When every AI solution is built from scratch, scaling is painful and slow. Teams duplicate work. Models are hard to govern. Talent doesn’t transfer easily between projects.
But with a platform approach, time-to-deploy decreases, governance is standardized, and access control is consistent across tools. Cross-team collaboration improves: data scientists, engineers, and business owners share a common foundation. And impact compounds. Improvements to shared services start benefitting every use case that relies on them, not just one team that operates in a siloed environment.
Moving to a platform model isn’t just about software architecture, it’s an organizational shift. It means prioritizing enablement over ownership, and giving teams the tools and standards to build responsibly and independently, rather than gatekeeping AI through a central team.
