GHD, a global professional services company, has appointed David McLaren as its new Enterprise Data & AI Leader, strengthening the firm’s global capability in enterprise data platforms, advanced analytics and responsible use of artificial intelligence. He will be based in the firm’s Toronto office.
McLaren brings extensive experience in building enterprise-scale data platforms and enabling analytics and AI adoption across complex organizations. Most recently, he led data and enterprise automation at Coca-Cola Canada Bottling, where he established enterprise data, automation and governance capabilities that supported commercial growth and improved operational efficiency at scale.
At GHD, he will lead the ongoing development of GHD’s Enterprise Data Platform and foundational AI capabilities. As a member of GHD’s IS & Technology leadership team, McLaren will strengthen how GHD uses data and AI, helping teams deliver more informed, efficient and consistent outcomes for clients worldwide.
McLaren’s focus will be on building the foundations that allow GHD to embed practical, responsible AI and data-driven decision-making into how the organization operates and delivers value for clients globally.
“Data and AI are most powerful when they are practical, trusted, and embedded into everyday decision-making,” McLaren said. “I’m excited to join GHD and help continue to build the enterprise foundations that support scalable AI, advanced insights and real-world outcomes for both our teams and our clients.”
“Our aim is to unlock the potential of AI and data-driven design and decision making for our people and our clients,” said Paul Murphy, GHD’s Chief Information Officer. “David’s leadership will help us further progress the development of trusted data and AI foundations for our teams as well as for collaboration with partners, clients and contractors.”
As an early adopter of AI tools, the firm recently joined the D^3 Frontier Firm initiative, a collaboration between the Digital Data Design Institute at Harvard (D^3) and Microsoft.
