The corporation has been, and remains, the core around which every city, every government, and most jobs are built. It is the principal employer, and for those not directly employed by the corporation, it supports a secondary economy of restaurants, realtors, suppliers, and delivery services, to name just a few. The taxes it pays fund city governments, public schools, and the infrastructure we all depend on. The deductions (taxes) taken from workers’ wages fund medical, retirement, and social services.

But this model is under pressure and is slowly unraveling. To understand why we need to understand why large corporations exist in the first place. In classical economic theory, firms exist because using the open market is expensive. Finding specialized individuals, verifying competence, negotiating terms, and monitoring performance all carry transaction costs. When costs are high, bringing work inside and directing it via payroll and managers is cheaper than contracting piece by piece. It was necessary for manufacturers to have large numbers of workers to make, assemble, and distribute their products.

However, the evolving capabilities of AI, along with the near-universal availability of Wi-Fi, advanced robotics, and the growth of complex global networks, make communication, task matching, coordination, manufacturing, and verification nearly frictionless. The economic boundary of the enterprise is dissolving.

The Architecture of the Thin Firm
The corporation has been slowly reorganizing into a model defined by a small, high-judgment core of people surrounded by a flexible network. This means that maintaining large pools of permanent headcount, primarily for administrative or coordination work, is now a liability.

This core keeps only what cannot be safely outsourced or automated: strategic vision, institutional trust, brand ownership, proprietary intellectual property, and high-consequence judgment calls. Everything else eventually migrates into an ecosystem of part-time specialists, independent consultants, digital platforms, robots, and autonomous workflows.

What Is the New Model?
The fading traditional corporation is being replaced by several different dynamic, disaggregated operating models:

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Perhaps due to our insecurity or fear of losing our jobs, we have made human-in-the-loop the centerpiece of both law and AI governance, often without asking whether the human in that loop is competent or capable of sound reasoning.

The National Institute of Standards and Technology, an agency of the U.S. Department of Commerce, and its Artificial Intelligence Risk Management Framework emphasize defining human roles and responsibilities and understanding the limitations of human-AI interactions. They do not recommend just accepting human involvement as sufficient.

An AI algorithm may be as competent as or more competent than a human recruiter, and there is the rub. How to tell?

If an algorithm says that Candidate A fits the job requirements at 85% and Candidate B at only 63%, and a recruiter recommends the first candidate, can they explain why, or did they just rubber-stamp the recommendation?

To have faith that the recruiter is competent and at least as good as the algorithm, I believe they should be qualified at three levels.

The first is recruiting competence. The recruiter needs to understand both what the hiring manager is seeking and what the organization needs. They need skills in assessment techniques and their limitations, and in interpreting a candidate’s answers in relation to the job and the hiring manager, not just the specific requirements. They need to “read between the lines,” so to speak, and have some level of intuition.

Second is AI competence. They must understand the algorithm performing the evaluation and know what it was designed to interpret. They must know what it uses for inputs, its limitations, and possible failure modes. This does not require being a data scientist but does require a basic understanding of how these systems work,

An example is an airline pilot trained to this level of competence. They do not need to understand every line of code, but they must understand what the algorithm or software is supposed to do and how to recognize when it becomes unreliable.

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This year marks our 20th anniversary, and the program couldn’t be more relevant to the challenges facing our profession.

AI, automation, shifting demographics, and an increasingly volatile world are reshaping recruiting at an unprecedented pace. There has never been a more important time to learn, challenge assumptions, and prepare for what’s next.

Our theme this year is Uplift—a reminder that, even amid profound change, we can build a stronger future for talent acquisition.

I hope you will join us for an outstanding lineup of speakers and workshop leaders and be part of the conversation that will help shape the next twenty years.

So many changes, so little time to absorb everything that is happening around us. I thought that things were happening fast twenty years ago, but I never imagined the impact of AI and the changing economic and demographic environment we are in.

I really look forward to engaging in conversations with you, debating, exploring, and imagining what tomorrow may look like.

Can we count on your involvement?

