Reliable data. Useful AI.
Data, reporting and responsible AI
Data/AI lead owns definitions, quality, use-case selection and output review.
AI-assisted workflows, reviewed by people
Useful AI starts with information that has defined ownership, sources and review rules. A model cannot resolve conflicting entity definitions or decide which document is authoritative on its own. We establish the reporting and data foundations first, then select specific use cases where controlled assistance can improve preparation work without obscuring accountability.
Typical needs include conflicting asset definitions, inconsistent reports, repeated searches through sensitive documents and AI experimentation without agreed handling terms. We assess the data perimeter, access groups and expected outputs before selecting models or vendors. Hosting, residency, retention and provider terms are chosen and agreed for the implementation. A use case is approved against a defined task, source set and evaluation method. The office can see who reviews an output, what constitutes an unacceptable error and how that error is escalated. Evaluation distinguishes a useful draft from an authorized final record. Changes in data sources, permissions or vendor terms trigger reassessment where relevant. Benefit reviews compare measured preparation work with the agreed baseline while retaining the checks needed for reliable evidence and accountable decisions.
Senior availability and reviewer responsibilities are agreed per mandate; the role need not be a dedicated full-time appointment.
02 / Scope and deliverables
- 01inventory/classification and data owners
- 02common entity/account/asset/obligation definitions
- 03quality checks and exception workflow
- 04source-linked reporting model
- 05permission-aware knowledge index where scoped
- 06prioritized AI-use-case register
- 07model/vendor data-handling assessment
- 08quality evaluations
- 09review and escalation points
- 10retention/logging/access policy
- 11staff training
- 12periodic benefit/quality review and improvement backlog
03 / AI-assisted work, reviewed
- Extract and search approved documents through permission-aware access; reviewers verify retrieved evidence and citations.
- Draft factual reporting from controlled sources, keeping source dates and unresolved reconciliation issues visible.
- Triage data exceptions and routine tasks against agreed rules, with a designated owner confirming disposition.
- Prepare meeting material and summaries, evaluate accuracy on representative examples and route consequential conclusions for authorization.
04 / Practical delivery
- 01
Inventory and classify data, assign owners and agree common definitions and quality checks.
- 02
Prioritize use cases, assess vendor handling terms and evaluate outputs against representative, permission-appropriate evidence.
- 03
Operate approved sources → extraction → reconciliation/checks → senior review → authorized decision, then review quality and measured benefit.
05 / Outputs and cadence
Outputs include a data dictionary, quality exception register, source-linked reporting model and AI-use-case register. Where scoped, the knowledge index respects access permissions. A periodic quality and benefit review examines evaluated output accuracy, reviewer coverage and the improvement backlog; it also considers changes to retention, logging or vendors.
06 / Measures agreed per mandate
Examples to agree for the selected scope and baseline; not promised numerical results.
- Data completeness
- Exception age
- Evaluated output accuracy
- Source-reference coverage
- Measured preparation time
- Assigned reviewer coverage
07 / Working interfaces
The data/AI lead coordinates source owners, finance, governance, security, technology providers and relevant advisers. Accountable people retain payment, hiring, investment, filing, access and legal decisions. Approved tools prepare evidence and drafts; they do not acquire authority simply because a workflow has been automated.
Cybersecurity, privacy and fraud controls
Protect the office's information and decisions.
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Tell us where the office stands today and what you want to improve. Use an initial conversation to understand the work, assess fit and define the next step.