Saxon AI

  AI cashflow forecasting is reshaping how enterprises understand, predict, and control liquidity. This blog explores the rise of the cash-aware enterprise—where AI connects operational signals with financial outcomes to deliver real-time visibility, smarter forecasting, and more confident decision-making across finance and business teams. Traditional cashflow forecasting can no longer keep pace with today’s dynamic business environment. Discover how AI-driven cashflow forecasting enables the cash-aware enterprise—providing continuous insights, predictive intelligence, and enterprise-wide alignment to manage risk, improve liquidity, and drive resilient growth.

Finance operations have steadily evolved from manual, fragmented processes to system-driven workflows supported by automation and controls. Yet, despite this progress, challenges such as delayed period closes, growing exception queues, and constant manual follow-ups remain common. This signals a deeper need—not just for automation, but for systems that can actively manage work toward outcomes. The future of AI in finance is no longer defined by how many tasks are automated, but by how intelligently work progresses across systems. Traditional automation approaches like RPA and IPA focus on execution and interpretation, but they still depend heavily on human intervention to move work forward during exceptions or unique scenarios. Robotic Process Automation (RPA) excels at handling repetitive, rule-based tasks such as data entry and reconciliations. Intelligent Process Automation (IPA) builds on this by interpreting semi-structured data using OCR and machine learning to classify invoic...

Invoice matching plays a critical role in controlling payments, enforcing policies, and maintaining audit accuracy in accounts payable. Methods like 2-way, 3-way, and 4-way matching are well established, but in real-world operations they often struggle with partial deliveries, delayed goods receipts, and inconsistent invoice formats. Traditional automation checks whether numbers align. When something is missing or arrives late, invoices are pushed into exceptions, increasing manual follow-ups and extending processing cycles. This is where AI agents are starting to change how invoice matching works in practice. Why traditional invoice matching breaks down In day-to-day AP operations, mismatches are common. A purchase order may be created for 100 items, while only 80 are delivered initially. Standard 3-way matching flags this as a mismatch even though it reflects a valid partial shipment. Similar issues arise from outdated POs, unit-of-measure differences, or minor pricing adjustments...