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.

The post Eightfold’s Cultivate Recap: Answering the Question- What Does Eightfold Do? appeared first on Aptitude Research.

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.

The post Indeed Launches Smart Screening appeared first on Aptitude Research.

When Aptitude Research published its first report on the recruiter experience in 2021, it was filling a gap. Nearly all the talent acquisition research at the time spoke to leaders and executives — the people buying and implementing technology — while the recruiters actually doing the work every day went largely unstudied.

Five years on, we’re releasing a new report, The State of the Recruiter Experience, sponsored by Greenhouse. The premise of the original still holds. The urgency is greater.

The role got harder. The environment didn’t keep up.

Recruiters today juggle more systems, more complexity, and rising expectations around speed, quality, and candidate experience — all while being asked to adopt AI and partner more closely across the business. Yet the workflows and tools around them have barely moved.

The numbers tell the story:

• 47% of recruiters describe their day-to-day experience as reactive and overloaded.
• Only 18% say their technology supports their workflows seamlessly.
• More than half spend at least 50% of their time on non-strategic work like scheduling, coordination, and manual data entry.
• 61% name poor integration as their single biggest frustration with recruiting tech, and 57% say they leave their ATS just to complete sourcing.

The gap with leadership is widening

One of the clearest signals of how little has improved comes from comparing the two reports directly. In 2021, 58% of recruiters said executives didn’t fully understand the work they do. Today that figure has climbed to 64%. The disconnect isn’t closing — it’s growing.

AI is helping with tasks, not outcomes

AI is the newest layer of complexity. Most organizations have bolted it onto existing workflows rather than rethinking them: 38% are using AI mainly to automate tasks, while just 22% say it meaningfully supports decision-making. The takeaway from the report is blunt — AI without structure, integration, and trust tends to amplify existing inefficiencies rather than fix them.

What the report covers

The new report builds on the 2021 research across three sections:

1. The Current State — where recruiting work breaks down today across process, technology, and roles.
2. AI as the Catalyst — why AI is falling short, and what it would take to move from efficiency to genuine enablement.
3. The Future State — what a redesigned recruiter experience looks like, and what organizations need to do to get there.

The throughline: improving the recruiter experience isn’t about adding more tools, data, or AI. It’s about redesigning how the work gets done — so recruiters spend their time on decisions, not tasks.

The recruiter experience isn’t just changing. It’s being redefined. The question is whether organizations are ready to lead that shift, or whether they’ll keep layering tools onto a model that was never built for this moment.

The post Five Years Later, We are Revisiting the Recruiter Experience appeared first on Aptitude Research.

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

The post Introducing the State of Hiring Automation 2026: the gap is larger than you think appeared first on Aptitude Research.

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.

The post Visier Outsmart Event: Community, Talent Decisions, and Workforce AI appeared first on Aptitude Research.

I love the ATS market. I always have and I always will. I have talked about it so much over the past 20 years that even my kids can define an ATS.

More than a decade ago, we published our first ATS Index report. I wrote that “this is not your grandma’s ATS.” At the time, it was a reaction to systems that were rigid, compliance-heavy, and built more for process than for people.

We are able to publish our latest ATS Index and that statement still holds. But for a very different reason. The ATS has changed more in the past three years than it did in the decade before it. And yet, many of the expectations around it have not caught up.

As we prepare to publish this year’s ATS Index, one thing is clear… the conversation about the ATS needs to shift. Not just in how we evaluate it, but in what we expect it to deliver.

From System of Record to System of Execution to System of Experience

The ATS was never meant to just store data. But for years, that’s how it was used.

Today, that model is breaking. The ATS is becoming a system of execution. It is where decisions are made, where workflows happen, and increasingly, where AI operates. This shift is especially evident with the rise of agentic capabilities.

Agentic ATS is not about adding another feature or chatbot. It is about systems that can take action. Screening candidates. Scheduling interviews. Driving follow-up. Supporting hiring managers directly. But most organizations are not ready for this shift. Not because the technology is not there, but because the workflows are not defined, the data is not connected, and the expectations are unclear. The work needs to clearly be defined first. I can’t stress this enough. The real question is not whether your ATS has AI. It is whether it can actually execute work on your behalf.

If It Doesn’t Fit the Recruiter Workflow, It Doesn’t Work

For all the innovation happening in this space, one issue remains unchanged: the disconnect between systems and how recruiters actually work. Recruiters are still navigating too many systems. Too many clicks. Too many manual steps.

