Director of Revenue Cycle
Evaluate and implement revenue cycle technology
What You Do Today
Assess vendors for RCM automation, run pilots, measure ROI, and make build-vs-buy decisions for things like AI coding, automated eligibility, and robotic process automation.
AI That Applies
RPA and intelligent automation — bots handle repetitive tasks like eligibility checks, claim status inquiries, and payment posting. AI handles the judgment calls like coding and denial prediction.
Technologies
How It Works
The system pulls financial data from operational systems — transactions, forecasts, actuals, and variance history. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
You move from evaluating whether to automate to deciding what to automate next. The question isn't 'should we use AI?' — it's 'which 20% of remaining manual work is worth automating?'
What Stays
Vendor evaluation, change management, and integration planning require human judgment about organizational readiness, not just technology capability.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for evaluate and implement revenue cycle technology, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long evaluate and implement revenue cycle technology takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your CFO or VP Finance
“What data do we already have that could improve how we handle evaluate and implement revenue cycle technology?”
They're prioritizing which finance processes to automate first
your ERP or finance systems admin
“Who on our team has the deepest experience with evaluate and implement revenue cycle technology, and what tools are they already using?”
They know what automation capabilities exist in your current stack
your FP&A counterpart at a peer company
“If we brought in AI tools for evaluate and implement revenue cycle technology, what would we measure before and after to know it actually helped?”
They can share what worked and what didn't in their AI rollout
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.