Enterprise resource planning (ERP) systems have shaped the rhythm of corporate finance. They centralize transactions, support compliance, and anchor the month-end close — a model that defined finance operations for decades.
But the expectations placed on CFOs have changed faster than the infrastructure supporting them.
The global ERP software market was valued at approximately $64.8 billion in 2024 and is projected to reach more than $123 billion by 2030, according to Grand View Research, reflecting growing demand for systems capable of handling increasing operational complexity. Yet growth in the category has not necessarily meant structural reinvention. Many ERP systems were originally designed around batch processing and end-of-month reconciliation.
Finance teams are now expected to provide near-real-time visibility into cash flow, entity-level performance, and budget variances — often across layered operating structures. That disconnect between real-time expectations and close-cycle infrastructure is particularly apparent in mid-market and multi-entity businesses, where operational complexity can outpace reporting agility.
Against this market backdrop, LiveFlow, the New York–based FP&A and reporting automation platform, has launched Flow, an AI-native ERP built for growing, multi-entity companies.
The Next Phase of ERP Development
For decades, ERP software has been optimized for recording activity—not necessarily for surfacing insights in real time. These systems capture and store financial transactions, ensuring ledger integrity and regulatory compliance. Analysis, forecasting, and strategic modeling are often conducted outside the core system — typically in spreadsheets or external planning tools.
As CFOs take on more strategic responsibilities, however, the lag between transaction and insight has become more consequential.
That pressure is reflected in finance leaders’ priorities. In Deloitte’s CFO Signals survey, respondents consistently rank automation, data integration, and AI-enabled insight among their top transformation initiatives, with a majority indicating that AI will play a significant role in finance operations in the near term.
LiveFlow developed Flow as an AI-native ERP, embedding intelligence into the platform’s core design from the outset. Rather than centering workflows around month-end reconciliation cycles, the system is designed to process and organize financial data on an ongoing basis.
The approach mirrors a broader push within the ERP market to better align financial reporting with real-time operational activity.
Built for Operational Complexity at Scale
One of the defining characteristics of modern mid-market businesses is structural complexity. Companies often operate across subsidiaries, locations, and inventory environments earlier in their lifecycle than in previous decades.
Historically, multi-entity functionality has often been added to ERP systems over time as companies expanded, rather than designed into their architecture from the start.
Flow was built with a multi-entity framework from day one, according to the company. The platform consolidates reporting across operating units within a single system, aiming to reduce reliance on manual consolidation and spreadsheet-based adjustments.
For CFOs overseeing expansion into new regions or managing intercompany activity across business lines, that design choice could help address one of the more persistent friction points in finance operations.
Bridging Accounting and FP&A
Another longstanding challenge in finance technology is the separation between accounting systems and financial planning and analysis (FP&A) tools.
Accounting closes the books. FP&A develops forward-looking projections— often using exported data that must be restructured and reconciled before it can inform forecasts.
Flow is intended to bring those functions into a single workflow. By combining ledger accuracy with live forecasting inputs, the platform aims to reduce version-control issues and minimize delays caused by cross-platform workflows.
If successful, that integration could move finance teams toward a more integrated finance system in which accounting records and forward-looking models operate within the same platform— not just for reporting, but for ongoing decision-making.
Rethinking AI’s Role in ERP
Across the ERP market, AI is increasingly embedded as a core feature rather than an experimental add-on. In many systems, it appears as a layer that flags anomalies, generates summaries, or automates discrete workflows.
What is shifting is not whether AI is present, but how deeply it is embedded.
Rather than functioning solely as an add-on to existing systems, some ERP providers are incorporating intelligence directly into core financial processes. The broader goal is to reduce reliance on periodic reconciliation cycles and enable continuous processing and organization of financial data.
LiveFlow’s Flow is built around that approach, embedding AI within transaction processing and reporting structures rather than limiting it to analysis overlays.
Whether this approach defines the next phase of ERP development remains uncertain. But as CFO expectations continue to move toward real-time visibility and faster operational insight, ERP providers are increasingly exploring ways to make intelligence more foundational to how financial systems operate.
Beyond the Month-End Close
As CFO responsibilities expand beyond reporting into capital allocation, risk modeling, and operational strategy, the cadence of financial insights is changing. Executive teams increasingly expect visibility into performance on a weekly — and in some cases daily — basis, compressing what was once a month-driven reporting rhythm.
Periodic reconciliation remains essential to finance operations, but it is no longer sufficient as the primary source of strategic clarity.
As businesses scale, financial complexity tends to compound — while traditional reporting cycles remain fixed. That dynamic is prompting renewed scrutiny of how finance systems are structured — and how quickly insight can move from transaction to decision.
For CFOs navigating multi-entity complexity, that change may be difficult to overlook.
