In 2026, HR leaders will face a workplace characterized by accelerating AI adoption, evolving employee expectations and ongoing economic uncertainty. As these forces converge, they are reshaping roles, skill requirements and how organizations think about the future of work, making HR’s strategic and stabilizing leadership more critical than ever.

Drawing on conversations with talent leaders across industries and insights from our global community, Avature Founder and CEO Dimitri Boylan outlines the key trends set to define HR in 2026. From the spread of agentic AI to new approaches to skills, culture and adaptability, this outlook offers HR leaders a practical guide to navigating change and driving impact in the year ahead.

HR Trend 1: Turning AI Productivity Into Enterprise Value

In 2025, many organizations saw a surge in individual productivity fueled by AI tools. But as AI usage increased, a clear pattern began to emerge: productivity gains, while welcome, were largely isolated. They helped individuals work more efficiently, but they didn’t fundamentally change how the enterprise operated.

If all you get from AI is that individual users become more efficient, you probably as a business don’t end up on the right side of disruption, in the winners’ circle.

This challenge is reflected in MIT research, which found that 95 percent of AI pilots fail to deliver measurable business results. The underlying issue doesn’t stem from how the technology works at a fundamental level, but rather from a lack of integration.

Without context and connection to core data and processes, AI is unable to achieve the enterprise-scale efficiency gains organizations seek. The key to unlocking that value lies in operationalizing knowledge across the enterprise by embedding it directly into the workflows teams rely on every day.

If you’re really focused on getting to the ROI, you have to get beyond support for the individual. The AI has to be context-aware, workflow-embedded and fully integrated into the way the organisation operates. It’s a big challenge.

As we head into 2026, organizations that continue deploying standalone, consumer-oriented AI features will see diminishing returns, while those that embed AI deeply into their workflows, content ecosystems and operating models will drive value that moves the business forward—not just increased efficiency, but better decisions, stronger alignment and a workforce equipped for long-term competitiveness.

HR Trend 2: The Spread of Agentic AI—Hybrid Takes on a New Meaning

If 2025 was marked by the rise of agentic AI, 2026 is the year it will spread across the enterprise, with 69 percent of business leaders expecting AI agents to transform their operations this year—a clear signal that organizations are moving beyond curiosity into widespread adoption.

As agentic AI becomes woven into core workflows, it’s also prompting organizations to reimagine the future of the workforce itself. One company already leaning into this shift is IBM. At #AvatureUpfront EU 2025, IBM introduced the idea of becoming an “AI-hybrid organization”, outlining a 2027 vision in which humans and AI agents collaborate fluidly across workflows. In 2026, we foresee more organizations following suit, but their success will depend on agents having the right context.

To unlock the full potential of enterprise-grade agentic AI, interconnectivity must become a strategic priority. Disconnected tasks need to evolve into coordinated, cross-workflow automation. That requires more than just powerful models—it demands infrastructure. Organizations should ensure their vendors are investing in integration-first design, including robust support for the Model Context Protocol and best-in-class frameworks that enable intelligent interoperability at scale.

And with the rise of agents, the best-in-class platforms in the market are beginning to offer agent builders—tools that allow users to quickly and autonomously design their own agents based on their unique processes and governance requirements, while maintaining transparency and control. As such, 2026 will mark a significant step toward scalable autonomy, where control and transparency are built in from the start.

Designing Guardrails for Human–Agent Collaboration

But as agentic AI grows more powerful and autonomous, trust, ethics and AI governance become paramount. As custodians of organizational culture and ethical decision-making, HR has a vital role in ensuring that agents behave in line with company values and operate within clear guardrails.

Achieving this requires close collaboration between HR, IT and Legal to define where AI can act autonomously, which decisions must remain human-led and what data agents can access in compliance with emerging regulations. As a result, human-in-the-loop governance, where people supervise, guide and intervene when needed, is likely to become the cornerstone of responsible agentic AI adoption in 2026.

HR Trend 3: From “Admin” to “Architect”—HR as the Chief Designer of Work

As artificial intelligence reshapes how work gets done, it’s also altering roles, skills and organizational structures in ways we can’t yet fully anticipate. With 39 percent of core U.S. workforce skills expected to change by 2030, the ability to forecast and respond to emerging skills needs will become a defining capability for HR. Yet only 11 percent of HR professionals feel very confident they can do so today, according to Avature’s recent AI Impact Survey.

