Explore AI-assisted accounting workflows with BIATConsultant. Start with reliable books, a defined review process and measurable finance outcomes.
Explore AI-assisted accounting workflows with BIATConsultant. Start with reliable books, a defined review process and measurable finance outcomes.
This advisory service is designed for finance teams, owner-managed businesses and growing companies. Begin with the process you want to improve, the information available and the person who will approve the output. BIAT’s consultancy approach connects the business requirement to a defined scope, data preparation and adoption plan.
These are potential pilot areas to evaluate against your records, systems and review capacity. Select one bounded workflow before planning broader adoption.
A finance team wants to reduce invoice-processing effort. A pilot uses a controlled sample to extract invoice fields and suggest account classifications. Reviewers check tax treatment, duplicates and exceptions before posting. The evaluation measures field accuracy, false matches, review effort and traceability back to the invoice.
This is an illustrative engagement, not a published client case or a claim of measured savings. Agree the baseline, sample, acceptance criteria and stop conditions before testing. Include difficult and incomplete inputs so the evaluation reflects the work users actually face.
Agree the required deliverables in a written proposal. A readiness assessment and a production implementation are different scopes; identify which work is needed now and which depends on specialist technical delivery.
The roadmap should name business and technical owners, unresolved dependencies and the decisions needed before implementation. Software licensing, integrations, custom development and ongoing operation are scoped separately when required.
Bring a description of the workflow, its users and the intended output. Start with an inventory and redacted examples; do not send confidential source material to an unapproved AI tool simply to demonstrate the problem.
Keep a human approval step for material classifications, journal entries, tax positions and reports. Separate model suggestions from authorised ledger changes. Check totals, source documents and period cut-offs. AI adoption does not replace accounting responsibility, statutory audit independence or a qualified signatory.
Specify authorised tools and users, source access, review steps and incident escalation. Changes to the model, prompts, data or workflow can change performance; agree how those changes will be evaluated before wider use.
Compare the pilot with a documented baseline. Measure relevant output quality, exception detection, review effort and traceability. A faster draft is only useful if the team can verify it and the combined preparation and review work makes sense.
Record what passed, what failed and which inputs should be excluded. The next decision may be to improve the data, revise the workflow, change the tool or stop the pilot. Do not assume every process needs generative AI.
Fees depend on the number of workflows, source complexity, stakeholder interviews and the depth of pilot planning or evaluation. Confirm the deliverables, sample size, access arrangements and exclusions before work begins.
Share your business context, immediate problem and current systems in the enquiry. The first step is to clarify whether you need use-case advice, data preparation, a controlled pilot or a broader adoption roadmap.
These advisory services focus on workflow design and controlled pilots. Filings, accounting judgments and regulated reports remain subject to the relevant professional review and authorisation. An AI-generated output is not a signed audit opinion.
