Most companies are using AI in HR. That debate is over. Our latest research found that 69% of companies are using AI in talent acquisition and 74% are using it in HR. But only 12% are using agentic AI.

Aptitude Research just released The Agentic Moment: HR’s Opportunity to Lead the Next Era of Work, sponsored by Eightfold. We surveyed HR leaders on what they understand about agentic AI, where they want it, what is blocking them, and how ready they actually are. The results are not what the market narrative suggests.

Most HR Leaders Cannot Define Agentic AI

Only 31% of HR leaders say they are very familiar with agentic AI or consider themselves experts. Twenty-six percent told us our survey was the first time they had seen a clear definition.

That matters more than it sounds. Vendors are calling chatbots agentic. Analysts are calling copilots agentic. Some companies are calling any automated follow-up agentic.

Here is the test. Does the system wait to be told what to do at each step, or does it decide that itself? If it waits, it is not agentic. A chatbot answering policy questions responds to inputs. A copilot drafting job descriptions generates content. A dashboard flagging attrition reports. None of them execute.

Organizations that cannot make this distinction cannot evaluate vendors accurately. They will buy the wrong thing and govern it the wrong way.

Nobody Owns AI Strategy

Thirty-three percent of companies have no clear AI vendor strategy at all. Forty-four percent have no clear ownership of AI or are unsure who owns it. Only 13% say the CHRO or CPO owns it directly.

Ownership shapes speed. Organizations where the CHRO or CPO owns AI strategy deploy faster and govern better. Where ownership is undefined or sits outside HR, adoption is slower and more reactive.

The Foundation Is Cracked

The systems HR relies on today were not built for the intelligence era, let alone the agentic one.

Only 28% of companies are satisfied with their ATS decisions. Eighty-two percent say their ATS has significant gaps. Forty-eight percent cite scheduling as the top gap. Thirty-four percent are increasing ATS investment in 2025, and one in four are replacing their system entirely.

Spending more on the same category of tool does not close a capability gap. What changes the equation is the nature of the technology itself.

Where Companies Want Agents

When we asked where companies would most want agentic AI to take autonomous action, the answers were specific and operational.

Screening and interview orchestration came first at 34%. High-volume and frontline recruiting followed at 31%. Internal mobility landed at 21%.

These are not experimental use cases. They are the workflows where volume overwhelms capacity and where inconsistency is most expensive. High-volume hiring is the clearest example. Only 8% of companies are managing it agentically today. Twenty-four percent are still primarily manual.

The Real Barrier Is Not Budget

Only 12% of companies cite unclear ROI as a barrier. Only 6% cite leadership resistance. Only 5% cite vendor capability gaps.

The top barrier is integration. Thirty percent say they cannot integrate across existing HR systems and platforms. Data readiness and internal AI literacy tie at 24%.

This is a plumbing problem and a people problem. Agentic AI requires orchestration. A system that autonomously sources, screens, schedules, and communicates cannot function if its components cannot talk to each other. Companies managing fragmented best-of-breed estates face a compounding readiness challenge that vendor selection alone will not solve.

And only 38% of HR leaders agree their team has the skills to implement and manage AI effectively. Infrastructure can be procured. Workforce readiness has to be built.

One in Two Companies Have No Governance Framework

Half of companies have no AI governance framework in HR at all. Governance scored 3.15 out of 5 on our readiness assessment, one of the lowest dimensions we measured.

You do not need a comprehensive framework before you start. You need a clear policy for the specific use case you are deploying. What the agent can decide. What requires human review. How decisions are documented. Who is accountable.

Build governance use case by use case and let it grow into a framework. Waiting for the perfect policy is how organizations end up with neither policy nor progress.

What To Do Now

Assess honestly. Every readiness dimension we measured scored between 3.15 and 3.34 out of 5. Nobody is far ahead. The lowest score is your starting point.

Sequence deliberately. Start with the workflows where agentic AI is most mature and the ROI case is clearest. Expand into use cases that require richer data. Then orchestrate across the lifecycle.

Buy for connection, not features. The right question to ask a vendor is not what their agent does in isolation. It is how it connects to the rest of the stack.

The agentic moment is not coming. It is here. The question for HR leaders is not whether to act. It is how fast and how well.

