What support model is needed after implementation?

Implementing a new process for handling delegated authority data, whether a bordereaux mapping tool, a validation workflow or an AI-assisted system, is only the first half of the work.

Once the system goes live, someone has to own it day to day. Coverholders will change their reporting formats. Exceptions will need reviewing. Questions will arise that the original project team may no longer be around to answer.

Without a defined support model, the improvements delivered during implementation can quietly erode within months.

This article explains what a sustainable post-implementation support model looks like, and how AI-assisted tools change what that support needs to cover.

Why support needs change after go-live

During implementation, a project team is focused on design, testing and getting a new process live. That team typically has a defined end date and a specific set of deliverables.

Once the process goes live, the demands on the organisation shift. The question is no longer "does this work?" but "is this still working, every month, as conditions change?"

This shift is often where things go wrong. Project teams disband, ownership becomes unclear, and the detailed knowledge of how the process was designed leaves with the people who built it. Meanwhile, coverholders continue to change formats, submit unusual data, and raise queries that need a timely response.

If no one is clearly responsible for handling these day-to-day issues, small problems accumulate. Exception queues grow unreviewed. Data quality issues that should have been caught early instead surface much later, often during a renewal or an audit, when they are more expensive to fix.

Traditional components of a support model

Organisations that manage this transition well typically put a small number of established support elements in place before go-live, regardless of whether the underlying process is manual, semi-automated or AI-assisted.

Common components include:

• A helpdesk or query-handling function, so coverholders, brokers and internal stakeholders have a clear route for questions.
• Defined service levels, setting expectations for how quickly queries and exceptions should be addressed.
• An exception queue and review process, ensuring records that fail validation are actively worked rather than left unresolved.
• Periodic reconciliation, checking that totals, counts and key figures continue to match expectations over time.
• Formal change control, so that when a coverholder changes its reporting template or a new product is added, the change is assessed and the process updated deliberately rather than discovered by accident.

These elements are not new. They reflect the same operational discipline that any ongoing business process requires, whether or not any part of it is automated.

Where AI-assisted tools change support needs

AI-assisted mapping and validation tools change what this support model needs to cover, without removing the need for it.

Because these tools interpret bordereaux structure and terminology rather than relying on fixed templates, they reduce the manual effort previously spent reformatting spreadsheets and maintaining rigid mappings. That is a genuine operational gain.

However, it introduces new monitoring requirements. Support teams need visibility into:

• Confidence levels on automated field matches, so a rise in low-confidence matches can be investigated before it causes downstream errors.
• Data or format drift, where a coverholder's submissions gradually change in ways that affect mapping accuracy.
• Periodic review of mapping rules, to confirm they remain accurate as products, coverholders or terminology evolve.

Crucially, none of this replaces human judgement. Where an AI-assisted tool flags uncertainty or an unusual pattern, an experienced DA professional still needs to review it and decide what happens next. The support model simply needs to route that review to the right person, at the right time.

Building a practical support model

A workable support model can be established with a small number of practical decisions, ideally agreed before go-live rather than worked out reactively afterwards.

Key considerations include:

• Assign clear ownership. Someone, typically an operations manager, should own the process on a business-as-usual basis, distinct from the original project sponsor.
• Define escalation paths. Exceptions requiring underwriting judgement should have a clear, direct route to the people qualified to make that call.
• Set a review cadence. A regular schedule, weekly or monthly depending on volume and risk, for reviewing exceptions, confidence levels and reconciliation results keeps small issues from becoming large ones.
• Retain access to implementation knowledge. Even if the project team disbands, someone should remain reachable who understands why the process was designed the way it was.

Treating these as deliberate decisions, rather than assumptions, is what separates a support model that holds up over time from one that quietly decays.

Example

A London managing agent has recently implemented an AI-assisted bordereaux mapping tool for its agricultural risk coverholders. Three months after go-live, one coverholder changes its reporting template without notice. The mapping tool flags a rise in low-confidence field matches, and this is picked up during a scheduled weekly review rather than being missed entirely.

Because a support model was agreed at go-live, including a weekly review of confidence-level exceptions, the operations manager identifies the format change quickly, updates the mapping rules, and avoids a backlog of unresolved records. The underwriting team is not involved until the mapping is confirmed, preserving their time for genuine judgement calls.

FAQs

• Who should own the support model after implementation?

  Ownership should sit with an operational role, such as a DA operations manager, rather than remaining with the project team that delivered the implementation. This should be agreed before go-live so there is no gap in responsibility once the project formally closes.

• Does AI reduce the amount of ongoing support needed?

  AI-assisted tools reduce the manual effort spent interpreting and reformatting inconsistent bordereaux, but they shift effort towards monitoring, exception review and periodic checks for data or format drift. They do not remove the need for an ongoing support model or for human oversight of judgement calls.

• How often should a DA data process be reviewed once live?

  Review cadence depends on volume and risk, but a regular schedule, such as weekly or monthly, for exception review and periodic reconciliation is a common practical baseline. Higher volume or higher risk processes typically warrant more frequent review.