Standardize Before You Scale: A CFO/COO Playbook for Best-of-Breed Without the Bloat
Every plant in a multi-site network ends up with its own stack: different sensors, dashboards, line apps, and even naming conventions. When you start planning for agentic AI—software that can coordinate and recommend actions across operations—the tempting move is a rip-and-replace. The smarter move is the opposite: standardize signals and require interoperability, then let agents work across what you already own. That was the clear takeaway from the AI Frontiers session featuring Infinite Uptime’s leadership: protect capital, trim the integration tax, and speed rollout.
Why should this be led by finance and operations
This isn’t about consolidating on a single vendor. It’s about consistency (especially at the sensing layer) and systems that talk to each other without heroics. If you don’t fix those two, every deployment becomes a custom project and costs drift upward.
The playbook to make best-of-breed work at scale
1) Define “standard” at the signal level
Standardization isn’t a logo—it’s semantics and suitability. For each application, choose the right sensor class (vibration, acoustic, torque, thermal), specify the units and sampling rates, and hold those choices across sites. The goal: Asset A in Plant 1 should look like Asset A in Plant 7 to downstream systems. If sensing is wrong for the job, no AI layer will rescue it later.
2) Put interoperability into procurement
Make integration a contract requirement. New machines and software must publish the agreed signals and metadata and subscribe to schedules and work orders via documented APIs. If they can’t, don’t buy them. This single rule prevents tomorrow’s one-off adapters and the hidden tax of rework.
3) Run the factory as a system of systems
Keep core platforms doing what they do best—your Manufacturing Execution System (MES), Computerized Maintenance Management System (CMMS), Supervisory Control and Data Acquisition (SCADA), and Product Lifecycle Management (PLM). Give the agent a clean lane to read state, propose actions, and push approved work—without hard-wiring brittle logic into every endpoint.
4) Put quality control where it matters most: on sensors
If sensing is poor or inconsistent, everything above it struggles. Treat the sensor bill of materials as a corporate standard and audit it like any other critical asset—particularly on lines where downtime minutes are expensive.
5) Fund reuse, not rebuild
Invest once in shared data contracts (names, units, states) and common connectors, then reuse them plant to plant. Track “time to first action” for each new site. If you’re measuring in quarters, you’re rebuilding. Weeks means you’re scaling.
6) Keep safety margins intact
As you introduce agentic AI, gate higher-risk actions with people until performance holds across shifts and seasons. Validate thoroughly before authorizing narrow auto-actions; keep audit trails and instant rollback in place.
What you gain
- Faster multi-site rollout because signals and semantics are already aligned
- Lower integration spend by avoiding per-plant adapters and custom glue
- Cleaner ROI attribution because recommendations run through standard workflows
- Protected cash by extending the useful life of tools you already own
Standardization and interoperability are force multipliers. They convert a fragmented toolset into an operational platform where agentic AI can actually deliver—without a risky rip-and-replace. Start at the sensing layer, require integration in procurement, and let best-of-breed continue to win where it’s strong.
Source: “Agentic AI in Manufacturing: From Copilots to Autonomous Systems” (AI Frontiers 2025 event), sponsored by Cybus and Infinite Uptime, Inc.