I’ve spent the last few months on the road more than I’ve spent at my desk. Conferences, client sessions, vendor briefings, and a lot of hallway conversations that turned out to be more useful than anything on the agenda. When you talk to enough talent leaders in a short window, patterns start to emerge because everyone is wrestling with the same shifts at the same time.

Here are the six themes I kept hearing.

1. AI is moving from efficiency to decision-making

For two years, the AI conversation in talent acquisition has been about efficiency. Faster screening, fewer manual tasks, time back in the day. That made sense, because it is where most of us started. Half of companies still name efficiency as their top reason for adopting AI; only about a quarter point to better decisions.

But the questions are changing. Talent leaders are no longer asking me whether AI can save their recruiters time. They have accepted that it can. They are asking something harder: is it helping us make better decisions, and how would we even know? That is a different phase of maturity, and most organizations have not yet built the muscle to measure it.

2. Candidate fraud is no longer a fringe concern

A year ago, candidate fraud came up occasionally and usually as a story someone heard secondhand. Now it comes up in nearly every conversation, and it comes up early. AI-generated resumes, candidates being fed answers in real time, identity that does not match the person who shows up on day one. What used to be a rare headache has become a real operational risk, and most teams do not yet have a process built for it. Expect this to be one of the defining talent acquisition conversations of the next year.

3. The recruiter experience is still badly misunderstood

This is the one I feel most strongly about. The gap between what recruiters actually do and what leadership thinks they do is not closing — it is widening. When asked, 64% of recruiters say executives do not fully understand their work, up from 58% just a few years ago. We keep asking recruiters to move faster, adopt new tools, and become more strategic, while leaving the underlying workflows, systems, and expectations largely unchanged. You cannot redesign the role by adding pressure to it.

4. The build-versus-buy conversation is back

For a while, build versus buy felt settled — most organizations bought. AI has reopened the question. Teams that never would have considered building are now asking whether they should stand up their own capability rather than wait for a vendor roadmap, and teams that started building are discovering how much it actually takes to maintain. And the pricing in the token economy is much much different. With some vendors, they can do both. There is no universal right answer here, but the fact that the question is live again tells you how much AI has scrambled the old assumptions about what is core and what is bought.

5. Voice is showing up in screening

One of the more interesting things I am seeing is voice moving into screening — conversational, voice-based interactions earlier in the process than we are used to. It is promising, because it can feel more human and accessible than another form to fill out. It also raises real questions about consistency, bias, transparency, and candidate comfort that we have not fully worked through yet. This is early, and it is worth watching closely rather than adopting blindly.

6. Frontline hiring is finally getting the attention it deserves

The frontline now makes up roughly 70% of the workforce, and for years our technology, our processes, and our research treated it as an afterthought. We built for corporate hiring and bolted it onto high-volume roles. That is starting to change. More leaders are asking how to apply AI, candidate experience, and speed to frontline hiring specifically, rather than assuming what works for professional roles will translate. It is overdue. The frontline is where the volume is, where the turnover is, and where a better experience would matter most.

Where this leaves us

If there is a thread connecting all five, it is that the easy phase of this work is ending and the harder, more interesting phase is beginning. The tools have changed faster than the operating models around them, and the organizations that pull ahead will be the ones willing to rethink the work itself, not just layer new technology on top of it.

I went deeper on a couple of these — the move from efficiency to decision-making, and the realities of the recruiter experience — in recent research with iCIMS and Greenhouse. If these are the conversations happening inside your own team right now, that is a good place to keep going.

And if you are at one of the next stops on my calendar, come find me. The hallway conversations are still the best part.

I have been covering Eightfold for the better part of a decade, and I attend a lot of conferences. This is not going to be one of my usual recaps. I want to use this year’s Cultivate in Napa to answer the question I get asked more than any other, what does Eightfold do? It is not a question specific to Eightfold. With so many vendors moving quickly, it is hard to keep up when where they started is not always where they are going. For Eightfold, the product has grown into something bigger than the product most of us first met.

Where Eightfold Started

When Eightfold entered the market, it was an AI matching company. The deep-learning models matched people to jobs and jobs to people, surfaced talent that traditional keyword search missed, and made an early, credible case for skills over pedigree. That was differentiated, and at the time that was enough.

Over the years, Eightfold stopped being a feature and became a foundation. The matching engine turned into a talent intelligence platform, and the platform started powering acquisition, mobility, and resource management rather than a single use case.

