Saxon AI

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 Similar issues arise from outdated POs, unit-of-measure differences, or minor pricing adjustments...

  Manufacturers have invested heavily in forecasting, deploying new planning tools, analytics dashboards, and data lakes to analyze data coming from all directions—sales pipelines, supplier metrics, production schedules, logistics feeds. Yet, despite the analytics and dashboards, accuracy still slips.  The problem isn’t a lack of data. It’s a lack of connection.  Sales teams plan in CRM, operations in ERP, procurement in SRM, and logistics in WMS. Each function sees a part of demand, but no one sees the whole. When a major customer changes an order or a distributor delays a shipment, that signal takes days to ripple through the organization. By the time procurement adjusts or production recalibrates, the opportunity or risk has already passed.  That’s why accuracy remains low even when data is rich. This latency in signal propagation directly impacts the bottom line; delayed responses contribute to an average inventory holding cost spike of 8-12% of COGS due to unnec...

  For years, enterprises have tried to make customer service faster — automating workflows, tightening SLAs, launching 24/7 chatbots. Yet customers still wait — not only for responses, but for reassurance that someone understands. Speed alone doesn’t feel like care anymore. Because real-time isn’t defined by seconds — it’s defined by intelligence that understands intent and acts with empathy. That’s the new frontier of customer experience emerging through Agentic   AI for customer service  — a system of intelligent agents that doesn’t just respond instantly but reasons, learns, and collaborates with humans to make service truly real-time. Are We Solving Problems or Just Replying Faster? Most customer service journeys still begin the same way they did a decade ago — a ticket raised, a call logged, an email sent. Every step that follows is a reaction. Agentic AI for customer service redefines that flow. Instead of waiting for a customer to report an issue, intelligent agent...

We are almost at the end of the 2025 second quarter, and the CIO forums' discussions have shifted from experimenting with AI to incorporating AI into the core. The discussions have evolved from virtual assistants to  AI assistants . Today, the competitive advantage lies not in experimenting with AI, but in quantifying its value and proving its impact across sales, HR, IT, and customer support. For business leaders, ROI is the ultimate lens that distinguishes between hype and the true AI transformation. The primary step to move up the ladder from AI pilots to strategic ROI is to define the potential use case. This article explores how to define, measure, and communicate the ROI of AI assistants through frameworks, KPIs, and real-world examples, so executives can lead AI adoption with clarity and confidence. We have also decoded a Boardroom-ready equation for the ROI. Why ROI matters more than anything else? For today’s CIOs and business leaders, ROI is the ultimate proof point. It’s...