---
title: Finance Chiefs Spent Millions on ERPs They Hate. Maximor’s AI is Now Fixing That Without Starting Over
description: Finance leaders use AI with intelligent escalation to automate routine work, preserve judgement and deliver audit-ready trails for compliance and risk control.
author: Darie Nani (Editor-in-Chief)
date: 2026-01-29T09:25:57.000Z
updated: 2026-02-26T18:01:32.195Z
canonical: https://www.sovereignmagazine.com/article/finance-chiefs-spent-millions-on-erps-they-hate-maximor-s-ai-is-now-fixing-that-without-start
image: https://cdn.nanimediahouse.com/7654131.jpeg
categories: Finance
content_type: Analysis
region: United States
publication: Sovereign Magazine
---

Ninety-six per cent of CFOs report that the primary benefit of AI in finance is freeing time for strategic work. Fourteen per cent completely trust AI to deliver accurate accounting data without oversight. A [Wakefield Research study](https://cfotech.news/story/us-cfos-back-ai-for-finance-but-insist-on-oversight) of 100 CFOs at mid-market US companies with revenue between $50 million and $500 million identified this gap in AI adoption.

The study found that 60 to 77 per cent of CFOs plan to adopt AI, depending on the use case. Ninety-seven per cent require human oversight.

## Finance AI Models and CFO Responses

The AI finance tools market offers two models. First, [AI copilots](https://www.sovereignmagazine.com/article/4-startups-showing-how-ai-in-finance-automation-pays-off-in-weeks), whether standalone or embedded in legacy platforms, require accountants to review transactions individually. They reduce repetitive work but do not eliminate it. Productivity gains remain in single digits because humans remain the bottleneck.

Second, AI agents automate transactions without human intervention, close books faster, and scale without adding headcount. Most operate as black boxes, however, providing no audit trail or verification of accuracy. Errors are discovered during audits or board reviews, not at the time of decision.

CFOs reject both models. Copilots transfer verification burdens to humans without decision-making authority, while agents automate errors at scale without accountability.

## Intelligent Escalation in Practice

Intelligent escalation programs AI to recognise the limits of its judgment. The system operates autonomously on routine transactions with clear patterns, unambiguous rules, and low risk. When it encounters ambiguity, missing data, or scenarios outside established parameters, it escalates decisions to humans with context: observations, reasons for flagging, and missing information.

An AI system trained on company policies, regulatory requirements, and historical decisions evaluates thousands of transactions in the time a human reviews dozens. It stops when uncertain.

The technical challenge involves distinguishing between confidence and correctness. A model may assign high probability to incorrect classifications if training data contains errors or business context shifts.  Data quality determines intelligent escalation effectiveness. Poor data leads to false positives, where AI escalates routine transactions unnecessarily, or false negatives, where it processes exceptions without flagging them. Contextual awareness requires understanding accounting rules and company-specific policies, such as capitalisation thresholds, approval hierarchies, and business unit variations.

## Market Adoption of Verifiable Automation

[Gartner’s 2025 AI in Finance Survey](https://www.gartner.com/en/newsroom/press-releases/2025-11-18-gartner-survey-shows-finance-ai-adoption-remains-steady-in-2025) found AI adoption in finance reached 58 per cent in 2024. Organisations overcoming data, complexity, and talent barriers saw the highest returns. The survey projects AI will become a core enabler of finance operations by 2026 for teams that address the trust gap.

A [2026 CFO guide](https://softco.com/guides/ai-in-finance-2026-the-cfo-guide-to-automation-compliance-ap-efficiency/) by SoftCo shows 81 per cent of finance leaders plan to use AI for risk management, 74 per cent for financial reporting, and 68 per cent for treasury management. Intelligent process automation leads adoption at 44 per cent, followed by anomaly detection at 39 per cent. These operational deployments handle volume, while humans retain decision authority on exceptions.

[Maximor](http://maximor.ai)‘s [Audit-Ready Agent architecture](https://www.sovereignmagazine.com/article/finance-chiefs-spent-millions-on-erps-they-hate-maximor-s-ai-is-now-fixing-that-without-start-2) integrates with existing ERP, payroll, and billing systems to automate workflows such as revenue recognition, cash reconciliation, and financial close. It generates audit-ready outputs, including workpapers, reviewer notes, and decision traces for every action. Dominic Rand, CFO of Kiva Brands, selected Maximor after evaluating multiple AI solutions. The platform delivered AI that handled routine tasks with speed and precision, while escalating ambiguous decisions with context.

