“We’re seeing a marked shift from high-volume experimentation to more focused, outcome-driven AI deployment,” he says. “Instead of launching dozens of proofs of concept in parallel, organizations are prioritizing a few use cases where AI can be embedded deeply into operational workflows and drive measurable results.”
For example, the finance department at one AArete customer identified invoicing as a high-friction workflow, then created an AI-driven fix that included generative AI, natural language processing, and optical character recognition, Pange says.
“This effort, sourced from within the [finance] function itself, delivered measurable improvements in cycle time and accuracy — outperforming several parallel experiments that lacked operational anchoring,” he adds. “This focused approach reflects a practical shift: AI delivers the most value when data, business context, and operational urgency come together in a few well-defined initiatives that span the enterprise.”
