How do you select the right coverholders to pilot AI-assisted bordereaux processing with first?
Selecting coverholders for an initial AI-assisted bordereaux pilot should be a deliberate decision, not simply a matter of convenience. A useful pilot group typically includes a mix of submission complexity and data quality, representing a reasonable cross-section of the wider coverholder panel, rather than only the cleanest or only the most problematic submissions. Choosing too narrow a group in either direction risks giving a misleadingly positive or misleadingly negative picture of how AI-assisted processing will perform once rolled out more widely.
Key takeaways
- Coverholder selection for a pilot materially shapes what the pilot actually demonstrates.
- A useful pilot group represents a reasonable cross-section of submission complexity and data quality.
- A pilot built only around the cleanest coverholders risks overstating readiness for wider rollout.
- A pilot built only around the most difficult coverholders risks understating early benefit and stalling momentum.
Once an organisation decides to pilot AI-assisted bordereaux processing, an easy but consequential decision follows: which coverholders should be included first.
It is tempting to start with whichever coverholders are easiest to work with, either because their submissions are the cleanest or because the relationship is the most cooperative. It is equally tempting, in organisations keen to stress-test the technology, to start with the most difficult submissions in the portfolio.
Both instincts are understandable, and both can produce a pilot that tells the organisation less than it thinks it does about readiness for wider rollout.
Why coverholder selection shapes pilot outcomes
A pilot is only useful if its results generalise reasonably well to the wider rollout that follows it.
If a pilot is built entirely around an organisation's cleanest, most consistent coverholders, the results will likely look strong, but they say little about how the system will cope with the messier, more varied submissions that make up much of the real portfolio. Confidence built on that basis can be misplaced once wider rollout begins.
If a pilot is built entirely around the most difficult coverholders, early results may look weak or slow, even where the underlying approach is sound, simply because the pilot group represents an unusually challenging subset rather than a typical one. This can stall momentum and support for the wider programme unnecessarily.
How organisations traditionally chose where to start
In practice, pilot coverholders are often chosen for reasons unrelated to representativeness: the largest coverholder by premium volume, the most cooperative relationship, or simply whichever coverholder happens to be due for a system review at the time.
These are understandable practical considerations, but on their own they do not guarantee that the pilot group reflects the range of formats, data quality and complexity the organisation will eventually need to handle.
Practical criteria for selecting a pilot group
A useful pilot group generally reflects a deliberate mix rather than a single characteristic.
Submission format and data quality variation matters: including coverholders with both straightforward, well-structured bordereaux and more complex or inconsistent ones gives a more honest picture of what the system can and cannot yet handle well.
Transaction volume matters too, though moderation is usually sensible: a volume large enough to produce a meaningful sample of exceptions and edge cases, without being so large that reviewing results becomes unmanageable during the pilot period.
Willingness to engage constructively also matters. Coverholders who understand they are part of a pilot, and who are willing to provide feedback or tolerate a period of closer scrutiny, tend to produce a more useful and less fraught pilot experience than those brought in without context.
Moving from pilot to wider rollout
Pilot results, including the cases where the system struggled or required more human review than expected, are valuable input for planning wider rollout, not a final verdict on the technology.
Where a pilot surfaces genuine difficulty with a particular submission type or coverholder pattern, that information should shape how the wider rollout is sequenced, for example by addressing the underlying mapping or data dictionary gap before extending to other coverholders with similar submission characteristics.
Treating pilot difficulty as useful diagnostic information, rather than as a sign the pilot has failed, tends to produce a more realistic and better-prepared wider rollout.
Example
A managing agent oversees bordereaux from sixty coverholders across several classes of business and plans to pilot AI-assisted mapping and validation before wider rollout.
Rather than choosing the five coverholders with the cleanest, most consistent submissions, the implementation team selects a pilot group of eight coverholders spanning straightforward and more complex submission formats, moderate transaction volumes, and two classes of business. The pilot surfaces genuine mapping challenges early, giving the team a realistic view of what wider rollout will require, rather than an artificially smooth result.
FAQs
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How many coverholders should be included in an initial pilot?
The right number depends on the organisation's total coverholder count and its operational capacity to review pilot results closely. A small, manageable group focused on representativeness is generally more useful than either a single coverholder or an overly large initial rollout.
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Should the largest coverholder by volume always be included in the pilot?
Volume is one useful factor but not the only one. Including the largest coverholder can be valuable for demonstrating scale, but this should not come at the expense of also including coverholders that represent typical format and data quality variation.
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What should happen if a pilot coverholder's submissions turn out to be too difficult for the system to handle well?
This is useful information rather than a failure of the pilot. It identifies genuine limitations or configuration gaps that need addressing before wider rollout, and should be treated as an input to refining the approach rather than a reason to abandon it.
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