Systems | Development | Analytics | API | Testing

If your organization is still running SAP ECC, the clock is ticking. SAP has set 2027 as the end of mainstream maintenance for SAP ECC 6.0. This announcement means no more standard support, security patches, or bug fixes after that date. For large enterprises in manufacturing, food and beverage, pharma, chemicals, and logistics, the pressure to complete an S/4HANA migration before that deadline is becoming impossible to ignore. The risk isn’t just technical.

Product teams tasked with providing an AI analytics and BI platform to finance organizations see a unique set of challenges. Finance organizations are subject to SOX, GDPR, EU AI Act compliance on top of accurately closing the books and preparing for the potential of an audit. In a highly regulated industry like finance, product leaders building solutions for finance leaders need accurate insights they can trust that hold up to audits and regulatory scrutiny.

Most AI pilots fail to reach production not because the models don’t work, but because enterprises struggle with data governance. While pilot-phase AI projects demonstrate impressive results in controlled environments, they hit governance walls when moving to enterprise-scale deployments. This post examines why AI initiatives stall before production and provides a governance-focused approach for breaking the cycle.

In 2025, insightsoftware was recognized in Gartner’s Magic Quadrant for Financial Planning Software. The recognition focuses on insightsoftware’s JustPerform product, which offers web-based budgeting, planning, and forecasting with an Excel-like interface and self-service reporting, dashboards, and analytics.

The AI revolution in finance is no longer a question of “if” but “how fast” and “how responsibly.” While our previous posts explored AI auditability frameworks, agentic workflows that transform finance operations, and building AI native Finance teams, today’s CFOs face an equally critical challenge: successfully navigating the complex and rapidly evolving landscape of AI compliance.

Any time an organization closes the books or practices the governance needed to meet regulatory demand, it relies on reporting. Reporting technology is designed to save time while increasing accuracy, but many long-standing reporting vendors are existing or de-emphasizing this still-essential space. This leaves a gap for teams that need a modern, production-grade reporting solution. And when choosing between reporting tools, architecture is important.