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Recruiters have treated artificial intelligence as a collection of tools. We have AI sourcing tools, AI chatbots, AI assessment platforms, AI scheduling applications, and AI assistants embedded insid…

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For most of the industrial era, workforce planning rested on an assumption: organizations hired people because those people possessed skills the organization needed. Employees developed those skills, accumulated experience and organizational knowledge, and became more valuable over time.

That assumption is becoming less reliable.

The World Economic Forum reported in 2025 that employers expect roughly 39 percent of workers’ existing core skills to change or become outdated by 2030. Whether the exact number proves correct is less important than what it tells us about employer expectations. Companies increasingly believe that skills are changing too quickly for occasional training and periodic hiring to keep pace.

For decades, when an organization needed more software capabilities, it hired more programmers. Coding ability was scarce, and the programmer was the container in which that capability resided. Artificial intelligence is beginning to loosen that relationship.

Research on AI-assisted software development suggests that AI can significantly increase developer productivity. At the same time, companies adopting these tools do not necessarily stop hiring engineers. Instead, the nature of engineering work changes. Greater value lies in defining problems, understanding the business context, choosing the architecture, evaluating AI-generated work, identifying failure modes, and exercising judgment.

The skill has not disappeared. The capability has been redistributed. That distinction may become one of the most important ideas in workforce strategy.

A skill belongs to an individual. Capability is the organization’s ability to get something done.

For much of the twentieth century, these were almost synonymous because the primary way organizations acquired capability was by hiring people with the necessary skills. If a company needed accountants, engineers, recruiters, lawyers, designers, or marketers, it put more of them on the payroll. The workforce became the warehouse in which organizational capability was stored.

That is beginning to change.

Recruiting Is Changing Too
Recruiting has been mediated by technology for decades. Applicant-tracking systems collect résumés, organize candidates, search for keywords and help recruiters manage enormous quantities of information. Increasingly sophisticated systems source candidates, match people to jobs, administer assessments, schedule interviews and recommend priorities. The critical issue is not whether machines are making every hiring decision. They are not.

The issue is that technology increasingly determines the information environment in which human decisions are made. A recruiter may never see every application. A hiring manager may encounter only candidates that an algorithm has already prioritized. The human may still make the final decision, but technology increasingly determines which decisions reach the human.

Generative AI has simultaneously transformed the candidate side of recruiting. Today virtually anyone can generate a polished résumé, tailor an application to a specific position, prepare answers to likely interview questions, research the organization and rehearse responses at almost no cost. There is nothing inherently wrong with this. Candidates have always sought help presenting themselves.

But when polished presentation becomes nearly universal, presentation becomes a weaker signal. A perfectly tailored résumé tells us less about the effort someone invested in understanding the position. A polished response to a predictable interview question tells us less about how the person thinks when facing an unfamiliar problem.

Consequently, evidence that is harder to manufacture becomes more valuable, such as demonstrated performance, work samples, reputation, validated skills, references, professional relationships, and direct interaction.

As the cost of producing information falls, the value of interpreting information rises.

This is why I believe the future belongs partly to what I have written about previously, and I called the artisan recruiter. The artisan recruiter is not anti-technology. Quite the opposite. AI should handle research, sourcing, scheduling, administration, and analysis wherever it can do these things effectively. What the recruiter should not give up is her judgment.

Stop Asking Whom to Hire
The larger transformation goes beyond recruiting.

The traditional workforce model asks:

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In a recent issue of This Week in Recruiting, Hung Lee argued that recruiters should support open-source, on-premises AI rather than allow organizations to become dependent on proprietary cloud providers.

His argument raises the make-or-buy decision that has been a discussion that has kept talent leaders up at night for as long as I can remember. Simply put: should employers create their own AI-enabled screening, assessment, and interviewing capabilities or buy one from a vendor?

The argument to make your own is appealing on the surface. An internally developed system could, in theory, better reflect the organization’s culture, jobs, skills, performance standards, and workforce strategy than a commercial product. It might also give the employer greater control over its data, algorithms and intellectual property.

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A hiring manager can personally evaluate maybe five candidates. A recruiter can write thoughtful outreach to a few dozen people. Workforce planning happens once a year because continuous analysis costs too much or is impossible without data. Scarcity forces prioritization and prioritization forces discipline.

