The agentic
maturity model
for reconciliation

All recs carried out manually, using end-user computing solutions (EUCs), often on tools with embedded AI capabilities. Little governance, high risk of error and lack of auditability. Operations has become even more complicated with the addition of point AI workflow solutions.

AI’s potential is widely recognised. Numerous pilots and use cases, often driven by a focus on where to implement AI rather than where it adds value. A natural step in the journey as firms figure out the difference between hype and reality.

A defined governance framework enables teams across the organisation to replace manual Ops work with AI-powered automation that harnesses machine learning and deterministic rules. Efficiency is up, risk is down and teams are focussed on managing the exceptions that really need their attention.

Hybrid human-agent teams are established. Agents do the heavy lifting around process setup and optimisation, and exceptions investigation, but are largely constrained to operating within individual platforms. People log into software and oversee agent’s work. Reconciliation is elevated from a last line of defence to an adaptive upstream control.

Digital employees are connected to other agents across the org and access headless systems independently. Ops teams no longer log into reconciliation platforms – they review and approve proposed actions centrally, and focus on value adding tasks around efficiency and scale while the bulk of reconciliation and exceptions management is handled by transparent, governed AI.