What AI-Led Claims Orchestration Changes for Insurers, Adjusters and Policyholders
7 min read
Claims and InsurTech organizations have spent the last few years putting AI to work on individual steps: reading documents, summarizing files, suggesting reserves. Each step got faster. The claim often didn’t, because moving it to the next stage still depended on an adjuster opening the file and acting. Nothing in the system is responsible for taking the next step without a person authorizing it.
Five Sigma’s newest capabilities, Adjuster’s Cockpit and Clive Claim Conductor, are built around that gap. The Conductor progresses the claim through its stages inside rules the insurer sets. Cockpit gives each adjuster one view of the automated work they’re responsible for and flags where a person is needed. In Five Sigma’s Automation First vision, this is the third stage: AI carries more of the responsibility for moving each file forward, and people decide how far that responsibility extends.
The more interesting question is what happens to a claims operation once the file can move on its own.
What you will learn:
- How the two new capabilities split the work of moving claims and supervising them
- The five design decisions behind an AI-led claims operating model
- Why the industry’s AI gap sits between reading a claim and progressing it
- What changes for claims leaders, adjusters and policyholders
What Clive Claim Conductor and Adjuster’s Cockpit do in claims orchestration
Claims orchestration is the layer that advances a claim from one lifecycle stage to the next once each stage’s goal is met, within limits the insurer sets.
Clive Claim Conductor handles orchestration. For each claim, it tracks the current stage and the goal that stage has to reach, completes the work that gets it there, and passes the claim forward when the goal is met. As long as the insurer’s rules allow it, the file keeps progressing without an adjuster signing off each move.
Adjuster’s Cockpit handles supervision. Each adjuster sees all the automated claims assigned to them in one place: those progressing on their own, those waiting on the adjuster’s decision, and those that can’t move. Where a decision is needed, Clive’s proposed action comes with a confidence level and the reasoning behind it. The adjuster can run it as proposed, change it, or reject it.
Between them, the claim keeps its momentum and the adjuster keeps sight of every file that depends on their judgment.
The five principles behind the AI-led operating model in claims
An AI-led operating model in claims is the set of rules that decides what AI executes on its own, what stops for a person, and how work moves between the two. Five Sigma built Cockpit and Conductor around five design decisions.
1. Claims move without waiting on a person at every step. Clive Claim Conductor understands the claim’s current stage, executes the work that stage requires, and advances it when the stage goal is met. Progress no longer waits for someone to open the file and check.
2. Human attention goes to the files that need it. Adjuster’s Cockpit shows in one view claims that are moving on their own, claims that are sitting with a person and those that have stalled, so supervision happens across the whole caseload at once.
3. Adjusters get the context before the decision. Every recommendation arrives with its confidence level and its supporting explanation, so the review takes minutes rather than a morning of reading the file to reconstruct what happened.
4. Automation runs inside insurer-defined guardrails. The insurer decides which actions can execute automatically. Guardrails halt anything that requires a person, which is what makes both no-touch and low-touch handling workable in a regulated line of business.
5. Insurers widen the scope at their own pace. Automation can be configured by action, by sub-organization, and by line of business, which gives insurers control over where AI can act and where human approval stays required.
That last point matters more than it sounds. Graduated autonomy levels with guardrails behind each one are a configuration decision the insurer owns, not a setting a vendor ships.
Want to see the new AI operating model running on claims like yours?
Claims orchestration vs task automation: where the industry’s AI gap sits
Most carriers already use AI to understand claims. Far fewer let it act on them, and that’s where cycle time is decided.
Almost every carrier already has AI reading claims. In the NAIC’s survey of private passenger auto writers, 70% of reporting companies were using AI or machine learning in claims, with another 10% building models. Far fewer let those models act. WTW’s 2026 Advanced Analytics and AI Survey of 59 North American P&C insurers found 14% using straight-through processing in claims today, with a further 36% planning to introduce it.
The intelligence is deployed and the decisions are still queued behind a person. The common objection is that claims are too complex to run without a human at every stage. Complexity is real, and it argues for low-touch rather than against automation.
A no-touch claim completes end to end inside the guardrails, which suits simple, clean losses. A low-touch claim gets automated up to the specific moments that need judgment, authority, or empathy, then hands those moments to an adjuster. Most claims are low-touch candidates. Very few are no-touch candidates, which is why STP rate as a metric stalls near 10% and badly understates how much automation is running inside complex files.
