Systems | Development | Analytics | API | Testing

Why Your Rolling Forecast Is Always Stale

Every FP&A team knows the feeling. The reforecast was published on Monday. By Wednesday, someone in sales has closed a deal that changes the revenue picture. By Friday, procurement has flagged a cost overrun that nobody modelled. The forecast is four days old and already partially wrong. This is not a forecasting problem. It is a data pipeline problem. Finance teams hired analysts for their analytical skills.

Why Your Chart of Accounts Breaks at Every Acquisition

You closed the deal. The press release went out. Integration planning is underway. And somewhere in the finance team, a controller is opening a spreadsheet and starting to map 1,400 account codes from the acquired company's ERP into your group chart of accounts. This is the moment the chart of accounts breaks. Not dramatically. Not all at once.

How Structured Content Improves Financial Product Communication

Financial product communication has to be clear, accurate, and easy to understand. Customers often compare banking products, insurance options, investment services, loans, credit cards, payment solutions, and savings accounts before making a decision. Each product may include detailed information about fees, eligibility, benefits, terms, risks, application steps, and support options. When this information is presented in a confusing or inconsistent way, customers may struggle to understand what a product offers and whether it is right for their needs.

The internal war over who owns AI.

There is a massive boardroom fight happening right now over who gets to control AI. Should it be IT? A centralized lab? The executives? Boris Rabkin from Ligentia drops a truth bomb: AI belongs wherever value is actually created. If your AI strategy is locked inside an isolated corporate lab instead of in the hands of your product, engineering, and customer teams, it’s going to fail. Full episode out now!

Reporting Intelligence UI Flyover

See how Reporting Intelligence from insightsoftware transforms the way finance teams build, manage, and distribute ERP-connected reports, without IT involvement, data engineering, or infrastructure overhead. This walkthrough covers the full product experience, from self-serve report building in Excel to cloud-based scheduling and distribution, embedded Lineos AI assistants, and the autonomous Intelligence Layer that monitors your KPIs and surfaces insights around the clock.

Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail

AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments. Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.

Stop Rebuilding Data Models From Scratch: Meet SpotterModel

Your data engineering team shouldn't be the bottleneck between a business question and a governed answer. SpotterModel turns a natural language prompt into a deployable data model. This release does the heavy lifting on complex calculations, and lets you roll back to any previous model state, anytime, so a bad change never costs you hours of rebuilding. It maps your relationships, dimensions, and measures instantly, and you stay in control of table selection and the build process the whole way.

Ready Set Code! The Telemetry Tsunami

Welcome to Ready Set Code! The game show where data engineers face off to prove who can build faster. In today's episode, "The Telemetry Tsunami," three contestants face a massive flood of nested JSON telemetry data. Their mission: flatten the arrays, join it to customer tables, and deploy a secure automated pipeline. Who will separate themselves as a data driver vs. a data downer? Find out now! Type Less. Build More.