AI and automation for accountants
Most practices do not have an AI problem. They have an admin problem: chasing clients for records, answering the same emails and tracking deadlines in a spreadsheet one person maintains. Some of that is fixed by AI, more of it by well-built automation, and knowing which is which is the actual skill.
Book the free AI consultation
Astraeus implements both for accountancy practices, with one rule that never bends: nothing files, sends or posts without a person approving it. Judgement on tax positions, filings and anti-money-laundering decisions stays with your people. AI drafts and classifies; it does not decide.
If your practice is between 3 and 30 fee earners, the pattern is usually the same. Staff spend January and October chasing clients for records instead of preparing accounts. The partner inbox is the practice operating system. Invoices still get rekeyed even though the practice already pays for capture tools nobody fully adopted. None of this needs a person doing it end to end, and in most practices all of it is still done by hand.
Workflow catalogue
Each row is a pattern to assess, not a claim that every business should implement it.
Before
Staff send individual reminder emails through busy season and track responses from memory.
After
Reminder sequences run from the deadline list, and what a client sends is checked against what was requested, with the gaps flagged in plain English.
Human checkpoint
The accountant reviews the gap list before any chase escalates.
Before
The partner inbox absorbs deadline questions, document requests and booking requests, answered one by one.
After
Incoming mail is classified and routine replies arrive as drafts awaiting one-click approval.
Human checkpoint
Nothing sends without a person clicking send.
Before
CRO, VAT, income tax and payroll dates live in a spreadsheet held together by one person.
After
A deadline engine notifies clients on schedule and escalates internally when responses do not arrive.
Human checkpoint
A partner owns the exception report.
Before
A new client means days of forms, identity checks and engagement-letter drafting.
After
Intake, document collection and e-signature run as one workflow, with the engagement letter drafted from the intake data.
Human checkpoint
The partner reviews and signs every letter, and anti-money-laundering judgement stays human.
Before
Unbilled work in progress sits unnoticed and fee reminders go out late, or not at all.
After
Work-in-progress alerts, invoice generation from time data and polite escalating reminder sequences run on schedule.
Human checkpoint
Write-offs and final notices remain a partner decision.
Most of what hurts a practice is fixed by deterministic automation: rules and integrations that do the same thing every time. That is a feature, not a limitation. AI earns its place where language and judgement calls are involved.
Checking what a client actually sent against what was asked for, and flagging the gaps before a person spends time on them.
Replies to routine queries, engagement letters from intake data, and management accounts commentary the accountant edits rather than writes from scratch.
No AI opinions on tax positions, no automated filing to Revenue or the CRO, no anti-money-laundering judgement. If a vendor offers those, ask who carries the liability.
Client data stays in EU-hosted tools, nothing is used to train anyone’s models, and every automated step has a named human checkpoint you can show a client or reviewer.
The first step is a free 45-minute consultation, not a project.
We walk through how work actually moves through the practice: where records arrive, what gets chased, what gets rekeyed and where the partner inbox does the coordinating.
Within 48 hours you get a one-page note with one recommended next step and its price. If the honest answer is to spend nothing yet, the note says that.
A first quick-win automation is typically live within weeks, not months. The consultation identifies which one is worth doing first.
The honest boundary
Automation cannot rescue a practice that has not agreed who owns which work, and AI drafting is only as good as the approved material behind it. If the honest finding is that the existing tools just need configuring and adopting properly, that is the recommendation, and it is a short note.
Direct answers
Usually not. Most builds connect the tools the practice already has. If a tool genuinely is the bottleneck, the findings note says so and explains why.
No. Tools are configured so client data is not used for training, and data stays in EU-hosted systems. The data path is documented so it can be shown to any client or reviewer who asks.
No. AI drafts and classifies. Judgement on tax positions, filings and anti-money-laundering stays with your people, and every automated output has an approval step before anything sends or files.
That is an adoption and configuration gap, not a tool gap, and it is exactly the kind of thing an implementation engagement fixes. Most practices use about half of what they already pay for.
The consultation is free. Every engagement after that is a fixed, quoted price agreed before work starts, and the findings note only ever recommends one next step, not a menu.
A first quick-win automation is typically live within weeks, not months. The consultation identifies which workflow is worth doing first and what it would take.
If you run an Irish practice with 3 to 30 fee earners and your team is losing hours to chasing, triage and rekeying, book the free 45-minute consultation. You get a one-page findings note and one priced recommendation, implemented by Astraeus.
Book the free AI consultation