Quality of Earnings analysis is an important part of buying a business. It helps a buyer understand whether reported earnings accurately reflect the company’s ongoing financial performance.
It is also expensive.
Even for smaller transactions, professional Quality of Earnings work can cost $25,000 to $30,000 or more. That can be difficult for an independent sponsor, search fund, or individual buyer to justify—especially before the buyer knows whether the transaction will close.
The cost is not simply the result of complicated financial analysis. A large portion of the effort happens before the analysis can begin.
Financial professionals must collect records from different sources, clean the data, standardize accounts, reconcile conflicting numbers, and build a usable financial model. Only then can they begin investigating the quality and sustainability of the company’s earnings.
That creates an opportunity for better software.
The goal should not be to have AI perform Quality of Earnings work without professional oversight. The goal should be to remove more of the repetitive preparation that makes the work slow, expensive, and difficult to scale.
The Real Bottleneck Is Messy Financial Data
Financial records rarely arrive in one clean, consistent format.
A business may provide QuickBooks exports, tax returns, income statements, scanned bank records, spreadsheets, broker documents, and reports produced by different accounting systems. The chart of accounts may have changed several times. The same type of expense may appear in different accounts depending on the year, employee, or accounting firm involved.
In some cases, the books may never have been formally closed. Transactions continue to change after financial reports are generated, making it difficult to determine which numbers represent the final period.
For a Quality of Earnings professional, this creates a significant amount of front-end work.
Before examining customer concentration, recurring revenue, owner adjustments, margins, or working-capital requirements, someone must first create a reliable financial foundation.
That often means:
- Extracting information from multiple documents and accounting systems
- Creating trial balances for each relevant period
- Standardizing different charts of accounts
- Mapping historical transactions into a common structure
- Identifying discrepancies between tax returns and internal financial statements
- Preserving a record of every adjustment
- Determining which exceptions require further investigation
This is necessary work, but much of it is repetitive. It also consumes time that experienced financial professionals could spend analyzing the business.
General AI Tools Help, but They Do Not Create a Workflow
AI is already making parts of the process easier.
Accounting firms can use general AI tools to extract tables from documents, interpret poor-quality scans, organize financial information, and assist with spreadsheet analysis. In some cases, tasks that previously required complicated Excel formulas can now be completed more quickly.
But there is a major difference between using AI as a tool and building AI into a professional workflow.
Uploading individual PDFs into an AI platform may help an experienced partner complete a task. It does not necessarily provide a process that can be consistently followed by an entire team.
A professional Quality of Earnings workflow needs more than document extraction. It needs structure, controls, review steps, and auditability.
Employees need to know:
- Which documents have been processed
- Where each number came from
- Which accounts were reclassified
- Why an adjustment was made
- Which information was verified
- Which items remain uncertain
- Who reviewed and approved the result
Without those controls, AI can become another layer of “duct tape and bailing wire”—useful in the hands of an experienced professional, but difficult to deploy reliably across a firm.
The answer is not to replace traditional software with AI. It is to combine the two.
Traditional software can manage business rules, permissions, calculations, approvals, and audit trails. AI can assist with the parts that require interpretation, such as recognizing inconsistent documents, matching similar accounts, or identifying unusual activity.
Together, they can create a controlled process rather than a collection of prompts and spreadsheets.
A Better System Would Prepare the Data for the Professional
Most Quality of Earnings work follows a recognizable pattern.
The beginning involves substantial data collection and cleanup. The middle includes repeatable calculations and analyses. The final stage requires professional judgment as unusual transactions, inconsistencies, and potential risks are investigated.
Software is particularly well suited to the first two stages.
A Quality of Earnings platform could ingest financial statements, tax returns, trial balances, and supporting records. It could then standardize the information, create a common chart of accounts, and identify inconsistencies between periods or sources.
The system could assign confidence levels to extracted information. A clearly readable number that reconciles across multiple records might receive a high-confidence score. A number taken from a blurry scan or contradicted by another document could be flagged for human review.
That distinction matters.
A reliable system should not quietly make assumptions when the records disagree. It should surface the disagreement and allow the professional to determine whether it represents an error, timing difference, accounting adjustment, or legitimate change in the business.
The software could also preserve the chain between the original record and the final presentation. When an account is moved, combined, or adjusted, the reviewer should be able to see exactly what happened.
