Post Go-Live: Why CFOs Are Using Process Mining to Take Control of Their D365 Programme

Going live on D365 is a significant achievement. But for many CFOs, the weeks and months that follow bring a quieter, more persistent set of problems. Process mining is the tool that brings those problems into sharp focus — and gives finance leaders the evidence they need to fix them.

The post go-live period is one of the most uncomfortable phases of any D365 programme. The implementation partner has rolled off. The hypercare window has closed. The finance team is live on the system and working hard to make it operate smoothly. And yet something is not quite right.

Month-end close is taking longer than the business case projected. Invoice exceptions are accumulating in a queue that is difficult to manage. A recent internal audit raised questions about control effectiveness that nobody could answer with confidence. And the CFO, presenting to the board, is relying on a combination of management judgement and anecdotal evidence rather than hard data when asked whether the D365 investment is delivering what was promised.

These are not signs of a failed implementation. They are signs of the normal, uncomfortable gap that exists between a functioning system and a fully optimised one. Almost every D365 finance programme has this gap. The question is how large it is, where it sits, and what it is costing the organisation.

Process mining and process intelligence answer that question directly. Rather than relying on workshops, user interviews, or periodic audits, they read the actual event data inside D365 and reconstruct precisely how your finance processes are running in reality. The result is an objective, quantified picture of where value is being created, where it is being lost, and what needs to change.

Below are the four areas where post go-live process intelligence is generating the most measurable impact for D365 finance teams.

1. Accounts payable and invoice processing

For most finance teams, accounts payable is the first place post go-live pain becomes visible. Invoice volumes are high, the process touches multiple systems and approval chains, and the consequences of poor performance are immediate: late payment penalties, strained supplier relationships, and a growing exception backlog that consumes significant manual effort.

The common experience is that D365 has improved AP performance relative to the legacy system, but has not delivered the automation levels that were central to the business case. An implementation designed to automate 80 percent of invoice processing is running at 50 to 60 percent in practice. The gap is real, it is costing money, and nobody can point to exactly why it exists.

Process mining closes that gap in visibility. It analyses every invoice journey through D365, reconstructing the actual flow for each transaction. It identifies where invoices are stalling, which approval steps are creating bottlenecks, how many invoices are bypassing the designed three-way match process, and where duplicate payment risk is accumulating undetected.

Critically, it does not just surface the problem. It identifies the specific point in the process where each failure originates, allowing the finance team to make targeted configuration or workflow changes rather than undertaking broad system reviews that consume time and rarely resolve the root cause.

2. Financial close acceleration

The financial close cycle is where the promise of D365 is most directly tested against reality. CFOs invest in modern ERP precisely because it should compress close timelines, improve reporting quality, and reduce the manual effort that consumes the finance team at the end of every period. When the post go-live close cycle falls short of those expectations, it is both a practical problem and a credibility problem at board level.

The challenge is that close cycle problems are difficult to diagnose without process-level visibility. A seven-day close that should be five days contains hundreds of individual tasks, dependencies, and handoffs. Understanding which specific activities are on the critical path, which are consuming disproportionate time relative to their complexity, and which are being repeated because of upstream errors requires a level of analytical granularity that standard reporting cannot provide.

Process intelligence maps every task in the close sequence and quantifies exactly where time is being lost. It distinguishes between tasks that are genuinely complex and tasks that are slow because of rework, waiting time, or process sequencing that was not optimised at implementation. It identifies the intercompany reconciliation steps that D365 should be handling automatically but is not, the journal approval workflows that are creating unnecessary delays, and the reporting dependencies that are forcing the consolidated close to wait on individual entity timelines that could be restructured.

For CFOs operating across multiple entities and currencies, this visibility is particularly valuable. It provides a consolidated view of the close process across the entire organisation and surfaces the specific dependencies that are extending the timeline for board reporting.

3. Compliance and audit readiness

One of the most uncomfortable conversations a CFO can have with an auditor is being unable to demonstrate, with evidence, that financial controls are operating as designed. D365 implementations are built with robust control frameworks. Segregation of duties, approval hierarchies, and automated validation rules are configured carefully during the programme. But between configuration and operation, things change.

Users find workarounds. Approval chains are bypassed informally when speed is prioritised over process. Journal entries are posted outside standard workflows because a deadline is approaching and the normal route is too slow. None of this is necessarily visible in the system configuration. It only becomes visible when you analyse what the system is actually doing with live transactions.

Process mining performs continuous conformance checking against the designed control framework. It flags every instance where a purchase order was approved by the same person who raised it, where a payment was released without a matched invoice, or where a journal was posted outside the standard approval workflow. It does this across every transaction, not just the sample that an internal audit team would test.

The audit readiness implication is significant. A CFO who can present continuous, evidence-based monitoring of control effectiveness is in a materially stronger position than one relying on periodic testing and management representation. It shifts the compliance conversation from assurance to evidence.

Process intelligence also identifies training gaps with precision. When a control is bypassed consistently by the same team or individual, it is almost always a training or process design issue rather than deliberate non-compliance. Identifying the pattern allows targeted intervention and removes the need for disruptive blanket retraining.

4. Fraud detection and anomaly identification

Financial reporting is designed to summarise. It aggregates transactions, applies business rules, and presents outcomes in a format that supports decision-making. It is not designed to detect the subtle behavioural patterns that precede financial irregularities. By the time a fraud or a material control failure is visible in a financial report, the loss has already occurred.

Process mining operates at a different level of granularity. By analysing the sequence, timing, and characteristics of transactions at process level, it identifies anomalies that fall well below the threshold of any financial report. Unusually fast approval cycles on high-value invoices. Vendors added to the system and paid within the same period without standard onboarding verification. Journal entries posted at unusual times by users whose standard activity pattern makes this behaviour inconsistent.

These signals are not proof of fraud. They are patterns that warrant investigation before a problem becomes a loss. For the CFO, the value is proactive rather than retrospective. You are not discovering an issue during an annual audit. You are seeing the leading indicators in time to act.

In a D365 environment, where access controls and segregation of duties configurations can be genuinely complex, process-level anomaly detection provides an oversight layer that configuration management alone cannot deliver. It is the difference between knowing your controls are correctly configured and knowing whether they are actually working under live operating conditions.

The post go-live question that deserves a better answer

Every CFO who has been through a D365 implementation knows the question that follows go-live: are we getting the value we said we would? In most organisations, that question is answered with a combination of positive sentiment, selective evidence, and a reasonable degree of hope.

Process mining replaces that uncertainty with precision. It shows you, in quantified terms, exactly where your D365 investment is performing as intended and exactly where it is not. It gives the CFO the evidence to make the case for targeted remediation investment, the confidence to present control effectiveness to an audit committee, and the visibility to hold the organisation accountable for continuous improvement.

The gap between a live D365 system and a fully optimised one is real in almost every post go-live environment. The organisations closing that gap fastest are the ones that have stopped relying on intuition and started using process intelligence to find it.

We are currently offering process mining to a small number of D365 finance teams at no cost, in exchange for a case study. If you are a CFO or Finance Director who wants to see precisely where your post go-live programme is leaving value unrealised, we would welcome a short conversation with the team at Tierpoint Partners.