AI is changing that fast.

AI is now the substrate or infrastructure for cognition: the ability to perceive information, reason about it, draw conclusions, solve problems, make judgments, and communicate those conclusions. Readily available cognition means thinking gets embedded inside workflows that previously contained very little thinking at all. Every application can be analyzed against every job requirement. Every candidate can receive a personalized response. Every interview can be transcribed, summarized, and scored against a competency framework. Every hiring decision can draw on larger bodies of evidence than any human recruiter could process in a career.

That sounds great, but in practice it raises a question: what happens when you pour cognition into processes that were never designed to handle this much of it?

The process itself still depends on vague job descriptions, inconsistent evaluation standards, poorly trained managers, and weak accountability. The result is not necessarily better hiring. It may simply be more analysis, more recommendations, and more automated activity flowing through a system that cannot distinguish useful judgment from noise.

What Abundant Cognition Actually Unlocks in Recruiting
But the upside is also useful and real, and it comes in three forms.

Volume. A small company with two recruiters has always competed for talent against companies with twenty. Abundant cognition closes that gap. Two recruiters with good AI-assisted tools can now source, screen, personalize, and follow up at a scale that used to require a much larger team. That’s a big plus for organizations that previously couldn’t afford to be thoughtful at every step of the process. It also reduces the need for as many recruiters in larger firms.

Consistency. Human recruiters are inconsistent. Research on decision fatigue shows that evaluators score candidates more harshly as the day wears on and their cognitive resources deplete. Machine cognition applied consistently to every candidate doesn’t have those inconsistencies. Done right, it reduces the noise in decisions that currently have too much of it.

Candidate experience. Most candidates today get silence or a form rejection. Abundant cognition means every candidate can get a response that feels human and specific, even if most of the process is automated underneath. That shapes employer brand and influences who applies next time. It’s one of the most obvious places where the old model produced outcomes that nobody wanted.

These three gains address the same underlying problem: recruiting has historically been a high-touch process that only large, well-resourced organizations could do well. Abundant cognition democratizes that capability.

The Efficiency Trap
Here is the paradox. More analysis or thinking doesn’t automatically produce better decisions. It can produce worse ones.

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I have watched this movie too many times.

The first show ran from about 2005 to 2015. Skills databases and competency frameworks. SAP, PeopleSoft, SuccessFactors. Vast taxonomies. Committee meetings a…

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Ask a stranger what they do, and they will usually tell you their job.

Not their passions. Not their family. Not what matters most to them. The job is not just something we do, but has become what we are. It is the first question at a party, the defining line in a biography, and often the foundation of our identity. When we are laid off, we lose that identity, and that is hard.

After spending decades studying recruiting and the labor market, I have come to appreciate something that economic statistics rarely reveal. When people lose a job, the paycheck is often not the first thing they grieve. A job is almost never just a job. It is a container into which society has packed a remarkable number of responsibilities. We have carried that container for so long that we have forgotten what is inside.

The obvious function of a job is income, but that is the least interesting part of the story.

A job is also how governments finance themselves. Before wages ever reach a worker’s bank account, payroll taxes are collected automatically to fund public services, retirement systems, unemployment insurance, and healthcare in many countries. The paycheck is not simply the worker’s income; it is also one of the state’s principal sources of revenue. Employment quietly connects citizens to government through the payroll system.

Jobs also provide protection. In the United States, health insurance, retirement savings, disability insurance, and life insurance are frequently tied to employment. This arrangement was never the result of a grand design. Much of it emerged during the wage controls of the Second World War, when employers competed for workers by offering benefits they could not provide through higher wages. A temporary solution gradually became the backbone of the American safety net.

Other countries organize these benefits differently, but they have not escaped the same dependency. Where governments provide healthcare or pensions directly, those programs are still largely financed through payroll taxes collected from employment. Whether employment delivers benefits directly or finances them indirectly, the modern social contract remains closely connected to paid work.

Employment also shapes identity.

Ask someone who they are, and the answer often begins with what they do. Titles become part of our self-image. Careers become measures of accomplishment. When people lose their jobs, they often lose more than just income.