Once human involvement can vary by claim and by individual action, adjusters join the stages that need them and stay out of the ones that don’t.
The operating model Cockpit and Conductor create lets the level of human involvement vary by claim and by individual action. An adjuster stops being a mandatory checkpoint at every stage and joins the stages that need them.
How AI claim orchestration changes claims operations management
When the file moves on its own, the leader’s job shifts toward deciding what AI may do, setting up the boundaries and watching where human effort lands.
The first change is where supervision happens. Team leads have long managed by sampling files and chasing diaries. With automated claims visible by status across each adjuster’s caseload, a claims team can see which files are moving, where decisions are piling up and which claims have stalled, and step in where the queue backs up.
The second change is what gets measured. Straight-through rate counts only claims with zero human involvement. The more useful picture shows how many human decisions each claim needed, how long the file waited for them, and how much of its lifecycle ran without intervention.
The third change is governance. Carriers already grant settlement authority to people in tiers, by role and by amount. Extending AI’s scope can follow the same discipline: which actions, on which lines, in which sub-organizations, reviewed and widened as performance earns it. That keeps every expansion deliberate and auditable.
The payoff is capacity. Experienced claims judgment reaches more claims when less of each adjuster’s day goes to the administration that keeps routine files moving.
What changes for adjusters and for policyholders with AI-led claims processing
AI-led claims processing gives adjusters a queue of decisions to make, shifting their time from checking a dairy on which files moved to approvals, coverage questions and negotiations. As a result, policyholders experience smoother claims progress with fewer waits and less chasing.
Adjusters feel this first in how the day starts. Rather than walking the diary to find out which files moved overnight, the adjuster opens a queue of claims that are ready for a decision: an estimate needing approval, a coverage question the file cannot settle on its own, a demand that crossed a reserve threshold. The skill set shifts from throughput to oversight, which is where the adjuster’s role has been heading for some time.
Policyholders feel it as speed and quiet. J.D. Power found that 22% of customers report high effort on claims settled in under a week, rising to 48% on claims that take 30 days or more. Effort roughly doubles as the claim ages, and nearly 40% of those high-effort claimants contacted their insurer four or more times. Celent’s 2026 analysis found delays in claim handling accounting for 22% of all 2025 claims complaints, the largest single category, with total complaints up 7% for a second consecutive year.
A claim is one of the worst weeks in a policyholder’s year. An operating model that keeps the file progressing shortens that week for the person living through it.
How Clive runs a claim end to end: stages, actions and guardrails
Clive Claim Conductor moves a claim through its lifecycle stages, runs each action under its own behavior mode, and brings in an adjuster only when confidence is low, a guardrail trips, or a decision belongs to a person.
Each stage has a standard to meet. A claim runs from FNOL through coverage decision, damage assessment and settlement to close. Each stage has a defined standard: fully automated intake completed at FNOL, exposures opened and coverage determined, value and reserve set during damage assessment, payment made on the agreed amount at settlement, and every condition satisfied before close. Clive Claim Conductor advances the claim once a stage’s standard is met and records what it verified before moving on. Five Sigma configures the stages and the steps each agent executes from the insurer’s own SOPs, per sub-organization, so the number of stages can change with the line of business.
Every action has a behavior mode. Within each stage, Clive takes specific actions, such as opening an exposure, creating an inspection, updating a reserve, settling a payment or closing the claim. Each action runs in one of three modes, within limits the insurer sets:
- Always AI. The action runs unattended. Advancing the claim to its next stage typically sits here.
- Confidence threshold. The action runs unattended when Clive’s confidence clears a set level, for example 0.9 for a settlement payment or 0.7 for a reserve update. Below that level, a person approves it.
- Guardrail. An extra rule is checked before the action runs, such as routing any payment above an authority limit to an adjuster. A tripped guardrail can raise a warning, block the action or route it to a person.
Every step is written to the claim file. For each action, Clive records the evidence it relied on, with links to the source documents, its reasoning and what it wrote to the claim, building a full audit trail.
Adjusters step in only when needed. Low confidence, a tripped guardrail or a decision reserved for a person pauses the action and alerts the adjuster. Once the adjuster acts, the automation picks the claim back up.
