Process Improvement + AI, from Operators
Fix the process first. Then put AI to work.
We define the problem, find where work actually breaks down, remove the waste, add AI only where it earns its place, and measure the result. Built and tested inside the insurance agency we run.
The Difference
Anyone can demo an AI agent. The value is knowing what it should do.
AI tools are easy to assemble. Redesigning how a business works around them, and proving the result, is the hard part.
A demo says
“I built an agent that reads your email and updates the CRM.”
An operator says
“Here is how much of your team’s week goes to inbound requests, and a third of it is rework from missing information. Fix the intake form first. Then these request types can be routed automatically, and these stay with a person. Here is the review queue, the test set that keeps routing accuracy where we agreed, and the four numbers we will report at 30, 60, and 90 days.”
Why Workflows, Not Tools
AI impact comes from how the work is organized.
In Microsoft’s 2026 Work Trend Index, a survey of 20,000 knowledge workers in 10 countries, organizational factors like culture, manager support, and talent practices accounted for more than 2x the reported AI impact of individual factors. Handing people better tools is not enough. The work has to be redesigned.
52% of professionals surveyed believe their organization has no generative AI policy (Thomson Reuters, 2025).
64% of professionals surveyed have received no generative AI training at work (Thomson Reuters, 2025).
Only 20% of professionals surveyed know their organization is measuring generative AI ROI (Thomson Reuters, 2025).
Don’t Automate Waste
One process is really a dozen decisions.
Take an inbound customer request, something every service business handles. First we ask which steps add value. Then we decide who or what should own the ones that remain.
- Request arrives by phone, email, text, or webOne queue instead of four inboxes. No judgment needed, so no AI needed.Software
- Classify and route itRequest type, urgency, and owner, with low-confidence cases sent to a person.AI
- Pull the customer’s accountAn integration, not a person switching between three systems.Software
- Chase missing informationOften pure waste. The first fix is usually a better intake form, not AI.Software
- Draft the responseDrafted from the account and your procedures; a person approves it.AI + review
- Handle the exceptionJudgment, relationships, and anything with legal or financial risk stay with people.Person
How We Work
Five stages of improvement, with AI at every one.
A disciplined improvement cycle, applied to a new kind of tool. You can stop after any engagement and keep everything it produced.
| Stage | What happens | Where AI helps |
|---|---|---|
| Define | The problem, the customer, the scope, and the one number that matters | Reads months of emails, tickets, and call notes to show where volume and complaints actually are |
| Measure | A baseline: volume, cycle time, defect rate, cost | Classifies unstructured history into a baseline, which becomes the test set later |
| Analyze | Map the process, find the waste and root causes, score the opportunities | Clusters defects and delays to surface root causes a spreadsheet would miss |
| Improve | Remove the waste, then prototype, test, and deploy AI on what remains | Does the steps it is suited for, tested against the Measure baseline |
| Control | Keep the gains: owners, monitoring, a response plan | Tracks its own accuracy, cost, and drift; evaluations re-run before every change |
Define · Measure · Analyze · 2 weeks
AI Opportunity Audit
Baseline the workflows, map the waste, and rank opportunities by feasibility, impact, risk, and effort, with the economics written out. Then prototype the top candidate and test it against your own history.
Audit detailsImprove · 6–12 weeks
Build & Deploy
Fix the process, then build the AI into it: integrations, permissions, review queues, logging, an evaluation set, cost controls, training, and adoption tracking.
Build detailsControl · Monthly
AI Operations
A fractional AI officer. Results reported at 30, 60, and 90 days, evaluations re-run before every change, and the next workflow taken through the cycle.
Operations detailsHow We Know It Works
We test systems against the right answer. Including our own.
The insurance agency we run ingests carrier downloads automatically. On September 1, 2026 we tested that parsing against the agency management system, policy by policy.
| Field | Policies compared | Matched |
|---|---|---|
| Effective date | 585 | 100% |
| Expiration date | 585 | 100% |
| Line of business | 585 | 100% |
| Carrier | 585 | 99.8% |
| Premium | 585 | 29% |
The first version of the test reported dates as only 73% accurate. The parser was fine; the test was pairing different terms of the same policy. Once the test was fixed, it pointed at a real defect: 201 policies came through with no premium at all, including every download from one carrier feed that puts the amount in a remarks field the parser was not reading. Much of the remaining premium gap was the test itself, comparing six-month term premiums against annualized ones. Finding that is what testing is for.
WaiveFlow is a new practice. We publish client results as engagements complete, with measured numbers only.
Where Businesses Start
The same four patterns show up everywhere.
Different industries, same bottlenecks: requests, documents, deadlines, and knowledge stuck in people’s heads.
Inbound Request Triage
Service requests at an insurance agency, document requests at a CPA firm, maintenance tickets at a property manager. Sorted into one queue, routine ones drafted, exceptions kept with people.
Intake & Triage packDocuments to Data
Applications, invoices, statements, contracts, and forms re-keyed into a system of record. Extracted, validated, and flagged when something is missing.
Document Processing packRenewals, Deadlines & Follow-Up
Policy renewals, engagement letters, contract expirations, and quotes that were never followed up. Surfaced early and ranked by risk, with outreach drafted for review.
How we find theseAnswers from Internal Knowledge
“How do we handle this?” answered from your procedures, guidelines, and past work, with citations, instead of from whoever happens to remember.
Knowledge Assistant packIndustry pages: insurance agencies (the business we run) and accounting and tax firms. Is your business a fit?
Luke Royal
Founder, WaiveFlow · Partner, The Way Agency
I am a partner at The Way Agency, an independent insurance agency in Kentucky, and I built the platform it runs on: carrier download ingestion, AMS sync, call and text logging, e-signature, lead intake, and servicing pipelines. I approach improvement with discipline: define the problem, measure it, find the root cause, fix it, and keep it fixed. AI is a powerful new tool inside that discipline, not a replacement for it. WaiveFlow brings that work to other businesses.
More about WaiveFlow →Risk & Governance
Built for businesses that handle other people’s data.
Controls are part of the design, not an add-on after launch.
NIST AI Risk Management Framework
Governance organized around NIST AI RMF and its Generative AI Profile.
Regulated Data, Handled Up Front
Your industry’s data rules (GLBA for insurance and financial data, for example), vendor data-retention terms, and access controls reviewed before anything is built.
Audit Logs & Prompt-Injection Defenses
Every automated action is logged. Email, documents, and web content are treated as data, never as instructions.
People Own the High-Stakes Calls
Decisions that carry legal, financial, or client risk route to people. AI prepares; people decide.
FAQ