CIOs disrupt IT operating models to align with digital business, exploit AI


To adapt to an increasingly digital world, CIOs are changing the way they run their IT organizations, spurring adoption of agile and DevOps programming and multithreaded operating models and creating software to automate tasks. CIOs are instituting startup-like operating models in which their software engineers experiment with emerging technologies, says Bill Briggs, CTO and managing director of Deloitte Consulting.

“We’re in a moment now of [asking], how do you reinvent what it means to deliver technology?” Briggs tells “Technology is at the heart of business strategy and at the heart of next-generation products and services, customer engagement and how work gets done.”

CEOs and their corporate boards expect to harness technology to search for new pockets of growth. As a result, CIOs must adopt a broader strategy called “unbounded IT,” says Briggs, who described the approach in Deloitte’s 8th Tech Trends report, published last week.

Different operating models require change

Unbounded IT, for some companies, means establishing different working groups and models within IT.

For example, Ford Motor CIO Marcy Klevorn has turned to bimodal IT, consisting of two primary operating models. The core development team focuses on risk-intensive areas such as design and manufacturing while a separate development squad targets emerging technologies, including its FordPass mobile application and connected and self-driving cars — areas for which there is a greater appetite for risk. “We encouraged them to take risks, fail in the process and move on quickly to the next idea,” Klevorn told Deloitte.

While automation is causing some consternation among economists, CIOs are leveraging artificial intelligence to gain operational efficiencies. In 2015, Mike Brady, CTO for American International Group, deployed “virtual engineers,” codenamed ARIES, to help resolve network incidents. Leveraging machine learning, the co-bots operated alongside humans for 90 days, assessing outages, and determining probable causes and responses. Within six months of deployment, ARIES resolved more than 60 percent of outages.

The results forced AIG leaders to consider using co-bots to augment business operations, Brady said. “We want business to use machine learning instead of requesting more resources,” Brady says.