Like many organizations, Medtronic began its AI journey by giving employees tools and prompt training. But a familiar question soon emerged: how did any of it fit into their actual work?
“Great, but training for what?” Abby Rich recalls. “Like, how am I supposed to use this?”
For Abby, who leads enterprise and workforce design at Medtronic, this became the starting point for a deeper shift: understanding the work behind each role, deciding where people and AI could contribute, and building an internal team to keep that work evolving.
In her conversation on The Work Blueprint with Siobhan Savage, CEO and Founder of Reejig, she describes a journey that began in HR and is shaping a broader enterprise approach to AI work design. Her next focus: measuring how redesigned workflows improve outcomes and deciding how to reinvest the time AI frees up.
Abby’s path into work design ran through leadership development, HR strategy and strategic workforce planning. Understanding the workforce increasingly meant understanding the work itself.
Tool deployment exposed that need. Employees could learn how to prompt AI without knowing where to use it in their roles. Meanwhile, bottom-up experimentation was creating another challenge: teams were building duplicate agents and establishing different ways of doing similar work.
People were doing what the organization had asked them to do. But improving those workflows later would mean navigating the variations they had created.
For Abby, the response required a clearer view of the work and deliberate decisions about how it should happen.
Within HR, Abby and her team examined the work behind roles: what belonged with a business partner, what could sit in shared services, and where AI could contribute.
Working with Reejig helped provide the work data to examine roles at that level. Abby’s team used that understanding to understand where AI can support people’s work and where human expertise remains essential.
Her starting point is straightforward: what does someone in this role need to achieve, and how can we better support them to do it?
From there, the team examines the tasks and subtasks that make up a workflow. Abby uses month-end financial reporting as an example: understanding the inputs and steps involved helps identify where AI could assist and where people’s expertise is needed.
That examination also helps identify activities that take up time without contributing enough to those outcomes. In HR’s own evolution, this meant questioning work that continued simply because it had always been done and considering how that time could be better spent.
Abby says HR’s own transformation demonstrated progress and built credibility with other executive leaders. Their interest brought a practical question: how could they do this in their functions?
Supporting that demand required an internal team with the expertise to keep redesigning work as needs and technology changed.
Abby sees a place for consultants in getting started. As she puts it, “It’s a bridge. It’s not the destination.” An ongoing capability needs expertise inside the organization as work will never stop changing.
That does not mean rebuilding every role. Her approach distinguishes between opportunities to streamline work and disruption significant enough to require a role to be redesigned. As work changes, employees need clear expectations, relevant development and support to succeed.
Examining AI’s potential also sharpened the view of the human elements of a role - and where development investment could matter most.
“What are the human elements of a role that need to remain uniquely human,” Abby asks, “and then how it can signal for you how you do additional upskilling, reskilling, redeployment.”
The work-level view also opened conversations with talent management partners about career mobility. Different job titles or families can obscure similarities in the tasks people perform. Seeing those similarities creates a basis for considering less traditional pathways for people with the interest and capability to move.
These were emerging insights and opportunities, rather than a claim that every pathway had already been put into practice.
At the time of the interview, Abby described enterprise priorities spanning commercial work, product and R&D innovation, business services, supply chain and operations. The aim was a more consistent approach to major workflows, alongside continued employee experimentation.
But deploying technology is only part of the challenge. Abby is focused on defining new expectations and workflow-level KPIs so that people understand how to use the time AI frees up, with clear priorities and support from their leaders.
Her own experience makes the problem tangible: becoming more productive can simply leave room for more meetings. Leaders need to decide what people should do with the time they gain.
Employees also need clarity about what AI means for them. Abby stresses that a role being affected by AI does not automatically mean it disappears. People need context, answers from their leaders, and support as expectations change.
For CHROs, her journey brings the next decision into focus: once you understand where AI can change the work, what will you ask your people to do differently - and how will you help them do it?
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