Tacit knowledge shapes how experienced claims professionals interpret complex information and make difficult decisions. Learn why this expertise is becoming harder to scale—and how human-centric AI can help insurers put it to better use.

Claims teams work with enormous amounts of information.

Medical records. Legal documents. Correspondence. Adjuster notes. Statements. Images. Reports.

But information alone doesn't resolve a claim.

Experienced claims professionals know how to interpret what they see. They recognize which details deserve closer attention, spot inconsistencies across a file, understand when a situation requires a more nuanced approach, and know when the facts don't tell the whole story.

Much of that expertise is difficult to capture in a manual or workflow.

It's called tacit knowledge: the judgment, intuition, and practical understanding people develop through experience.

For insurers, tacit knowledge is incredibly valuable. It's also fragile.

As claim files grow, experienced professionals leave the workforce, and automation takes over more routine work, insurers face a new challenge: How do you scale efficiency without losing the human expertise that makes good claims decisions possible?

Tacit knowledge is experience-based knowledge that can be difficult to fully explain, document, or transfer to someone else.

The concept is often associated with philosopher Michael Polanyi's observation that people can know more than they can explicitly communicate.

In insurance claims, the distinction matters.

A claims manual can describe a process. A policy can establish requirements. A checklist can make sure important steps aren't overlooked.

But those tools can't fully reproduce the judgment that develops after years of working through complicated claims.

Tacit knowledge can influence how a claims professional:

• recognizes inconsistencies across medical records and other claim documents;
• determines which information warrants further attention;
• interprets ambiguous or incomplete circumstances;
• communicates effectively in sensitive situations; and
• applies professional judgment when the appropriate next step isn't obvious.

That expertise develops over time through experience, observation, feedback, and exposure to real claims.

And that makes it difficult to scale.

The challenge isn't simply that experienced professionals eventually retire or change roles.

The environment surrounding claims work is changing too.

Claims professionals work across large volumes of structured and unstructured information, including medical records, legal documents, correspondence, reports, notes, images, and other sources.

The problem isn't necessarily access to information.

It's finding what matters.

When important facts are buried across fragmented systems and lengthy files, experienced professionals spend valuable time locating and organizing information before they can apply their expertise to it.

The more information there is to process, the greater the burden on the people responsible for understanding it.

Claims expertise takes years to develop.

As experienced professionals leave an organization, some of what they know can be documented and transferred. Some cannot.

The U.S. Bureau of Labor Statistics projects thousands of openings for claims adjusters, appraisers, examiners, and investigators each year as workers transfer occupations or leave the labor force.

Replacing a role, however, isn't the same as replacing experience.

New claims professionals need exposure, feedback, mentoring, and time before complex patterns become familiar and professional judgment becomes instinctive.

That makes tacit knowledge both an organizational advantage and an organizational risk.

At the same time, claims organizations face pressure to control costs, reduce leakage, detect fraud, maintain compliance, improve customer experiences, and process claims more efficiently.

Those demands leave less time for the mentoring and deliberate practice through which expertise has traditionally developed.

Technology can relieve some of that pressure.

But it can also change the way expertise develops inside a claims organization.

Automation has brought meaningful improvements to insurance claims.

Routine information can be classified. Documents can be processed faster. Repetitive workflows can move forward with less manual intervention.

For claims professionals, that can mean less administrative work and more capacity for higher-value tasks.

But automation also changes the mix of work that claims professionals experience.

Human expertise develops partly through repeated exposure. Professionals encounter situations, recognize patterns, make judgments, receive feedback, and gradually develop the intuition that helps them navigate more complicated cases.

As technology takes on more routine and intermediate work, the experiences through which claims professionals develop expertise may change too.

That doesn't necessarily mean automation weakens expertise. It means insurers need to be more deliberate about how people continue to learn—through mentoring, feedback, case review, and exposure to complex claims.

That doesn't mean insurers should automate less.

It means the goal of automation matters.

The objective shouldn't be to remove people from claims. It should be to remove the work that prevents people from using their expertise while preserving meaningful opportunities to develop it.

AI creates an opportunity to rethink that relationship.

The goal isn't to somehow extract everything an experienced examiner knows and put it into an algorithm. Truly tacit knowledge cannot simply be downloaded from someone's experience.

Nor should insurers assume that technology can fully preserve the judgment, intuition, or contextual understanding of an experienced professional.

Instead, AI can reduce the information burden surrounding human judgment.

It can read large claim files, structure complex information, surface relevant facts, identify connections across documents, and help claims professionals understand the evidence in front of them.

It can also make documented knowledge—such as policies, rationales, case notes, and other explicit information—easier to access and apply.

The human remains responsible for interpreting that information in the context of the individual claim.

