OpenAI pushes deeper into enterprise workflows with ChatGPT tools for Excel

New Skills and Apps features move AI into repeatable business processes, with finance teams among early use cases.

ChatGPT in Excel introduces AI-powered workflows, connecting live data, automating analysis, and improving productivity for finance teams.

OpenAI is expanding the role of ChatGPT in enterprise workflows with new Skills and Apps functionality for Excel, targeting repeatable processes such as forecasting, reporting, and financial modeling.

The update focuses on embedding AI into structured workflows rather than one-off tasks, allowing teams to standardize how work is executed across functions. Early use cases highlight finance teams, where processes are often rebuilt manually despite being repeated across reporting cycles.

The new functionality introduces two components. Skills are designed to capture how teams structure their work, including logic, formatting, and expected outputs. Apps connect these workflows to live data sources and tools directly within Excel.

This allows users to run predefined processes without starting from scratch, reducing variability in outputs and aligning results with internal standards.

In a LinkedIn post, Sarah Friar, Chief Financial Officer at OpenAI, said: “Every finance team relies on a similar set of repeatable workflows - forecasts, KPIs, reconciliations, and models.”

She added: “These are high-stakes, time-intensive, and still rebuilt more often than they should be.”

Friar also outlined how the new system changes how teams approach these tasks: “Instead of starting from a blank sheet, teams can now: run standardized workflows on demand; pull from trusted, connected data sources; generate outputs that match internal expectations the first time.”

Focus on consistency, speed, and shared systems

The shift toward structured workflows reflects a broader move in enterprise AI adoption, where organizations are looking to reduce duplication and improve consistency across teams.

Friar said the impact extends beyond individual productivity: “The result is less rebuilding, fewer inconsistencies, and faster cycles from analysis to decision.”

She added: “Having seen this play out firsthand, this is where things really start to compound. It's not just individual productivity, but shared systems that scale across functions.”

The focus on shared systems points to a shift from standalone AI tools toward integration within core business processes.