Integration Over Invention: A 3-Pillar Framework for AI That Actually Drives Returns

The role of AI in enterprise is quickly shifting from simple chatbot assistant to full-on cross-functional coworker. As the technology advances, there’s an upward trend in corporate leaders using AI for operational use cases to create more cohesion between departments. Take, for example, how ARCO Construction is streamlining the notoriously complicated building process: their GenAI ecosystem is assisting the team today in legal case intake, identifying risk factors, and triggering automated workflows in other enterprise systems. Many leaders think it holds promise across many industries to hold this same administrative role, but remain cautious about full, “process-level” automation.

Robin Patra, Director of Data, Analytics & AI at ARCO Construction Company, has spent over two decades leading digital and AI transformations at multi-billion-dollar enterprises like BlackRock and Cisco. As a leader who regularly transforms $3B+ enterprises and scales global AI teams, he said the move to orchestration requires leaders rethinking how they measure success, manage risk, and build trust.

"AI is an enabler, not a product," Patra said, meaning the future implementation of AI technology is a means to shave off project timelines through the use of agentic AI and process automators. "Before investing, leaders need to measure whether the total cost of ownership matches the value it brings to their core business. The move to orchestrated workflows is prompting many leaders to call for a new strategic framework built on three pillars:

• Integration over invention: First, proper implementation requires a deep focus on skillfully weaving AI into existing business processes to create powerful data-driven workflows. "Enterprises are not in the business of creating AI; we are in the business of deploying AI," Patra said. "This means the priority must be understanding the tool and how it integrates into the workflow. This is the most critical piece most leaders overlook."

• Driven by domain: The second pillar is people alignment. As AI connects disparate business units, the human experts in those units must be brought together to co-design the new processes. "AI-driven transformation must be led by domain experts. The people who own the process are the ones who must trust the AI's outcome, so they must also be the ones who own its integration into their daily workflow."

• Tying AI back to returns: And finally, a call for the executive-level push for workflow transformation to unlock ROI is key. Patra offered a simple litmus test when evaluating AI's success once implemented: "Is the tool you’re deploying actually moving the needle on the product you’re selling?” If it’s not accelerating your core business, it’s just overhead." AI, he added, needs to move out of isolated silos so you can integrate it directly into core business workflows.

But realizing the benefits also means confronting the risks of a poorly governed rollout. Patra illustrated this by distinguishing between AI as a powerful enabler (like providing a doctor with patient history and a suggested diagnosis) and AI in a high-risk autonomous role (like prescribing medicine and sending the order directly to a pharmacy). A rush to deploy powerful tools in business without first establishing a proper AI governance framework can lead to costly failures.

• The salary slip-up: "We had a case where an analyst asking the AI Copilot for the CEO's salary resulted in that information being shared across the enterprise," Patra said. "Currently tool providers will sell you on the power of their AI, but they will never tell you how to govern your data. The responsibility to build those guardrails falls squarely on the enterprise leaders who are deploying the technology."

So what does competent implementation look like? Patra offered a practical governance playbook for AI leadership built on a couple principles:

• Limit data access to relevant roles: Patra suggested restricting data access to relevant roles in order to reduce the risk of internal data leaks. "In enterprise, every individual has a role, a responsibility, and a specific relationship with data. For example, I should be able to see my own team’s salary information, but I shouldn't see another manager’s team data. We are now applying that same level of data classification to AI."

• Bring in all the subject matter experts: To have process experts define where human sign-off is required for security purposes. "Currently, we're classifying our data to prevent security slips. When I implement AI that cuts across two or three business groups, I bring the task experts from every department into a room."

• Practice healthy suspicion: He also advised refining AI based on its errors rather than its successes. He pushes leaders to ask proper questions to identify corner cases. "The agent may be working great overall, but where did it fail, and in what specific scenario?" he postures. Staying critical is how you create exceptions reports for these instances and reduce error.

Patra has seen the payoff first hand of integrating AI correctly within his own work at ARCO. "My design and estimation for $2 billion to $5 billion projects used to take three to four months. With the help of AI, we're reducing that timeline that to two months." Two months of saved time certainly demonstrates the payoff for scaling AI adoption across enterprise systems, but Patra urged leaders to understand that effective governance means a continuous cycle of monitoring and alignment. It requires strong AI data governance to build the trust needed to move from simple task automation to the interwoven, cross-functional orchestration that complex industries need.