AI in Healthcare: What COOs Need to Get Right in 2027

Introduction

Healthcare COOs are heading into 2027 under pressure from every direction. Margins are thin. Staffing gaps haven't closed. Patients expect the same speed and convenience they get from their bank or their favorite retailer. And boards want to know what last year's AI pilots actually delivered.

That last question matters most. A few years ago, trying AI was enough to look forward-thinking. Today, AI budgets are judged the same way as any other operating expense: did it lower costs, free up staff time, or improve the patient experience?

COOs are in the best position to answer that. You own the workflows where AI either removes friction or adds to it. You see how scheduling, the contact center, clinical operations, billing, and staffing connect. And you are the one who has to make a new tool work on a Tuesday morning when call volume spikes and two people called in sick.

This article is a practical guide for that role. It covers where AI is creating real operational value, why so many projects stall, how to govern AI safely, and how to prove a return before you scale. The core idea is simple: the AI investments worth making in 2027 are the ones that reduce operational friction, improve patient experience, and show up clearly in your numbers.

From Pilots to Everyday Operations: What Changed for 2027?

Three shifts are shaping how healthcare COOs should think about AI this year.

Pilot fatigue is real. Many health systems have run small AI experiments that never moved into daily use. Leadership is now less interested in new pilots and more interested in tools that run in production and deliver results month after month. If you're curious why the gap between pilot and production is so wide, this breakdown of why AI deployment in healthcare often takes 6 to 12 months is a useful read.

The tools can now do more than make suggestions. AI used to mostly draft text or flag patterns for a person to act on. Now it can complete whole tasks inside a workflow, such as booking an appointment, answering a refill question, or summarizing a call and logging it in the right system.

The bar for approval is higher. Before any AI project gets funded, COOs should be able to answer four questions:

• Does it solve a specific problem that costs us real money or time today?

• Can we measure the improvement against a clear baseline?

• Does it fit into the systems and workflows our teams already use?

• Can we govern it safely, with the right people reviewing the right decisions?

If the answer to any of these is no, the project probably isn't ready.

Where AI Creates the Most Operational Value

Not every use case is equally ready. These five areas are where healthcare organizations are seeing practical results today, along with what COOs should measure in each.

1. Patient Access and the Contact Center

The contact center is the front door to care. It is also where operational problems show up first. When scheduling is confusing, a bill is unclear, or discharge instructions are hard to follow, patients pick up the phone.

AI can take on a large share of routine calls and support staff on the rest:

• Scheduling and rescheduling. AI can book, move, and confirm appointments over the phone at any hour. Here's a closer look at how AI voice agents handle patient scheduling.

• Shorter waits. Handling simple requests automatically frees staff for complex ones, which brings hold times down. See how to reduce patient wait times using AI agents.

• Fewer no-shows. AI can spot patients likely to miss an appointment and reach out with reminders or easy rescheduling options.

• Follow-up after discharge. Automated check-in calls catch problems early and reduce avoidable readmissions. Learn how to automate post-discharge follow-up calls.

• Prescription refills. These high-volume, rule-based calls are a natural fit. One global healthcare provider automated 45% of its prescription refill requests this way.

What to measure: Call abandonment rate, hold time, first contact resolution, repeat calls, no-show rate, and patient satisfaction.

2. Workforce Capacity

Staffing is usually the largest operating cost and the hardest problem to solve. AI helps in three ways:

• Matching staff to demand. Forecasting patient volume by site, department, and shift helps avoid both overtime and idle time.

• Cutting paperwork. Automating documentation, data entry, and call notes gives clinicians and support staff more time with patients.

• Better coaching. AI can review real interactions and point managers to the specific skills each person needs to work on, instead of relying on gut feel.

Many organizations underestimate how much poor staffing decisions cost them. This piece on what healthcare contact centers get wrong about staffing lays out the common mistakes.

What to measure: Overtime spend, agency and temp labor costs, time saved per employee, and turnover.

