AI is profoundly transforming work—influencing careers, talent strategies, and organizational structures. AI leaders Athena Karp and Danielle Li explore how AI is reshaping the workplace, from accelerating learning to fostering human-AI collaboration.
AI is reshaping work from the ground up. It’s accelerating learning curves, redefining job architecture, and forcing a reexamination of how organizations manage, develop, and retain talent. For enterprise leaders, this shift is operational, and it’s urgent.
Athena Karp, founder of HiredScore and SVP of Product Marketing at Workday, joined MIT professor Danielle Li, PhD, to discuss the real implications of AI in the workplace. Their conversation explored the general optimism surrounding AI by grounding it in practical considerations: how AI is impacting performance, equity, and organizational design.
Li brought rigorous research. Karp brought firsthand insight from working with some of the world’s largest enterprises. Together, they offered a blueprint for how leaders can move from experimentation to execution.
Here are the four major takeaways.
Li’s research on generative AI in customer service roles offers a powerful glimpse into the very essence of democratizing expertise. Her findings revealed a striking data point: employees with access to AI reached nine-month proficiency levels in just two months. The system closed the skill gap by serving up best practices drawn from top performers, effectively embedding mentorship into everyday tasks.
“What happens is that you essentially have this kind of senior colleague, coach, sitting with you,” said Li.
The significance goes beyond productivity. AI is collapsing the traditional learning curve, enabling people to contribute at a higher level far earlier in their tenure. This redefines how we think about entry-level work, readiness for promotion, and internal mobility.
AI is collapsing the traditional learning curve, enabling people to contribute at a higher level far earlier in their tenure.
Skeptics worry AI might make workers dependent or less thoughtful. But system outages in Li's study revealed the opposite. Employees who had previously used AI performed better even when the tool went offline. And the more they had used it, the better they adapted.
“Rather than people becoming so reliant that they become numb, the tool was actually helping them access skills in a durable way,” Li explained.
Employees weren’t just parroting suggestions. They were internalizing them. In practice, AI became more like a highly available coach than a crutch—shaping both confidence and skill depth through repetition and feedback.
The conversation emphasized AI’s potential to capture and scale tacit knowledge—the unwritten expertise that usually lives inside experienced employees' heads.
“So much of our company's capital lives within people, and people are forgetful...AI has the potential to serve as a repository for all of that expertise,” said Li.
Instead of automating tasks in isolation, AI tools can preserve what makes top performers excellent. This knowledge can then be distributed across teams, used in onboarding, and even serve as the foundation for digital twins, which Li described as AI-powered representations of individual expertise.
These models can capture what a person does, how they do it, and why they do it. With enough participation, these models could enable employees to scale their insight across geographies and time zones.
One colleague, she noted, even began training a domestic version of her digital twin to support childcare coordination—showing how personal, practical, and wide-ranging these applications could be.
Karp introduced a new concept: the Chief Work Officer (CWO). This role isn’t just about IT or HR. It’s about orchestrating how AI and humans collaborate across the entire organization.
Karp explained that the Chief Work Officer is not necessarily a singular role, but a cross-functional leadership mindset focused on continuously optimizing how work is performed in the era of AI augmentation. The Chief Work Officer will connect the dots between business strategy, AI technologies, employee readiness, and organizational learning. Think of it as a conductor for human-machine workflows.
The CWO collaborates across HR, IT, and business units to ensure:
Work is thoughtfully decomposed between humans and AI
Employees are supported with real-time tools and training
AI is implemented to unlock strategic value, not just reduce costs
Organizational learning keeps pace with technological change
This role is forward-looking by design. It isn’t just about managing current systems, but preparing the organization for roles, skills, and workflows that don’t exist yet.
"The most important part of that role is: how do you have every employee be ready—and continually be ready—for success in that human-machine collaboration?” said Karp.
As AI continues to evolve, the CWO must prioritize adaptability as the real key to long-term organizational success. The role must also ensure that strategic growth, not just cost-cutting, is the lens through which AI adoption is managed.
If there's one thread that runs through this conversation, it's this: AI is a multiplier, not a replacement. It can amplify human capability, accelerate learning, and rebuild trust in systems that have historically excluded people. But only if we design for that outcome.
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