It's a simple division of strengths:

AI handles the information burden. People provide the judgment.

That gives claims professionals more time for the work where human expertise matters most: analyzing complex circumstances, communicating with claimants, exercising empathy, investigating uncertainty, negotiating, and making informed decisions.

Claims organizations don't need more information.

They need to make the information they already have easier to understand and use.

Human-centric AI can help close that gap in several ways.

Critical details can be buried hundreds of pages apart.

AI designed for claims can process large volumes of medical, legal, and other claim documentation, bringing relevant information together without requiring professionals to manually search every page.

The result isn't a replacement for human review.

It's a stronger starting point for it.

Unstructured information becomes more valuable when claims professionals can understand it in context.

Summaries, chronologies, extracted facts, and organized claim data can reduce the time spent assembling the story of a claim and increase the time available to evaluate what that story means.

In insurance, an answer isn't enough.

Claims professionals need to understand where information came from and why it matters.

Evidence-backed insights and citations to source documents allow people to verify what AI surfaces against the underlying claim file. That transparency supports human control and makes AI more useful in decisions that need to be explained and defended.

When professionals spend less time searching, sorting, and summarizing, they gain more capacity to apply their experience.

Experienced team members can focus on difficult cases that genuinely require their expertise. Developing professionals can work from clearer, more comprehensive information as they build their own judgment.

AI doesn't preserve tacit knowledge by itself.

But by reducing the information burden around claims work, it can give human expertise more room to be applied, shared, and developed.

Technology alone won't preserve institutional expertise.

Claims organizations also need to think deliberately about how knowledge is developed, shared, and applied.

Where appropriate, document the reasoning behind important decisions—not only the final outcome.

Decision rationales, feedback, case reviews, and playbooks can turn parts of individual experience into knowledge others can learn from.

Not all tacit knowledge can become explicit. But organizations can capture more of the context surrounding expert decisions.

The goal is not to convert every element of experience into data. It is to preserve the parts of expert reasoning that can genuinely be documented and shared.

AI should support professional judgment, not obscure or replace it.

Claims professionals should be able to review AI-generated information, trace insights back to their sources, provide feedback, and determine how the information applies to the individual claim.

The best candidates for automation are often the tasks that consume expertise without requiring it.

Searching lengthy documents, organizing information, extracting facts, and producing repetitive summaries can take significant time without making full use of an experienced professional's judgment.

Removing that burden creates space for higher-value work.

Efficiency shouldn't come at the expense of developing the next generation of claims expertise.

Mentoring, case reviews, feedback, and exposure to complex situations remain essential—even in highly automated environments.

As automation changes the kinds of tasks people perform, insurers may need to create these learning opportunities more intentionally rather than relying on experience to develop naturally through repetition.

Processing time and cost matter.

But a human-centric claims operation should also consider consistency, quality, defensibility, employee development, and the ability to apply expertise where it creates the most value.

The future of claims isn't a choice between human expertise and artificial intelligence.

It's about giving claims professionals better access to the knowledge they need to do their best work.

That's the idea behind Claims Intelligence.

Owl's human-centric AI is built to read and reason over the claim file, helping claims teams turn complex and dispersed information into knowledge they can use.

Rather than predicting what will happen next, Owl analyzes what the claim actually says.

That distinction keeps people at the center of the process.

Claims Intelligence combines three principles:

• Accountable AI provides transparent logic, auditability, and evidence that claims professionals can review and validate.
• Effective AI processes complex information accurately and efficiently, reducing repetitive work and helping teams focus on the claim itself.
• Ethical AI supports fair, compliant outcomes grounded in the facts of the individual claim rather than speculative predictions.

The technology isn't there to replace what an experienced claims professional knows.

Nor is it there to claim that human judgment can be fully captured or reproduced.

It's there to reduce the information burden around that judgment—and give expertise more room to work.

Tacit knowledge will always matter in insurance.

No system can fully reproduce the judgment, empathy, creativity, and intuition professionals develop through years of experience.

But insurers can decide how much of their experts' time is spent applying those skills—and how much is lost searching through documents, reconciling fragmented information, and completing repetitive work.

Human-centric AI offers a different path.

By turning complex claim information into clear, traceable knowledge, insurers can give experienced professionals more capacity for difficult work while giving developing professionals a stronger foundation on which to build their own expertise.

At the same time, organizations still need deliberate mentoring, knowledge-sharing, and professional development. AI can support those efforts, but it cannot replace them.

The opportunity isn't to automate human judgment out of insurance.

It's to build claims operations where technology and human expertise make each other stronger.

Because the most valuable knowledge in claims isn't just what's written in the file. It's knowing what to do with it.