3. Revenue Cycle

Small errors in the revenue cycle add up fast. AI can help with:

• Suggesting accurate codes and checking for inconsistencies.

• Reviewing claims for likely errors before they are submitted.

• Finding patterns behind denied claims so the root cause gets fixed.

• Speeding up prior authorization paperwork.

What to measure: Denial rate, clean claim rate, days in accounts receivable, and cost to collect.

4. Clinical and Operational Workflows

Clinicians spend a large share of their day on documentation and administrative follow-up. AI can draft notes from a visit, summarize patient history, and make sure referrals don't fall through the cracks.

It can also show where patients get stuck. By looking at how patients move from first call to appointment to follow-up, COOs can spot repeated handoffs, long waits between steps, and work that gets done twice.

What to measure: Documentation time per visit, patient throughput, turnaround times, and length of stay.

5. Supply Chain

AI can forecast supply needs based on patient volume and past usage, flag items that are overstocked or close to expiring, and warn about likely shortages before they hit.

What to measure: Inventory carrying costs, expired stock, and how often items run out.

The Most Overlooked Source of Operational Insight: Patient Conversations

Every day, patients tell your organization exactly what is broken. They call about bills they don't understand, appointments they can't book, instructions they can't follow, and medications they can't get refilled. Each of those calls is a signal about how well your operations are working.

Most of that signal is lost. In a typical contact center, quality teams manually review only a small sample of calls, often just a few percent. The rest go unheard. This article on what's hiding in the 97% of conversations you don't analyze explains what that blind spot costs.

When AI reviews every conversation instead of a sample, COOs can:

• See why patients are calling. Group calls by reason and spot the top drivers of volume and repeat contacts.

• Catch new problems early. A sudden jump in calls about a new billing statement or a broken online scheduling link shows up in days, not months.

• Check quality and compliance on every call. Confirm that staff verify identity correctly, follow required scripts, and handle sensitive information properly. Here's a practical guide on how to audit 100% of healthcare calls.

• Coach staff on real calls. Managers can use actual patient interactions to show what good looks like and where each person can improve.

A simple example: Say calls about a specific billing statement double in two weeks. A conversation review shows patients are confused by a new line item. That finding goes to the revenue cycle team, who rewrite the statement. Call volume drops, patients are less frustrated, and payments come in faster. One insight improved three parts of the operation.

This is the kind of work Level AI's voice of the customer insights are built for. They turn thousands of patient conversations into clear, actionable findings that operations, finance, and patient experience teams can use.

Why AI Projects Stall (and How to Avoid It)

The biggest AI challenge for most healthcare organizations isn't choosing the right technology. It's getting that technology to work inside a complex operation. Three problems come up again and again.

1. Data Lives in Too Many Places

Patient information sits in the EHR. Call recordings sit in the contact center platform. Billing data sits in the revenue cycle system. Staffing data sits somewhere else. When these systems don't connect, AI only sees part of the picture, and its results are only as good as that partial view.

Before scaling any AI project, make sure you have clean data, consistent definitions (everyone should mean the same thing by "no-show" or "resolved"), and secure, controlled access to patient information.

2. Tools Sit Outside the Daily Workflow

If staff have to open a separate screen, log into another system, or copy information by hand, they won't use the tool for long. The AI projects that last are the ones built into the systems people already use every day.

3. Too Many Disconnected Tools

It's tempting to buy a separate AI product for every problem. But ten disconnected tools mean ten integration projects, ten vendor contracts, and ten sets of data that don't talk to each other. When one tool answers patient calls and a separate one reviews quality, gaps appear between them. This piece explains why siloed automation in healthcare contact centers fails patients.

The better approach is to favor platforms that can serve several departments and use cases, so the first project makes the second one easier.

Governance Is a COO Responsibility

AI governance often gets handed to IT or compliance. But because AI changes how work gets done, COOs need a seat at the table. Three areas deserve attention.

