Asana’s manufacturing AI Teammates are purpose-built agents that run inside real manufacturing workflows. The NPI Assistant generates gate task sets and tracks conditional items to closure. The Kaizen Coordinator turns improvement events into permanent SOPs and routes updated standard work for approval. The Maintenance Assistant structures service records and escalates overdue maintenance before it causes a line stop. The Quality Coordinator triages quality issues, routes RMAs, and drafts ECNs from prior context. Unlike generic AI tools that start cold, all four are built on the Enterprise Work Graph, so they carry your program history, improvement outcomes, equipment context, and quality learnings forward with every cycle. All are governed, permissioned, and auditable at the admin level.

Asana is where human-agent manufacturing teams thrive, sitting alongside the ERPs, MES platforms, and QMS systems your teams already rely on, not replacing them. While those systems manage transactions, records, and production data, Asana manages the work: the cross-functional AI Teammate actions, gate transitions, escalations, and workflow coordination that live between your systems of record. Asana integrates natively with Salesforce, Slack, Microsoft Teams, Google Workspace, Hubspot, and OneDrive, among other tools. 

Asana gives every team from engineering and QA to procurement a single live program plan to work from, so no NPI relies on fragmented tools or missed gate handoffs. Program timelines are structured from kickoff through the production readiness gate, with milestones, owners, and conditional items tracked automatically. With the Enterprise Work Graph, context from every previous NPI program is carried forward, so gate reviews, handoff packages, and cross-team coordination get smarter and more complete with every cycle.

Enterprise-grade governance is built into every agentic action in Asana. Admin-level controls govern how AI Teammates are deployed, who can action them, and what they can do, with full visibility into usage, activity, and outputs across the organisation. For manufacturing, this means quality standards, gate criteria, and approval workflows are enforced at each step, so every AI Teammate action is permissioned, monitored, and auditable, and all outputs meet the compliance standards your production environment requires.