Remember when marketing was just about creative campaigns? Before the proliferation of digital channels, automation platforms, and analytics tools, a marketing team could operate on instinct and creativity alone.
Then came Marketing Ops.
Not because anyone wanted more complexity, but because the complexity arrived whether organizations were ready or not. Someone had to manage the MarTech stack, ensure data flowed between systems, and translate analytics into action. Marketing Ops emerged to handle what had become too complex and data-driven to manage without structure.
The Pattern Repeats
This same evolution has played out across every major business function:
Marketing Ops emerged to handle campaign complexity, marketing automation, and the explosion of digital channels. What started as "someone who knows the tools" became a strategic function.
Sales Ops arose to manage CRM systems, sales analytics, and the increasing sophistication of pipeline management. Sales leaders needed operational support to make sense of the data.
Revenue Ops unified marketing, sales, and customer success operations to break down silos that were creating friction across the customer journey.
Finance Ops developed to ensure governance, enable real-time reporting, and manage the complexity of modern financial systems.
Each of these functions emerged for the same reason: the underlying work became too complex and data-driven to manage without dedicated operational expertise.
AI Is Following the Same Pattern
AI introduces unprecedented operational complexity. It's not just another tool to add to the stack—it fundamentally changes how work gets done across every function. That level of transformation requires systematic management.
AI Operations is emerging because organizations are discovering what happens without it:
- Fragmented initiatives that don't scale beyond pilot projects
- Compliance and bias issues that create legal and reputational risk
- Unreliable outputs that erode trust in AI capabilities
- Wasted investments in tools that never deliver promised value
These aren't technology problems. They're operational problems. And they require an operational solution.
What Makes AI Ops Different
The key difference between AI Ops and its predecessors isn't the complexity—it's the scope.
Marketing Ops transformed marketing. Sales Ops transformed sales. AI Ops transforms the entire workforce.
The focus isn't just managing tools or optimizing processes within a single function. It's about creating what we might call "superhuman" employees across the organization—eliminating mundane tasks so people can focus on high-value, creative work that actually requires human judgment.
This is a different kind of operations role. It's not about making one department more efficient. It's about fundamentally changing what's possible for every employee.
The Organizational Question
Every operations function faced the same question at its emergence: do we treat this as a strategic capability or as ad hoc support?
Organizations that treated Marketing Ops as strategic gained competitive advantage. Those that treated it as "whoever knows the tools" struggled to realize value from their marketing technology investments.
The same choice is presenting itself with AI. Organizations can build intentional AI Operations capability, or they can let AI adoption happen haphazardly and hope for the best.
The pattern suggests which approach works better. The question is whether you'll learn from history or repeat it.