This piece is by Norm Hudson, Staff Boom CEO, and it looks at how AI can make a positive impact on admin for insurance brands;
Artificial intelligence has become the default answer to nearly every operational challenge in insurance. Claims leaders are asked when they will automate the backlog. Underwriting teams are pressured to use AI for triage. Policy servicing groups are promised that AI will eliminate manual work. And across the market, vendors are eager to demonstrate impressive proofs of concept that appear to turn messy, document-heavy processes into clean, instant outcomes.
But AI is not cure-all for operational inconsistency. In reality, it’s far more effective as an accelerator for organizations that already have disciplined processes, standardized decision rules, and reliable data foundations. Until those fundamentals exist, though, many AI projects will continue to disappoint once they move from demo to production.
AI is no longer a “what if” in the insurance industry. It’s inevitable.
AI’s Progress Today, And Its Limitations
This gap between perception and reality is most visible in insurance administration, which is filled with high-volume work that keeps policies accurate, customers served, and transactions moving. Administration is also where organizations aim to improve customer service and save money. Yet it remains the area where AI is least likely to succeed without significant preparation.
In current insurance operations, most AI deployments remain narrow and task-specific. Common use cases include document classification, basic data extraction, and quality monitoring of customer service or claims calls. In these applications AI can deliver real value, especially when inputs are consistent and the expected output is constrained.
What AI has not consistently delivered, however, is enterprise-wide ROI at scale. Showing true cross-departmental ROI is difficult, to say the least. There are a lot of gaps and inconsistencies across organizations that many leaders aren’t considering to truly have a cohesive AI use case. Things like fragmented core systems and legacy platforms, along with inconsistent data quality and unstructured inputs, are where many struggle to bridge that gap. At the end of the day, it truly is about data, data, data.
Human-in-the-loop remains the standard. In many environments, humans are still doing a lot of the heavy lifting required to make automated workflows function reliably. The result is that automation sometimes becomes an added layer of work rather than a replacement for it.
The Production Reality: Why Demos Succeed and Deployments Stall
Insurance leaders aren’t wrong to expect AI to reduce manual work. The problem is that many pilots are optimized for a controlled scenario: clean samples, constrained document types, known edge cases, and cooperative users. Real operational environments look very different.
Recurring patterns across carriers, brokers, and managing general agents (MGAs) include pilot programs that impress in demos but falter in production, AI tools that raise processing costs rather than lowering them, and employee resistance driven by workflow friction and retraining fatigue.
These issues rarely come down to model intelligence alone. They come down to operational context: unclear handoffs, undocumented exception paths, inconsistent business rules, and data that is not ready to support automation at scale.
Why AI Alone Will Not Fix Insurance Administration
The costliest misconception in insurance administration is the assumption that AI can serve as an end-to-end replacement for broken workflows. AI struggles when rules aren’t standardized, data is messy or incomplete, and when exception handling goes undocumented. This is why human judgment is still critical with AI implementation. The limiting factors become operational maturity, not model sophistication.
A useful way to frame this is to look at unit economics, not just time savings. Consider the following policy check example: A 30-minute process may cost about $5.78 using a human-supported approach, and when AI is layered in, efficiency may improve by around 30%. Yet the cost can rise to nearly $13 per policy check due to added input requirements, tooling overhead, and the need to manage exceptions and verification.
The uncomfortable truth behind many “automation” rollouts is that automation can be more expensive than manual work, especially when the workflow isn’t ready.
Your AI Checklist Before Scaling
To unlock AI value in administration, the sequence matters. AI cannot be the first step. It must be the amplification layer applied after operational readiness exists. Four prerequisites stand out.
1) Process standardization
Workflows must be documented end-to-end, including handoffs, decision points, and exception paths. Unnecessary variations and redundant steps must be removed.
Standardization is not glamorous, but it is the difference between automation that scales and automation that collapses under edge cases.
2) Data readiness
AI is only as useful as the data environment that supports it. Organizations must clean, label, and normalize historical data across the entire enterprise, not just within one team. Data ownership and quality standards must be defined, with visibility into performance through dashboards and governance routines.
This “data readiness” work delivers value even before any AI model is deployed.
3) Human-in-the-loop design
Some steps must remain human-verified, mainly where the cost of error is high or regulatory scrutiny demands explainability. The key is to design human verification intentionally: define escalation paths and document exception handling to ensure employees know where judgment is required versus where automation should be trusted.
4) Measurable baselines
AI ROI cannot be proven without clear pre-AI baselines: true cost per transaction, cycle time benchmarks, and error rates. Many insurers underestimate the “AI enablement work” required to establish these measurements, but without them, AI programs are vulnerable to perception-based reporting and unclear outcomes.
Preparing Your Organization for the AI Revolution
Equipping your organization and data for the AI revolution starts with understanding what you already have. Insurance companies should begin by inventorying every data source that touches administrative workflows, then distinguishing between structured and unstructured inputs. The next step involves cleaning historical records to remove duplicates, standardizing field definitions and business rules across the organization, and ensuring training data is labeled consistently. Finally, establish clear data governance and quality ownership. This foundational work delivers measurable ROI even before any AI system goes live, making it valuable regardless of your automation timeline.
This is the unglamorous “preseason training” of AI. It is also where many organizations will either build a durable advantage or fall into a cycle of pilots that never become production systems.
The Real Opportunity: Build for AI. Don’t Wait for It.
The strongest long-term strategy in insurance administration is not chasing the flashiest tool. It is building operational foundations that make AI useful. The insurers that win will be the ones that standardize operations, clean their data, and build flexible admin capacity that can absorb change.
AI is coming, but it will not rescue broken processes. In insurance administration, successful AI belongs to organizations that do the hard work first and then let technology multiply the results.
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