Quality of Earnings Software: What Advisory Firms Are Using in 2026
Most advisory firms doing QoE work are still doing it in Excel. That is not a criticism. Excel is flexible, familiar and controllable. But it does not scale and it does not get faster with repetition. Quality of earnings software exists precisely to solve that problem. Here is what purpose-built tools actually do and what to look for when evaluating them.
This is a practical look at what QoE software actually does, what to look for and what the market looks like right now.
What QoE software actually means
The term gets used loosely. Before evaluating anything, it is worth being clear about what you are actually looking for.
At the basic end, some tools offer data visualisation and analysis features that can help with QoE work. These are useful but they are not QoE tools. They require a professional to do all the interpretation.
At the more sophisticated end, purpose-built platforms ingest financial data and automatically surface candidates for QoE adjustment. Unusual cost items, revenue that does not recur consistently, margin movements that are not explained by the business narrative. The platform does the pattern recognition. The professional decides what is actually an adjustment.
The second category is where the meaningful time saving sits.
What good QoE software does
Surfaces adjustment candidates automatically. The tool should identify items in the P&L that warrant investigation without you having to find them manually. One-off costs, non-recurring revenue, intercompany items, period-specific anomalies. This is where a well-built tool saves the most time.
Provides traceable reasoning. Every flagged item should have a clear explanation of why it was flagged. Not just a number but a reason. That reasoning needs to hold up when a senior professional reviews it and when a client questions it.
Works with real data. QoE analysis runs on management accounts that come out of data rooms. These are not always clean or consistently formatted. Software that requires pre-formatted input significantly reduces the practical time saving.
Separates identification from conclusion. This is the most important distinction. Good QoE software identifies candidates for adjustment. It does not present those candidates as confirmed adjustments. The professional review step is not optional and the tool should make that clear in how it presents its output.
Integrates with report output. The QoE schedule needs to end up in the report. Tools that produce analysis in isolation, disconnected from the final deliverable, create additional work rather than reducing it.
What advisory firms are actually using
Most advisory firms are still doing QoE work manually or with general-purpose spreadsheet tools. Excel remains the dominant platform for QoE analysis across the market.
A growing number of firms, typically those doing higher deal volumes or facing margin pressure, are moving to purpose-built platforms. The driver is usually a combination of delivery time and capacity. Firms that need to run multiple deals concurrently cannot afford to have senior professionals spending the first week of every engagement building the QoE workstream from scratch.
General-purpose AI tools are being used informally by individual team members for tasks like drafting adjustment narratives or summarising documents. These are useful but they are not a substitute for a platform that processes the underlying financial data.
The security question
QoE work involves highly sensitive client financial data. Before adopting any software, the security credentials matter.
ISO 27001 certification is the recognised standard for information security management. GDPR compliance is a baseline requirement for firms operating in or with clients in the UK and Europe. Understanding where data is processed and whether it is used in any model training is a reasonable question to ask any vendor.
What to expect from implementation
Purpose-built QoE platforms typically require a short onboarding period. The main variable is how quickly the team adapts to reviewing AI-generated output rather than building analysis from scratch. Firms that invest time in understanding how to get the best out of the tool in the first few engagements tend to see a faster return.
PinpointAI
PinpointAI includes a dedicated QoE workstream. It surfaces adjustment candidates automatically from uploaded financial data, shows the reasoning behind every flagged item and feeds directly into the branded report output. No separate QoE schedule to build. No unvalidated conclusions. Just a working candidate list your team can review and refine from day one.
Book a demo to see the QoE output on a real dataset.
