Your AI sprawl is DevOps sprawl all over again—fix it
“A man who carries a cat by the tail learns something he can learn in no other way,” said Mark Twain. Discovering hard truths through hands-on experience is how everyone is learning AI . That includes this CTO of a Fortune 500 solutions integrator. So take it from me: You’re likely repeating the sins of our past. How do I know? Confession time. We didn’t used to have a global contract with the…
It seems the AI revolution is facing the same issues we encountered with DevOps sprawl, and addressing them is crucial. In the early days of AI adoption, CIOs and teams were quick to create separate AI teams for various business functions, each using its preferred model and tools. This led to a scattered approach, similar to how DevOps teams initially proliferated without proper controls, resulting in a tangled mess of incompatible technologies.
Companies realized the need for a platform engineering team that could provide a consistent and supported approach, much like the platforms that emerged to streamline developer processes in the DevOps era. With AI now becoming a hot topic in corporate earnings calls, the same lessons apply. The focus should be on consolidating tools and processes, much like FinOps did for cloud costs.
By building on proven foundations and adding capabilities incrementally, organizations can avoid the tangled web of technical debt that emerges when multiple, independent AI stacks are maintained. It's essential to recognize that some complexities can only be navigated with the guidance of experts, and entrusting others to carry the 'cat' can prevent the costly consequences of an uncontrolled AI sprawl.
Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
