Shifting the AI Mentality: How Manufacturing Leaders Are Turning Strategy into Adoption

From AI Conversation to AI Execution in Manufacturing

At a recent Women in Manufacturing (WiM) Illinois panel in Chicago, “Shifting the AI Mentality of Your Company,” industry practitioners and leadership voices came together to discuss a question many manufacturers are actively facing: not whether to adopt AI, but how to make it work inside real organizations.

The panel featured Heather Baker, Client Partner & Global Co-Lead, Digital & AI Practice at Alexander Hughes, alongside Teetee Roberts Brown, Reid Bloomfield, and moderator Aaron Lober, from CADDi. Each brought a distinct perspective, from executive strategy to hands-on operational implementation.

Unlike theory-driven conversations, this panel reflected what is actually happening inside manufacturing organizations today. With an audience largely made up of mid-level operators and cross-functional leaders, the discussion provided a grounded view of adoption, where progress is happening, where it is stalling, and what is required to move forward.

The consensus was clear: AI is no longer optional. The challenge is not access to tools, but the ability to integrate them into workflows, culture, and decision-making in a way that creates real value.

AI Adoption Is Happening But Not the Way Many Expected

While AI investment is accelerating across industries, real transformation remains uneven. Organizations are not struggling to access tools, they are struggling to embed them into day-to-day operations.

Teetee Roberts Brown highlighted the importance of structured training and trust-building, explaining how employees use tools like Copilot with intentional guidance. By identifying internal champions and creating a safe environment for experimentation, organizations can build confidence and accelerate adoption.

Heather Baker reinforced that AI adoption varies widely depending on business objectives and leadership priorities. In her conversations with CEOs, she sees companies focusing on targeted, practical applications rather than broad, undefined transformation efforts, often aligning AI initiatives with specific operational outcomes.

Reid Bloomfield emphasized that innovation often comes from employees themselves when they are given the tools and guardrails to explore. Providing space for experimentation allows ideas to emerge organically from the shop floor.

Aaron Lober added that many of the most impactful insights come from individuals engaging with AI for the first time, bringing fresh perspectives that leadership teams alone may not anticipate.

This reflects a broader reality. In a recent article in the Wall Street Journal, “AI Adoption Is Slower Than Expected, and It’s Not Just About the Tech”, it is reported that while companies are rapidly investing in AI, many initiatives stall because they require changes to workflows, governance, and employee behavior—not just new tools.

The Real Barrier to AI Adoption in Manufacturing: Curiosity, Culture, and Leadership Alignment

A common assumption is that AI adoption is divided along generational lines. The panel challenged this directly.

Reid Bloomfield described what he calls a “curiosity gap,” not a generational gap. In his organization, even employees approaching retirement have become active users of AI tools when given access and encouragement. By creating shared prompt libraries and collaborative environments, teams can learn from one another regardless of experience level.

Heather Baker echoed this perspective, emphasizing that adoption accelerates when leaders clearly connect AI initiatives to business outcomes. Many executives are proactively educating themselves through formal programs and structured learning to better understand how to guide their organizations.

Aaron Lober pointed to the importance of phased implementation, introducing AI capabilities in manageable steps rather than overwhelming teams with large-scale change. Heather also highlighted data centralization as a critical enabler—without it, AI initiatives often stall.

Studies from Cornell University, “Artificial Intelligence, Scientific Discovery, and Product Innovation”, highlight that only a small percentage of organizations are seeing meaningful impact from AI, largely because success depends on building internal capabilities and integrating AI into how people actually work.

Who Owns AI? Aligning Executive Leadership and AI Execution

One of the most important, and often unresolved, questions in AI transformation is ownership.

Heather Baker was clear: AI strategy must be CEO-led. When leadership is actively engaged, organizations can align initiatives across departments and move beyond isolated experimentation.

At the same time, Aaron Lober highlighted a common tension between technology leadership and operational teams. While strategy may sit with the CTO or digital leadership, the real impact is realized within operations.

