Hi {{firstname|everyone}},
Most firms talk about AI in accounting as if the only goal is speed. Faster reconciliations. Faster posting. Faster close. Faster reports.
That obsession is understandable. Accounting teams are under pressure. Volumes are rising. Clients want answers sooner. Talent is stretched thin. So the natural instinct is to push automation harder and measure success in minutes saved.
But that framing misses the real problem. The real bottleneck is not how quickly numbers move through the system. It is how confidently people can interpret them, question them, and explain them to clients.
This is where most AI conversations quietly go wrong. Firms use powerful tools to accelerate output, but they leave the cognitive burden exactly where it was before. The work moves faster, but the thinking does not.
The best AI use cases flip that equation. They reduce mental load so accountants can focus on decisions, not data wrangling.
Below are 3 shifts worth paying attention to.
1. AI should surface questions, not just answers
Most accounting systems are designed to give you outputs. They tell you what the numbers are, but they do not tell you where to focus or what deserves judgment. As volumes grow, this becomes a problem. Accountants end up reviewing everything because the system cannot tell them what is important.
This is where AI changes the game when it is applied correctly. Instead of producing more reports, it reduces the field of attention.
Fact is, 40% of firms say AI that includes historical context delivers more value than simple automation.
When AI is used this way, it draws a line around what has changed, what looks inconsistent with prior behaviour, and what might affect a decision if left unchecked.
Ultimately, AI stops being a reporting layer and starts acting like an experienced reviewer. Not one that decides for you, but one that points you to the right questions early, while there is still time to act.
In practice, this is how you can put this to work:
Configure AI to flag material changes and behavioural breaks rather than generate additional summaries
Use AI prompts that ask what is different and why, not just what happened
Treat AI outputs as starting points for judgment, not conclusions to accept
2. Judgment improves when context stays attached to the numbers
One of the quiet failures of automation is how easily context gets stripped away. Transactions move through systems cleanly, but the story behind them disappears. When that happens, accountants are forced to reverse engineer intent during review.
The strongest AI systems do the opposite. They keep memory close to the data. They recognise that judgment in accounting is built over time by seeing the same client patterns repeat.
To that end, 55% of finance professionals say they do not trust AI outputs until they can see how a decision was reached.
When context is preserved, decisions feel easier because they are familiar, not because they are simplified.
This is where AI can genuinely support professional thinking. By linking entries to prior months, known client behaviour, and past resolutions, it reduces mental friction. Accountants spend less time asking what is going on and more time deciding what to do about it.
Here's how this thinking translates into day-to-day accounting work:
Train AI models on historical client data and behaviour, not just accounting rules
Store explanations, exceptions, and resolutions alongside transactions
Use AI to summarise what changed month on month in clear language
3. The real outcome is calmer decisions, not faster closes
Speed is easy to measure, so it becomes the default goal. But faster does not always feel better. In many firms, the close moves quicker, yet stress increases.
When AI is working properly, the tone of the work changes. Issues surface earlier. Patterns are recognised sooner. Reviews become confirmatory rather than investigative. That is when accountants start to trust the system.
Organisations that use AI earlier in the workflow report up to 40% fewer late-stage exceptions.
This is the difference between automating activity and supporting judgment. Clients do not value speed in isolation. They value answers that hold up under scrutiny. AI earns its place when it helps accountants deliver that confidence consistently.
Here are 3 practical ways your firm can put this into practice:
Deploy AI earlier in the process to surface exceptions before review
Focus automation on explanation and anomaly handling, not just throughput
Track fewer late-stage issues and review queries as a success measure
Shaping the Future with Samera.AI
At Samera, this is how we think about AI in accounting. Not as a race to automate everything, but as a way to reduce noise, preserve context, and support better judgment at every level of the firm.
With Samera.ai, the aim is not to replace accountants or speed them up blindly. It is to help them see clearly, decide confidently, and spend more time where their judgment actually matters.
If this resonates, take a look at what we are building at:
Cheers,
Arun