By: Alwyn Pretorius - General Manager, Infinitus Reporting Solutions

Finance leaders may be feeling the pressure to adopt AI into their reporting and planning environments, and it’s understandable. CFOs are driven by board expectations, and many are of the opinion that staying ahead means adopting technology. At the same time, vendors are promoting it as the latest must-have, and these contribute to the wider narrative that its use in reporting is now unavoidable. 

Yet, in most organisations, the basic work of gathering reliable data from multiple sources and producing a consolidated group view that stands up to scrutiny remains the core priority, and until that foundation is secure, AI cannot add meaningful value.

Finance teams working across multiple entities know the reality all too well. 

Business units run different accounting systems, use different GL structures and apply policies with small but material variations. A group financial manager trying to produce a single income statement must translate these mismatched trial balances into one reporting language. This often requires manual mapping across accounting systems, repeated every month and redone whenever a late journal appears, draining time that should be spent analysing performance.

Consolidation can add another layer of strain, particularly when data sources don’t line up cleanly. Where intercompany transactions and balances must be reconciled and eliminated from group results, in many organisations this work is still done in Excel schedules that grow unwieldy as the group expands. Without a doubt, it’s repetitive work with high risk, but also the step that determines whether the final group view is credible.

The environment becomes even more complex when commentary, narrative insight and entity-level explanations must be included in the reporting pack. Where group teams depend on inputs from individual business units, which have to be collected, reviewed and integrated into the final view, systems that promise automatic integration rarely account for the human layer where teams refine, interpret and validate their numbers.

These are not peripheral issues, but the backbone of group reporting. When they remain fragmented, no higher-order tool can operate reliably, and even the most advanced models depend on consistent structures, clean mappings and stable data flows. Without that foundation, outputs become inconsistent and trust breaks down quickly.

With Finnivo, for instance, we have focused our efforts on solving these fundamentals in a way that gives finance teams control without adding complexity. Finnivo is built on a ‘front-to-back’ architecture that allows organisations to automate their reporting outputs based on their unique reporting formats, reporting structures and data flows. Once you upload your report formats and group structure, Finnivo will auto generate the back end for you, including the database structures and various automation tools. Implementation is quick, and users are able to integrate to the various source systems that data needs to be imported from. Only then, can the underlying data be translated into a uniformed format, providing a single language model that is useful for AI. 

Automating data collection and consolidation is not a new message, but its relevance has sharpened as the industry’s appetite for AI grows. Organisations cannot skip these steps; reliable data flows must come first, automated reconciliation must be in place, and group structures must be aligned and repeatable. Only then do advanced capabilities become practical.

There is room, however, for technology to support insight generation and reporting efficiency in future phases, and we continue to invest in that path, but it must be done responsibly. The job today is to help finance teams stabilise their environments, reduce manual intervention and create a repeatable base they can trust.

Finance leaders who achieve this foundation gain the headspace to interrogate trends, engage with business performance and influence strategic decisions, which is the real goal. In short, AI may shape the future of reporting, but strong fundamentals determine who will be ready for it when the time comes.