Reid Bloomfield summarized this dynamic: leadership sets the vision, but employees determine the outcome. Organizations that invest in training and enablement are the ones that translate strategy into results.

Teetee Roberts Brown added that housing digital transformation within a single function can limit adoption. Broader engagement across teams is necessary for AI to become embedded in everyday workflows.

Data Readiness and Digital Infrastructure: The AI Foundation Most Manufacturers Underestimate

While AI often dominates the conversation, the panel emphasized that data remains the true foundation.

Heather Baker pointed to data centralization as one of the most significant challenges organizations face. Companies with more mature data environments, such as those in MedTech, are further along in their AI journey, while others struggle with fragmented systems and inconsistent data quality.

Aaron Lober reinforced that data readiness is often the determining factor in whether AI initiatives succeed or fail. Without a strong data foundation, even the most advanced tools cannot deliver meaningful insights.

Reid Bloomfield also highlighted the importance of data security, noting concerns around sending proprietary information to cloud-based systems. Some organizations are addressing this by developing internal AI models trained on their own data.

This reflects a broader industry challenge. Reuters notes in “The Future of AI Will Be Written in Nuts and Bolts”, that manufacturing remains one of the least digitized sectors, meaning the greatest barrier to AI adoption is not access to tools, but the ability to structure, connect, and leverage operational data effectively.

Moving Beyond the Hype: A “Lights On” Future

Despite rapid advancements, the panelists were aligned on one key point: fully autonomous, “lights-out” manufacturing is not the near-term reality.

Reid Bloomfield described a “lights-on” future, where AI enhances human capability rather than replacing it. Knowledge is multiplied, decision-making is accelerated, but people remain central to operations.

Teetee Roberts Brown reinforced this, emphasizing that her organization remains fundamentally people-centered.

Heather Baker added that one of the biggest barriers to progress is not technology, but culture. Fear and uncertainty, sometimes even at the leadership level, can slow adoption if not addressed directly.

Reid Bloomfield offered a perspective that captures the shift:

AI should not be measured by how much it replaces, but by how effectively it elevates people.

Why Manufacturing Companies Are Re-Evaluating Executive Leadership in Chicago

As AI adoption accelerates across manufacturing, organizations are increasingly evaluating whether their current leadership teams are equipped to guide digital transformation initiatives at scale. Many companies are turning to executive search firms in Chicago to identify leaders who can align AI strategy, operational execution, workforce transformation, and long-term business growth.

For manufacturers, the challenge is no longer simply implementing AI tools. It is identifying leadership capable of integrating technology into organizational culture, operations, and decision-making in a sustainable way.

As a global executive search and leadership advisory firm, Alexander Hughes USA works with organizations navigating executive leadership recruitment, succession planning, digital transformation, and operational modernization across manufacturing and industrial sectors.

The Companies That Will Lead

The discussion at Women in Manufacturing highlighted a clear and consistent reality:

AI transformation is not a technology initiative; it is a leadership and cultural shift.

Organizations that succeed will be those that:

• Align leadership around a clear AI strategy
• Build trust through structured adoption and training
• Invest in data as a foundational capability
• Empower employees to explore, learn, and apply AI in their roles

The shift to a digital-first mindset is already underway. The differentiator will not be access to AI, but the ability to make it part of how the organization thinks, operates, and evolves.

As AI continues to reshape industries, the organizations that succeed will be those led by executives who can translate innovation into action. At Alexander Hughes USA, we partner with manufacturing and industrial organizations to identify leaders who not only understand digital transformation, but can embed it into the fabric of the business, aligning people, processes, and strategy for long-term impact.

Through our Chicago executive search office, alongside our leadership advisory teams in New York and Boston, Alexander Hughes USA supports organizations navigating AI adoption, operational modernization, executive leadership recruitment, succession planning, and global leadership transformation initiatives across manufacturing and industrial sectors.