Filip Tack

Filip Tack's public writing circles a single conviction: that skills, made visible and structured, are the only honest foundation for how organizations hire, develop, and deploy people in the age of AI. He returns again and again to the same building blocks, tasks, tools, knowledge, languages, traits, and argues that vague concepts like "attitude" or "role" collapse without them. Responding to Simon Sinek's well-worn line that "you don't hire for skills, you hire for attitude," he doesn't reject it outright but refuses to let it become an excuse: "Attitude and skills were never opposites... Attitude doesn't sit above the skills profile. It sits inside it" . That move, refusing false dichotomies and instead decomposing them into their component parts, is his signature intellectual habit.

The AI anxiety question is where this framework does its most pointed work. Citing Harvard's Das Narayandas, Tack seizes on the image of professionals becoming "royalty without a kingdom" as AI hollows out roles from the inside, and on the finding that at least 30% of AI projects are quietly abandoned not for technical reasons but because "people protect their sense of who they are at work" . Where Narayandas prescribes "redeployment pathways," Tack pushes a step earlier: "You can't reassure people about a role you've never made visible" . His proposed antidote, "skills transparency," reframes AI adoption as fundamentally a visibility problem: break work into its actual skills and people can see for themselves what AI absorbs, what it supports, and what remains human . This is consistent with his broader claim that skills-based hiring, AI agents, hybrid work, and pay transparency are not four separate HR trends but one convergence, "old news" individually but new in combination, and that the gap between announcement and practice (companies dropping degree requirements yet barely changing who they hire, per Harvard/Burning Glass; companies rolling out AI with only 6% seeing real value, per McKinsey) reveals organizations that haven't done the harder structural work of deciding "which work goes to people, which to systems, what skills are required, and how you pay for it" .

His Dutch-language commentary on the ManpowerGroup Employment Outlook Survey extends the same logic to labor market strategy. As external hiring slows but critical skills shortages persist, he reads this as a shift from filling vacancies to securing talent, which "start[s] with two things getting sharp": first mapping what competencies the organization actually needs via a dynamic, competency-based job architecture, then auditing what competencies already exist in the workforce . Without that dual clarity, he implies, up- and reskilling efforts are guesswork. This is the same instinct as the skills-transparency argument, just applied to workforce planning rather than individual AI anxiety, don't reassure or restrategize around an unmapped reality.

Tack is also skeptical of proxy metrics that let organizations feel busy without confronting substance. His blog title "Hours Trained is a vanity metric" and his push at talentguide to let development plans move cleanly "from active to completed, with progress you can actually track" both reflect an insistence on outcomes and structure over activity and good intentions. He frames this product decision explicitly as customer-driven: "You pointed at the gap (pun intended), we built it" , suggesting he sees his company's roadmap as reactive to demonstrated organizational pain points rather than speculative innovation.

His Vlerick HR Day piece takes a longer historical view, tying together "Madame Soleil," decades-old robot panic, and Lawler III's early theories to argue that large language models have finally made the "skills-based organisation" practically achievable rather than aspirational, and that the purpose question in HR is "answered by ownership: your competencies, your development, your next step" . This is Tack's clearest statement that skills-based frameworks aren't just an operational fix but a source of individual meaning and agency, a throughline connecting his AI-anxiety argument, his critique of vanity metrics, and his labor-market commentary.

Taken together, the concrete takeaway from Tack's public thinking is procedural: don't debate abstractions like "attitude," "role," or "AI disruption" until you've broken the underlying work into visible skills. That decomposition, he argues, is what allows honest conversations about redeployment, hiring, development tracking, and even employee reassurance in the face of automation. It's a consistent, almost mechanical discipline applied across very different problems, hiring philosophy, AI adoption, labor market strategy, and product design, all treated as the same problem in different clothes.