What I heard at Cultivate this year is the next turn of that evolution. Eightfold is no longer asking customers to buy a product. It is asking them to build on a platform. As the team put it on stage, it is not about what they are building, but how they are building it. That line is the whole story.

The New Model: How the Stack Actually Fits Together

Here is my take on Eightfold’s model. Each layer depends on the one beneath it, and intelligence flows through all of them.

• Talent Intelligence Platform, the foundation. Everything sits on this, and it is built from three kinds of intelligence working together. Foundational Intelligence is the underlying large language models. Enterprise Intelligence is your own data across your HR systems and enterprise applications. Domain Intelligence is Eightfold’s proprietary data, with 1.6 billion career trajectories and 1.6 million skills behind it. The combination is what separates Eightfold from a generic AI layer, because most vendors have one or two of these and Eightfold has all three.
• Talent Tracking, the system of record. Sitting directly on the platform is the layer that tracks talent and movement across the organization. It gives every application above it a consistent source of truth to work from, which is what keeps the rest of the stack from fragmenting.
• Talent Applications, what people actually use. This is where the day-to-day work happens, and it is organized into three families. Talent Acquisition covers CRM, the ATS, candidate experience, and onboarding. Talent Management covers internal mobility, upskilling and development, employee experience, and workforce, talent, and succession planning. Resource Management covers availability, booking, and skill-based utilization for organizations that staff project-based work.
• Talent Agents the part that does the work for you. This is the newest and most important shift. On top of the applications sits an agentic platform and a growing set of agents, including the AI Interviewer Agent and the AI Interview Companion Agent. These do not simply surface insight and hand it back to a person. They execute inside the workflow, running interviews and supporting recruiters as the work happens.
• TalentForge, the build layer that runs alongside all of it. This is the bet that ties the Cultivate announcements together. Rather than buying fixed, one-size-fits-all software, enterprises can build custom HR applications on top of the platform, with talent intelligence available from day one.

The way to read this model is straightforward. The data foundation feeds the applications, the applications feed the agents, and TalentForge lets organizations build their own software on top of every layer. Nothing operates in isolation.

The Announcements, In Context

I am not going to walk through every announcement, but three matter, and they map to the model above.

• TalentForge is the headline. It moves Eightfold from a software company to a platform for building software, and it reflects the company’s view that the majority of enterprise software will eventually be custom-built rather than bought off the shelf.
• 360 Interview for AI Interviewer collapses multiple interview types, including screening, functional, coding, and language, into a single agent-orchestrated conversation. It is rebuilt around how agents work rather than around human scheduling constraints and multiple rounds.
• Workforce Readiness gives CHROs something they have not really had before, which is a board-ready metric for how AI adoption is actually progressing across the organization, instead of relying on lagging surveys and instinct.

Why This Matters

The reason “what does Eightfold do” has been such a hard question is the market has changed, Eightfold has changed, and the answer kept changing as the company grew. After this Cultivate, I think the honest answer is this. Eightfold gives organizations a talent intelligence foundation, a full set of applications across the talent lifecycle, agents that take action inside those applications, and now the ability to build their own software on top of all of it. This is what will power the workforce of the future, what they call the infinite workforce. The architecture to support it is real, and it is shipping. I will be watching how customers actually build on it, because that is where this next chapter gets decided.

For years, the top recruiting challenge that companies faced was reach. The ability to attract more candidates, generate more applicants, and expand the top of the funnel defined success in talent acquisition. Job boards, programmatic advertising, and sourcing tools were all designed around the same objective…get more candidates in the door or make it easier for more candidates to apply.

But, with advancement of AI, shifts in the labor market, and organizational restructuring, reach is no longer the main challenge. It is volume, signal, and decision-making.

Indeed’s recent announcement around Smart Fit Score and Smart Screening is helping companies address these challenges.

What Is This Announcement

Indeed has introduced a new capability through Smart Screening that moves beyond matching and into structured candidate evaluation. At its core, Smart Screening is embedded directly within the application experience through Indeed Apply. Smart Screening is not just another screening tool. It fundamentally changes where and how screening happens in the hiring process.

Instead of relying on recruiters to conduct phone screens days after an application is submitted, Smart Screening moves that step directly into the application itself. The moment a candidate applies, they are engaged through a structured, conversational experience that captures deeper context about their qualifications, experience, and intent. The system can ask follow-up questions, clarify responses, and gather information that would typically require a recruiter conversation. This creates a more complete and structured view of the candidate before any human interaction takes place.