The system prepares the information. The professional remains responsible for interpreting it.
The Interface Matters Too
Quality of Earnings work is commonly performed through large Excel workbooks containing complex formulas, multiple tabs, and carefully formatted schedules.
Those workbooks can support auditability, but they also create practical problems. They require significant setup and formatting. They are difficult for junior employees to navigate. Important details can become buried several layers deep.
A graphical interface could make the work easier to review without removing the underlying financial detail.
Variance analyses could be displayed visually. Users could examine revenue, margins, operating expenses, and seller adjustments over time. Sensitivity controls could show how specific assumptions affect adjusted earnings or valuation.
The professional would still be able to inspect the supporting calculations, but the system would present the information in a more consistent and understandable way.
This could be especially valuable for smaller accounting firms that do not have dedicated Power BI, Tableau, or software-development teams. They need the analytical benefits of a structured platform without building one internally.
More Affordable QoE Could Expand the Market
Reducing the manual burden could do more than improve margins for accounting firms.
It could make Quality of Earnings work available to buyers who currently cannot justify the cost.
A smaller buyer may understand that QoE is important but still hesitate to spend $25,000 or more during diligence. The alternative is often to perform an informal analysis, rely on a broker package, or attempt the work internally.
That creates risk.
At the same time, accounting firms may turn down smaller QoE engagements because the work requires too much manual effort for the available fee. Even when the firm has the expertise, the engagement may not be attractive enough to accept.
A structured software platform could change that calculation.
By reducing the time required for data cleanup, trial-balance preparation, account mapping, and workbook formatting, the firm could complete more engagements without reducing the quality of the professional review.
That could create a new service level between a basic financial screening and a fully manual, high-cost Quality of Earnings engagement.
It would not eliminate the need for an accountant or financial advisor. It would allow that professional to spend less time preparing the data and more time determining what the data means.
The Same Problem Appears in Job Costing and WIP
The underlying issue is not limited to acquisitions.
Construction companies and other project-based businesses face a similar problem when operational information remains disconnected from accounting.
A contractor may have a sophisticated project-management system that tracks schedules, phases, subcontractors, and field activity. But if that information does not reach the accounting system, the company may still struggle to understand job profitability and work in process.
Project managers may know that a phase is nearly complete. Accounting may only see the invoices that have been entered. Materials may have been purchased but not installed. Work may have been completed but not billed. Delays may be increasing carrying costs without appearing in the original forecast.
The result is two different versions of the business: the operational version and the financial version.
A focused software system could connect project progress, subcontractor invoices, QuickBooks data, WIP calculations, draw requests, and reporting. It could also generate regular progress reports for investors and lenders, including project status and supporting photographs.
As with Quality of Earnings, the value comes from creating a structured flow of information. The software brings the records together so the operator, accountant, lender, or investor can make a more informed decision.
Security Cannot Be an Afterthought
Financial and legal records are highly sensitive.
Accounting firms may be restricted in how they use public AI platforms by clients, insurers, professional standards, or internal policies. A tool may be technically capable of processing a document while still being inappropriate for confidential tax, banking, litigation, or acquisition information.
That is creating interest in more private AI environments, including locally hosted models and infrastructure.
Locally controlled AI could eventually allow firms to use document extraction and analysis tools while maintaining greater control over where client information is stored and processed.
The technology and economics are still developing, but the need is clear: firms want the efficiency of AI without giving up control over confidential information.
Any serious financial platform must therefore consider security, access controls, data retention, and deployment options from the beginning.
AI Should Remove the Burden, Not the Judgment
Quality of Earnings work will continue to require professional experience.
Software cannot decide whether a customer relationship is likely to continue, whether an expense is truly nonrecurring, or whether a management adjustment fairly represents future performance. Those conclusions depend on context, skepticism, and professional judgment.
But professionals should not have to spend most of their time cleaning spreadsheets before they can apply that judgment.
The strongest opportunity for AI in Quality of Earnings is not automated decision-making. It is automated preparation.
A well-designed system can ingest messy financial records, standardize accounts, organize trial balances, identify discrepancies, and present the information through a controlled and auditable workflow.
That could help accounting firms accept work they currently turn down. It could give junior employees better guardrails. It could reduce the cost of diligence for smaller buyers. Most importantly, it could allow qualified professionals to focus on the part of the work their clients are actually paying them to do:
Determine what the numbers really mean.
