How AI Leaders Are Reining In Bottom-Up Sprawl Through Unified Integration Layers

The views and opinions expressed are those of Nithin Ramachandran and do not represent the official policy or position of any organization.

Enterprise GenAI is entering its orchestration phase, where context and connectivity have become the primary discussion points between forward-looking AI builders. After a year of pilots and point solutions, leading organizations are moving away from isolated demos and toward coordinated systems that can operate across workflows, data, and teams. Catalyzed by early GenAI stumbles, the shift is forcing CIOs to confront the fact that scale comes not from more experimentation but from engineering discipline, operating structure, and decisions about where AI actually belongs in the business.

Nithin Ramachandran is the Global Vice President of Data Analytics, MDM and AI at industrial machinery manufacturing leader 3M, where he heads enterprise-wide data, analytics, and AI strategy. A senior data and AI executive with leadership experience at companies like Kohler Co. and Direct Supply, he has spent his career operating at the intersection of platform engineering, business transformation, and executive decision-making. That vantage point informs a clear, opinionated view on why scaling generative AI now demands far more discipline than experimentation.

"AI doesn’t create value in isolation. Value shows up when systems, data, and decisions are orchestrated end-to-end," said Ramachandran. That principle underpins his approach to enterprise GenAI evaluation, starting with a clear distinction between foundational capabilities and those that deliver real business impact. From there, Ramachandran advised breaking AI initiatives into three levels, each with a different role in how value is created, governed, and ultimately scaled.

• Three levels of impact: "Level one is pervasive generative AI that applies to every employee, supporting basic tasks like document search and email and delivering AI-assisted productivity that now amounts to competitive parity," he explained. "Level two is domain-specific, because the AI needed in marketing looks very different from what’s required in finance, legal, or supply chain, and this is where measurable business value starts to emerge. Level three is the most specialized, built on proprietary company information that truly differentiates the business and creates a sustained competitive edge."

With a strategy in place, the focus turns to execution. Ramachandran views stability and reliability as outcomes that must be deliberately engineered through structure and discipline. To avoid the day-one failures that quickly undermine trust, he advocated a three-tier operating model: a platform team responsible for core technical reliability, product teams accountable for translating AI capabilities into business value, and business teams focused on embedding those tools into real workflows and processes.

• Unchecked expansion: That discipline starts by recognizing that leaders are often debating the wrong issue. "The build versus buy conversation no longer holds, because nobody truly builds models from scratch anymore. They configure baseline services with their own data, while the real problem goes unaddressed," he said. "Most companies take a bottom-up approach, collecting ideas inside functional silos, which leads to a rush of disconnected copilots and tools. That’s how AI sprawl takes root across the enterprise."

The solution to this fragmentation is to invest engineering resources in creating a connective tissue that sits above these disparate applications, using APIs and emerging agent-to-agent protocols. This is how an organization makes the leap from simple information retrieval to orchestrated, task-based automation. And, Ramachandran argued, it’s where the true enterprise ROI is found.

• The wrapper layer: "Where you want to really invest your time is in creating that 'wrapper layer' over everything that connects to all of these systems and could orchestrate AI workflows across systems. That is another area, especially in large companies, where it's worthwhile to invest."

• Orchestration makes the impossible possible: "Companies can now manage an agentic interface to connect all agents across systems. Most of the industry is aligning to this because without that, productivity gains from generative AI in the enterprise are impossible."