DAVOS, Switzerland – Artificial intelligence is pushing companies to rethink how work is designed, delivered and measured, with executives increasingly arguing that technology adoption will fail unless human resources and technology leaders jointly redesign workflows.
As AI systems move from narrow automation tasks into core business processes, companies are discovering that existing organisational structures, job roles and skills frameworks are poorly suited to the way intelligent systems operate, executives said.
Pearson CTO Dave Treat, said organisations grappling with AI adoption were spending more time bringing together technology and human resources leaders to address the challenge jointly.
“HR leaders need to become product leaders,” Treat said, adding that technology executives had faced a similar transition in earlier phases of digital transformation.
The shift reflects a broader change in how companies approach technology. Earlier waves of digitisation focused on improving efficiency within established structures. AI systems, by contrast, require organisations to start with desired outcomes and work backwards to determine how humans and machines should interact.
Keith Ferrazzi, chairman, Ferrazzi Greenlight, said this required a fundamental change in mindset.
“HR is not a policy board,” Ferrazzi said. “They are work re-engineers.”
At Pearson, workforce design is increasingly treated as a joint responsibility between technology and HR functions. Ali Bebo, the company’s chief human resources officer (CHRO), said the value of AI lies in aligning systems with human capability.
“I like to think of Dave as the tech architect, and we are the capability architect,” Bebo said. “When that comes together, that’s designing the systems and the processes and how that comes together when it interfaces with humans.”
Executives from IBM said large organisations face particular challenges because AI-driven workflows typically cut across multiple departments.
Mohamad Ali, senior vice president and head of IBM Consulting, said the company had spent several years breaking down hundreds of internal workflows and rebuilding selected ones using a mix of human and digital labour.
“We decomposed our company into 490 workflows,” he said. “And one of those was actually the HR process itself.”
Ali said the effort reduced costs and cycle times but required sustained involvement from senior leadership.
“You have to start at the top,” he said. “These workflows cut across.”
A recurring theme was the difficulty of mapping skills in a way that reflects how work is actually performed. Treat said organisations lacked a shared language to describe skills at a task level, making it harder to align people and AI systems.
“You need to understand the data at a task, job role level,” he said. “We have a long way to go even in mapping humans to work in a traditional context.”
AI—Pixelisation of work
Some AI-native companies are approaching the problem differently, starting with what machines can do and then assigning human roles where needed. Ferrazzi described this approach as breaking work into smaller components.
“They called it the pixelisation of work,” he said.
Learning and reskilling also emerged as a central issue. Bebo said training needed to be embedded into daily work rather than treated as a separate activity.
“We don’t want them to give the answers,” she said, referring to AI systems. “We want them to teach.”
Executives warned that companies focusing on AI purely for automation risked missing its broader impact on productivity and growth.
“Companies that are focused on AI just for automation’s sake are hitting a wall,” Treat said.
The discussion took place on the sideline of World Economic Forum in Davos, where executives from multiple sectors said workforce design, rather than technology availability, was becoming the main constraint on AI adoption.