And in frontline and high-volume environments, the challenge is even greater. In many cases, there are no recruiters. Hiring is owned by managers who have limited time and little training on hiring technology. Yet most ATS platforms are still designed with a traditional recruiter in mind. This is where the gap is most visible.

An effective ATS must fit into the workflow. Not the other way around. That means:

• Embedded experiences, not separate systems
• Mobile-first design for frontline hiring
• Simplicity for hiring managers
• Automation that reduces steps, not adds to them

This is not about usability. It is about adoption. And ultimately, outcomes.

The ATS Is Expanding. But Not Always Intentionally

Another shift we see in this year’s Index is the expanding scope of the ATS. What used to be a defined category is now blurred.

Today’s ATS may include:

• CRM capabilities
• Interview scheduling and intelligence
• AI Interviews
• AI-driven screening and matching
• Candidate communications
• Analytics and reporting
• Internal mobility and talent marketplace features
• Sourcing

The list goes on and the ATS is no longer just about hiring. It is increasingly connected to broader talent strategies. But this expansion is not always strategic. Vendors are building more. Buyers are expecting more. But few organizations have clarity on what should live inside the ATS versus what should be integrated around it.

This is where the platform conversation becomes real. Not best of breed versus suite. But platform versus platform. And the ATS sits at the center of it.

We Need to Rethink How We Measure ROI

One of the biggest challenges in the ATS market today is how success is defined. For years, ROI was measured in efficiency:

• Time to fill
• Cost per hire
• Recruiter productivity

Those metrics still matter. But they are no longer enough. The value of the ATS today is tied to outcomes:

• Quality of hire
• Candidate experience
• Hiring manager effectiveness
• Ability to hire at scale
• Risk mitigation and compliance

And increasingly, the impact of AI.

If your ATS is making decisions or taking action, then ROI must also account for:

• Accuracy of those decisions
• Fairness and bias mitigation
• Transparency and trust

This requires a different level of accountability and measurement.

Frontline Hiring Is Forcing the Market to Change

If there is one area pushing ATS innovation forward, it is frontline hiring. High-volume environments have fundamentally different needs:

• Speed over process
• Managers over recruiters
• Mobile over desktop
• Simplicity over configurability

And yet, for years, these needs were underserved. That is changing. We are seeing more ATS solutions designed specifically for frontline environments. More automation. More conversational interfaces. More SMS capabilities. More focus on reducing friction in the apply and hiring process. Frontline hiring is not a niche. It is forcing a redesign of how hiring technology works. And the lessons from this segment will shape the broader market.

The ATS is no longer a static system. It is an evolving platform that sits at the center of talent acquisition and increasingly, talent management. But with that evolution comes complexity. More capability does not automatically mean more value.

The organizations that will get the most from their ATS are not the ones with the most features. They are the ones that:

• Align the ATS to real workflows
• Define clear roles for AI and automation
• Integrate data across systems
• Measure outcomes, not just activity

The post The ATS Isn’t What It Used to Be. And That’s the Point. appeared first on Aptitude Research.

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.

The post Crosschq’s Acquisition of Traitify: This is Not About Assessments appeared first on Aptitude Research.

There was no shortage of energy around AI. Every conversation, every booth, every session seemed to focus on AI and the future of AI. But beneath that excitement, a few themes stood out to me that feel more important than just the headlines.

1. AI Must Move Beyond Efficiency to Outcomes

We’ve spent the last few years focused on how AI can make things faster. Automate scheduling. Improve matching. Reduce manual work. That’s table stakes now.

In my conversation with Tyler Weeks, SVP of People Technology at Marriot, one point really stuck: AI needs to move beyond individual productivity and into enterprise impact. This isn’t about helping a recruiter move faster. It’s about whether organizations are making better hiring decisions, improving quality, and driving measurable outcomes.

Efficiency is easy to sell but outcomes are harder to prove. And, that’s where this needs to go.

2. The End of the Point Solution Era

It’s getting harder to find a true point solution. And the question of suite or best of breed is dated.

Every vendor is expanding. Every platform is becoming broader. What used to be sourcing, CRM, or assessment tools are now positioning themselves as end to end platforms. On one hand, this makes sense. Talent acquisition has been too fragmented for too long. But on the other hand, it raises a real question: If everyone is a platform, how do buyers evaluate where real depth and value exist?

