With decades of experience helping organizations navigate major technological shifts, HRTech expert Ling-Yi Tsai offers a unique perspective on the evolving landscape of artificial intelligence careers. Her work, which focuses on integrating technology across the entire employee lifecycle from recruitment to talent management, places her at the intersection of human capital and machine intelligence. Today, she unpacks the seismic shift in the AI job market, moving beyond pure technical roles to a new frontier of governance and safety. We’ll explore why companies like OpenAI are offering staggering six-figure salaries for positions that didn’t exist a few years ago, what it truly takes to succeed in these high-stakes roles, and how proactive risk management is becoming the most critical function in the AI industry.
That $550,000 salary for OpenAI’s Head of Preparedness is certainly attention-grabbing. If you were in that role, how would you approach the first 90 days to prove that value and start building a foundation for a safer AI future?
That’s the million-dollar question, or in this case, the half-a-million-dollar one. In the first 30 days, it’s all about assessment and alignment. You can’t build safeguards if you don’t have a crystal-clear map of the territory. I would initiate a full-scale audit of existing capabilities and conduct deep-dive sessions with the heads of policy, research, engineering, and product to understand their current workflows and pain points. The first key metric would be establishing a comprehensive risk register. By day 60, we’d move from assessment to action, running our first two threat modeling exercises on the most pressing emerging risks, like a specific cyber or bio domain threat. The tangible output wouldn’t just be a report; it would be a set of preliminary, actionable safeguard protocols for the engineering team. By day 90, my goal would be to present a fully-fledged, cross-functionally approved AI preparedness framework to leadership, demonstrating a clear, repeatable process for evaluating every future launch decision.
You mentioned coordinating across diverse teams like policy, research, and engineering. Could you walk us through how you would actually lead a threat modeling session for a new, complex risk?
Absolutely. It’s a process that has to be more structured than a simple brainstorm, especially with so many different expertises in the room. First, I would convene the core cross-functional team and present a detailed brief on the emerging threat—let’s say it’s a novel form of AI-driven biological risk. Step two is a silent, individual ideation phase where each expert—the policy lead, the lead researcher, the product manager—writes down every potential vulnerability and exploitation scenario from their unique perspective. Then, we move to a facilitated group discussion where we consolidate these ideas, clustering them into themes. This is where the magic happens; the engineer might see a technical vulnerability the policy expert would never consider, while the policy expert highlights a societal implication the engineer overlooked. Finally, we collaboratively rank these threats using a matrix of likelihood and impact. The outcome isn’t just a list of dangers; it’s a prioritized roadmap of what we need to build safeguards against first, with clear ownership assigned to each functional team.
The content points out that companies often act in hindsight, dealing with crises like “shadow AI” or tragic user outcomes after the fact. Can you share an example of a time you successfully implemented a governance framework proactively to get ahead of a potential risk?
I remember a situation with a large financial services client that was about to roll out a new AI-powered internal mobility platform. The excitement was palpable because it promised to identify hidden talent. However, I immediately felt a sense of unease about the potential for algorithmic bias and data privacy issues. Instead of waiting for a problem, I convinced leadership to pause the launch for three weeks. In that time, I assembled a small task force with representatives from HR, IT, and legal. We didn’t just write a policy; we co-created a “Responsible AI” charter for employee data. We ran simulations on the algorithm with dummy data to check for biases, established a clear process for employees to question or appeal the AI’s recommendations, and mandated training for all managers on how to interpret the tool’s output. The ultimate outcome was a slightly delayed but much smoother rollout. We avoided the employee mistrust and potential discrimination lawsuits that could have easily arisen if we’d just pushed the technology out the door.
OpenAI’s role is described as having “end-to-end” ownership of the preparedness strategy, which seems to place an immense amount of responsibility on one person. From your experience, what are the biggest challenges of that kind of complete ownership, and what strategies are key to success?
That “end-to-end” ownership is both a massive opportunity and a potential trap. The primary challenge is becoming a single point of failure. When every critical decision flows through you, you risk becoming a bottleneck that slows down innovation, which is the last thing a company like OpenAI wants. Another huge challenge is the sheer cognitive load of having to be fluent in the languages of policy, deep technical research, and product development simultaneously. To navigate this, you cannot be a micromanager. Your strategy must be to build a system, not to make every decision yourself. You have to empower the cross-functional teams with clear frameworks and principles so they can make 90% of the safety decisions autonomously. Your role then becomes managing the 10% of high-stakes, ambiguous decisions that truly require that top-level, integrated perspective. It’s about building trust and distributing responsibility, while retaining ultimate accountability.
Looking ahead to 2026, the prediction is a rise in AI Governance Specialists who must navigate complex internal teams and external government bodies. Could you describe a time you had to build a policy that satisfied both of those worlds?
I once worked with a tech startup that had developed an AI tool for screening job candidates. The engineering team was incredibly proud of its efficiency, but we were heading into a meeting with European regulators who were, understandably, very concerned about GDPR and algorithmic bias. The two groups were speaking completely different languages. My communication strategy was to act as a translator. First, I held an internal workshop with the engineering team where we documented every single data point the AI used and mapped out the decision-making logic in plain English, not code. Then, I met with our legal team to translate that technical map into a risk-mitigation document that addressed the regulators’ specific concerns head-on. In the final meeting with the government body, we didn’t just present our tool; we presented our transparent process, our framework for ongoing audits, and our appeals process for candidates. By showing them we had already thought through their concerns, we turned a potentially adversarial meeting into a collaborative one, satisfying both our internal need for innovation and the external demand for accountability.
What is your forecast for the evolution of AI safety and governance roles as they become standard outside of major tech hubs like San Francisco?
My forecast is that these roles will become both more widespread and more specialized. Right now, the focus is on foundational model safety at giants like OpenAI and Google DeepMind. As AI becomes deeply embedded in every industry—from healthcare to finance to manufacturing—we’ll see the rise of the “Applied AI Safety Officer.” A hospital will hire an AI Governance Specialist not to build a new large language model, but to ensure the AI diagnostic tool it purchased is free from bias and functions safely within clinical workflows. A bank will hire an AI Ethicist to oversee its automated loan-approval algorithms. The core competencies we’ve discussed—stakeholder management, risk planning, and policy creation—will remain critical, but they’ll be applied within specific industry contexts and regulatory environments. The demand will be enormous, because every company using powerful AI will realize that without robust governance, they are sitting on a massive, unmanaged operational and reputational risk.