s finding: Without observability, scaling from a few agents to hundreds becomes chaos.
Our lesson: If you can’t see how an agent made a decision, you can’t fix it.
When one client’s appointment confirmations dipped, our logs revealed that incomplete insurance data caused misclassification. We corrected the intake step—and restored accuracy in days, not weeks.
McKinsey’s finding: One-off agents create massive redundancy. Reusable components create advantage.
Our lesson: Stop reinventing workflows. Build a library.
We once rebuilt similar scheduling and verification logic for each client. Improvements didn’t propagate. It was unsustainable.
Now, our platform uses reusable agent components—conversation handlers, triage protocols, scheduling logic—that can be configured, not rebuilt.
Golden State Orthopedics implemented automation in weeks using our validated scheduling modules—benefiting from years of refinement across other practices.
McKinsey’s finding: Agents change the nature of work, not the need for people.
Our lesson: AI doesn’t replace people—it changes what people do.
When EmergeOrtho’s call volume doubled, they didn’t double staff. Instead, staff evolved:
The result: higher satisfaction, fewer errors, faster service.
True AI transformation is workflow transformation.
Here’s a diagnostic checklist to assess readiness:
| Pitfall | What Happens | Fix |
|---|---|---|
| Overengineering | Complex agents where rules would suffice | Start simple, scale complexity later |
| Trust erosion | Early failures destroy confidence | Evaluate rigorously before rollout |
| Technical debt | Custom builds that can’t evolve | Build reusable modules from day one |
| Change resistance | Staff fear replacement | Communicate role evolution early |
| Monitoring neglect | Failures go undetected | Treat observability as a core feature |
By 2027, agentic AI will be table stakes for patient access. Practices that delay won’t just lose efficiency—they’ll lose competitiveness.
Tooling is accelerating. What took months in 2015 now takes weeks—and soon, days.
Reusable ecosystems are emerging. Pre-built, healthcare-specific agents will commoditize. The winners will integrate best—tying AI, workflows, and people into one seamless system.
Human work is shifting. As AI handles routine complexity, human value moves toward empathy, oversight, and innovation. Those who embrace this shift will lead.
McKinsey’s six lessons mirror our own:
We’ve lived these for 13 years—and seen what happens when they’re ignored. The practices achieving breakthrough results didn’t just adopt technology. They transformed how they work.
Start small, but start smart.
The next generation of healthcare leaders won’t just buy AI—they’ll build their workflows around it.
Let’s discuss where you are, where you’re headed, and how to make sure your AI delivers real outcomes.