How Do We Measure Success After Go-Live?

Many delegated authority organisations invest significant time and budget into improving how bordereaux and other DA data is processed.

Fewer, however, agree upfront what "success" actually looks like once the new process or system goes live.

Without a clear framework, success is often assumed rather than demonstrated. That makes it difficult to justify further investment, identify remaining gaps, or reassure oversight stakeholders and regulators that a change genuinely delivered improvement.

This article sets out a practical way to measure outcomes after go-live, so implementation efforts can be judged on evidence rather than impression.

Why go-live is the start of measurement, not the end

It is tempting to treat go-live as the finish line for a project. The system is switched on, the new process is running, and attention moves elsewhere.

In reality, go-live is the point at which real measurement of operational impact begins. Whatever assumptions were made during planning now need to be tested against actual performance.

This matters particularly for delegated authority data flow. Oversight stakeholders and regulators expect organisations to be able to demonstrate that changes to bordereaux processing have improved data quality and control, not simply that a new tool or process exists. Without agreed measurement, an organisation can end up making claims about improvement that it cannot actually substantiate.

Traditional approaches to post-implementation review

DA organisations have traditionally assessed implementation success through a mix of structured and informal methods.

Common approaches include:

• Post-implementation reviews conducted a fixed period after go-live.
• Manual sampling of processed bordereaux to check for errors.
• Stakeholder surveys covering coverholders, underwriters and operations staff.
• Informal feedback loops, such as raised queries or complaints.

These approaches have real value. They capture qualitative issues that pure numbers might miss, and they involve the people who actually use the process day to day.

Their limitation is consistency. Manual sampling only checks a fraction of records, feedback tends to surface problems rather than confirm success, and without agreed metrics, different reviewers can reach different conclusions from the same underlying performance. The result is often an assessment based on anecdote rather than a consistent, comparable measure of outcomes.

Where AI changes what can be measured

Where AI is part of the new process, additional and more granular measurement becomes possible.

Instead of relying on sampling a subset of bordereaux, organisations can track:

• Exception rate: the proportion of records that the process cannot handle with confidence and flags for manual review.
• Override frequency: how often a human reviewer changes or rejects an AI-generated result.
• Accuracy trends: whether exception and override rates are falling, rising or holding steady across successive processing cycles.

Tracked over time, these measures give a more evidence-based view of whether operational performance is genuinely improving, rather than relying on periodic manual sampling alone.

This does not remove the need for human oversight. A falling exception rate does not, on its own, prove the process is sound. It is a signal that should inform where oversight attention is directed, not a substitute for judgement about whether the underlying decisions are correct.

Operational considerations for measuring success

Setting up measurement properly requires a few practical steps.

Agree the metrics and capture baseline data before go-live. Trying to define success criteria after the fact, using only the data that happens to be available, produces a weaker and more contestable picture.

Separate the stabilisation period from steady-state operation. Early weeks after go-live commonly involve teething problems, such as unfamiliar workflows or edge cases the new process was not designed for. These issues should be tracked, but they should not be mistaken for a verdict on the process's longer-term operational value.

Assign clear ownership for tracking metrics, so that measurement does not quietly stop once initial enthusiasm fades. Finally, revisit the metrics periodically as the process matures. What counts as an acceptable exception rate in month one may look very different by month six.

Example

A London market managing agent implements a new AI-supported validation process for bordereaux received from a portfolio of agricultural risk coverholders.

Ahead of go-live, the DA oversight team agrees a set of success metrics: data quality error rate, average bordereaux processing time, exception rate requiring manual review, and coverholder satisfaction.

Three months after go-live, the team compares steady-state performance against the pre-implementation baseline, separating out the first month's stabilisation issues from the following two months of steady-state operation.

The comparison shows that the data quality error rate has fallen significantly and processing time has reduced, while the exception rate requiring manual review has stabilised at an acceptable level after the first month.

This evidence gives the oversight team confidence to report the implementation as a success to senior management, while flagging two coverholders whose bordereaux still generate a disproportionate share of exceptions for targeted follow-up.

FAQs

• How soon after go-live should we start measuring success?

  Measurement should begin from day one of go-live, but early results should be treated as stabilisation data rather than a true reflection of steady-state performance. Many organisations use a stabilisation window of around four to eight weeks, though this varies depending on the scale of the change and how different the new process is from what came before.

• What if we didn't agree success metrics before go-live?

  This is common. Organisations can still retrospectively define reasonable metrics and use whatever baseline data is available, such as historical error rates or processing times from before the change. The resulting comparison will be weaker than one planned in advance, since the baseline may be incomplete or inconsistent, but it is still more useful than no comparison at all. Agreeing metrics before go-live next time avoids this problem entirely.

• How do we measure success if AI is part of the new process?

  Alongside standard metrics like data quality and processing time, track exception rates, override frequency and accuracy trends over successive processing cycles. These reveal whether confidence in the AI-supported process is increasing over time, and help identify where human oversight should remain focused, such as particular coverholders or record types that consistently generate exceptions.