Can AI Replace a Quality of Earnings Report?
Published February 2026
If you're preparing for a transaction, here's how to think about AI in the QoE process — not as a replacement for expertise, but as a force multiplier for deal velocity.
Want the shorter, opinionated take? Read AI Won't Do Your Quality of Earnings Analysis For You — our founder's manifesto on why "AI-assisted" beats "AI-generated" every time.
The Question
As AI tools enter the M&A landscape, deal professionals are asking a straightforward question: can AI replace the traditional Quality of Earnings engagement performed by a CPA firm?
The honest answer is nuanced. AI can automate the data-intensive work that consumes 70–80% of a traditional QoE engagement. But certain elements — professional judgment, management interviews, formal attestation — still require human expertise. The real question isn't "replace or not" — it's how to combine AI and human judgment for better, faster outcomes.
What AI Does Well
Data processing at scale
AI reviews 100% of GL transactions where humans can only sample. More coverage means fewer missed issues
Pattern recognition
Identifies anomalies, duplicates, round-dollar entries, and period-end clustering across thousands of transactions
Account mapping
Automatically classifies chart of accounts into standardized QoE categories — work that takes analysts days
Calculation consistency
Working capital ratios, EBITDA bridges, and trend analyses computed identically every time
Speed
Initial analysis in hours vs weeks — critical for competitive deal timelines
Keyword intelligence
NLP-powered detection of personal expenses, related-party indicators, and adjustment candidates in transaction descriptions
What AI Can't Do (Yet)
Management interviews
Understanding why a number is what it is requires conversation, body language, and follow-up questions
Business context
Knowing that a $200K expense is truly non-recurring requires understanding the business, the industry, and the deal context
Formal attestation
Lenders and institutions that require a CPA's signature and professional liability coverage can't accept AI-only analysis
Judgment calls
Is a recurring legal expense truly non-recurring? Is the owner's salary replacement $150K or $200K? These require human judgment
Relationship navigation
Negotiating adjustment positions with the other side of the deal is inherently human
Complex structures
Multi-entity carve-outs, international tax structures, and bespoke accounting require specialist expertise
AI vs CPA Firm: Where Each Excels
| Capability | AI-Assisted | Traditional CPA |
|---|---|---|
| Transaction coverage | 100% of GL | Sample-based |
| Time to first findings | Hours | Weeks |
| Cost | Fraction of traditional | $20K+ |
| Consistency | Standardized methodology | Varies by team |
| Management interviews | Not applicable | Deep qualitative insight |
| Professional attestation | No | Yes (CPA liability coverage) |
| Complex judgment calls | Flags for review | Expert resolution |
| Scalability | Parallel processing | Linear (headcount) |
The Hybrid Model
The most effective approach combines AI automation with human expertise. This isn't a compromise — it's a genuinely better outcome for all parties:
AI handles data processing
Account mapping, anomaly detection, initial adjustment identification, calculations — the 70-80% of work that's data-intensive
Humans provide judgment
Evaluating flagged items, conducting management interviews, making nuanced decisions about adjustment treatment
Faster deal timelines
Preliminary AI findings available in hours, giving the team a head start before formal engagement begins
Better coverage
100% transaction review by AI + focused human analysis on flagged items = more thorough than either alone
Where This Is Headed
The trajectory is clear: AI will handle an increasing share of the analytical work in QoE, while human judgment remains essential for the subjective, relational, and attestation components. The firms and professionals who adopt AI tools will outcompete those who don't — not because AI replaces them, but because it makes them faster, more thorough, and more scalable.
Learn how Shepi implements this hybrid approach.