The Smart Fit Score is a key part of this. It introduces a more consistent way to evaluate candidates against a defined hiring bar. Instead of relying on resume keywords or subjective interpretation, it uses candidate-provided data to assess alignment with role-specific criteria. This helps standardize what “qualified” actually means which is something most organizations still struggle with.

There are also two important layers that reflect where the market is heading. First, bot mitigation. As AI-generated applications increase, the ability to verify that candidates are real and that responses are authentic becomes critical. This is less about fraud and more about preserving signal quality in a high-volume environment.

Second, credential verification. Allowing candidates to upload proof of licenses or certifications early in the process adds a level of validation that typically happens much later. This is especially relevant in frontline and regulated roles where compliance matters.

Smart Screening is doing three things:

• Shifting screening upstream into the application
• Structuring candidate data earlier in the process
• Improving signal in a high-volume environment

This is not about making screening faster. It is about making it more consistent, more scalable, and more aligned to how hiring actually happens today.

Why This Is Significant

This announcement signals a shift in how we should think about the role of platforms like Indeed. Indeed has historically been positioned as a platform that connects employers and candidates and drives application volume.

But volume without structure creates new problems. Employers are overwhelmed with applicants. We found that 68% of companies have more applicants this year than last.  And, 58% of companies have less recruiters on their teams this year. Recruiters are spending more time sorting than engaging. And candidates are often indistinguishable from one another, especially in an environment where AI-generated resumes and applications are becoming more common.

By introducing structured screening and fit scoring at the top of the funnel, Indeed is moving into a new role of not just driving applications, but helping employers make smarter decisions around them.

Benefits of Screening at Scale

The benefits of this announcement extend to both employers and candidates, but they are most immediate for employers who are under increasing pressure to deliver better outcomes with fewer resources.

Efficiency: At a foundational level, Smart Screening helps address one of the biggest challenges in talent acquisition today: efficiency at scale. Recruiters are spending a disproportionate amount of time reviewing applications, conducting initial screens, and managing follow-up communication. At the same time, teams are smaller. Aptitude Research found that 58% of companies have reduced their TA teams, even as hiring demands and expectations continue to rise. This creates a clear need for solutions that can expand recruiter capacity without adding headcount.

Quality of Applicant: It also has a direct impact on quality of applicant. One of the persistent challenges in hiring is not just attracting candidates, but identifying which candidates actually meet the hiring bar. Aptitude Research found that quality of hire is the top priority for 70% of companies, yet many still lack a consistent way to define or measure it. Structured screening and fit scoring introduce a more standardized approach to evaluating candidates, helping employers focus on those who are most aligned with role requirements earlier in the process.

Candidate Drop-Off: Candidate drop-off is another critical area of impact. Engagement at the point of application is often the difference between completion and abandonment. We have found that when candidates receive communication within the first 15 minutes, application completion rates can reach 85%, but when there is no engagement, that drops to 35%. By introducing immediate, conversational interaction, Smart Screening helps maintain candidate momentum and reduce drop-off at a stage where most organizations lose talent.

Decision-Making: Finally, this approach improves overall decision-making. Instead of relying solely on resumes or fragmented notes from phone screens, recruiters are provided with structured, consistent inputs that include candidate responses, summaries, and alignment to defined criteria. This creates a more informed starting point for hiring decisions and reduces variability across recruiters and hiring managers.

These benefits reflect a broader shift in talent acquisition. The focus is no longer just on generating applicants, but on improving how those applicants are evaluated, engaged, and converted into hires.

Enabling Hiring at Scale Without Losing Signal

The real value of this approach is not just efficiency. It is signal quality. In high-volume hiring environments, the challenge is not just processing candidates quickly. It is ensuring that the right candidates are not lost in the noise. By introducing structured inputs and aligning them to defined hiring criteria, Smart Screening helps organizations move from keyword-based filtering to criteria-based evaluation

This aligns with a broader shift in the market toward more structured, explainable decision-making. It also supports a more consistent definition of the hiring bar, which remains one of the most persistent challenges in talent acquisition.

