Rockwell Automation, a global leader in industrial automation and digital transformation, is powering industries across more than 100 countries. The company has made major strides in AI integration — especially when it comes to doing it responsibly.

In the first part of this two-part interview series, Andrea Ruotolo, Global Head of Customer Sustainability, Digital, and Responsible AI at Rockwell Automation, joins Clyde Gillard, North American AI Go-to-market Leader at HPE, for a conversation on responsible AI, human-first and AI-enabled organizations, data infrastructure challenges, AI governance gaps, and the importance of leadership vision in prioritizing projects to drive business value. 

A dual perspective on responsible AI and sustainability

Ruotolo wears many hats. At Rockwell, she leads sustainability through digital transformation and AI, guiding industrial organizations to adopt technology in a strategic, purpose-driven way.

In addition to her corporate role, Ruotolo serves as President for Canada on the Global Responsible AI Council, the world’s first global body dedicated to the implementation of responsible AI. She also engages with the Toronto Machine Learning Society and groups that promote women leaders in AI, providing her with what she calls “multiple perspectives” on technology implementation, policy, and ethics.

Human-first, AI-enabled: Rethinking AI strategy

When asked about the challenges industries face in deploying AI, Ruotolo is quick to counter a prevalent paradigm. “I do not agree much, if at all, with this concept of AI-first. I think corporations need to be human first and AI-enabled.”

According to her, the distinction matters. A truly human-first approach empowers employees, fosters better experiences, and aligns more closely with the real mission of most businesses — serving people, not just optimizing processes.

Ruotolo notes that 78% of companies use AI in at least one function, but many are rethinking “all-in” public cloud strategies due to concerns around data sovereignty, vendor lock-in, performance, and cybersecurity.

That’s particularly crucial in the industrial sector, where organizations grapple with fragmented data sources and outdated legacy systems.

These conditions can complicate AI readiness, but Ruotolo sees a hidden upside: “One could view fragmented data sources as a challenge, but at the same time, it’s a blessing in disguise because that’s preventing organizations from moving and breaking too fast. And that would not be a good thing to do with AI.”

AI governance gaps: The case for caution and collective understanding

Despite the buzz around AI, a much deeper issue in many organizations is the lack of AI governance, says Ruotolo. “I see numbers such as 90% plus of organizations using AI in general, but only about 8% have fully embedded governance frameworks or integrated governance into the development life cycles.”

While frameworks like the EU AI Act and ISO 42001 exist, most companies are still figuring out how to translate those standards into internal governance structures, she adds. As Ruotolo states, “We don’t truly understand this technology, and that’s where slowing down would be the right approach.”

Adding to that, Ruotolo recounts a startling example of three AI agents that began communicating in a new, human-unintelligible language once they realized they were speaking to each other.

“There’s so much we don’t understand,” she emphasizes. That is why her approach advocates for caution, clarity, and the involvement of employees in the deployment of AI, particularly in an era of widespread workforce anxiety.

“If you have your employees stressed out in fight-or-flight mode, they are probably looking to work somewhere else because there’s no clarity from a leadership standpoint,” Ruotolo adds.

Avoiding “Death by a thousand pilots”: Aligning AI projects with ESG impact

While many organizations embrace AI pilots enthusiastically, Ruotolo warns against an overzealous, uncoordinated approach.

Referring to a report published by BCG, she shares that 75% of organizations struggle to scale impact. Even with widespread AI adoption, the impact is minimal.

According to Ruotolo, organizations must focus on the multifaceted value that AI implementation could bring. She further stresses having a vision.

“If the leadership doesn’t have a clear vision about whether they are building an AI-enabled organization or an AI-first company, this could lead to death by a thousand pilots.”

Without vision or governance, employees may launch scattered initiatives, wasting time and resources. Instead, she calls for a structured framework that aligns with both business goals and employee needs.

For organizations looking to prioritize AI projects to drive sustainable business value, Ruotolo suggests focusing on initiatives that meet multiple objectives — especially those tied to environmental, social, and governance (ESG) goals.

“A good framework starts by mapping these high-frequency operational use cases, assessing, for example, ESG impact metrics, and prioritizing projects that meet dual or multiple objectives.”

In conclusion, Ruotolo provides a practical example: Reducing energy consumption. A project that achieves this does not just lower costs — it minimizes operational risks and empowers frontline employees to respond to energy deviations in real time.

Disclaimer: The views and opinions expressed in this interview are solely those of the speaker and do not necessarily reflect the views of her current or past employers.

CDO Magazine appreciates Andrea Ruotolo for sharing her insights with our global community.