Closing this gap will require more than reactive planning—it demands real-time visibility across the talent lifecycle. That’s why we’re launching Avature’s Workforce Planning Solution, designed to provide a holistic view of talent acquisition, talent management and skills intelligence within a single ecosystem.

As organizations work to close this confidence gap, we’ll also see HR’s role shift from process administration to architecting the future of work. This means defining how roles evolve, how AI and humans collaborate, and how skills are built, bought or borrowed across the enterprise. The question is no longer if AI will reshape work, but whether HR will lead that transformation or be left reacting to it. We believe it’s HR’s moment to lead, and doing so requires a platform purpose-built to navigate complexity, support agility and provide a truly connected view of talent.

Yet as this shift plays out, it’s clear that skills alone may not offer the level of precision required. That’s why we foresee a growing emphasis on tasks in 2026, not as a replacement for skills, but as a more actionable, context-rich layer.

As Jennifer Shappley, Global Talent & HR Leader, put it in her episode of The Talent Transformation Podcast: “Getting an understanding of the tasks of a job is just a smart place to be spending time at a point where more automation and AI are coming, and it’s going to change the tasks.”

HR Trend 4: The Rise of People- and Culture-Led Transformation

AI adoption is not a tech transition, it’s a people transition,”

Dr. Alberto Rossi
Director of the AI, Analytics and Future of Work Initiative, Georgetown University

As organizations pursue increasingly ambitious AI initiatives, it’s becoming clear that technology alone won’t deliver transformation. Enterprise innovation hinges on three inseparable pillars: people, culture and technology. It’s the organizations that master all three that will stay ahead.

Even the most sophisticated AI won’t give an organization a competitive advantage if its people lack the skills or confidence to use it in their day-to-day work. As Dr. Rossi explains, “Success is a people transition as much as a tech transition. Teams that upskill early and design great employee UX will out-execute those with better models but clunkier adoption.” In a landscape where technology evolves at unprecedented speed, the ability to continuously learn, test and adapt becomes a critical differentiator.

Culture, too, becomes a strategic asset in this shift. Innovation slows to a halt in environments where employees fear making mistakes or challenging old ways of working. As David Swanz, HR & Talent Transformation Service Line Leader at IBM, highlights, “You need to have permission to fail; otherwise, you don’t learn. There needs to be permission to experiment with new things.” Creating this psychological safety—where experimentation is rewarded, and iteration is expected—is essential for scaling AI responsibly and unlocking its full organizational impact.

Given its proximity to people, insight into workforce dynamics and influence over how change is experienced, HR is uniquely positioned to lead the charge in shaping the enterprise’s cultural and capability foundations that make transformation possible. In 2026, HR teams that cultivate continuous learning, normalizing experimentation and embedding adaptability into daily work can turn AI disruption into a strategic advantage—not by reacting to change, but by shaping the conditions that allow the entire organization to thrive in it.

HR Trend 5: The Revival of CRM

In 2026, skills shortages remain a defining challenge for HR, with 48% of talent leaders citing critical gaps in hard-to-fill roles, according to Avature’s AI Impact Survey. In this environment, passive hiring is no longer sufficient. What’s needed is sustained engagement, long before a role opens and long after a candidate hits “apply.”

That’s why CRM is being redefined as the strategic foundation for building talent supply chain independence. In 2026, we anticipate a significant increase in organizations utilizing CRM to foster direct relationships with both external and internal talent, ensuring their pipelines are well-stocked before demand surges.

Organizations like ManTech are showing what’s possible. By leveraging Avature CRM to build self-sustaining communities, the government contractor has significantly reduced third-party spend and achieved remarkable results: 57% of external hires now come directly from their career page, and applicant flow has grown tenfold in just three years.

In a market where speed and relevance are everything, CRM is no longer a “nice to have.” It’s the infrastructure for a proactive, data-driven and truly resilient talent strategy.

HR Trend 6: Adaptable Technology Becomes Mission-Critical

In nature and in business, survival belongs to those who adapt. And in 2026, as the trends above take hold, adaptability is no longer a differentiator—it’s expected. This makes selecting technology that enables adaptability mission-critical.

The ability to quickly modify workflows, update data models and adjust governance structures without disruption will be essential, necessitating flexible platforms that can evolve alongside changing processes, operating models and emerging capabilities. That’s why at Avature, our objective is to enable you to adapt quickly and effectively so you have no digital or technical execution risk as you respond to market conditions.

In 2026, the most future-ready organizations will be those that choose technology built for continuous evolution—systems that support experimentation, allow teams to redesign processes as roles change and scale new ideas without technical bottlenecks. In a world where transformation never pauses, the right technology becomes the quiet enabler of agility, resilience and innovation.

