A practical checklist for CFOs, Controllers, and Finance Transformation Leaders evaluating whether their data foundations are ready for AI - or setting it up to fail.

Inside, you’ll find the 5 data requirements every finance AI initiative needs to succeed – but that most finance stacks can’t currently deliver:

  • Real-time granularity.  AI needs transaction-level data as events happen, not batch summaries after the fact.

  • Transaction-level lineage.  Every number must be traceable from source event through to report.

  • Continuous processing.  Finance workflows that run always on, not only at month end.

  • Embedded controls.  Governance and AI oversight built into the architecture, not bolted on top.

  • Audit-ready outputs.  AI outputs that are transparent, explainable, and repeatable by design.

For each one, the framework explains what it costs you not to have it, and what becomes possible when you do.

What You'll Learn

  • Why AI projects fail even when the technology works

  • The hidden architecture gaps preventing finance teams from scaling AI

  • How delayed, aggregated data limits forecasting, anomaly detection, and automation

  • Why transaction-level lineage is becoming a prerequisite for trusted AI

  • The governance and auditability requirements regulators increasingly expect

  • A practical checklist to assess your organisation's AI readiness today

Who Should Read This?

CFOs: Preparing finance functions for AI-enabled decision-making.

Controllers: Looking to improve trust, governance, and financial visibility.

Finance Transformation Leaders: Modernising finance architecture and operating models.

Finance Systems & ERP Leaders: Evaluating the infrastructure required to support AI initiatives.

Data & Technology Leaders: Supporting finance modernisation and AI readiness programs.

The answer isn't better AI.

It's better architecture.

Download the framework and discover the five foundations every finance organisation needs before AI can succeed.