New playbook: when your own AI model pays. Read
Uber (mobility and delivery) reported this on 1 Oct 2026 in company self-report (source). It belongs to the data and ontology layer.
An MCP Gateway and Registry that auto-crawls Uber's internal service definitions and exposes existing HTTP/gRPC APIs as agent tools, with tool-level authorisation and redaction for humans, services and agents.
Hundreds of MCP servers and thousands of tools catalogued, exposed with zero changes to downstream services.
The number covers infrastructure scale; productivity outcome published is not reported.
“…existing APIs are the fastest way to provide tools to an agent,” Uber said.
Shared definitions of your metrics and records, used by both staff and agents.
Read the guide to data and ontology.
10 minutes
to scope a supply shortage, down from 3 months and 30 people
An enterprise data layer on Palantir Foundry unifying 33 ERPs (~3.5M data connections), feeding a Sector Control Tower for executives and a Program Digital Cockpit for program teams (source).
27 days
faster time-to-sign for candidates
A recruiting and hiring agent in Microsoft Copilot Studio, grounded in the Celonis Context Model (process-mining data on how hiring actually flows), used by recruiters and hiring managers (source).
2 months
for a 1% forecast-error gain that last took a year
A Genie-based research agent on Databricks that answers finance and store-operations questions about the labour-demand forecast, with explanations, replacing data scientists answering stakeholder emails (source).