Hi {{firstname|everyone}},
Over the past few years, I’ve noticed something that almost every partner complains about, but very few firms properly diagnose.
Not client acquisition. Not regulation. Not even pricing pressure. It’s reviewing.
More specifically, the steady stream of avoidable review points that absorb senior time week after week.
In fact, up to 30% of audit and accounting effort is spent on rework and review corrections rather than first-time completion.
When I sit with partners, the frustration is rarely about complex technical debates. The frustration comes from clearing comments that feel repetitive. Missing explanations. Inconsistent documentation. Variances that should have been addressed before the file ever landed on their desk.
Individually, these issues look small. Collectively, they become one of the most expensive habits inside the firm.
And here is the uncomfortable part. Most of these review notes are predictable. That is where AI becomes interesting, not as a speed tool, but as a quality filter embedded earlier in the workflow.
Here are three ways I believe firms should be thinking about this shift.
1. Catch the predictable inconsistencies upstream
When you analyse review notes across a firm, you quickly see themes. Variances without explanation. Movements that are technically correct but commercially unexplained. Disclosures that are incomplete or inconsistently structured.
They are pattern gaps.
To that end, a Gartner report revealed that over 60% of routine variance analysis and anomaly detection tasks can be automated using AI and advanced analytics.
Instead of waiting for a partner to notice that something “doesn’t feel right”, AI can be trained to flag inconsistencies before a file is marked ready for review. It can compare prior year numbers, identify unexplained percentage shift, and highlight where commentary does not align with ledger movements.
By the time the partner opens the file, the obvious friction points have already been addressed. What remains is genuine professional judgment.
What this looks like in practice
Flag material variances against prior periods and require documented explanations before sign off
Compare trial balance movements with financial statement commentary to detect misalignment
Identify missing disclosures or incomplete workpapers based on templates and prior engagement data
2. Use your own review history as a quality blueprint
Most firms are sitting on a goldmine of data and do not realise it.
Every review note ever written tells you something about recurring weaknesses in preparation. Yet very few firms step back and ask: What are we repeatedly correcting across teams?
Whether the issue is incomplete revenue explanations or inconsistent treatment of certain expenses, AI can analyse historical review comments and surface common themes. Once you know the patterns, you can build prompts directly into the workflow.
Stats reveal, AI-assisted financial controls report error reductions of 20–50% in reconciliations and reporting workflows.
So now, instead of relying on partners to catch the same issue twenty times, you create guardrails earlier.
This is how you can ensure your reviews are no longer clogged with repetition:
Analyse past review notes to identify recurring correction themes across teams
Build structured prompts into working papers for areas that frequently attract comments
Require standardised documentation fields for high judgment areas rather than relying on informal narrative
3. Protect partner time for thinking, not fixing
If I ask partners where their time creates the most value, the answer is rarely “clearing minor documentation gaps”.
Their value sits in risk assessment. In commercial interpretation. In conversations with clients that shape decisions. Yet too often, review turns them into advanced proofreaders.
McKinsey reports, nearly 40% of finance teams’ time is spent on data validation, error checking, and manual review activities.
AI, when used properly, acts as an early stage challenger. It can flag unusual margin swings. It can question abrupt changes in accounting treatment. It can prompt for reasoning when ratios shift unexpectedly.
By the time the file reaches a partner, the only real questions should be about risk, judgment, and what it means for the client.
This is how reviews can become a layer of experienced judgement:
Generate automated alerts for significant movements in revenue, margins, or expense ratios
Require documented reasoning for changes in accounting treatment before advancing to review
Prevent files from progressing when key reconciliations or explanations are missing
How Samera Thinks with AI
We are not interested in making bookkeeping faster for the sake of it. We are interested in reducing avoidable cognitive load across the firm so that senior professionals can focus on the decisions that actually require experience.
If review notes are quietly consuming more partner hours than anyone is comfortable admitting, the issue is not capability.
You can see how we are building this thinking into Samera AI here:
Cheers,
Arun