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Welcome to July! Here in the Northern Hemisphere, summer really gets underway, while Down Under it is mid-winter. As we get ready for the Fourth of July holiday here in the U.S., this is a shorter ar…

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I have noticed that something interesting is happening in our society. Just when technology is making it possible to create almost anything instantly, people seem to be rediscovering the value of craftsmanship.

I have taken up film photography again after decades of only using digital cameras. A local vinyl shop has opened, and the owner is surprised by the interest. Handmade furniture commands premium prices even though Chinese and other Asian mass-produced alternatives are available. And data from the American Booksellers Association shows that between 2020 and 2025, the number of independent bookstores in the U.S. jumped by 70%.

These trends may seem contradictory. If technology can make things faster, cheaper, and easier, why would people deliberately choose slower and more demanding alternatives?

The answer may be that abundance changes what we value. When something is scarce, we value access. When something becomes abundant, we begin to value craftsmanship, quality, authenticity, and the human effort behind the finished product.

We have seen this pattern before.

During the Industrial Revolution, factories transformed production. Furniture, clothing, tools, and household goods that once required artisans' skills could suddenly be manufactured at scale. Products became more affordable and more widely available than ever before.

Yet craftsmanship did not disappear. In many cases, it became more valuable. A handcrafted table was no longer competing against another handcrafted table. Instead, it was competing against thousands of factory-produced alternatives. The artisan’s value shifted from production to judgment, from volume to quality, and from efficiency to mastery.

I believe recruiting is approaching a similar moment.

The Industrial Recruiter
For the past three decades, recruiting has steadily evolved into an industrial process. We have made every effort to streamline, speed up, and improve efficiency. Organizations grew larger, hiring became more complex, and technology enabled talent acquisition to be managed at scale.

As a result, recruiters have been using industrial metrics like time-to-fill, cost-per-hire, sourcing activity, requisition load, and candidate throughput. The profession has adopted manufacturing thinking, in which efficiency, consistency, and scale are paramount.

Technology has sped this. By embedding AI into applicant tracking systems, sourcing platforms, candidate relationship management tools, and workflow automation, we have tried to make recruiting faster and more scalable.

However, we lost something important along the way.

Back to the Future
Many recruiters around my age remember a very different version of the profession. Before dashboards dominated every conversation and before every activity could be measured, recruiting was a relationship business. The paperwork was incidental, and none of us liked dealing with the administrative requirements.

Recruiters spent time getting to know candidates as people rather than simply evaluating them as applicants. We met candidates for lunch, attended industry events together, and maintained relationships that often spanned years. Conversations explored career aspirations, personal motivations, family considerations, and long-term goals.

The recruiter was not simply moving people through a process. The recruiter exercised judgment and focused on getting to know the person and their goals.

The best recruiters developed deep knowledge of their industries and maintained networks built over many years. They understood both their clients and their candidates at a level that enabled them to identify opportunities long before a requisition was issued. Our success was measured less by speed and more by outcomes. Did the right person join the right organization? Did the individual succeed? Were both they and their manager happy? Did the relationship endure?

In many ways, recruiting was closer to a craft than a process.

Carpenters, and Recruiters
The distinction between a craft and a process helps explain what may happen next.

Consider the difference between a factory worker and a master carpenter. The factory worker follows a predefined process designed for consistency and efficiency. The carpenter practices a craft that relies on experience, judgment, and skill.

The carpenter’s tools matter, but the tools themselves do not create the value. Two carpenters can own identical equipment and produce dramatically different results. The difference lies in their understanding of materials, their sense of design, and their ability to make hundreds of small decisions that ultimately determine the quality of the finished product.

Recruiting is no different. The most successful recruiters have never distinguished themselves through the sheer volume of resumes processed or messages sent. Their value has come from their ability to understand people, recognize potential, identify fit, and build trust. These are forms of judgment that cannot easily be reduced to a formula.

The Hung Lee Example
One of the most interesting examples of this principle can be found within our own profession. Consider the success of Hung Lee and Recruiting Brainfood.

