Thought Leadership

Most enterprise sourcing processes are built to compare. They produce a structured list of requirements, issue an RFP to a broad field of providers, score responses against a compliance matrix, and select the vendor that best matches the specification.

For decades, enterprise IT outsourcing followed a familiar pattern. Organizations assessed their current environment, documented requirements, issued an RFP, compared provider responses, negotiated a long-term contract, and transitioned services into a managed delivery model.

Artificial intelligence is rapidly becoming the centerpiece of modern IT operations. Nearly every managed service provider (MSP) now claims to offer AI-powered capabilities, intelligent automation, or next-generation operations support. For CIOs evaluating providers, the challenge is no longer finding vendors that mention AI—it’s identifying which partners can actually operationalize it at scale.

For most enterprise organizations, AI is still viewed through a capital expenditure lens. It is often positioned as a large, incremental investment layered on top of already constrained IT budgets. For companies operating at $100M+ scale, that perception creates hesitation. The concern is simple: how do you fund transformation without impacting margins?

For decades, IT Infrastructure and Operations (I&O) has relied on a familiar model: scale the team as demand increases. Meaning, with more users, systems, and tickets, your company will require more people. That model is reaching its limits.

In 2026, AI is no longer just embedded in software as a feature or enhancement. It is rapidly becoming the workforce that IT teams are expected to manage, govern, and scale.