By Jeff Pirino, SVP Sales and Account Management, Jopari Solutions, an Office Ally Company. Originally published in WorkCompWire’s Leaders Speak
An adjuster opens a prior authorization request on Tuesday morning. The injured worker is fourteen months post-injury, and the treating physician is requesting a second lumbar surgery. The file has grown across that time. MRI reports from two facilities, physical therapy notes from three different providers, a surgical summary, medication history, and eleven prior PA requests, some approved, some denied, some with conditions she can barely reconstruct from the notes left in the system.
She has the PA response deadline in forty-eight hours. She needs to determine whether this surgery is medically necessary, consistent with the applicable treatment guidelines, and defensible under audit, without sending a denial that unravels if she’s missed something buried in month seven.
This is not an unusual scenario. It’s the daily reality of claims work in 2026.
Workers’ compensation systems have never been richer in data. Every electronic bill carries structured data. Every clinical attachment carries unstructured data. Every payment generates reconciliation data. Every provider inquiry, adjudication decision, and appeal creates another layer of record. The industry has digitized to a point that would have been unimaginable fifteen years ago.
And yet the adjusters who sit at the center of all this data report feeling more overwhelmed, not less. More data hasn’t made adjudication better. Better intelligence might.
It’s worth being precise about what those terms actually mean, because they get used interchangeably in ways that obscure the real problem. Data is raw input: the 837, the 275 attachment, and scanned clinical documents such as operative reports, therapy notes, and diagnostic imaging reports. Information is data that has been organized into something usable — a structured record, an indexed document, a searchable timeline. Intelligence is information applied to a decision, surfaced at the right moment, in the right context, to the person who needs to act on it.
Most claims operations have solved the data problem. Many have made real progress on the information problem. Very few have closed the gap to intelligence. And that gap, coupled with the volume of incoming data, is what’s slowing adjudication, straining examiner workloads, and creating the conditions for inconsistent decisions across teams, regions, and time.
The case for intelligence in claims is usually made on efficiency grounds: faster decisions, less manual review, lower operational cost. Those are real benefits. There is one less talked about benefit: defensibility. A defensible claim isn’t one that is merely reviewed quickly. It’s one that can be explained later.
When an adjudication decision gets challenged by a provider, an attorney, an auditor, or a regulator the question isn’t how fast the decision was made. The question is whether the evidence supports it, whether the reasoning is documented, and whether the decision is consistent with similar claims handled by the same organization. Under those criteria, a decision made by a rushed adjuster flipping through fourteen months of accumulated records is inherently harder to defend than a decision made by an adjuster working from a structured, auditable summary of the same records.
This is where intelligence applied at intake changes what’s possible downstream. When a claim enters the system, the documentation that arrives with it can be indexed, summarized, timelined, and cross-referenced before it ever reaches an adjuster’s queue. Severity signals can be surfaced early, based on structured clinical indicators rather than gut pattern recognition. Inconsistencies between the bill, the records, and the prior authorization can be flagged for review before adjudication begins. None of this replaces adjuster judgment. All of it supports it.
The infrastructure for this already exists at scale. In workers’ compensation, the majority of electronic bills arrive with clinical attachments, and those attachments flow through clearinghouse rails before they ever reach a payer’s adjudication system. That position in the workflow, between submission and adjudication, is where intelligence can be applied most efficiently and with the least disruption to existing processes. Paper-based records from providers operating outside electronic submission channels present a similar opportunity. The same infrastructure that processes electronic attachments can ingest, normalize, and apply intelligence to scanned and digitized documents, folding them into the same evolving clinical picture. As additional transactions and documents arrive on a claim over weeks and months, the underlying picture doesn’t just grow, it sharpens. Each new attachment refines the clinical timeline and surfaces new inconsistencies or confirmations. Intelligence applied at this layer isn’t a one-time event at the moment of submission. It’s a continuous process that evolves with the claim.
The distinction matters because intelligence applied after the fact, as a post-decision review tool or a reporting layer, offers limited leverage. It can catch errors, but it can’t prevent them. It can describe what happened, but it can’t change what is happening. Intelligence applied at intake is different. It shapes the conditions under which decisions get made, which is where the leverage actually lives.
Intelligence applied at intake changes what’s possible downstream. It means the adjuster opens a file and sees a structured timeline, not a stack of PDFs. It means the severity indicators are already flagged, not waiting to be discovered. It means the adjudication decision, when it’s made, rests on documented evidence that can be surfaced again months later, in the same form, with the same audit trail, ready for whatever challenge comes next.
The real benchmark for intelligence in claims isn’t speed. It’s durability. Decisions made today that still hold up when they’re examined tomorrow, next quarter, or three years into litigation. That’s the shift that separates organizations still accumulating data from the ones actually learning how to use it.
The future of claims work won’t be defined by how much data flows through the system. It will be defined by how much of that data reaches the adjuster as intelligence and how many of those decisions, made under pressure and at scale, can still be explained when someone asks why.