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I am old enough to remember recruiting before any ATS systems or computers. We had file cabinets, folders, and Rolodexes for contact information.

I think it was around 1990 when Resumix, one of the first ATSs, appeared. Its resume-parsing/extraction technology promised to make it easy to search resumes and connect with candidates. It was revolutionary and exciting. We felt like we were on the cusp of an easier, more automated life. It didn’t prove to be either easier or more automated. It actually created more work. Not much different in a way than some recruiters now feel about AI.

But applicant tracking systems have evolved to become the most entrenched technology in recruiting. They have grown beyond our wildest expectations into the system of record, the compliance archive, the workflow engine, the reporting tool, and, in many organizations, the closest thing recruiting has to an operating system. For most recruiters and HR executives, imagining recruiting without an ATS is almost impossible.

The ATS exists because recruiting was built around applications. A job is approved. A requisition is opened. A job is posted. Candidates apply—recruiters screen. Hiring managers interview. Offers are made. Compliance records are stored. Reports are generated.

The entire architecture assumes that hiring begins with a requisition and that candidates enter the process by applying.

But AI challenges these assumptions.

Will the ATS remain the center of recruiting once AI agents can identify talent, infer skills, verify credentials, conduct preliminary assessments, schedule interviews, generate summaries, recommend finalists, and maintain auditable decision trails?

I doubt the ATS will vanish, but it will morph to become infrastructure. It will become like plumbing as an almost invisible foundation.

Why the ATS Became Core
The ATS became critical because it solved several problems. It brought order to a messy, paper-based, and eventually email-driven process. It created a common workflow. It stored candidate information. It provided requisition control. It gave recruiters a place to track activity and legal, compliance, and audit teams a record of what happened.

The ATS is not only a recruiting tool. It preserves records, documents candidate disposition, supports equal employment opportunity reporting, and creates evidence that the organization followed a defined process.

This is why the ATS has survived despite decades of complaints. Recruiters dislike most ATSs. Candidates dislike them even more. Hiring managers don’t know they exist or ignore them. Yet companies keep them because they provide structure, control, and compliance.

AI Does Not Need the Same Workflow
With AI, we no longer need the old sequential model that has defined recruiting for so long.

Today’s model looks like this:

Requisition → Posting → Application → Screening → Interview → Offer → Hire

But an AI-enabled model looks more like this:

Business need → Work analysis → Talent discovery → Skills inference → Assessment → Matching → Decision support → Hire

This is a major change, and we are just beginning to understand what it means.

The current ATS is built around requisitions and applicants. AI is built around talent, skills, work, availability, and fit. The ATS asks, “Who applied?” AI asks, “Who could do this work?”

Those are very different questions.

If the future of recruiting is still based on job postings and applications, the ATS remains central. If the future shifts toward continuous talent discovery, internal mobility, skills-based matching, verified credentials, and agentic sourcing, the ATS becomes a record layer beneath a broader talent operating system.

The Application May Be the Weakest Link

Let’s ask this question.

What happens when nobody needs to apply?

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Talent intelligence is the current rave. Several vendors are working on or already offering talent planning data. Their offer is seductive. They promise that if you feed AI the org chart, financial info, a skills inventory, if you have one, and some market data, you will get a workforce plan. That plan will include a recommendation for how many people you need, with what skills, in which locations, at what cost, eighteen months from now.

The vendors offering this break into three camps. Workday leads on connected, scenario-driven financial planning; Oracle and SAP offer suite-native depth; Visier, Eightfold, Gloat, and Reejig cover skills intelligence, analytics, and mobility. Add Draup and the former Skyhive for job-to-automation estimation. But how good are the results? And can Ai really do this now?

Not close, yet, and maybe never.

The First Problem is the Data
Workforce planning runs on data. Bad data produces bad plans, and most companies have bad data. I have not encountered one firm with good, verifiable data about its workforce or its skills.

Start with the skills inventory. Almost no organization knows what skills its people have. The HRIS has job titles and pay grades, but it does not record the quiet data. It doesn’t know that the financial analyst taught herself Python last year or that the warehouse supervisor ran a logistics operation in the military. Skills data is self-reported, out of date, or absent. The taxonomies are not consistent. One system calls it “project management,” another “team leadership,” and a third does not capture it at all.

