The Cost of Dirty Data

Why Clean Data Comes Before AI in Life Insurance

By Zane Gray

Every carrier is racing to deploy AI for faster underwriting, automated document review, and predictive analytics. Yet the technology keeps underdelivering. The culprit isn’t the software. It’s the data feeding it.

“Garbage in, garbage out” is an old adage, but AI raises the stakes. Dirty data no longer merely affects isolated workflows. It poisons every recommendation, automated decision, and customer interaction. Automation doesn’t fix bad data. It scales it.

None of this is news to the industry. In a 2025 study by LIMRA and Equisoft, 87% of life insurers said they already use AI in underwriting, operations, or new business. Yet 78% called data readiness the biggest obstacle to getting value from it.

AI Can Only Work With What You Give It

Insurance operations have accumulated decades of data across fragmented systems, acquisitions, and legacy platforms. The problem surfaces every time one agency absorbs another’s book of business: ownership fields and address formats that never agree, and carrier names spelled differently in every system.

Across our 160-plus clients, we’ve found more than 30 variations of Global Atlantic’s name alone. Until someone reconciles those records, real data insight is impossible.

When AI enters the picture, these inconsistencies become part of the model’s foundation. The model doesn’t know the records are wrong; it assumes that’s what your business looks like.

Data Cleanup Is an Insurance Operations Problem

Data remediation is often treated as a purely technical exercise. In insurance, it is fundamentally operational.

Cleaning data goes beyond removing duplicates or correcting spelling. It requires deep industry knowledge to distinguish between a legitimate exception and a flawed process — to recognize whether a policy ownership structure makes sense, why beneficiary information appears inconsistent, or whether missing licensing details are significant.

Clean data isn’t simply standardized data. It’s accurate data that makes sense in the context of the business.

A generic data team can make your records match. It takes insurance professionals to know what those records are supposed to say. That expertise is the difference between a clean database and a functioning one.

That is where insurance-specific operational expertise becomes important.

Employee Pooling’s teams work in the systems and workflows used by carriers and distributors every day. That includes CMS, AMS, and CRM environments, policy records, agent records, contracting information, and other operational data. The goal isn’t simply to make records look cleaner. It’s to make them more useful, reliable, and ready for the work that depends on them.

The Hidden Cost of Waiting

Many organizations defer data cleanup until after they implement a new platform. This delay makes the work significantly more expensive. And the clock is running: Gartner predicts that through 2026, organizations will abandon 60% of AI projects lacking AI-ready data.

Dirty data taxes everything around it: employees burn hours re-verifying records, compliance risk stacks up, and customer service falters when systems disagree. Technology projects stall because manual intervention for data exceptions was never part of the implementation plan.

AI magnifies these challenges. Rather than realizing productivity gains, organizations end up hiring people to babysit the automation, correcting mistakes that were in the data long before the software arrived.

The lesson isn’t that organizations should wait to invest in AI. It’s that data readiness needs to be part of the AI strategy from the beginning.

Technology needs operational expertise

The organizations seeing real returns on AI have one thing in common: they got operationally ready first. While technology excels at structured tasks, it lacks the nuanced judgment required for the complexities of insurance.

Prioritize remediation before automation. Before deploying smart systems, data must be audited and standardized. Rightsourcing provides the immediate, skilled capacity to clean operational data without the overhead of internal hiring and training cycles.

Establish continuous governance. Data quality is a discipline, not a project. A rightsourcing model ensures consistent access to specialists who maintain integrity, validate inputs, and prevent errors from propagating through automated workflows.

Resolve complex exceptions. Algorithms often fail when faced with edge cases or regulatory shifts. Rightsourcing bridges this gap by providing expert personnel to manage the anomalies that would otherwise stall automated systems.

Where Employee Pooling Can Help

Getting data ready for smarter systems doesn’t have to become another permanent project for an already-stretched internal team.

Employee Pooling provides insurance-trained operational professionals who can take on data-intensive work while working within the systems, processes, and quality standards of the client organization.

That can include:

Data, System & Hierarchy Administration: EP’s Special Projects team supports operational data maintenance inside CMS, AMS and CRM environments, including records updates, hierarchy administration, reconciliations, duplicate-name removal, policy data maintenance and Excel-based data cleanup. Explore Special Projects.

New Business & Application Processing: Data quality begins upstream. EP’s New Business team supports application processing, case creation and updates, suitability review, document preparation and other workflows that help reduce errors and keep information moving correctly through the process. Explore New Business Services.

Agent Contracting & Licensing: Producer information is another critical source of operational data. EP manages agent appointments, profile builds, carrier requirements, contracting updates, and other activities that keep producer records accurate and current. Explore Agent Contracting.

Policy & In-Force Data: For existing business, EP supports in-force policy cleanup and management, advisor and agent record updates, policy status work, and other data-intensive policy administration tasks. Explore Policy Services.

Data Readiness Is an Ongoing Discipline

One cleanup project can make a meaningful difference. It doesn’t solve the underlying issue if inconsistent data continues to enter the system.

That’s why organizations should think about data quality as an operational discipline.

Every new record, policy change, agent update, and system integration creates another opportunity for errors to enter the environment. Establishing consistent processes, validating information, and addressing exceptions before they compound can make future automation far more effective.

For carriers and distributors considering AI, modernization, or a major systems initiative, that means asking a basic question before asking what the technology can do:

Can your data support it?

Give AI Something Worth Learning From

Life insurers are already investing heavily in AI. The opportunity now is to make those investments work.

That starts with data.

Clean, reliable data supports better automation, stronger reporting, more efficient operations, and better experiences for advisors and policyholders. Employee Pooling combines insurance expertise, technology, and scalable operational capacity to help organizations get the underlying work into shape.

Before you make your systems smarter, make sure the data they’re learning from is worth trusting.

Talk to Employee Pooling about your data and operational needs.

Zane Gray is Director of Technology and Partnerships at Employee Pooling, where he streamlines processes, improves data quality, and helps internal teams and clients get more value from the technology and automation they use.

A version of this article appeared in the 2026 September/October issue of Aspire Magazine.