Insurers should start by treating data quality, organization, and governance as the foundation for any automation programme. The practical move is to map where unstructured inputs enter core workflows, then standardise ingestion before expanding analytics or straight-through processing. Without that discipline, spreadsheets, paper, and narrative documents keep creating error, delay, and weak operational control.
How insurers should reduce manual data handling before automation
The first step is to reduce variability at the point where data enters the organisation. In insurance operations, manual handling usually grows out of inconsistent intake, duplicate transcription, and exceptions that are not designed out of the process. Standardising source formats, fields, and ownership makes later automation safer because the workflow is dealing with cleaner inputs, not compensating for noise.
A useful test is whether a human is still needed to interpret the same information more than once. If staff must re-key, reclassify, or reconcile the same customer or policy data across systems, the process is not ready for scale. The right sequence is to remove avoidable interpretation work first, then automate the stable parts of the workflow.
What manual handling usually signals about the process
Manual data handling is often a symptom of weak data organisation rather than a standalone efficiency problem. Paper forms, spreadsheets, email attachments, and free-text narratives create ambiguous handoffs that force staff to make judgement calls outside the system of record. That increases error rates, slows throughput, and makes it harder to prove that the same rule was applied consistently.
For insurers, the issue is not only speed. Unstructured intake also makes downstream analytics and straight-through processing unreliable because the business has not separated data capture from data interpretation. A process can look automated on paper while still depending on hidden human review for every exception, which caps scalability and distorts operational metrics.
How to prepare the workflow for safe automation
The practical move is to map each workflow and identify where unstructured inputs enter, where decisions are made, and where data is copied or transformed. Once those handoffs are visible, standardise the inputs first, then simplify the exception path, and only then expand automation beyond basic routing or validation. That order matters because automation amplifies whatever process discipline already exists.
Data governance is the control layer that keeps this from becoming a one-time cleanup exercise. Clear definitions for source, owner, format, retention, and exception handling stop local workarounds from reappearing. Insurers should also measure whether the automation reduces manual touches per case, rather than simply moving the manual work into a queue that is harder to see.
Risk and Threat Considerations
Manual handling creates operational and control risk when it persists inside high-volume or regulated workflows. Each spreadsheet, email attachment, or narrative exception is another chance for transcription error, missing approval, inconsistent treatment, or silent data drift, and those failures become more expensive once automation starts depending on the same inputs.
Failure mechanism: Unstandardised intake forces staff to interpret, copy, and reconcile data outside controlled systems, which preserves error-prone exception handling and weakens traceability.
Impact: The insurer can automate the wrong process, scale inconsistent decisions, and create data-quality problems that spread into pricing, claims handling, reporting, and operational oversight.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0 and CIS Controls v8 set the technical controls, while ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC-01 — Organizational Context | Insurance automation depends on clear process ownership and operating context. |
| ID.IM-01 — Improvement | Reducing manual handling is an iterative process-improvement effort. | |
| Recommendation — Define workflow ownership and data boundaries before expanding automation. Measure manual touchpoints and improve the intake process before scaling automation. | ||
| CIS Controls v8 | CIS-1 — Inventory and Control of Enterprise Assets | Mapping input sources and handoffs requires knowing where data enters and moves. |
| Recommendation — Inventory intake sources and system handoffs that feed automated workflows. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | Controlled handling of workflow data depends on defined access and ownership boundaries. |
| A.5.37 — Documented operating procedures | Standardised intake and exception handling rely on documented procedures. | |
| Recommendation — Restrict who can alter intake data and exception records. Document the standard intake process and the approved exception path. | ||
Practitioner Guidance
What to prioritise: Focus first on the highest-volume workflows where manual re-entry, document parsing, or exception handling is most common. Those are usually the best candidates for standardisation because small improvements there produce the largest reduction in operational noise.
What to verify: Before trusting automation, confirm that the upstream data can be validated at ingestion, that exception categories are explicit, and that owners can explain why a case leaves the normal path. If that cannot be shown, the process still depends too heavily on human interpretation.
What good looks like: A stable intake layer, limited exception types, and a clear trail from source document to system record. When those are in place, automation becomes an execution tool rather than a substitute for process design.
Practitioner takeaway: Insurers should not scale automation by adding more robots to a messy workflow. They should first remove unnecessary manual interpretation, because safe automation depends on controlled inputs, disciplined exceptions, and measurable data quality.
Related resources from NHI Mgmt Group
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- Why do organisations need PCI data discovery before they can reduce cardholder data risk?
- How should security teams automate cloud data discovery before they can govern sensitive information at scale?
- How should financial institutions implement data discovery before they can govern sensitive information at scale?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