AI does not fix this. AI just inherits whatever data there is, whether good or bad. And, a model trained on garbage produces polished garbage.

The talent intelligence everyone wants requires a clean, current, structured view of the workforce that very few companies possess. Until that exists, AI makes assumptions, and you cannot tell which data or numbers are real and which are invented.

External labor market data is better, but still messy and incomplete. Job posting aggregators, compensation benchmarks, and skills demand signals exist and improve every year. But they describe the past and the present. They do not know your business is about to enter a new market or shut a product line. The most important inputs to a workforce plan are decisions that have not yet been made.

The Second is That Planning is Not Prediction
Workforce planning follows strategy, and strategy is based on human judgment about the uncertain future.

How many engineers you need next year depends on what you decide to make. What you decide to make depends on where you think the market is going, what your competitors do, what capital you can raise, and what your leadership has the appetite to attempt. These are not data problems. Or AI problems. They are judgment problems, full of politics, ego, and incomplete information.

The most significant limitation of AI is its complete blindness to non-codified knowledge. Strategic corporate planning relies heavily on the unwritten: the boardroom negotiations, impending executive transitions, politics, cultural appetites for risk, or confidential M&A considerations, and what leadership thinks the market will do.

An executive leadership team may intentionally hire too many in a specific region to signal market commitment to investors or to deliberately deprive a competitor of talent. They may decide to cut positions to force efficiency or cut costs. These decisions are not recorded in a database. They exist solely in human conversations and strategic intent. An AI model, blind to these unquantifiable variables, will recommend unachievable plans based on this perfectly imperfect data.

An AI model can take “we will grow the European business forty percent” and tell you what that means in terms of headcount needs and cost. That is useful and achievable. What it cannot do is decide whether to grow the European business forty percent or whether that is possible or wise. Scenario generation is only as good as the scenarios you feed it. Again, garbage in, polished garbage out.

The job of AI is not to generate multiple futures and recommend an optimal one. The future scenarios come from the business.

What AI Can Do
None of this means AI is useless. It means the useful applications are narrower and less exciting than the promise.

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Most of the conversation about AI runs on industrial age assumptions. We talk about jobs. Productivity. Labor. Efficiency. Augmentation. All of it is surface stuff and first-order effects. The deeper…

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For as long as I can remember, there has been a gentle tug of war between the hiring manager and the recruiter. From a recruiter's perspective, the hiring manager has been on the edge of the process. They owned the requisition, had the budget, and made the decision about who to hire. But the work of hiring, sourcing, screening, scheduling, and negotiating was the recruiter's responsibility. The hiring manager critiqued. The recruiter delivered or didn’t.

That arrangement is ending.

AI agents can now do most of what recruiters used to do. And over the next few years, they will take over virtually all search, screening, assessment, scheduling, at least early-stage negotiation, and offer preparation. They will do it faster, more cheaply, and more precisely than any human. By 2030, the recruiter, as a facilitator/coordinator, will largely be gone. What will remain is a small senior layer of strategists, advisors, and governance specialists. The operational work will belong to the agent.

But this article is not about recruiters losing their jobs. It is about the hiring manager finally being forced to do what should have always been their job.

The End of the Alibi
The hiring manager’s traditional attitude was based on a useful alibi. If a hire went wrong, the recruiter sent the wrong people. If the role stayed open too long, the recruiter was slow. If diversity numbers slipped, the recruiter had not sourced widely enough. The hiring manager had clean hands and little responsibility.

But with the recruiter gone, the alibi disappears. The AI agent does not get blamed in the same way. The agent does what it is told to do based on the inputs it receives from the manager, and a handful of senior advisors who can offer guidance when needed. When the agent underperforms, the responsibility belongs to the hiring manager.

The hiring manager of 2030 cannot stand on the edge of the process. They are the process.

What Does the New Hiring Manager Actually Do?

