Workflow Teardowns

What changes when AI becomes part of the operating system?

Most organizations do not suffer from a lack of AI capability. They suffer from workflows that were designed before that capability existed.

These teardowns examine consequential business processes from the decision backward — identifying where information, automation, AI, and human judgment should sit to improve the economics of the work.

Explore the teardowns

Raw information

Context

Judgment

Decision

Action

Outcome

Rivington's work focuses on the point where technical capability becomes operating behavior.

Commercial Insurance

Commercial Underwriting

How AI can increase underwriting capacity without removing underwriting judgment.

Operating problem

Highly paid underwriters spend significant time assembling information before they can exercise the judgment they were hired for.

Primary decision

Should we accept this risk, under what conditions, at what price, and does anything require escalation?

Outcome opportunity

Release expert underwriting capacity by moving information assembly out of the underwriter's day.

Insurance Operations

Claims Intake & Triage

How AI can get every claim to the right adjuster faster without letting a machine decide the outcome.

Operating problem

Adjusters spend expert capacity gathering FNOL details, checking coverage, and manually routing claims before any real adjusting begins.

Primary decision

What happened, how complex or risky is this claim, and what should happen next?

Outcome opportunity

Release adjuster capacity and cut cycle time by assembling and classifying each claim before it reaches a human.

B2B Revenue

Enterprise Lead Qualification

How AI can put the right account in front of the right rep fast enough to matter, without automating away deal judgment.

Operating problem

Signals for a single account are scattered across seven systems, so reps rebuild account context by hand before deciding whether to engage.

Primary decision

Is this account worth pursuing now, why, and who should own it?

Outcome opportunity

Release selling capacity and shorten response time by assembling account context automatically instead of one lead at a time.

Customer Operations

Support Escalation

How AI can shorten resolution time and lift agent capacity without letting a machine decide what is owed.

Operating problem

Agents spend most of a ticket assembling account context before they can judge whether a case can be resolved or must escalate.

Primary decision

What is happening, what action is allowed, and does this require an exception or escalation?

Outcome opportunity

Release agent capacity by assembling account context and policy automatically before the agent opens the ticket.

Professional Services

Expert Application Review

How AI can increase expert review throughput without removing the discretion the review exists to provide.

Operating problem

Scarce experts spend most of a review retrieving, reading, and normalizing evidence before they can apply the judgment that determines the outcome.

Primary decision

Does this case meet the required standard, what evidence matters, and where is expert discretion necessary?

Outcome opportunity

Release expert review capacity by moving evidence assembly and criteria mapping out of the reviewer's queue.

The operating question

Where is expensive judgment being used as middleware?

If experienced people are spending their time retrieving information, rebuilding context, interpreting routine inputs, coordinating handoffs, or moving work between systems before they can exercise judgment, the workflow may be the problem.

Rivington examines one consequential workflow and determines where data, automation, AI, and human judgment should sit — then redesigns the operating model around the decision that matters.

Bring us one consequential workflow Explore the AI Workflow Diagnostic