Protecting patient data. Any AI tool that touches patient information must meet HIPAA requirements. That means strict access controls, strong security, a signed business associate agreement, and a clear review of each vendor's practices. Here's what to look for in HIPAA-compliant AI voice agents.

Checking accuracy and fairness. AI can make mistakes, and its performance can drift over time. Set up regular reviews of its output, track error rates, and watch for patterns that treat some patient groups differently from others. For a deeper look, read about the risks of AI in healthcare and what purpose-built AI looks like.

Deciding where people stay involved. Not every decision carries the same risk. A simple way to set rules is to sort tasks by risk level:

Finally, define clear escalation paths before you launch. Everyone should know exactly when and how a patient gets handed to a person, and that person should receive the full context of the conversation so the patient doesn't have to start over.

How to Measure the ROI of AI

The number of AI tools you've deployed tells you nothing about whether they're working. What matters is whether the KPIs your business already tracks are moving in the right direction.

For a contact center specific list, see this guide to healthcare voice AI KPIs.

Build the Business Case Before You Scale

1. Define the problem and what it costs today. For example, "Missed appointments cost us an estimated $X per month."

2. Record a baseline. Capture current performance before anything changes.

3. Set a clear target. Decide what success looks like and by when.

4. Run a controlled pilot. Compare a group using the AI against a group that isn't, over the same period.

5. Scale only when the results hold. If the numbers don't move, adjust or stop before spending more.

This breakdown of the ROI of AI in healthcare call centers shows how these numbers come together in practice. You can also estimate your own potential savings with the Level AI ROI calculator.

Where to Start: A Practical Way to Prioritize

With so many possible uses, the hardest part is often deciding what to do first. Three simple rules help.

Start with high-volume, repetitive work. Patient calls, scheduling, reminders, refill requests, and claims happen thousands of times a month. Even small improvements add up quickly, and results are easy to see.

Pick problems with a clear price tag. Overtime, claim denials, no-shows, and repeat calls are easy to measure before and after. That makes it much easier to prove value and win support for the next project. This guide to cutting call center costs in healthcare is a good place to find quick wins.

Choose tools that can grow with you. Your first project should make the second one easier, not create another island. Look for a platform that connects with your existing systems and can support more than one department.

A good first project usually sits where these three rules overlap. For many health systems, that's the patient contact center: high call volume, clear costs, and a direct line to patient experience.

The 2027 COO AI Checklist

Before approving any AI investment, work through these questions with your team.

Problem and value

• What specific operational problem are we solving?

• What does this problem cost us today?

• How will we measure improvement, and what return do we expect?

Integration and adoption

• What data does this need, and is that data reliable?

• Will it work inside the systems our teams already use?

• Can it grow beyond the first pilot to other departments?

Risk and governance

• How is patient data protected, and does the vendor sign a BAA?

• Which decisions need a person to review or approve them?

• What happens when the AI can't handle a request?

Conclusion: AI Is Now Part of How Healthcare Runs

AI in healthcare is moving past scattered experiments and into the daily operation. The organizations that get the most from it won't be the ones with the most AI tools. They'll be the ones that tie every investment to a clear operational result and keep patient experience and quality at the center.

COOs are best placed to lead that shift. You understand the workflows, the costs, and the people involved. In 2027, the most useful way to think about AI is as part of how your organization operates, managed with the same discipline as staffing, scheduling, or the budget.

Hear What Your Patients Are Already Telling You

Thousands of patient conversations happen every week, and most of what they reveal goes unheard. Level AI reviews every interaction to automate quality checks, surface recurring issues, and give operations teams the insight they need to improve patient experience and contact center performance.

Improve Patient Experiences With Every Conversation

Identify service gaps, uncover patient needs, and improve the quality of every patient interaction with Level AI

Schedule a demo

Improve Patient Experiences With Every Conversation

Identify service gaps, uncover patient needs, and improve the quality of every patient interaction with Level AI

Schedule a demo