With decades of experience guiding organizations through technological shifts, HRTech expert Ling-Yi Tsai specializes in harnessing the power of analytics and new systems to transform how companies recruit, develop, and manage their people. As organizations move beyond early AI experiments toward a full-scale operational transformation, her insights have become essential for leaders navigating this new landscape.
Our conversation explores the critical skills, strategies, and mindset shifts required in an AI-powered workplace. We delve into the move from generic AI literacy to highly specialized fluency and how to assess it. We also examine the evolving job market for both new graduates and seasoned professionals, the profound psychological impact of AI on the workforce, and the new leadership capabilities needed to manage integrated teams of humans and AI agents. Finally, we look at how to measure true transformation and build the new roles necessary to sustain it.
As companies shift from broad AI literacy to role-specific training, how can HR design effective, targeted programs for diverse functions like legal or engineering? What practical screening methods can be used during hiring to assess this specialized fluency? Please provide a step-by-step example.
This is a crucial evolution. The era of one-size-fits-all AI training is over because the application of AI is so context-dependent. Look at a company like Indeed; they’ve completely moved to role-specific training with astounding results. For their engineering team, this meant focusing on AI coding tools, which led to over 85% of the team using them weekly and a 20% jump in productivity without sacrificing quality. For the legal team, the training was entirely different, focusing on automating contract review. They identified that 20% of their tasks could be automated, and they cut the time to review a contract from a staggering 26 hours down to just two. The key is to start by deeply understanding the workflows of a specific department and identifying the highest-value opportunities for AI augmentation, then building a curriculum around those use cases.
When it comes to screening, you have to embed the assessment directly into the hiring process. Zapier treats this as a “Code Red” moment, fundamentally reimagining its application. First, they ask a direct question: “How do you use AI in your current work?” This provides a baseline. The second, more revealing step is a practical challenge: “Describe a workflow relevant to your role and explain how AI could improve it.” This separates those who have a theoretical understanding from those who can apply it. It shows you their problem-solving process and their creativity in using these new tools, which is far more valuable than a certificate.
With a 16% decrease in some entry-level jobs and a disconnect between university curricula and employer needs, what practical steps can companies take to cultivate entry-level talent? How can they partner with educators to ensure graduates possess the career-relevant skills required for a changing workplace?
It’s tempting to blame AI entirely for the squeeze on entry-level roles, but the reality is much more nuanced. While we’ve seen a 16% dip in jobs highly exposed to automation, we’re also seeing a fundamental redesign of what an “entry-level” job even is. Research from the University of Phoenix shows that a third of HR leaders are already planning to create new roles specifically designed for junior employees to partner with AI. The bigger challenge is the glaring skills mismatch. According to Cengage Group, only 30% of recent graduates found a job in their chosen field, and it’s because there’s a massive divide between what employers need and what universities teach.
To bridge this, companies must stop being passive consumers of talent and become active co-creators. This means forging deep, strategic partnerships with educational institutions. They can co-develop curricula, provide guest lecturers from the industry, and create project-based learning opportunities that solve real-world business problems. It’s what students are crying out for; a third of them wish their universities worked more closely with employers. When educators see that employers prioritize job-specific skills and are willing to help build them, they are more likely to shift from purely theoretical instruction. It’s about creating a direct pipeline where students graduate with not just a degree, but with demonstrable, career-relevant competence.
Many employees experience a fear of becoming obsolete as AI expands. Beyond providing training guides, what specific strategies can leaders use to foster psychological safety and coach their teams to collaborate effectively with AI as a new kind of team member? Share an example of this in action.
This fear is palpable in the workforce right now; the Pew Research findings that workers are more worried than hopeful are not an exaggeration. We call it FOBO—Fear of Becoming Obsolete—and a simple training guide isn’t going to solve it. It’s an emotional issue that requires an empathetic, human-centric leadership approach. The most effective strategy is to create a culture of open dialogue and active listening. A company like Synchrony does this beautifully. They don’t just push out resources; they start by pulling in feedback through town halls, active listening sessions, and surveys to truly understand the anxieties and needs of their people.
Only after understanding the fear can you effectively address it. Synchrony’s AI Field Guide is a great example because it’s not just a technical manual. It’s filled with real employee stories showcasing how their peers are successfully collaborating with AI. This makes the concept feel accessible and less threatening. It reframes AI not as a replacement, but as a new kind of teammate—which is exactly what employees want. Four out of ten workers have said they want to learn to partner with AI, not just operate it. By creating psychological safety, leaders give employees permission to be curious, to experiment, and even to fail, which is essential for learning to work effectively with this new technology.
The shift from AI adoption to genuine transformation requires embedding AI into core operations. Can you describe how a company can measure this transformation beyond simple efficiency gains, and what metrics indicate that AI is truly changing how work gets done across the enterprise?
The distinction between adoption and transformation is everything. Adoption is using AI to do the same old things a bit faster. Transformation is using AI to fundamentally change what you do and how you do it. To measure this, you have to move beyond narrow efficiency metrics like time saved and look at broader, more strategic indicators. Zapier offers a compelling roadmap. A key metric for them was usage saturation: today, an incredible 97% of their employees use AI in their core work. That number doesn’t just show adoption; it signals a complete operational shift.