To Conclude

The year ahead will intensify the divide between organizations that embrace change and those overwhelmed by it. As AI becomes more deeply embedded across workflows, companies that invest in adaptable platforms and deploy agentic intelligence with purpose will accelerate, while those constrained by legacy systems and fragmented data will struggle to keep pace.

HR stands at the center of this shift. By designing hybrid human–AI operating models, strengthening cultural readiness and enabling employees to learn and evolve alongside new technologies, HR can ensure the organization moves forward with clarity and momentum. The future will not wait for those who hesitate—and HR is uniquely positioned to help organizations move decisively into what comes next.

We all knew AI was going to be transformational.

And although it’s still early days, we’re already seeing vast changes in how we go about our everyday business. From planning an itinerary to drafting an email, AI is redefining how we seek solutions and perform tasks, both personal and professional.

The world of talent acquisition is no exception, with skills matching and agentic automation among the many positive innovations winning back wasted hours for overworked talent teams and boosting the quality of hires. This extends throughout the HR function. Citing a recent Gallup survey, Forbes stated that 93 percent of CHROs have integrated AI into their everyday activities, with ideation and reduced administrative work identified as areas for quick wins.

There is, however, one area in particular where AI is proving problematic.

The Rise of the AI-Powered Serial Applicant

As I speak with clients, prospects and peers, we all agree that one area that has become immensely difficult to deal with is that of ‘AI-powered candidates’.

They have always existed in some form or another: the serial applicant who applies for a hundred jobs in your organization while barely meeting the criteria for even a handful. And while they would often cause some noise, the issue was manageable. With a few automations and rules, an ATS could easily spot them, and the recruiter could filter them out in favour of the truly committed talent.

But all that’s now changed. Serial applicants are getting to grips with large language models (LLMs) and using them to tailor resumes to positions and apply in a matter of seconds. The issue is further compounded by some candidates employing AI bots to comb job boards and apply for roles on their behalf, automatically tailoring their CV and cover letter to the job description. And ATS filters are having a much harder time catching them.

An AI Arms Race Emerging Between Candidates and Recruiters

In hindsight, it was only a matter of time until this happened. Interestingly, a perfect analogy of the issue can be found in a core component of how today’s AI tools make self-improvements: generative adversarial networks (GANs).

GANs mirror human communication: there’s a sender and a recipient. And the purpose of them is to improve the technology’s ability to determine whether a message is authentic or fake. Over the course of countless iterations, the recipient learns from its mistakes to become better at spotting fakes, while the sender also learns to become better at crafting its deceptions.

If we were to transpose this digital game of cat and mouse to the talent world, we would be the cat, or the recipient. Our responsibility, therefore, is to sharpen our ability to ‘detect the fake‘ and sift out these serial candidates. As I see it, there are two key ways to do this:

1. Strengthen the filtering layer of your ATS
2. Incorporate sourcing strategies an algorithm can’t game

Let’s dive deeper into these paths:

1. Strengthen the Filtering Layer of Your ATS

Savvy process design and the right technology can work together to provide extra checks, without tying up recruiters’ time. Assessments, limit-setting and recorded interviews will play a key role here.

• Pre-Interview Screening Assessments:
  Not only do these provide an additional friction layer to help verify intent, but they also allow us to gain a greater understanding of the candidate’s skills and capabilities on a quantitative level.
• Setting Application Limits:
  Recently, we have been seeing clients implement restrictions around how often candidates can apply (e.g., 3 applications within a 12-month period). Enforcing these limits gives candidates pause to consider whether the job they are applying for really is the best fit for them.
• Recorded interviews:
  Removing the synchronous aspect of interviews reduces the risk of a bottleneck of interviewer availability. But it also provides an opportunity to assess candidate suitability more qualitatively without compromising on efficiency. As these tools evolve, it will become interesting to see whether AI can uplift the quality of these questions by tailoring them dynamically based on recruiter prompts and the candidate’s previous responses.

2. Incorporate Sourcing Strategies an Algorithm Can’t Game

Get Face-to-Face at Recruiting Events:

In-person meetups, careers fairs and networking gatherings have taken on a new importance in an era of AI-driven hiring. While CVs can be made to look flawless, they can’t shake your hand, hold eye contact or navigate a spontaneous conversation. These events give recruiters a chance to pick up on non-verbal cues, communication style and signs of genuine enthusiasm – something an algorithm can’t fake.