A Responsible Approach to AI in Hiring

One of the more important aspects of this announcement is what Indeed is not doing. Smart Fit Score is not designed to:

• compare candidates against one another
• make autonomous hiring decisions
• replace recruiter judgment

Instead, it is built around employer-defined criteria  and candidate-provided information . This reflects a more responsible and controlled approach to AI in hiring. Aptitude Research has found that trust remains one of the biggest barriers to AI adoption in talent acquisition. Solutions that prioritize transparency and human oversight will be better positioned for long-term adoption.

Aptitude Research and Phenom released the State of Hiring Automation 2026 this week. It was a labor of love that we started last year. It was a project that combined an audit of more than 200 enterprise hiring experiences with survey data from over 300 organizations. Together, we mapped where companies are investing in automation, where candidate experiences are breaking down, and what the inline hiring journey actually looks like today.

One of the most interesting findings: organizations are not as far behind as they think and not as far ahead as they hope. Across industries, role types, and maturity levels, the patterns were remarkably consistent. Frontline hiring, knowledge worker hiring, healthcare, retail, manufacturing, hospitality … all showed similar results. Most organizations are in the same place.

That consistency is both reassuring and clarifying. It means companies still have a real window to differentiate without feeling like they’re chasing competitors who are years ahead.

Where investment has paid off

A decade of investment in attraction and candidate engagement has delivered real results. Career sites, job discovery, applications, and communication have all improved meaningfully. Across attraction, engagement, and conversion, organizations are operating at roughly 62% of their potential — a solid foundation.

• 62% — average maturity in attraction, engagement & conversion
• 21% — average hiring automation score post-apply
• 94% — of organizations not scheduling interviews inline
• <1% — with a fully orchestrated inline qualification workflow

Where the gap lives

Once candidates apply, the experience changes dramatically. Hiring automation scores averaged just 21%. The breakdown isn’t in attracting talent. It’s in qualification, assessment, screening, interview scheduling, and decision-making. Too many organizations still push candidates into fragmented post-apply workflows, losing top talent at the moment of peak intent.

Automation inline: the current reality

• 11% use role-specific qualification early in the process
• 11% deploy assessments during the application flow
• 7% automate interview scheduling inline
• 1% are using voice-based screening agents

What comes next

For years, the market focused on attracting more candidates. The next phase is about qualifying the right candidates faster and with greater consistency investing in assessments, interview orchestration, scheduling, screening, and AI that supports decision-making inside the workflow, not just around it.

The organizations that move fastest won’t necessarily be the ones with the biggest applicant pools or the most AI announcements. They’ll be the ones that create a more connected inline experience that compresses the time between apply, qualification, assessment, and interview. That is where hiring automation becomes meaningful.

We are sharing these results May 20 at 11am EST: Mind the Gap: What the 2026 State of Hiring Automation Report Reveals | Phenom + Aptitude Research Webinar

Last week at Visier Outsmart was a different event from most I have attended. Not just the content or the product announcements. But the experience itself. I left feeling like this was a community and I left with a very clear vision of how Visier supports its clients and where it is going in the future. In the messy world of HRTech, this type of clarity is rare.

I had the opportunity to moderate a panel on talent decisions and host a fireside chat with CEO, Ryan Wong, on the big themes of 2026 including SaaS-pocalyse and strategic workforce planning.

Here are my takeaways:

1. The message was consistent: AI is about decisions, not efficiency

For years, the narrative has been: AI will make HR more efficient and save time. That’s not the conversation anymore. The shift is toward using AI to make better decisions—about hiring, workforce planning, retention, and organizational design. Not faster reporting. Better judgment.

2. We are moving from insights to outcomes

One of the clearest themes across sessions: Insights alone are not enough. Companies have spent years building dashboards, reports, and analytics capabilities. But as was said during the event, if no one acts on an insight, it doesn’t matter.

The focus now is on:

• What should I do?
• What action should be taken?
• What outcome will this drive?

This is where workforce AI is heading—closing the gap between data and action.

3. Recommendations are the new frontier

There’s a growing expectation that systems don’t just show data. They guide action.

That means:

• Recommending workforce decisions
• Aligning those recommendations to company policy and context
• Embedding them into workflows where decisions actually happen

This is a big shift. It takes the fear out of AI-enabled recommendations and puts power behind it. It also raises new questions around trust, governance, and accountability.

4. The future is a continuous decision loop

The most important concept reinforced throughout the event is that workforce intelligence is no longer linear.

It’s a loop:

• Understand the workforce
• Plan based on that understanding
• Get recommendations
• Take action
• Improve continuously

This “decision loop” is what turns AI into something operational and not theoretical.