We are moving from a best of breed conversation to a platform consolidation reality. But clarity has not caught up.

3. Agentic AI Is Everywhere but Still Undefined

You could not walk the expo floor without seeing the word “agentic.” It’s on every booth. In every demo. Across every category. But the definition is inconsistent. Some vendors are talking about true orchestration of tasks across systems. Others are rebranding automation or copilots. And very few are showing what this actually looks like in a live, end to end workflow.

4. Trust Is the Foundation of Transformation

In my workshop on TA transformation, one topic kept coming up over and over again: trust.

Trust with candidates. Trust internally across teams. Trust in how AI is being used. And trust in vendors. Candidates expect transparency and communication. Internally, teams are being asked to adopt technologies they don’t fully understand. And buyers are being asked to invest in solutions that often sound identical. Without trust, none of this scales. And right now, trust is fragile.

5. Differentiation Is Getting Harder

Everyone sounds the same. Every vendor is talking about AI. About skills. About platforms. About improving experience. About outcomes. But when messaging converges, differentiation disappears.This puts more pressure on buyers to go deeper. Not just what a vendor says, but what they can prove. What outcomes they can show. What problems they actually solve in production environments. The market is getting louder, but not necessarily clearer.

6. Transformation Requires Stages, Not Just Incremental Change

We talk a lot about transformation. But most organizations are still operating in incremental improvement mode.Adding AI to existing workflows. Layering on new tools. Optimizing pieces of the process. That’s not transformation.

Real transformation requires stages. Moving from automation to AI to more intelligent, orchestrated systems. It requires rethinking how work gets done, not just improving how it gets done today. And most organizations are still early in that journey.

The next phase of this market will not be defined by who has AI. It will be defined by who can prove it works.

The post Notes from UNLEASH: AI, Trust, and the Blur of Differentiation appeared first on Aptitude Research.

Frontline hiring is at a breaking point. Across industries, more than 60 percent of companies that have increased frontline hiring in the past year. Nearly half report growth between 10 and 25 percent, and another 15 percent have seen increases above 25 percent.

And yet 41 percent of organizations are still using the same hiring process for professional and frontline roles. Forty two percent are using the same technology for both. That is not scale. That is strain.

The result is friction across the funnel.

Seventy percent of organizations say their biggest challenge is finding enough qualified candidates. Forty eight percent struggle to screen and rank candidates efficiently. Twenty eight percent cite high cost per hire. Twenty six percent point to candidate drop off during the hiring process.

When we look at where the process actually breaks down, screening is the biggest bottleneck at 44 percent. Background checks and compliance follow at 36 percent. Interview scheduling comes in at 34 percent. These are not strategic conversations. These are operational pressure points.

And the cost of inefficiency extends far beyond recruiting.

Unfilled frontline roles impact revenue. Rushed hires increase turnover. Recruiters burn out. Customer experience declines. In fact, when organizations were asked what would happen if they reduced time to fill by just five days, 45 percent said quality of hire would improve. Thirty six percent said candidate experience would improve. Thirty three percent said recruiter workload would decrease. Thirty one percent said customer experience would improve.

Frontline candidates want speed, clarity, simplicity, and connection. If the process is not mobile first and responsive, they move on.

Technology should help solve this. But only 25 percent of talent acquisition leaders believe their current technology is very effective in high volume hiring.

When asked what they would like to use, 57 percent point to better sourcing technology. Fifty two percent want interview scheduling automation. Forty five percent highlight AI matching.

The benefits of AI are clear in perception. Forty five percent say it increases quality of hire. Forty two percent cite cost savings in sourcing and screening. Twenty seven percent say it reduces time to fill. Yet 23 percent admit they do not currently measure its impact.

Frontline hiring is not lacking tools. It is lacking intentional design.

This is exactly why Tim Sackett and I are launching a twice monthly podcast focused entirely on frontline hiring in partnership with Paradox.

Frontline hiring deserves its own conversation. It is not a scaled down version of corporate recruiting. It operates under different pressures, different economics, and different candidate expectations.

In the podcast, we will unpack what these numbers actually mean. Where organizations are misaligned. How conversational technology and automation are reshaping speed and experience. And what leaders should be measuring beyond time to fill.

We will talk to practitioners who are managing thousands of hires. We will challenge assumptions about volume and quality. And we will connect frontline hiring directly to business performance.

Frontline hiring is no longer operational overhead.

