Forbes Business Council Article: The Connected Enterprise Comes First: Build The Operating Model, Then Let AI Do Its Job

Enforce accountability before investing in algorithms.

​Before AI can improve forecasts, spot problems or suggest next steps, the organization needs a few basics in place:

• Data that links directly to business goals
• Continuous data quality management, not one-off cleanups
• Clear ownership for different data domains

This is where many efforts break down. Traditional data governance focused heavily on policies and repositories. There was structure, but not much accountability.

In a connected enterprise, that changes. Business leaders take ownership of their data. Stewards keep definitions and quality in check. Teams know what data is meant for and how to use it. Governance becomes practical and tied to real decisions, not abstract rules.

When accountability is clear, AI finally has a solid, reliable foundation to work from. Without that foundation, AI doesn’t create clarity; it just adds more noise.

Deploy AI only where it improves efficiency.

When the fundamentals are in place—connected systems, clear ownership and stable processes—AI can start delivering real value. Automation can eliminate repetitive work, and AI is able to build on those gains by sharpening forecasts, flagging issues sooner and offering practical next steps rather than more dashboards. It can also strengthen governance behind the scenes by classifying data, tracking lineage and monitoring policies without adding more manual effort. The ultimate goal is for AI efforts to turn into reliable, scalable capabilities that become part of how the business runs. Those who skip the groundwork typically end up with pilots that look promising in a meeting but never grow beyond a test.

Own operational change at the leadership level.

The CEOs who get this right build discipline into the way the organization actually runs.

They put clear decision forums in place where everyone knows who has authority and who doesn’t. Accountability isn’t vague; it’s explicitly assigned. Escalation paths are simple and known. And governance moves at a steady rhythm tied to real delivery, not to calendar meetings.

Execution follows a repeatable loop: discover the problem, design the solution, build it and then operationalize it—with clear decision points at each stage. This structure doesn’t slow teams down. It removes friction. Priorities don’t get renegotiated every week. Leaders aren’t settling disputes through email chains. Work moves forward because the path is defined.

When CEOs run the system this way, transformation stops being fragile and becomes part of how the company operates.​

Get the sequence right before accelerating execution.

In the rush to “do something with AI,” many organizations flip the order. They chase intelligence before they’ve built connectivity. They automate work before anyone is accountable for it. They try to scale before they have the controls to support scale.

The outcomes are predictable. Pilots look impressive in a demo but never mature. Leaders don’t fully trust the models because the underlying data isn’t stable. Governance is always playing catch-up, trying to clean up issues after the fact.

The answer isn’t to slow down; it’s to move in the right sequence. Build the connected enterprise first. Put ownership and operating discipline in place. Then, bring AI in as the multiplier it’s meant to be.

In my experience, organizations that follow this order operate differently. Decisions move faster because they’re grounded in shared data. Risk decreases because controls are built into the workflow. And AI accelerates responsible execution instead of substituting for leadership.​