5. This is the SaaS-pocalypse moment—and vendors matter more than ever

For the past decade, the model was simple: Buy more technology. Add more tools. Solve for features. That model is breaking. Companies are overwhelmed with systems, unclear on value, and facing pricing models for AI that are still largely undefined. Consumption-based pricing, token-based models, bundled AI—it’s all still evolving.

This is the SaaS-pocalypse.

But organizations can’t navigate it alone.They need vendors that don’t just provide technology—but provide context, guidance, and operational support. The burden is shifting from “buying software” to depending on partners to make sense of it.

What made this event stand out wasn’t just the technology. It was the clarity of direction. The market is moving beyond experimentation toward something much bigger:
Using AI to drive better workforce decisions—and ultimately, better outcomes. And what stood out most is that this community isn’t waiting to figure it out. They’re already doing it.

Today, Crosschq announced its acquisition of Traitify. There is a tendency in this market to immediately categorize acquisitions. A company acquires an assessment provider and the assumption is straightforward: they are filling a gap in their product portfolio.

That is not what this is. Crosschq’s acquisition of Traitify is less about adding another capability and more about addressing a set of challenges that organizations have not been able to solve, despite years of investment in hiring technology.

Hiring has become more efficient. It has not become more effective. Organizations are still overwhelmed with volume, struggling to differentiate candidates, and making decisions with limited connection to outcomes.

Why Traitify

Assessments have always played an important role in hiring, but they have also been one of the most problematic parts of the experience.

They are often too long, too complex, and too disconnected from how candidates actually apply for jobs today. This is especially true in frontline hiring. Traditional assessments are still 45 minutes- 1 hour for these roles.

Candidates are applying on their phones, often in between shifts or alongside other responsibilities. Any friction in the process increases drop off and reduces the likelihood of completion. At the same time, organizations need better signal earlier in the process.

Traitify addresses this in a way that most assessment providers have not. It delivers:

• a fast, visual, and mobile-first experience
• high completion rates in high-volume environments
• structured behavioral data that can be used consistently across candidates

What stands out is not just the experience, but the ability to generate usable signal without disrupting the process. As part of Paradox, Traitify had a long history of supporting organizations with frontline hiring needs. Traitify improves both the candidate experience and the quality of inputs into the hiring process.

What This Means for Crosschq

This acquisition reflects the direction Crosschq has been moving toward over the past several years. The focus is no longer on individual steps in the hiring process. It is on how those steps connect and how they ultimately contribute to better outcomes.

Crosschq has built capabilities across:

• reference checking
• interview intelligence
• fraud and identity verification
• post-hire outcome data

The addition of Traitify introduces structured behavioral data earlier in the process. This is important because hiring decisions are still largely made on fragmented and inconsistent inputs.

A resume. A set of interviews. A reference check at the end.

There is limited standardization and very little alignment to what happens after the hire is made. What Crosschq is building is a more connected system. One where signals across the hiring lifecycle are combined and tied to outcomes such as performance, retention, and quality of hire. This is a shift from managing process to enabling better decisions.

It is also where the frontline workforce becomes more central. Frontline hiring represents one of the largest and most complex segments of the labor market, yet it has historically lacked structure and consistency. Traitify provides a way to introduce structured, scalable, and candidate-friendly inputs into that environment without slowing it down.

What This Means for the Market

This acquisition reflects broader shifts in the HR technology market.

First, it reinforces the move away from fragmented point solutions. Organizations have spent years assembling stacks of tools across sourcing, screening, assessments, interviews, and reference checks. These tools often operate independently, producing disconnected data and inconsistent decision making.There is increasing demand for more integrated systems that connect these signals and provide a clearer view of candidate quality.

Second, it highlights a more important shift in how AI is being applied in hiring. Much of the current focus on AI has been on efficiency. Automating tasks, improving speed, and reducing manual work. While these gains are important, they do not address the underlying challenge. Organizations still struggle to determine which candidates are most likely to succeed. To move beyond efficiency, AI in hiring must be grounded in:

• structured and validated inputs
• consistent data across candidates
• clear connections to outcomes

The combination of Traitify’s validated assessment data and Crosschq’s outcome-based insights creates a stronger foundation for this next phase. It also raises expectations for what hiring technology should deliver. Not just faster processes, but better decisions.