The Human Algorithm | Legacy Maker

AI is no longer a future-state consideration — it is a present-tense operational reality. The organizations navigating it well are not the ones with the most sophisticated technology. They are the ones that have answered one question clearly: are we training our people to work with AI, or training AI to replace our people?

I was sitting in a Wharton CHRO executive session last year when someone asked the question that had been hovering unspoken over the entire room: “Are we training our people to work with AI, or are we training AI to replace our people?”

The silence that followed was instructive. Not because no one had an answer — but because everyone had a different one.

That tension is at the heart of every workforce transformation conversation I am having right now with HR executives, CFOs, and business owners across California. Artificial intelligence is no longer a future-state consideration. It is a present-tense operational reality, and the organizations that are navigating it well are not the ones with the most sophisticated technology stack. They are the ones that have answered that question clearly — and built their people strategy around the answer.

The Scale of the Shift

Let me start with the data, because the numbers are genuinely striking. SHRM’s State of AI in HR 2026 report, drawing on nearly 2,000 HR professionals, found that 90 percent of CHROs expect AI integration to accelerate significantly in 2025, and 87 percent believe it will be critical to boosting organizational productivity. Those are not aspirational figures — they reflect decisions already being made in boardrooms and budget cycles right now.

At the same time, the World Economic Forum’s Future of Jobs Report 2025 projects that 85 million jobs will be displaced by automation by 2030, while 97 million new roles will emerge that are better adapted to the new division of labor between humans and machines. The net is positive — but only for organizations and individuals who invest in the transition. For those who do not, the displacement is real and the timeline is shorter than most people expect.

McKinsey’s research adds important nuance: generative AI alone could automate work activities that currently absorb 60 to 70 percent of employees’ time. But the same research is clear that the highest-value human capabilities — judgment, empathy, creative problem-solving, ethical reasoning — are precisely the ones that AI cannot replicate. The strategic imperative is not to compete with AI. It is to become indispensable alongside it.

What I Am Seeing on the Ground

In my work with mid-market employers across California — organizations ranging from 200 to 5,000 employees — I am seeing three distinct responses to AI-driven workforce transformation, and they are producing very different outcomes.

The first group is waiting. They are watching the technology evolve, deferring investment, and hoping the landscape stabilizes before they commit. This is an understandable instinct, but it is becoming increasingly costly. Every quarter spent waiting is a quarter in which competitors are building AI-fluent workforces, and the gap in capability compounds quickly.

The second group is deploying without designing. They are implementing AI tools — copilots, automated workflows, AI-assisted recruiting and performance management platforms — without a coherent strategy for how those tools change the nature of work, the skills required, or the employee experience. The technology works. The people do not follow. Engagement drops, turnover rises, and the ROI on the technology investment evaporates.

The third group — the ones I am most encouraged by — is treating AI as a workforce design challenge, not a technology procurement decision. They are asking: What work do we want humans to own? What capabilities do our people need to thrive in an AI-augmented environment? How do we redesign roles, rewards, and development pathways to reflect that new reality? These organizations are not just implementing AI — they are building AI-ready cultures.

The Three Capabilities That Matter Most

Based on my Wharton CHRO Program training, my work with the MIT Agentic AI professional education cohort, and direct experience advising HR leadership teams, I have identified three human capabilities that are becoming disproportionately valuable in AI-augmented workforces.

1. AI Fluency — Not AI Expertise

There is an important distinction here. Organizations do not need every employee to be a data scientist or a prompt engineer. They need every employee to understand what AI can and cannot do, to know when to trust an AI-generated output and when to question it, and to be comfortable working in environments where AI is a collaborator rather than a tool. This is a literacy challenge, not a technical training challenge, and it requires a fundamentally different approach to learning and development.

2. Judgment Under Uncertainty

AI systems are extraordinarily good at pattern recognition and optimization within defined parameters. They are not good at navigating genuinely novel situations, weighing competing ethical considerations, or making decisions where the right answer depends on context that is difficult to quantify. As AI handles more of the routine cognitive work, the premium on human judgment — particularly in high-stakes, ambiguous situations — increases dramatically. Organizations that develop this capability in their people will have a durable competitive advantage.

3. Relational Intelligence

The Wharton HR Management & Analytics curriculum’s emphasis on collaboration networks is directly relevant here. Research on organizational network analysis consistently shows that the most valuable employees in AI-augmented organizations are not necessarily the most technically skilled — they are the ones who connect people, build trust across functions, and create the conditions for collective intelligence. These capabilities are deeply human, and they become more valuable, not less, as AI handles more of the transactional work.

The People Analytics Imperative

One of the most significant shifts I am seeing in forward-thinking HR organizations is the move from intuition-based to data-driven workforce decisions. This is not new in concept — HR has been talking about people analytics for a decade. But AI is making it genuinely operational in ways that were not previously possible.

The Wharton HR Management & Analytics program’s curriculum on attrition prediction is a good illustration. Using regression analysis to identify the leading indicators of employee departure — not just the lagging indicator of an exit interview — allows organizations to intervene before they lose people they cannot afford to lose. The same analytical approach applies to performance evaluation, strategic staffing, and collaboration network analysis.

“The data you need to make better workforce decisions almost certainly already exists in your HRIS, your performance management system, and your benefits claims data. The question is whether you have the analytical capability — and the organizational will — to use it.”

— Claudia Lin

The Benefits Connection

There is a dimension of AI-driven workforce transformation that does not get enough attention in the HR conversation: the connection to total rewards and benefits strategy. Aon’s 2025 Employee Sentiment Study found that employees with access to comprehensive benefits — including mental health support, financial wellness programs, and flexible work arrangements — are 10 percent less likely to report chronic stress. In a workforce transformation context, this matters enormously.

Change is inherently stressful. Organizations that are asking their people to learn new skills, adapt to new roles, and work alongside AI systems need to be investing simultaneously in the psychological safety and financial security that make that kind of sustained adaptation possible. This is why I approach workforce transformation as a holistic strategy — one that connects HR transformation, benefits design, and risk management into a coherent whole.

A Note on California

For California employers specifically, the AI and workforce transformation landscape has additional dimensions worth noting. California’s robust employment law framework — including some of the nation’s most stringent protections around algorithmic decision-making in employment — means that AI-assisted hiring, performance management, and workforce planning tools carry legal risk that must be actively managed. Employment Practices Liability Insurance (EPLI) is becoming an increasingly important component of the risk management strategy for any California employer deploying AI in HR functions.

The California Privacy Rights Act (CPRA) also has direct implications for the employee data that powers people analytics systems. Organizations that are building AI-driven HR capabilities need to be doing so in close coordination with their legal, compliance, and information security teams — not as an afterthought, but as a foundational design principle.

What to Do Next

If you are an HR leader, CFO, or business owner reading this and wondering where to start, here is my practical guidance. Begin with an honest assessment of your current AI readiness — not your technology readiness, but your people readiness. Survey your workforce on their comfort with AI tools, their understanding of how AI is being used in your organization, and their confidence in their ability to adapt. The results will tell you more about your transformation risk than any technology audit.

Then design your upskilling strategy around the three capabilities described above: AI fluency, judgment under uncertainty, and relational intelligence. These are not skills that can be developed in a one-day training. They require sustained investment in learning culture, manager development, and organizational design.

Finally, make sure your benefits and total rewards strategy is aligned with the demands you are placing on your people. Transformation requires resilience. Resilience requires support. The organizations that get this right will not just survive the AI transition — they will emerge from it stronger, more capable, and more competitive than they were before.