AI is different from earlier technology waves because it doesn’t just digitize existing processes; it reshapes how work is designed, who does what, and how decisions get made. With digital, mobile, and cloud, the standard IT playbook worked: pick a platform, run pilots, write policies, train people, and move on. Those technologies mostly sat “behind” the work and made existing processes faster or more efficient. AI, however, changes the work itself: - Ownership blurs: When a model helps make a decision, who owns the outcome—the person, the system, or both? - Roles shift: People are no longer just “users” of tools; they become partners to systems that learn alongside them. - Decisions move: AI can take on parts of judgment and analysis that used to be purely human. Because of this, AI is less of an IT project and more of an organizational design challenge. The biggest risk is not technical; it’s human. If we only focus on tools and platforms, we miss the need to redesign roles, workflows, decision rights, and success metrics so people and AI can create value together.

Recruiting is a useful example of how AI reshapes work rather than simply speeding it up. Traditionally, recruiters spent much of their time on administrative tasks: screening résumés, scheduling interviews, collecting feedback, and coordinating across systems. This work was necessary but largely transactional, and it kept the focus on matching candidates to job descriptions instead of aligning capabilities with broader organizational needs. With AI in place, many of these administrative steps can be handled automatically—summarizing résumés, scheduling interviews, and synthesizing feedback. In one acquisition example described by Mary Alice Vuicic, Chief People Officer at Thomson Reuters, curiosity about what AI could do led to a redesign of the process that removed about 95% of the administrative work while improving the quality of the output and reducing the time required. This shift allows recruiters to focus on higher-value activities: - Understanding potential, not just past experience - Orchestrating how skills move across the organization - Ensuring the company retains critical talent and competitive advantages during changes like acquisitions In this model, recruiters start to operate less like traditional “requisition fillers” and more like talent architects—designing systems and experiences where technology amplifies human judgment instead of replacing it. The expertise doesn’t shrink; it expands into more strategic, human-centered work.

A practical way to approach AI-enabled work is to use an AI Work Redesign Loop—an ongoing process for rethinking how humans and systems share work. Key steps include: 1. Start with outcomes, not activities - Define what success really means in your context. - Go beyond efficiency metrics like speed or volume. - Examples: - Sales: prioritize deeper customer relationships, not just more calls. - Healthcare: focus on better recovery rates, not just shorter visits. These outcomes guide where AI can make the most meaningful impact. 2. Deconstruct the work - Break work into tasks and ask: - What can be automated? - What should be augmented by AI? - What must remain distinctly human? - This reveals how AI changes not just the tools, but the nature of people’s contribution. 3. Design the human–AI collaboration - Decide how people and AI will interact day to day: - What will you still verify or interpret yourself? - Where is human context essential? - What data, guardrails, and escalation paths are needed? - This is where new skills emerge: questioning AI outputs, guiding models, and explaining how AI fits into decisions. 4. Rethink how you measure success - Traditional metrics reward efficiency: number of calls, turnaround time, cost. - In an AI-enabled environment, you also need to track: - Quality of outcomes - Trust in the system - Adaptability and learning over time - Over time, a key measure becomes how well people and systems learn from each other, not just how much is automated. To make this work, AI can’t sit only in IT. At Thomson Reuters, for example, the CIO and CHRO co-sponsor the AI transformation framework, reflecting shared ownership between technology and talent. That kind of operating model—where people, processes, and intelligent systems are aligned in a shared flow of work—helps ensure AI is treated as a new teammate, not just another tool. Organizations that lean into this kind of redesign are better positioned to let AI handle scale while humans focus on meaning, relationships, and strategic choices.