The company’s funding announcement reported customer outcomes: a 40 per cent increase in team capacity, faster close cycles, and cleaner audits. The platform deploys specialised agents to automate repetitive tasks while maintaining compliance and transparency.

## AI as a Force Multiplier

Early AI implementations measured success by headcount reduction. This approach failed in finance, however, where accuracy and auditability matter more than speed. The current model extends existing team capacity without replacing judgment.

Regulatory requirements under GDPR and CCPA mandate explanations for automated decisions affecting individuals. In finance, this extends to decisions with material impact on financial statements, tax filings, or investor disclosures. Intelligent escalation satisfies this requirement by design. Every autonomous decision includes a decision trace showing data used, rules applied, and classification rationale. Every escalation provides the same context, along with an explanation for human judgment.

Scalability drives adoption. Finance teams at mid-market companies face the same complexity as enterprise organisations but with fewer resources. Hiring is expensive and slow, training takes months, and turnover disrupts operations. Intelligent escalation allows small teams to process the volume of larger ones by automating routine work while preserving senior accountant judgment for decisions requiring business context.

Research on [AI agents in accounting](https://www.sovereignmagazine.com/article/the-ai-revolution-in-small-business-finance-how-intuit-s-new-tools-signal-the-future-of-autom) shows systems like Docyt’s GARY and Vic.ai automate invoice processing and fraud detection while maintaining timestamped audit trails. These agents provide detailed logs to ensure transparency and compliance.

Remaining challenges include building team trust in escalation decisions and integrating with legacy systems. CFOs report that training teams to trust AI is harder than implementation. Trust is earned through consistency: the AI must escalate the right transactions, provide useful context, and demonstrate alignment with company standards over time.

## Judgment in AI Finance Tools

Foundation models classify transactions, extract invoice data, and reconcile accounts with high accuracy. They cannot reliably determine when a transaction requires human review, however. Speed and accuracy are baseline requirements. Judgment distinguishes automation from intelligent escalation.

Ramnandan Krishnamurthy, co-founder and CEO of Maximor, states that judgment becomes the competitive advantage when intelligence is commoditised. CFOs require systems that are verifiable, operate autonomously when appropriate, and demonstrate judgment about when to act or escalate.

A mid-market CFO using intelligent escalation with three accountants and an AI system achieves the output of six people. The AI processes 80 per cent of transactions autonomously, escalates 15 per cent for human review with context, and flags five per cent for policy clarification or system updates. The team reviews escalations in hours instead of days, closes books faster, and spends more time analysing results.

## Further context

**Q: Why do finance teams dislike traditional ERP systems?**
Traditional ERP systems are designed for general business operations, not finance-specific workflows. They often lack the flexibility to handle complex accounting rules, regulatory changes, or real-time financial close processes. Additionally, ERPs typically require manual intervention for exceptions, reconciliations, and audits, which slows down finance teams. Their rigid structures also make it difficult to adapt to new reporting standards or business models, leading to frustration among CFOs and accountants.

**Q: How does intelligent escalation differ from other forms of AI automation?**
Intelligent escalation is a hybrid approach that combines automation with human oversight. Unlike black-box AI (which operates without transparency) or rule-based automation (which follows rigid scripts), intelligent escalation uses AI to handle routine tasks autonomously while flagging ambiguous or high-risk decisions for human review. It provides context for escalations, such as the reasoning behind a flagged transaction, and maintains an audit trail for compliance. This balances efficiency with accountability, addressing the limitations of both fully automated and manual systems.

**Q: What are the compliance requirements for AI-driven audit trails in finance?**
AI-driven audit trails in finance must meet regulatory standards such as GDPR, CCPA, and GAAP (or IFRS). These requirements include:

Failure to meet these standards can result in penalties, restatements, or loss of stakeholder trust.

**Q: Why is data quality a critical challenge for AI in finance?**
Finance data is often fragmented across systems (e.g., ERPs, payroll, billing), inconsistent in format, or outdated. Poor data quality leads to:

**Q: Why do mid-market finance teams struggle with scalability compared to enterprises?**
Mid-market teams face the same regulatory and operational complexity as enterprises but lack the resources to hire specialised staff or invest in customised solutions. Key challenges include:

AI addresses this gap by automating routine tasks (e.g., reconciliations, invoice processing) while escalating complex decisions to existing staff, effectively multiplying their capacity without adding headcount.

**Q: How do finance teams build trust in AI systems?**
Trust in AI is built through consistency, transparency, and validation. Key steps include:

Over time, teams trust AI when it demonstrates alignment with company policies, reduces errors, and accelerates workflows without creating additional risks.