Vulcan Materials Company produces an essential ingredient of the built environment. Its aggregates are found in roads, bridges, buildings and other structures across the United States. The company, which generated approximately $7.8 billion in revenue in 2025, operates more than 400 aggregate facilities across 24 states and shipped more than 220 million tons of aggregates last year.

“If you’re in a building, on a road, on a bridge or anything made of concrete or asphalt, chances are very likely our product is in it,” said Chief Information Officer Krzysztof Soltan.

Since becoming CIO in 2021, Soltan has led a multiyear transformation spanning commercial operations, finance, human resources, supply chain, cybersecurity and data. Although cloud platforms, artificial intelligence and automation are important components of that work, Soltan does not define his role primarily through technology. “First and foremost, I look at myself and my role as being a business partner to everybody across the company,” he emphasized. “I also think of my role as being the change agent for the company through technology, to be an educator of what’s possible.”

Simplifying the Business Before Applying AI

Vulcan’s transformation began with a focus on commercial and marketing capabilities. The timing aligned with the company’s Vulcan Way of Selling initiative, while the organization had both the momentum and readiness necessary to embrace change. The program was intended to help salespeople spend more time with customers and less time completing administrative work, while creating the data foundation for more sophisticated analytics and AI.

Soltan and his team are now applying the lessons from that work to a broader back-office transformation encompassing finance, HR and supply chain. The objective is not merely to move existing processes into the cloud. Vulcan is using the transition to challenge those processes, reduce unnecessary complexity and equip employees with better tools.

The focus has been on modernizing capabilities, moving to the cloud and using SaaS platforms, as well as simplifing and challenging every process at the same time, according to Soltan. That distinction is important. Replacing an old system without reconsidering the work around it can preserve inefficiencies in a more modern technical environment.

System consolidation is therefore a central element of Vulcan’s strategy. “My team will tell you, everywhere I turn around, I talk about data first,” Soltan underscored. “Less systems, less to maintain, less to integrate, less to train, will result in more value.”

Treating Data Governance as a Business Discipline

Vulcan’s emphasis on simplification is directly connected to its AI ambitions. Reducing the number of systems can improve data consistency and make it easier to establish a trusted view of the business. Yet Soltan emphasized that improving data at the point of migration is not enough. Its quality must be actively maintained.

“[AI] fundamentally relies on really good data,” he said. “All companies have lots of data, but we have good data, and we will have even better data as time goes on.”

That requires clear accountability across the enterprise. Vulcan has established a master data governance program that defines business ownership, identifies data stewards and gives the technology organization responsibility for managing, governing and monitoring the underlying information. What began as part of the transformation program has become part of the company’s operating culture.

“This is hard, but it’s critical,” Soltan acknowledged. Establishing governance involves more than selecting tools or creating policies. It requires leaders to assign ownership, resolve disagreements and educate employees about why consistent data matters.

Talent represents another part of the foundation. Vulcan recruited a new data leader, built cross-functional expertise and continues to combine external hiring with the development of existing employees. “Nothing happens without people,” Soltan said. “We’re constantly upskilling our existing talent to ensure that the data is a strategic asset for Vulcan Materials.”

Concentrating AI on the Biggest Opportunities

Vulcan has used machine learning, analytics and other forms of AI since before generative AI became a boardroom priority. In its operations, the company has standardized capabilities that apply AI to improve how it produces finished materials. Its commercial transformation has also created a stronger base of information upon which new AI applications can be built.

Soltan is now exploring generative and agentic AI, including through an employee hackathon focused on solving real business problems. However, he is wary of allowing a proliferation of experiments to substitute for enterprise impact.

“What we are focused on is identifying those few big needle-mover opportunities and driving that use of AI for those, versus trying to solve a lot of little problems,” he said. Operations, commercial activities and back-office functions all offer potential, but investments must be prioritized according to their value.

The company takes a similarly pragmatic approach to buying versus building AI. If a useful capability is already embedded in an enterprise platform, Vulcan will generally begin there. “Why reinvent the wheel?” Soltan asked. Commodity processes rarely warrant proprietary development.

Vulcan is more likely to build when an application can create competitive advantage, incorporates valuable intellectual property or addresses a need unique to the company and its industry. In other cases, it may combine an existing platform with custom components. “We always try to apply what works best for Vulcan Materials,” he highlighted.

Preparing for More Autonomous Operations

Over the next five to 10 years, Vulcan expects automation to play a larger role in its operations. Progress will depend not only on technical maturity but also on cybersecurity, risk management and the organization’s readiness for change. A technology that performs well in an experiment may not yet be supportable at scale in a rugged operational environment.

Autonomous mobility is one example. Quarry operations present different challenges than public roads, which have lanes, traffic signals and other predictable features. Physical AI will need to function safely around heavy equipment in far less structured environments.

Soltan also monitors human-agent collaboration, AI-assisted software development and AI at the edge. Processing data close to equipment could reduce latency and dependence on centralized data centers while enabling faster operational decisions.

Across these possibilities, his approach remains grounded in business value and workforce enablement. The purpose of automation is not simply to remove tasks, but to give employees the capacity to concentrate on work that matters more. “How will we enable our employees to focus on things that add even more value?” Soltan asked.

For Vulcan, that question ties the transformation together. Modern platforms create opportunities for simplification. Simplification improves data. Trusted data makes AI more useful, and AI can give employees new ways to improve a business that quite literally provides the foundation for much of the country’s physical infrastructure.

Peter High is President of Metis Strategy , a business and IT advisory firm. He has written three bestselling books, including his latest Getting to Nimble . He also moderates the Technovation podcast series and speaks at conferences around the world. Follow him on X @PeterAHigh .