The fast-moving target of AI literacy is quickly becoming a top CIO issue as the availability of tools outpaces workers’ capability to use them.
An enterprise skills gap is derailing enterprise AI ambitions, stopping projects before the adoption stage. It’s also forcing enterprises to turn outward for help, racking up bills in external consultants to complete projects, and adding costs for unused services. Plus, in-house training on how to use new tools is lagging, workers say.
Part of the issue is AI remains a relatively immature enterprise technology, according to Philippe Rambach, SVP and chief AI officer at France-based energy technology company Schneider Electric.
“It was never easy to deploy any new technology,” said Rambach, who first joined the company in 2010. “When I look at the time it took for companies to successfully deploy things which are relatively simple, like a CRM, it often took years.”
After deployment, companies still have an uphill battle to employee adoption. With AI, enterprises can’t take a one-size-fits-all approach to training, Rambach said. The company worked with online learning platform Coursera to create a four-tiered curriculum designed for the roles and functions of specific employees.
The first tier consists of fundamental AI training, which became a mandatory program for all of Schneider’s 160,000 employees. An additional training targeted at senior management team focuses on blending AI and strategy.
“They need to lead by example, so these teams need special attention with specific trainings that have been deployed to them,” Rambach said.
The third group is made up of experts involved in writing AI software, Rambach said, a team that reports directly to him. Finally, a fourth group — those in charge of transformation — also get access to tailored training.
“Those groups who are either in charge of building the customer roadmap or driving the internal transformation need a special focus,” said Rambach.
Scaling knowledge
Running training programs in a vacuum is unlikely to yield results, as workers need to be able to draw a clear line between the information they gain and how it can affect the work they do.
Schneider relies on cross-functional teams for the deployment and development of AI use cases — not just internally but for its customers, banding together business, IT and AI expertise to thread the needle on projects.
“Never underestimate the importance of these multidisciplinary teams,” Rambach said. “They learn by doing the project, they learn by working together.”
Schneider, which manufactures critical electric equipment for data centers, has leaned into the rapid buildout of infrastructure capacity and the ensuing need for power. The company partnered with Nvidia earlier this year to deploy testing and simulation capabilities for data center infrastructure.
The company posted record revenues of 21.2 billion euro (around $23.7 billion) for the first half of 2026, up 14 in organic growth year over year, with its data center segment as a lead driver of growth.
“Data centers need energy, energy needs data centers,” said CEO Olivier Blum, during a May earnings call. “What’s important is that we are present in both sides of the equation. We provide the infrastructure, but we can also leverage AI to make our solutions smarter for our customers, both in the world of energy and in the industrial process.”
With help from cross-functional teams, the company is using AI to help address challenges of the business, Rambach said. Use cases include the deployment of an AI-based tool to dispatch technicians more effectively, a customer-focused service to help reduce energy consumption and an internal tool to rapidly tailor sales pitches.
Rambach recommended executives continue to iterate on their AI playbooks, making the most of the positive friction that comes from getting domain experts to work closer together.
“There is no easy and 100% path,” said Rambach. “We are all learning.”
