Enterprise AI adoption has moved from experimentation to core infrastructure. As a technical executive, you're no longer asking whether to invest in AI-you're deciding how to scale platforms that dozens of product teams depend on. The stakes are high: a well-run AI/ML platform accelerates the whole organization; a poorly governed one creates cost overruns, compliance gaps, and rework.
The platform mandate
Centralized AI/ML platforms must deliver: model training and versioning, feature stores, inference at scale, observability, and governance. The bar is reliability and cost efficiency, not just capability. Teams expect the same rigor they get from their CI/CD or data platforms: clear SLAs, versioning, rollback, and visibility into usage and cost.
Start by defining the contract: what does the platform promise (latency, availability, model refresh cadence), and what do consuming teams need to do (data quality, evaluation, security review)? Document this in a platform charter and keep it updated as you add capabilities. Without a clear contract, you'll spend cycles on ad hoc support and misaligned expectations.
Governance without bottleneck
VP-level ownership means defining guardrails-data lineage, model risk tiers, approval workflows-without turning the platform into a gate that slows innovation. Partner with compliance and product to codify policy in platform contracts. The goal is to make the right thing the easy thing: if your controls are built into the pipeline and the UI, teams don't have to remember to do the right thing.
Implement risk tiers (e.g., low-risk internal tools vs. high-risk customer-facing or regulated use cases) and route accordingly. High-risk models may need additional review, monitoring, and rollback procedures. Keep the bar proportionate so that low-risk experiments don't wait in line behind heavy process.
Build vs. buy at scale
At enterprise scale, build is often justified for differentiation, cost, and control. Focus build investment on orchestration, security, and integration; leverage managed services for training and inference where it accelerates time-to-value. The build decision should be driven by where you need uniqueness: often that's in how you integrate models into your product, how you handle data and identity, and how you enforce policy.
Revisit the build vs. buy balance regularly. As the vendor landscape matures, capabilities that used to require custom build may become available as managed services. Conversely, if a capability becomes a key differentiator, bringing it in-house may be the right move. Document the rationale so the next leader can understand why the line was drawn where it was.
Your platform is only as good as the number of teams that ship on it. Optimize for adoption and clarity of contract, not feature count.