AI-Ready Data Assessment & Preparation

Make business records usable for a defined AI project. BIATConsultant helps assess data quality, document structure, access rights and the evidence needed to evaluate a pilot.

This advisory service is designed for businesses preparing document search, finance automation or knowledge assistants. 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.

• Discuss your AI advisory requirement

These are potential pilot areas to evaluate against your records, systems and review capacity. Select one bounded workflow before planning broader adoption.

• Document readiness: identify duplicates, versions, unreadable files and missing metadata.
• Finance data readiness: standardise identifiers, dates, categories and reconciliation rules.
• Knowledge-base readiness: identify authoritative sources, owners and access boundaries.
• Evaluation data: build representative examples and agreed correct outputs for testing.

A company wants an assistant to answer questions from internal policies. The readiness exercise identifies the current approved versions, removes duplicate sources from the pilot set and assigns owners. A sample question set checks whether answers cite the right source and decline when the documents do not support an answer.

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.

• Data/source inventory and readiness scorecard
• Quality findings with prioritised remediation actions
• Metadata, versioning and access recommendations
• Pilot dataset specification and evaluation checklist

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.

• Source inventory, file formats and system owners
• Access permissions and retention rules
• Representative redacted records and known quality issues
• Target use case, users and expected outputs

AI-ready does not mean unrestricted access or indiscriminate collection. Use only data that is appropriate for the agreed purpose and access model. Track provenance, document versions and retention requirements. Start with a controlled sample; training a model is not a prerequisite for every document-search or automation project.

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.

• Finance and accounting
• Internal controls
• AI advisory overview
• AI in Legal
• AI in Accounting