They achieved this because leadership declared AI a strategic business priority, not just an IT project. This means AI wasn’t a separate initiative; it was woven into the fabric of the company—integrated into planning cycles and even tracked through employee engagement surveys. The ultimate measure of transformation is linking AI efforts directly to business outcomes like revenue growth and operational efficiency. When you start measuring how AI contributes to top-line growth, not just bottom-line savings, you know it’s transforming the business. It’s a shift from asking, “How much faster are we working?” to “How is AI helping us win in the market?”
Research suggests experienced professionals who combine deep expertise with AI skills are increasingly in demand. How does this challenge the narrative of AI displacing senior roles, and what specific development paths should companies create for seasoned workers to help them amplify their strategic impact with AI?
This completely upends the simplistic narrative that AI is coming for everyone’s jobs, especially senior ones. The data from Toptal’s Job Report is clear: experienced professionals with over five years in the workforce are actually outperforming others in the job market, provided they pair their deep domain expertise with AI skills. The reason is simple, and it’s what the chief economist there pointed out: employers are desperate for people who can apply human judgment to the outputs AI generates. AI can analyze data, but it can’t provide context, understand market nuances, or make a wise strategic call. That’s where seasoned expertise becomes an irreplaceable asset.
For development, companies need to stop thinking about remedial AI training for their senior people. Instead, they should create pathways that focus on amplifying their existing strategic capabilities. This isn’t about teaching a 20-year marketing veteran how to write a prompt; it’s about showing them how to use AI for predictive market analysis or to model complex customer segmentation strategies they could only dream of before. The development should be focused on strategic application—how to use AI as a sparring partner to test hypotheses, how to question its outputs, and how to integrate its insights into the broader business strategy. It’s about turning their wisdom and experience into a superpower, augmented by AI.
Leaders will soon manage hybrid teams of humans and AI agents, with new metrics like the Human-Agent Ratio emerging. What new management skills will be essential for orchestrating work between people and AI, and how should leaders prepare now for measuring the performance of these integrated teams?
The idea of managing an all-human workforce is quickly becoming a relic of the past, as Marc Benioff so aptly put it. The future is the hybrid team, and it requires a completely new leadership playbook. The core skill will be orchestration—the ability to seamlessly assign tasks and manage workflows between human employees and their AI counterparts. Leaders will need to become experts at identifying which tasks are best suited for human ingenuity, creativity, and empathy, and which are best handled by the speed and analytical power of an AI agent. This requires a deep understanding of both human talent and technological capability.
To prepare, leaders must become fluent in the new metrics of this era. The Human-Agent Ratio, or HAR, which measures the number of AI agents per employee, will become as common as tracking headcount. Gartner’s forecast that AI agents will outnumber human salespeople ten-to-one by 2028 is a startling indicator of how fast this is coming. Measuring performance will no longer be about individual output; it will be about the combined output of the human-AI team. Leaders need to start building dashboards now that track not just revenue per employee, but the productivity and innovation generated by these integrated units. The focus will shift from managing people to orchestrating a complex, dynamic system of human and artificial intelligence.
As organizations create new AI-focused roles like Digital Ethics Advisor or AI Automation Engineer, how should they define the responsibilities for these positions? What is the best way to integrate these new roles into existing teams to ensure they drive transformation rather than create silos?
The emergence of these new roles is one of the most exciting signs of true AI transformation. We foresaw roles like the Future of Work Leader years ago, and now they are becoming commonplace. For newer roles like a Digital Ethics Advisor, the core responsibility is to be the conscience of the organization’s AI strategy, proactively building safety systems, ensuring regulatory compliance, and reviewing algorithms for bias to maintain human accountability. The AI Automation Engineer, on the other hand, is a more hands-on, enabling role, working directly with employees to integrate AI into their daily workflows, much like they are already doing at Zapier.
The absolute key to avoiding silos is to structure these roles as cross-functional enablers, not isolated gatekeepers. Initially, many of these responsibilities will emerge as temporary work streams or project-based assignments rather than full-time positions. This is an excellent way to integrate them organically. For example, an AI ethics review shouldn’t be a siloed process; it should be an integral part of every product development cycle, with the Digital Ethics Advisor embedded within that team. Their ultimate goal should be to build capabilities across the organization—increasing transparency, reducing bias, and ensuring accountability—so that responsible AI becomes everyone’s job, not just the mandate of a single department.
What is your forecast for the evolution of the CHRO role as AI becomes central to business strategy?
The CHRO is poised to become one of the most critical C-suite partners in the AI era, evolving from a leader of the HR function to the chief architect of the human-AI enterprise. Their role will no longer be about managing human capital in isolation; it will be about designing and orchestrating the entire socio-technical system of the company. They will be responsible for driving the cultural shift toward universal AI fluency, not just as a training exercise but as a core component of performance and promotion. They will need to spearhead the complete redesign of talent processes, from recruiting candidates skilled in AI collaboration to developing career paths for a hybrid workforce.
More importantly, the CHRO will become the primary steward of the human experience in an increasingly automated world. As employees grapple with fears of obsolescence, the CHRO must champion psychological safety, ensuring that technology serves and augments people, not the other way around. They will partner directly with the CEO and CTO to measure true transformation, linking AI initiatives to business outcomes and designing the organizational structures, new roles, and leadership models needed for a future where humans and AI agents work side-by-side. The CHRO of tomorrow won’t just be managing people; they will be building the very future of how work gets done.