Even a brief, five-minute interaction can reveal subtle insights into a candidate’s suitability that are far more difficult to glean from a pristine CV. Companies that consistently show up at industry-specific fairs or host their own community meetups build trust, brand recognition and a pipeline of candidates who have already demonstrated real-world engagement.

Tap Into Referral Programs:

There’s never been a more important time to have someone vouch for a candidate, to raise their hand and say “I know them, they can do it”. As recruiters drown in perfect-on-paper applications, the signals we used to see as measures of intent are becoming less relevant. Tailored keywords, cover letters, all of the little details and extra discretionary effort that once showed a candidate is genuinely keen are no longer differentiators in a world in which generative AI can provide this in seconds. They’re the bare minimum.

On the other hand, a referral from someone inside the organization still speaks volumes. According to an article from Mercer, referrals convert 4 times faster and stick 70 percent longer, and cost $1,000 less per hire.

Engage Your Talent Community:

AI has brought application sprees, but what happens when you don’t have jobs to offer to a candidate? A select group will be going out of their way and engaging with you even when there are no open jobs – providing you give them the opportunity. The leads you receive from your talent community will provide a reliable source of talent with a clear demonstration of intent.

The Tech is There to Help, But We Need Humans in the Loop

As AI-powered applicants flood the system, it’s no longer enough to rely on resumes, cover letters and keyword tailoring to determine their level of intent. We need to make sure we’re applying smarter filters and sharper instincts.

The future of hiring lies in combining the efficiency of technology with targeted human engagement. Assessments and automation can help, but real intent is best spotted through conversations, referrals and community engagement. The companies that win won’t just have the best tech; they’ll have the best read on people.

Interested in learning more about how Avature can help you cut to the quality candidates? Contact me here or on LinkedIn.

AI recruiting technology is continuing to yield quietly impressive results, with a recent analysis of Avature’s explainable matching tool demonstrating 80 percent accuracy in recommending candidates for a given role. This means that out of the top ten candidates suggested by AI, human talent experts deem eight as relevant.

In a similar analysis, Avature’s AI Resume Parser obtained 90 percent accuracy in extracting relevant candidate information and presenting it ready for use by recruiting teams.

This level of reliability makes it possible to relieve TA teams of often administratively burdensome, time-consuming tasks. Instead, they can focus on more value-added activities, such as engaging with applicants directly and providing strategically relevant market insights.

Focus On Integrated Use Cases to Derive Immediate Value from AI Recruiting Technology

Commentary on AI’s capacity to revolutionize the workplace is impossible to avoid. But a recent report from MIT states that in many fields, it is falling short of expectations and delivering only limited ROI.

The report suggests that to manage expectations and derive immediate value, it’s important to distinguish between an ambiguous AI-driven overhaul of operations predicted by many commentators and those AI tools that integrate easily into existing workflows, apply to defined use cases and are therefore able to provide an immediate boost to efficiency.

Avature’s Natural Language Processing (NLP) team carried out the research to determine the effectiveness of two AI features that fall very much into that second category. The news of their proven reliability comes at a critical time for many talent teams dealing with a frequently unmanageable number of applications.

AI Matching Tools Providing a Timely Productivity Boost For Talent Teams

A cursory glance at the jobs on LinkedIn should be enough to tell you that TA teams have a lot on their hands. In an age of ‘easy apply’ options and AI-assisted serial applicants, it’s not uncommon for companies of all sizes to receive hundreds of applications for a single position.

Manually sifting through this volume of applications can use up much of a recruiter’s capacity, distracting them from value-added work while increasing the risk of errors.

By contrast, AI recruiting technology can efficiently rank candidates according to their fit with a position’s requirements. To do so, matching AI identifies and compares job titles and key skills data from the job description and candidate resume and scores suitability according to predefined weighted factors. Alternatively, recruiters looking to model their search on a previously successful hire can find similar candidates based on experience and skills.

But one of the most compelling advantages of AI in recruiting goes beyond efficiency: it lies in the ability to make hiring decisions more objective.

AI Helps Achieve Fairer, Skills-First Hiring Based on Merit

Traditional resume screening can sometimes favour candidates who “look good on paper” but who are later found to lack what it takes to succeed.

Avature’s explainable AI addresses this by ranking candidates based on the skills and experiences most relevant to a role, ensuring suitability is determined by capability rather than surface impressions or job titles alone. Recruiters can then use the interview process to explore these areas in more detail.

