With decades of experience helping organizations navigate change through technology, HRTech expert Ling-yi Tsai joins us to dissect the new “precision era” of hiring. We’ll explore how the disappearance of traditional hiring seasons demands an “always-on” recruiting mindset, why validating foundational skills like tech and communication proficiency has become non-negotiable, and the urgent need for HR leaders to establish responsible AI governance. This conversation will also delve into the hidden dangers of rigid flexibility policies and how to foster a culture of trust and true engagement.
With hiring now spread more evenly throughout the year and September emerging as a new peak, how should HR leaders adjust their resource planning for this “always-on” reality? Could you provide a step-by-step example of how to maintain a consistent talent pipeline?
It’s a fundamental shift, and it’s jarring for teams built around traditional January and spring hiring surges. The data is clear: no single month accounted for more than 12% of hiring last year, and the first three quarters were remarkably balanced. This means the old “ramp up, ramp down” model for recruiting resources is broken. To build a consistent pipeline, you first need to embrace continuous sourcing. This isn’t just about posting jobs; it’s about constantly engaging with talent communities on platforms where they live, even when you don’t have an open role. Second, you must invest in nurturing those relationships. This could be a monthly newsletter with valuable industry insights or invitations to exclusive webinars—anything that keeps your company top of mind. Finally, use your own internal data to forecast needs with more precision. If you see a consistent, low-level demand for a certain role, you can build a pre-vetted bench of candidates, so you’re not starting from scratch every time a need arises. It’s about smoothing out the peaks and valleys into a steady, manageable flow.
Data shows 1 in 4 candidates fail technology proficiency assessments, yet it’s the most tested hard skill. What are the best practices for integrating these crucial skill validations early in the hiring process without discouraging potentially strong candidates? Please share some practical advice.
That statistic is staggering, and it highlights a critical tension. You absolutely must validate skills, but you can’t create a process that feels like a cold, impersonal barrier. The key is to integrate assessments thoughtfully. Instead of a generic, standalone test, frame it as a realistic job preview or a mini-challenge. For a software role, it might be a small, relevant coding problem. For a marketing role, it could be a brief task using a common analytics tool. You want the candidate to feel like they’re getting a taste of the actual work. It’s also vital to be transparent. Explain why you’re using the assessment and what it measures. Providing immediate, constructive feedback can also transform the experience, making candidates feel valued even if they don’t pass. This approach shifts the assessment from a simple gatekeeper to a two-way evaluation, which is far less likely to alienate top talent.
As AI adoption in HR outpaces formal governance, what immediate risks do companies face regarding fairness and compliance? Can you walk us through the essential first steps for an HR team to create a responsible AI policy for its hiring tools?
The immediate risks are significant and deeply concerning. Without governance, you’re flying blind into potential bias and discrimination. An unvetted AI tool can inadvertently learn and amplify existing biases from historical hiring data, systematically filtering out qualified candidates from underrepresented groups. This opens the door to serious legal challenges and can do incredible damage to a company’s reputation. The first step for any HR team is to conduct an audit. You need to know every single place AI is touching your hiring process, from sourcing bots to screening algorithms. The second step is to establish a cross-functional governance committee that includes HR, legal, IT, and data science. This team must then create a clear set of principles for AI use—things like fairness, transparency, accountability, and the necessity of human oversight. Only with this foundation can you begin to build a robust policy that ensures your technology is serving your goals ethically and effectively.
Given that rigid flexibility policies can erode trust and push disengagement underground, how can leaders shift from mandates to a more collaborative work design? What specific metrics should they track to detect this hidden disengagement before it leads to attrition?
The era of top-down mandates is over; it’s a direct path to a culture of distrust. The shift to collaborative design begins with a change in mindset from “where work gets done” to “how work gets done best.” Leaders need to facilitate conversations with their teams, not issue edicts. Ask questions like, “What specific tasks require in-person collaboration, and which are better suited for focused, remote work?” Then, co-create a team charter that reflects those needs. To detect the “underground” disengagement, you have to look beyond traditional surveys. Are people still actively contributing to optional channels in your communication tools? Is cross-departmental collaboration decreasing? Are employees turning down developmental opportunities or mentorship roles? You can even look at network analytics to see if internal connections are weakening. These are the subtle but powerful indicators that your culture is under strain, giving you a chance to intervene before that quiet disengagement turns into a loud resignation.
Your research points to a new “precision era” where organizations must validate skills rather than assume them. Beyond tech proficiency, how should companies effectively assess critical communication skills, and what common mistakes should they avoid when interpreting those assessment results?
This is crucial because communication skills, which accounted for 27% of non-role-specific testing, are incredibly nuanced. The biggest mistake companies make is relying on simple grammar or multiple-choice tests. Perfect grammar doesn’t equal effective communication. A far better approach is to use work-sample simulations. Ask a candidate to draft a delicate email to a frustrated client, or to write a short internal memo summarizing a complex project update for a general audience. This allows you to assess their tone, clarity, empathy, and ability to tailor a message for a specific audience. When interpreting the results, the mistake is to be too rigid. Don’t disqualify a great communicator because of a minor typo. Instead, look for the core competencies: Did they understand the goal? Did they convey the information clearly and appropriately? Was the message persuasive? Focusing on the functional outcome rather than just the mechanics is the key to truly identifying strong communicators.
What is your forecast for the evolution of skills-based hiring over the next two years?
My forecast is that skills-based hiring will move from a strategic initiative to a core operational necessity. The “precision era” we’re entering means companies simply can’t afford the cost of a mis-hire, and validating skills is the best insurance against that. I expect to see a much tighter integration of assessments into the entire talent lifecycle, not just at the top of the hiring funnel. We’ll see more sophisticated simulations that test a blend of hard and soft skills simultaneously, reflecting how work actually gets done. Furthermore, as AI governance becomes standard, the data from these validated skill assessments will become the primary driver for internal mobility, upskilling, and workforce planning. The resume will become a historical document, while a verified skills profile will become a living, breathing passport to career opportunities within an organization.