Conductor can also pick a claim up partway through its lifecycle, and individual experts, such as coverage or demand analysis, can run on their own when a trigger event occurs, like a new demand arriving on the file.
How Five Sigma CMS and Clive AI support claims orchestration
Both capabilities run inside Five Sigma CMS, the AI-native claims management platform with Clive™ built in, and Five Sigma deploys either end to end as a full claims platform or modularly for a specific stage of the lifecycle. In both cases Clive combines agentic AI with the workflows that turn a decision into an action: automating routine work, planning claim handling as the file develops, and advancing claims according to the organization’s own procedures.
“The real transformation isn’t adding AI to existing claims workflows. It’s redesigning claims operations so AI takes the lead within defined guardrails, while adjusters supervise and step in when human judgment is needed. For insurers, this means applying experienced claims expertise across more claims, while reducing the administrative work required to keep routine claims moving.”
Gil Nechushtai, Chief Product Officer of Five Sigma
The gains show up at task level before they reach cycle time. In pet insurance, straight-through processing cut handling time by more than 90% on a line where losses are simple and volume is high.
Key takeaways
- Most carriers already use AI to read claims; far fewer let it act. NAIC found 70% of private passenger auto writers using AI or machine learning in claims, while WTW found 14% of North American P&C insurers running straight-through claims processing.
- Adjuster’s Cockpit and Clive Claim Conductor give insurers an AI operating model in which AI advances claims within insurer-defined guardrails and adjusters supervise.
- Clive Claim Conductor moves claims between lifecycle stages within insurer guardrails, and Adjuster’s Cockpit brings supervision of those claims into one caseload view.
- Automation configures by action, sub-organization, and line of business, so insurers widen the scope at a pace they control.
- Low-touch handling covers far more of the book than no-touch, which is why straight-through rate understates real automation progress.
- For claims leaders, the job becomes deciding which actions AI may take, on which lines, and tracking where human decisions still land on each claim.
- Policyholders gain most from fewer waits between stages, because effort and complaints rise the longer a claim stays open.
See Five Sigma’s Adjuster’s Cockpit and Clive Claim Conductor in action.
Get a walk through how AI-led claims orchestration would run on your book.
Frequently asked questions
What is claim orchestration?
Claims orchestration advances a claim from one lifecycle stage to the next once each stage’s goal is met, within rules the insurer sets, so routine progress happens between human decisions.
How is claims orchestration different from task automation?
Task automation speeds up individual steps, such as reading a document or calculating a reserve. Orchestration connects those steps and advances the claim itself, which is what shortens cycle time.
What is an AI operating model for claims?
It is the set of rules governing what AI executes on its own, what stops for human approval, and how work passes between the two. It covers guardrails, escalation paths, and the supervision view claims leaders use to oversee automated claims.
What do Adjuster’s Cockpit and Clive Claim Conductor do?
Conductor advances automated claims through their lifecycle stages within insurer guardrails. Cockpit gives each adjuster one view of those claims, showing which are progressing, which need a decision and which are stuck.
Which claims suit low-touch handling?
Most claims do: any file where much of the work is routine but a few moments, such as a coverage call, an approval above a threshold or a difficult conversation, need an adjuster’s judgment or authority.
How does AI-led claims handling change an adjuster’s job?
Adjusters spend less time checking which files moved and more time on approvals, coverage questions, negotiations and exceptions, plus oversight of the automated claims in their caseload.
What should claims leaders measure besides straight-through rate?
Track how many human decisions each claim needed, how long files waited for them, and how much of each lifecycle ran without intervention.
Why does claims orchestration matter to policyholders?
Effort and complaints rise the longer a claim stays open, so removing the waits between stages shortens the experience for the person who filed it.
Tirtza Bensoussan
Related resources
- Press Release: Five Sigma Unveils Adjuster’s Cockpit and Clive Claim Conductor to Enable No-Touch Claims Operations
- Blog: Agentic AI in Claims: Autonomy Levels and Guardrails
- Blog: Agentic AI vs STP: Why STP Rate Stalls Near 10%
- Blog: How Agentic AI Changes the Claims Operating Model
- Blog: The Agentic Era of Claims: The System of Record becomes the System of Action