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This is a fictional story about the looming future of work, HR, and recruiting. I used the childhood characters Dick, Jane, Spot, and Puff to link the recent past to the near future. It shows how age…

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When we talk about AI and recruiting, we discuss automation, efficiency, bias reduction, sourcing tools, assessment algorithms, and productivity gains. We ask whether AI can screen resumes more accur…

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Lots of TA people and hiring managers are thinking but not saying out loud that skills-based hiring is the most overhyped idea in TA right now. It has been for three years. And the only reason it has remained popular is that it seems to be a differentiator from the usual credentials and experience that most jobs require. And many very credible voices are too invested in it to admit what the data shows.

Harvard Business School and the Burning Glass Institute tracked actual hiring patterns and found that fewer than one in 700 hires in 2023 benefited from the shift to skills-based hiring. It isn’t any better today. Meanwhile, 85% of companies claim they practice it. This is a movement built almost entirely on press releases and lots of conference babble.

The Policy May Have Changed. The Behavior Did Not.
Quite some time ago, IBM dropped degree requirements. Then Accenture. Walmart rewrote its job descriptions. Bank of America announced changes. Sixteen states have removed four-year degree mandates for most public sector roles. Maryland was first in 2022. Pennsylvania, Virginia, and New Jersey followed. Tennessee wrote it into law.

All of that is good. Removing credential bias from job postings opens the door to many more candidates and improves diversity. A degree requirement on a job posting that does not need one automatically screens out 76% of Blacks and 83% of Latinos before anyone even looks at the resume.

But the problem is that the postings changed, but the hiring managers did not. Nor did many recruiters and HR folk.

When a job is posted without a degree requirement, the same hiring manager still reviews the resume. They still have the same mental model of what a qualified candidate looks like. They still ask the same interview questions. They still make the same offer to the person who looks most like the last person who did the job well. The policy changed. The behavior did not. And no one is measuring the behavior or working to change it.

Colorado is one of the few exceptions worth studying. The state government tracked actual outcomes and found that 25% of hires in 2023 and 2024 within reclassified job categories were people without degrees. That is what implementation looks like. But it required executive orders, hiring manager training, and a workforce board running workshops with employers on how to write different job descriptions. Most companies announce a policy change and call it done with no training or enforcement.

The Vendor Promise Is Also Broken
The pitch from companies like Eightfold, Gloat, and Beamery is that you can build a skills-based operating model for your workforce. Map skills to roles. Identify gaps. Move internal talent to where the work is. Hire based on verified capability rather than credentials. It sounds great; how can anyone object?

The problem is that this approach assumes you have clean, structured, and verified skills data for your workforce and candidates. Almost no organization has that. What vendors are doing is pattern-matching job titles to inferred skills and calling it talent intelligence. An engineer has “Python” on their profile because the job description said Python, not because anyone verified that they can actually write it. The candidate has “project management” listed because everyone does. There is little to no verification, assessment, or testing

The problem is the gap between what is promised and what is delivered.

Two Problems Dressed Up as One
The deeper issue is that skills-based hiring mixes two entirely different problems.

Problem one: reducing credential bias in hiring. This can only work when degree requirements are removed from job postings, hiring managers are trained to evaluate differently, and there is a way to track actual hiring outcomes. This is time-consuming work and requires the ability to influence and to enforce compliance.

Problem two: building a skills-based operating model for talent. This means developing the whole architecture of how you classify work, define roles, move people, and measure capability based on actual skills. This is a transformation of how organizations think about labor. It requires verified skills data, new job architectures, manager behavior change at scale, and compensation systems built around capability rather than title. It is a different and much harder problem.

The hype has merged these two problems so thoroughly that companies feel like they are addressing the second by doing the first. They drop the degree requirement. They feel progressive. They do not track or seriously evaluate whether any of it changed who actually got hired.

The Amplification Machine
Part of why this has lasted for three years without any serious pushback is the institutional weight behind it. The World Economic Forum has been pushing skills-based hiring as a core pillar of the future of work agenda. LinkedIn’s Economic Graph team constantly publishes data on skill trends. The Tear the Paper Ceiling coalition, backed by Opportunity@Work, runs campaigns and gets major corporate signatories. McKinsey has written about it. These are credible, well-resourced organizations. What CHRO or CEO is going to challenge these sources?