It is a strategic lever.

The post The Frontline Hiring Reckoning: A New Conversation appeared first on Aptitude Research.

For the last several years, HR technology has been defined by expansion. More vendors, more features, more promises. This year feels different. The conversation is shifting from what is possible to what actually works. From AI accountability to ecosystem clarity, HR leaders are being asked to make sharper decisions with clearer outcomes in mind.

Here is what I expect to define HR tech this year and where organizations should focus their attention.

1. Responsible AI moves from principle to practice

Responsible AI is no longer a positioning statement. It is a requirement. Buyers are asking harder questions about how models are trained, how decisions are explained, and how bias is mitigated in real workflows rather than policy documents.

What has changed is pressure. Regulatory scrutiny, employee trust, and brand risk are forcing providers to operationalize responsible AI across their products rather than bolt it on. This year, vendors that can demonstrate governance, transparency, and human oversight at scale will separate themselves from those still relying on aspirational messaging.

2. Companies must understand the ecosystem of how work gets done

HR technology has never existed in isolation, but the ecosystem now matters more than individual tools. Work happens across systems including HR, TA, CRM, finance, IT, and collaboration platforms, and AI only adds complexity.

Organizations are starting to map how work actually flows, not how org charts say it should. That means understanding dependencies, handoffs, and data movement across platforms. Providers that can show how their technology fits into this broader ecosystem of humans and AI and reduces friction rather than adding it will win.

This shift is less about buying another system and more about designing how work happens.

3. Talent acquisition responsibilities continue to expand

Talent acquisition is no longer just about filling roles. Teams are increasingly responsible for workforce planning, internal mobility, skills intelligence, employer branding, and analytics, often with the same headcount and budget.

This expansion changes buying behavior. Tools that only solve one narrow recruiting problem are harder to justify. TA leaders need technology that supports long term workforce decisions, not just short term hiring spikes. The expectation is broader impact, deeper insight, and closer alignment with the business.

4. ROI is no longer optional

For years, HR tech ROI was discussed but rarely enforced. That is over. Budget scrutiny, consolidation, and executive oversight mean every investment must show measurable impact.

This does not mean HR needs to justify itself in purely financial terms. It does mean tying technology to outcomes such as time saved, quality improved, risk reduced, and productivity increased. Vendors that help customers define, measure, and communicate ROI will be far more credible than those that avoid the conversation.

5. Integrated data is now a baseline expectation

AI, analytics, and workforce planning all depend on data that works together. Fragmented systems and disconnected datasets are holding organizations back.

This year, integration is no longer a nice to have. HR leaders expect systems to share data cleanly across HCM, TA, learning, performance, and beyond. Providers that rely on closed ecosystems or weak integrations will struggle as buyers prioritize platforms that enable insight across the entire employee lifecycle.

6. The conversation has shifted to platform versus platform

The old debate about best of breed versus ERP or HCM is no longer the point. The market has consolidated, and the real competition is now platform versus platform.

Providers are doing more through native expansion, acquisitions, and deeper partnerships. Buyers are evaluating which platforms can support scale, flexibility, and innovation over time rather than which tool is best at one specific function today. The expectation is broader capability with less operational burden.

7. The AI conversation must move from adoption to outcomes

Most organizations say they are using AI. Far fewer can point to meaningful results.

This year, the market will demand proof. Real examples of companies using agentic AI to automate work, support decisions, and improve outcomes. Not demos or pilots, but measurable impact.

The winners will be vendors and customers who can clearly articulate what AI is doing, where it is embedded, and how it changes work for people rather than simply stating that AI exists.

8. Frontline workers require a different technology strategy

Frontline work continues to expose a major divide in HR technology. Supporting desk based employees is not the same as supporting frontline workers, and that gap is becoming more visible.

Providers that truly serve frontline populations understand constraints around access, devices, connectivity, and time. Investment strategies must reflect those realities. This year, organizations will draw a clearer line between technology that can support frontline work at scale and technology that cannot.

Looking ahead

This year is not about more technology. It is about better decisions. HR leaders are being asked to move from experimentation to execution and from adoption to accountability.

The organizations that succeed will focus less on trends and more on outcomes. Responsible AI, integrated data, real ROI, and systems that reflect how work actually gets done will define the next phase of HR technology maturity.

The post What to Expect in HR Tech This Year from Adoption to Accountability appeared first on Aptitude Research.