MarvelX Team
MarvelX Team
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Your RPA bots break every time a claims document changes format. Here is what handles the exception instead.
Your RPA bots break every time a claims document changes format. Here is what handles the exception instead.
Your RPA bot maps to fixed field positions on a claims form. The moment a broker sends the same document in a new layout, the bot fails and the work lands back on a person. Every format change becomes an exception queue that your operations team clears by hand.
Why RPA breaks on every format change
Robotic process automation follows fixed rules. It expects the policy number in a set position and the loss date in a known cell. When a broker submits the same claim in a different template, the bot cannot find the fields and stops. The work drops into an exception queue for a person to clear. Your team then reads the document, keys the data into the core system, and routes the claim by hand. Each new intake channel and each partner format multiplies these breakages. The bot does not understand what a policy number is. It only knows where one sat last time. So every layout change, every scanned attachment, and every reordered form becomes manual review. That is the hidden cost of rule-based automation in a regulated operation: brittle logic that shifts effort back onto handlers instead of removing it.
How agentic AI handles the exception in practice
An agentic AI reads a claims document for meaning, not coordinates. It identifies the policy number, the loss date, and the claimant details wherever they appear on the page. A new template does not break it, because it was never mapped to positions. The agent extracts the data, runs policy validation against your rules, and either progresses the claim or flags it for human oversight. It works the same way across scanned PDFs, emailed forms, and partner portals. Each decision is logged with the source document and the reasoning, so you keep an audit trail. The handler stops re-keying data and starts reviewing the exceptions that genuinely need judgment. AI handles the repeatable extraction and routing. Your people handle the calls that require accountability, which is where regulated operations require a person to sign off.
What about incomplete or unreadable inputs
The common objection is that real claims arrive incomplete. A missing signature, an absent loss date, or an unreadable attachment would still stall automation. The agent handles this at intake, not days later. When a required item is missing, it asks the claimant for exactly that item straight away. It names the specific field or document rather than returning a generic rejection. The claim only progresses once the input is complete and readable. That closes the gap that usually surfaces when a handler opens a dossier mid-process and finds it unworkable. Incomplete inputs stop being a downstream surprise and become a resolved step at the front of the queue. Human oversight still governs the final decision, so an edge case routes to a person with the missing context already gathered.
What changes for your team, and the evidence
Your handlers stop clearing format-break exceptions and stop re-keying data. They review decisions and manage the cases that need judgment. The measurable result comes from an embedded insurance case study, where claim processing time fell from days to under an hour. Note that this is end-to-end processing time, not a per-keystroke speed. That same embedded client reached 10x claims capacity without a headcount increase, with decision accuracy at 90% in that case study. PwC's AI Agent Survey found that 79% of respondents say AI agents are already being adopted in their companies, with 66% of adopters reporting measurable value or productivity gains. For a regulated operation, the gain is capacity and consistency without adding people, and with an audit trail on every decision.
Key takeaway
RPA breaks on every format change because it maps to fixed positions. Agentic AI reads a claims document for meaning, so a new layout is just another document, not an exception.
Frequently asked questions
Why do RPA bots fail when a claims document changes format?
RPA follows fixed rules tied to field positions on a specific template. When the same claim arrives in a new layout, the bot cannot locate the data and stops. The claim then drops into an exception queue for a person to clear by hand.
How does agentic AI keep working when the layout changes?
It reads the document for meaning rather than coordinates, so it finds the policy number and loss date wherever they appear. A new template does not break it. Every decision is logged with the source and reasoning to maintain an audit trail.
What results has this delivered in practice?
In an embedded insurance case study, the client reached 10x claims capacity without a headcount increase. That was achieved at 90% decision accuracy in the same case study, with human oversight governing the final decision.
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Your RPA bot maps to fixed field positions on a claims form. The moment a broker sends the same document in a new layout, the bot fails and the work lands back on a person. Every format change becomes an exception queue that your operations team clears by hand.
Why RPA breaks on every format change
Robotic process automation follows fixed rules. It expects the policy number in a set position and the loss date in a known cell. When a broker submits the same claim in a different template, the bot cannot find the fields and stops. The work drops into an exception queue for a person to clear. Your team then reads the document, keys the data into the core system, and routes the claim by hand. Each new intake channel and each partner format multiplies these breakages. The bot does not understand what a policy number is. It only knows where one sat last time. So every layout change, every scanned attachment, and every reordered form becomes manual review. That is the hidden cost of rule-based automation in a regulated operation: brittle logic that shifts effort back onto handlers instead of removing it.
How agentic AI handles the exception in practice
An agentic AI reads a claims document for meaning, not coordinates. It identifies the policy number, the loss date, and the claimant details wherever they appear on the page. A new template does not break it, because it was never mapped to positions. The agent extracts the data, runs policy validation against your rules, and either progresses the claim or flags it for human oversight. It works the same way across scanned PDFs, emailed forms, and partner portals. Each decision is logged with the source document and the reasoning, so you keep an audit trail. The handler stops re-keying data and starts reviewing the exceptions that genuinely need judgment. AI handles the repeatable extraction and routing. Your people handle the calls that require accountability, which is where regulated operations require a person to sign off.
What about incomplete or unreadable inputs
The common objection is that real claims arrive incomplete. A missing signature, an absent loss date, or an unreadable attachment would still stall automation. The agent handles this at intake, not days later. When a required item is missing, it asks the claimant for exactly that item straight away. It names the specific field or document rather than returning a generic rejection. The claim only progresses once the input is complete and readable. That closes the gap that usually surfaces when a handler opens a dossier mid-process and finds it unworkable. Incomplete inputs stop being a downstream surprise and become a resolved step at the front of the queue. Human oversight still governs the final decision, so an edge case routes to a person with the missing context already gathered.
What changes for your team, and the evidence
Your handlers stop clearing format-break exceptions and stop re-keying data. They review decisions and manage the cases that need judgment. The measurable result comes from an embedded insurance case study, where claim processing time fell from days to under an hour. Note that this is end-to-end processing time, not a per-keystroke speed. That same embedded client reached 10x claims capacity without a headcount increase, with decision accuracy at 90% in that case study. PwC's AI Agent Survey found that 79% of respondents say AI agents are already being adopted in their companies, with 66% of adopters reporting measurable value or productivity gains. For a regulated operation, the gain is capacity and consistency without adding people, and with an audit trail on every decision.
Key takeaway
RPA breaks on every format change because it maps to fixed positions. Agentic AI reads a claims document for meaning, so a new layout is just another document, not an exception.
Frequently asked questions
Why do RPA bots fail when a claims document changes format?
RPA follows fixed rules tied to field positions on a specific template. When the same claim arrives in a new layout, the bot cannot locate the data and stops. The claim then drops into an exception queue for a person to clear by hand.
How does agentic AI keep working when the layout changes?
It reads the document for meaning rather than coordinates, so it finds the policy number and loss date wherever they appear. A new template does not break it. Every decision is logged with the source and reasoning to maintain an audit trail.
What results has this delivered in practice?
In an embedded insurance case study, the client reached 10x claims capacity without a headcount increase. That was achieved at 90% decision accuracy in the same case study, with human oversight governing the final decision.