Our most recent Insights post, Year-End Close with SAP AI: What to Automate Before December 31, focused on getting the books closed faster and with stronger evidence. But a faster close is only half of the value equation. The other half is what finance does with those numbers once they are final, such as planning, forecasting, explaining variances and helping leadership decide what to do next.

Our two most recent Insights posts looked at AI from the assurance side of finance. Autonomous Audits: Applying SAP AI to Continuous Assurance explored how continuous, population-level monitoring changes the audit, and Governing SAP AI Agents: Why the Control Plane Matters More Than the Model addressed how organizations keep AI agents accountable. In this post, we turn to the question that is on every controller's calendar right now: what can finance teams realistically do with SAP AI before the books close on December 31st?

Our most recent Insights post, Autonomous Audits: Applying SAP AI to Continuous Assurance, explored how SAP AI enables an autonomous approach to audits, shifting assurance from periodic sampling to continuous, population-level monitoring.

Our recent Insights posts have looked at how SAP AI and Joule are reshaping specific business areas, such as finance, compliance, quote-to-cash, supply chain and manufacturing, as well as industry-specific scenarios for software, telecom and utilities, and most recently the role of AI in clean core migrations to SAP Cloud ERP. In this post, we take on a topic that comes up in nearly every executive conversation we have: what happens to the audit when AI agents are doing the work?

For many SAP ECC customers, the journey to SAP S/4HANA is no longer just a technical upgrade decision. It is a business transformation decision shaped by rising maintenance risk, accumulated custom code, fragmented processes, limited data visibility, and growing expectations for AI-enabled operations.

For fast-scaling mid-market companies, replacing an entry-level ERP (such as Quickbooks or Netsuite) should be viewed as much more than simply an accounting-system upgrade. In reality, it offers an important opportunity to establish a scalable operating foundation; one that connects finance, procurement, sales, supply chain, and planning around trusted, real-time business data for companies that are growing in breadth and sophistication.

This is the newest installment in our industry-focused series within the broader SAP AI Insights collection. Building on four earlier posts covering SAP AI solutions for finance & compliance; project systems & supply chains; energy & datacenters; and telecom, this post focuses on software and Software-as-a-Service (SaaS), an industry that, in many ways, invented the modern recurring revenue playbook but is now being disrupted by the very technology it helped commercialize.

This new post in our SAP AI Insights series explores how SAP AI solutions are being applied within the telecommunications industry, where recurring revenue, complex monetization models, and rapidly evolving service offerings are becoming central to growth.

This Insights post is the first in a new set of industry-focused articles within the broader SAP AI Insights series. It looks at how SAP AI applies to three sectors where recurring revenue and complex monetization models are becoming central to growth: Utilities, Renewable Energy, and Datacenters.