Who we help · Accountancy firms
Scale AI without losing professional judgement or control.
For accountancy firms where client confidentiality, review quality, operational resilience and professional accountability must remain intact as AI use grows.
Who we typically work with
- Managing Partners setting the firm's AI position
- COOs standardising process across offices
- Heads of Risk and Quality accountable for review controls
- Innovation and technology leads selecting tooling
- Service line leaders automating repetitive engagement work
The position
Automation is arriving faster than the control design.
Mid-tier practices are automating preparation, data capture, reconciliation and reporting work - often several initiatives at once, sometimes across offices that joined the firm with different systems and habits.
The commercial case is straightforward. The governance case is what usually lags: the review controls, the working-paper trail and the evidence of professional judgement have to hold up when part of the process is machine-assisted. Getting that right is what allows a practice to automate further rather than pause.
What we organise the work around
Six control questions a practice has to answer well.
Client confidentiality
Engagement data moves between systems constantly. Governed adoption defines which categories of client data may reach which tools, and on what contractual terms.
Professional judgement
Preparation and analysis can be assisted. Conclusions remain the responsibility of a qualified professional, and the workflow should protect that line.
Review controls
Review procedures assume a preparer whose work can be interrogated. Where output is generated, reviewers need the same traceability to work from.
Repeatable evidence
Quality processes depend on consistency. Evidence should be produced the same way on every engagement, not assembled retrospectively.
Workforce training
Teams need to know what is permitted, what must be checked and what must never be entered into a tool - with worked examples from their own work.
Secure, governed automation
Extraction, validation, reconciliation and reporting workflows release capacity, provided controls and review points are designed into them.
Secure, governed automation
Controls inside the engagement workflow.
We design the control environment where the work is performed - data capture, preparation, review, sign-off and archive - so evidence accumulates as a by-product of doing the job.
- Data classification governing which client data may reach which tools
- Defined review thresholds for machine-assisted preparation
- Traceability from generated output back to source data
- Decision logs recording tool, version, reviewer and rationale
- Exception routing so anomalies reach a person, not a default
- Training and permitted-use guidance tied to each service line
AI system
The use case, data, authority and intended operating conditions are defined.
Material risk
Consequence, failure modes and misuse scenarios are identified.
Control
Permissions, boundaries, oversight and limits are specified.
Test
Each material control is exercised, not assumed to operate.
Evidence
Test results, logs, approvals and rationale are retained.
Residual risk
What remains after controls is stated rather than absorbed.
Accountable decision
A named leader approves, conditions or declines deployment.
Monitoring
Control performance, changes and incidents are reassessed.
How we engage
A path sized to the practice.
Most firms begin with the risk review, then govern and implement in the service lines with the highest volume of repetitive work.
Next step
Automate more, and be able to show how.
An independent assurance review gives partners an evidenced view of current AI use, tested control strength and the exposure that remains.