- AI reduces routine work in finance and strengthens decision-making: The more recurring tasks the Live Finance Platform executes, the more time remains for interpretation, decisions and business performance management.
- Control is embedded in execution: Rules, permissions, thresholds and escalations determine what AI agents may execute autonomously and when a case is submitted for review.
- Human judgement remains decisive: AI can execute processes and identify exceptions. Context, prioritisation, responsibility and decisions with business consequences remain with the CFO and finance team.
For this article in our ‘Inside Live Finance’ series, we spoke to CFOs whose finance organisations already use Aderis in production. We focused on what changes when recurring finance tasks are no longer handled manually but executed continuously by the platform.
These conversations show that the value of AI goes beyond completing individual tasks faster. It becomes relevant where finance spends less time on operational execution and can work with current information sooner.
AI is already established in the wider finance market. According to a Gartner survey, 59% of the CFOs and finance leaders surveyed already use AI in finance. Organisations with greater AI maturity are significantly more likely to report high value from it.
What changes for CFOs who already use AI in their daily work?
Discussions of AI in finance often begin with efficiency: how many invoices can be automated? How much faster is reconciliation? How much work can be removed from the month-end close?
Conversations with CFOs already using Live Finance reveal another aspect: when an AI-native finance platform takes on recurring finance tasks, more time remains for work that requires experience, context and business judgement.
One CFO described his role five to ten years from now as significantly different from today. This was not because he expected less responsibility, but because AI agents would increasingly take on routine work. For him, that is the opportunity: more time for tasks where experience, context and business judgement make the difference.
Other CFOs report having month-end close results available within minutes through Live Finance. Speed alone was not what mattered to them. It was what this made possible: figures were available earlier, and areas of the business requiring action became visible sooner.
The value of AI in finance therefore extends beyond more efficient processes. It also enables finance to work with current information sooner and focus more on decisions and business performance management.
Why less manual review does not mean less control
For many CFOs, the discussion about AI begins with a legitimate question: if AI agents execute tasks autonomously, how do I retain control?
In traditional finance processes, control often depends on review. A transaction is recorded, passed on, checked and approved. Attention is therefore also devoted to transactions that ultimately require no human judgement.
An AI-native model embeds some of these controls directly into the architecture. The CFO and finance team define:
- which rules apply,
- which permissions are in place,
- which thresholds must be observed,
- which transactions may be executed automatically,
- and when an exception is escalated for human review.
This also matters beyond the automation of individual processes. Finance leaders most frequently cite managing and expanding AI adoption as the reason for their growing responsibilities. Governance and compliance, alongside an expanding technology stack, are also among the main drivers.
The nature of control therefore changes. Not every individual action needs to be reviewed manually. What matters is how rules, permissions and escalations are designed, and whether every action remains traceable.
For CFOs, this means a shift in responsibility, not a reduction: they define rules, approvals and escalations, and determine where AI may act autonomously.
From every transaction to the exceptions that matter
Not every transaction is equally relevant to a CFO. In a payment run, for example, the amount, supplier and deviations from defined rules matter. By contrast, there is no need to check manually every time whether an invoice that can be coded unambiguously has been posted to the correct account.
This is where AI comes in. Clear-cut transactions are executed; uncertain matches or deviations from defined rules are submitted for review. If a bank transaction can be matched unambiguously to an outstanding invoice, no additional work step is created. If amounts do not match or a transaction deviates from defined rules, it becomes an exception.
What matters is that the finance team focuses its attention where human judgement is actually required.
What does the CFO gain?
When reconciliation, account coding and other recurring processes run continuously, relevant financial information becomes available sooner. For the CFO, this creates more than time savings: it provides greater scope to act.
Traditionally, many connections become fully visible only during the month-end close. Analysis then begins retrospectively: what happened? Where are the variances? Which entries need to be corrected?
Live Finance starts earlier: variances become visible as they arise, reconciliation runs continuously and every figure remains traceable to its source. The month-end close remains, but with Continuous Close it becomes less of a concentrated catch-up exercise, because reconciliation and close preparation already take place throughout the period.
The CFO can therefore ask questions relevant to business performance management sooner: what do the figures mean for the coming months, and what do we need to decide today?
The CFO's advantage also depends on the choice of architecture
AI is now part of many finance roadmaps. What matters, however, is where it sits within the architecture and which tasks it can actually take on. Many existing ERP systems extend their architecture with AI capabilities. These add-ons can accelerate individual tasks, summarise information or make existing processes more efficient.
AI-native finance goes deeper. AI is part of the underlying architecture, where data structures, rules, automation, context and human oversight work together from the outset. For CFOs, individual AI capabilities therefore matter less than whether the finance architecture is built to deploy AI across the entire finance function.
The difference becomes visible in day-to-day operations: financial data remains continuously available, recurring tasks are executed within defined rules and human judgement is applied where context and responsibility are required.
Why data quality begins at capture
As a platform takes on more finance processes, the more important the quality of its underlying data becomes. Data quality should therefore be established when information enters the system, rather than checked only at the end of a process.
Invoices, bank transactions and other financial information arrive from different sources and in different formats. Structuring and validating them from the outset, and linking them to their sources, creates a reliable foundation for further processing.
In an AI-native finance architecture, this structure is part of the system. For the CFO, that means spending less time asking whether the data is correct and more time asking what it means.
How Aderis supports this model
At Aderis, AI agents, financial data and controls are part of the same architecture. Rules, permissions and escalation paths determine which tasks may be executed autonomously and when a case returns to the finance team.
AI agents handle tasks including capture, account coding and reconciliation, as well as approval and payment processes. Transactions that meet defined rules can be processed automatically, while exceptions are submitted for review.
Every entry remains linked to its source and workflow, across entities, ledgers and currencies. This makes it possible to trace what was executed, on what basis, and where people granted approvals.
Conclusion: AI creates more time for decision-making
AI changes finance most where recurring tasks are executed continuously, controls are applied directly and exceptions are selectively submitted for review.
This gives CFOs more time for interpretation, decisions and business performance management. Responsibility remains clear: people define rules, approvals and escalations, while business judgement remains essential where context and consequences matter.
The value of AI therefore goes beyond more efficient processes. What matters is that finance works with current information sooner and the team focuses its attention where it contributes most to the business.
