Rethinking IT Leadership: How CIOs Are Shaping the Future of Manufacturing with AI and Data

The role of the CIO in manufacturing has never been more complex, and at the same time, more influential. As factories evolve into data-rich, digitally connected environments, CIOs are being asked to do far more than manage infrastructure or maintain uptime. They are expected to lead the charge in applying artificial intelligence (AI) to real business challenges, orchestrating cross-functional transformation while maintaining security, continuity, and trust across the enterprise.

For forward-looking leaders, this moment represents both a challenge and an opportunity. The technical possibilities of AI are expanding rapidly, but success depends on how well these tools are integrated into existing systems, workflows, and cultures. It is not just about building models or acquiring platforms. It is about aligning people, data, and priorities in a way that supports long-term impact and measurable outcomes.

May Yap, SVP and Chief Information Officer at Jabil, has spent the past several years navigating this exact terrain. Her approach offers a valuable playbook for AI leadership in manufacturing that unlocks the full potential of AI while ensuring it scales responsibly across every region, function, and team.

Today’s manufacturing leaders expect their CIOs to think like business strategists, prioritize operational outcomes, and drive transformation that goes beyond IT. This shift in expectations reflects a broader recognition that digital success depends on how well technology leaders can bridge the gap between tools and outcomes.

CIOs must now engage deeply with frontline teams, understand the pain points of engineering and supply chain stakeholders, and identify which AI projects will make the greatest impact. In this environment, technical literacy is no longer enough. The most effective CIOs combine fluency in systems architecture with a sharp understanding of business processes and performance metrics.

Yap describes this shift as moving from a function-oriented mindset to an enterprise-wide perspective. Rather than focusing solely on uptime or systems integration, CIOs must ask broader questions. What business problems are we trying to solve? Which processes are slowing us down? Where could AI help us move faster, reduce waste, or increase quality?

This mindset allows CIOs to become catalysts for transformation rather than custodians of infrastructure. They guide their organizations toward scalable solutions that improve outcomes, not just outputs.

Artificial intelligence can only be as effective as the data it relies on. For CIOs tasked with digital transformation and AI leadership in manufacturing, one of the most foundational challenges is ensuring that data is not only accurate but also understood, maintained, and shared by the right stakeholders. Building this kind of data culture requires more than new tools. It requires organizational alignment and shared responsibility.

In her leadership role, Yap prioritizes clarity around data ownership. One of her first steps to accomplish this objective was to establish a cross-functional council composed of senior representatives from every major business unit. These individuals are not just advisors. They are designated as data stewards, responsible for the quality and usability of the data their functions generated.

This model helps reinforce an important message across the organization: data is not owned by IT. It is created by business users, and it is their responsibility to ensure it can be trusted and reused. Yap encourages every member of the council to treat their data as a product that needs to serve others. That means data has to be clean, consistent, and meaningful.

Her team also uses a framework based on the FAIR principles: data should be Findable, Accessible, Interoperable, and Reusable. These criteria are the standard for evaluating whether datasets are ready to support machine learning models, forecasting tools, or AI-driven decision-making.

When teams begin to internalize these expectations, the conversation around AI shifts. It’s no longer about asking for insights from an algorithm. It’s a collaborative process where teams work together to prepare the inputs that power better outcomes.

For many manufacturing organizations, the promise of artificial intelligence remains stuck in isolated pilots and one-off experiments. But for CIOs who want to scale impact, the path forward requires discipline, prioritization, and a strong connection to operational needs. Under Yap’s leadership, Jabil has identified three categories of AI investment that offer the greatest potential for enterprise-wide value.

• Computer Vision: For Augmented Inspection Processes

The first focus area is computer vision, particularly for visual inspection tasks on the production floor. Using AI-powered cameras embedded in compact devices, teams are able to augment traditional inspection processes. This technology helps reduce error rates, ensures consistency in quality control, and allows operators to focus on more complex tasks. With adoption expanding across multiple regions, computer vision has become foundational to the company’s quality strategy.

• Machine Learning: For Forecasting and Optimization

The second area centers on machine learning for forecasting and optimization. After several years of work to clean and structure the organization’s data, teams are now able to develop predictive models to support everything from financial forecasting to supply planning and demand alignment. In some cases, machine learning has also been applied to improve the chemical composition of materials, driving better product outcomes. These efforts reflect a growing maturity in using data not just for visibility, but for actionable guidance.

• Generative AI and Agentic AI: For Scaling Insights and Augmenting Human Capacity

The third strategic focus is generative AI and agentic AI, which are being explored to support broader knowledge work. While the technology is still emerging, its application across departments such as finance, engineering, and operations has opened the door to new forms of automation and collaboration. Digital agents are being tested for a variety of use cases, helping to scale insights and free up human capacity.

Each of these initiatives is supported by a common foundation: clean data, a governed architecture, and a clear understanding of the business problems being solved. Rather than chasing every trend, Yap’s team has built a roadmap based on tangible value and long-term scalability. The result is not just a more intelligent factory, but a more connected and capable organization.

As enthusiasm for AI grows across manufacturing teams, so does the risk of misalignment. Technicians, engineers, and even frontline operators often bring forward creative ideas for how AI could improve processes. While this bottom-up energy is essential for innovation, it can also lead to fragmentation if there are no clear boundaries or support systems in place.

Yap recognizes this tension and has focused on developing a governance model that guides innovation without stifling it. Her approach combines education, policy, and enablement. Guardrails have been established that prevent sensitive data from being mishandled or exposed to unsecured environments. At the same time, technical resources are available to empower teams in the development of their own use cases within a well-defined framework.

Education has played a central role in this process. Employees are asked to review and sign AI policy guidelines, participate in awareness sessions, and understand the responsibilities associated with working with enterprise data. Rather than treating governance as a restriction, the organization frames it as a shared responsibility that protects customers, intellectual property, and operational integrity.

To further support innovation, Yap’s team developed a centralized technology stack and data marketplace. These resources provide a trusted foundation for teams to build on, reducing duplication and allowing insights to flow across business units.

The result is a culture where regional and site-level teams feel empowered to innovate, but within a system that keeps the broader organization aligned. Governance, in this context, was not about control. It was about coordination, clarity, and building trust across every layer of the enterprise.

Artificial intelligence is a rapidly emerging force that challenges how industrial operations are designed, executed, and improved. For CIOs and other digital leaders, the goal is not just to adopt AI but to rethink what manufacturing can become when data and intelligence are built into every layer of the enterprise.

Yap describes this future as one where AI helps organizations do anything possible, improve everything they touch, and move faster toward delivering value to their customers. That vision cannot be realized through technology alone. It requires AI leadership in manufacturing that can connect technical capability with business strategy and operational execution.

Looking ahead, manufacturing leaders will be called on to make decisions that shape the next generation of the industry. These decisions will span far beyond software choices or hardware upgrades. They will involve reshaping organizational culture, redefining talent priorities, and reimagining how factories operate in an increasingly connected world.

The CIOs who succeed in this next chapter will be those who see AI not as a tool to automate what exists, but as a catalyst to empower their people and propel them toward the demands of the future.

Listen to the full conversation CIO at the Helm: Scaling AI and Digital Transformation Across the Enterprise with May Yap, SVP and Chief Information Officer at Jabil, on The Manufacturing Intelligence Podcast.