When so many credible voices agree, it usually means the idea is correct in principle but much harder in practice than anyone is saying. That is what is happening. Credential bias is real. Degree inflation is real. The talent pool exclusion is real.

But the idea, while solid in principle, is failing by its own metrics. The University of Phoenix says that for every 100 job listings that remove degree requirements, fewer than four candidates without degrees are actually hired.

What Actually Works

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Last week, I presented an argument a CEO might make for eliminating or greatly reducing the internal TA function. This week, how a TA leader or CHRO might respond.

Speaking to the CEO:

You are right t…

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What is the probability of a CEO forcing the reduction or outsourcing of the current talent function to qualified and vetted RPOs, specialized external recruiters, and AI Agents?

I place this probability at 80-85% over the next 18 months for the following reasons. In many firms, this transition is already underway.

The Internal Resistance Is Real but Overrated
Every TA leader will make the case for keeping the function internal. They’ll talk about culture, institutional knowledge, employer brand, and the value of human judgment in hiring. Some of that is genuine, but most of it is self-preservation presented in what they think is strategic language. A CEO who has watched AI grow and become embedded in finance, legal, and administrative work is not going to find that argument as compelling today as it was in 2019.

The TA function has always struggled to prove its strategic value in terms that demonstrate effectiveness, not just activity. As I have written before, time-to-fill, cost-per-hire, and offer acceptance rates are operational metrics, not business impact metrics. If the CHRO cannot walk into the boardroom and demonstrate in dollar terms what the internal TA function produces that an RPO cannot, the function is already losing the argument.

The CEO Case for RPO Is Structurally Strong Right Now
Several conditions converge to make this a serious option, not just a cost-saving one.

Variable cost in an uncertain environment
With hiring mostly stagnant and the long-term outlook murky, a large fixed-cost TA team is a liability. RPO converts the fixed cost to variable. You pay for what you use. In a world where the CFO is scrutinizing every headcount line and AI is promising efficiency everywhere else, the TA function’s cost structure is hard to defend. A well-structured RPO contract tied to hiring volume is a good model for uncertainty.

The RPO market has matured
This isn’t 2005. Qualified enterprise RPO providers such as Cielo, Korn Ferry RPO, Allegis, and AMS all operate at Fortune 100 scale with sophisticated technology, data capabilities, and compliance infrastructure. They can almost always match or beat your argument that “they can’t match our quality. Many RPOs have built and are using AI agents for sourcing, screening, and more. By leveraging AI Agents and technology, they reduce costs, improve speed, and increase both their service and candidate quality.

Specialized recruiters solve the high-judgment problem
The only valid argument for keeping recruiting internal applies to senior, specialized, or culture-critical hires. But that alone does not justify the cost. It still makes sense to outsource 80% of volume hiring to an RPO while maintaining a small internal team that uses AI Agents and develops executive search relationships for the top of the house. This is not a compromise but a rational division of labor.

AI makes the handoff cleaner
The more of the sourcing and screening workflow that AI handles, the less advantage internal teams have. If an RPO can plug into your ATS, run AI agents, and operate within your compliance framework, the integration costs that historically made RPO transitions painful are declining.

Where the CEO Hesitates
But I am not predicting that this scenario is a 100% probability for a few reasons.

Control and data
Talent data and competitive intelligence are strategic assets. It is critical to know who you’re talking to, what the competitive talent landscape looks like, and who you have in your internal pipeline. Providing that knowledge to a third party makes the firm dependent on that party to maintain privacy and data security. It requires a high level of trust. A sophisticated CEO will want a strong contract giving clarity on data ownership before signing.

Culture at scale is real
Employer brand is vital to attract the right talent. RPO partners can be briefed and managed, but the candidate experience is harder to control at arm’s length. If external suppliers fail in a high-visibility hiring situation, it can cost more in employer brand damage than the operating costs saved.

This article continues below. Next week, I will present this case from the perspective of a CHRO. Be sure to subscribe to have access to the CHRO’s views on the probability of outsourcing TA.

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