CIO.com recently published an article featuring ModelOp’s perspective on what it really takes to move AI from “promising” to “productive” at enterprise scale. The piece, “From hype cycle to lifecycle: A CIO playbook for smarter AI investment and management,” speaks directly to a problem many IT leaders recognize: lots of AI activity across the business, but not enough visibility into what’s working, what’s stuck, and what’s introducing risk.
In the article, ModelOp is positioned as a “control tower” for enterprise AI—bringing structure to the full AI lifecycle, from ideation and development to deployment, monitoring, and retirement. That lifecycle approach creates a single system of record, so organizations can consistently track AI use cases across teams (and across AI types) instead of relying on fragmented reviews and manual tracking.
A key theme is that lifecycle management makes it possible to manage AI like a portfolio of investments. With centralized visibility into performance, cost, and risk, CIOs can shift from reactive oversight to strategic decision-making—identifying “home run” use cases worth scaling and spotting initiatives that are draining resources before they quietly grow legs.
The article also highlights what this looks like in practice with ModelOp: visibility into all AI (including ML, GenAI, agentic, internal, and third-party/vendor systems), enforceable policies, and integrations with existing ITSM, GRC, and data management systems to orchestrate governance across the enterprise. It also calls out workflow automation—like risk tiering, validation, and approvals—so reviews are structured, repeatable, and fully auditable.
If you’re working to scale AI responsibly—without slowing innovation—this is a quick, practical read.