This has a two-fold benefit of helping to filter out those candidates who seem impressive on first glance but lack the required skills to perform the job, while also bringing to the fore those hidden gems who might otherwise have slipped through the cracks.

Of course, any discussion of AI in recruitment must consider the risk of bias. Algorithms trained on historical data can inadvertently replicate past preferences, favoring certain demographics or educational backgrounds. To mitigate this, Avature’s machine learning team has taken proactive measures. Personal identifiers, such as name, gender, age and ethnicity are excluded from training datasets, reducing the risk of biased outcomes at the source.

By applying skills-first evaluations of a candidate, Avature’s AI empowers talent teams to build richer pipelines, surface hidden talent, and make hiring decisions that are both faster and fairer.

AI Agents Set to Further Shake Up The World of Recruiting

Resume parsing, skills extraction and matching, and candidate ranking are already having a huge impact on the work of many talent teams. However, things are about to take an even more significant leap forward.

The introduction of AI agents to undertake much of the repetitive, burdensome administration with greater autonomy is likely to provide a true game-changer. We are now entering an age in which a combination of AI agents drawing on a deep understanding of both HR-specific data and a company’s contextual information will be able to work together to take on whole sections of a recruiting workflow.

This may be as simple as drafting and sending an email in line with the organization’s brand voice, drawing up contract offers in line with company and local guidelines, scheduling and drafting questions for candidate interviews or transcribing and collating decision-maker feedback into a coherent document for review. In every case, human oversight is paramount, but the boost in productivity promised will be more than apparent.

We are only starting to explore the vast potential of AI recruiting technology to deliver new efficiencies and lead us to further evolve how we work. Given the sheer breadth of possibilities, it is difficult to predict every use case and benefit at this time.

For the time being, quick wins and real ROI can be found in the smart automation of mundane and time-consuming administrative tasks and the ordering of large volumes of applications into actionable lists of recommended candidates. But as new opportunities continue to emerge to refine how we engage and hire, it couldn’t be a more exciting time to work in talent.

Interested in learning more about Avature’s AI? Speak to our NLP team to learn more.

What if AI could finally solve HR’s biggest challenge: keeping employees engaged and loyal?

In our report, The State of the HR Landscape in 2025, we explored how AI and machine learning are reshaping HR strategies, with our survey revealing that a staggering 95 percent of respondents anticipated increased adoption of these technologies in the months to come.

AI is transforming how HR tackles one of its most persistent challenges: aligning workforce skills with evolving business needs. Our study revealed a growing shift toward skills-based approaches, with organizations using AI to map employee capabilities to emerging roles.

In fact, 46 percent of companies already applying skills-first strategies are using them to support internal mobility, helping employees transition into new positions and advance their careers. This is leading to more agile workforces, increased engagement and, ultimately, retention.

In this article, we’ll explore how integrating AI into employee engagement and talent management strategies can foster a workplace culture in which people thrive – and stay.

AI in Employee Engagement: A New Chapter in Understanding and Retaining Talent

Artificial intelligence’s ability to conduct deep contextual analysis enables the application of pattern recognition and behavioral understanding at a scale far beyond human capacity.

This could have a transformative impact on talent management strategies, particularly when considering the limitations of traditional methods used to measure subjective factors like employee engagement and sentiment.

Take employee surveys, for example: rating feelings on a numerical scale often falls short when it comes to capturing the nuances of human emotions, thoughts and experiences.

Now, AI could take measuring engagement from an intuition-based approach to one grounded in real, reliable data. Instead of asking employees to express how they feel in terms of predefined, rigid categories, imagine allowing them to express themselves in their own words and then use natural language processing (NLP) to analyze their responses and uncover insights.

By gaining access to this information, HR teams could get a deeper understanding of how employees really feel. More importantly, they would be able to detect attrition risk and take proactive measures to strengthen morale, in doing so helping to retain key talent and lessening the impact of high employee turnover on business performance.

This same pattern recognition at scale could also improve internal mobility processes. Beyond identifying and inferring a person’s specific skills, AI can also suggest positions or projects that better align with their profile and competencies.

Interestingly, in contrast to common perception, the adoption of AI can support tasks that require more human involvement.

According to our report, 42 percent of HR professionals already working with AI and automation technologies have seen increased productivity and a reduction in repetitive administrative tasks. The most powerful consequence of this is that it allows professionals to shift their attention to the more strategic aspects of their work, those in which human insight and perspective are irreplaceable.

As a result, AI is helping shape workplaces where employees find greater meaning in their contributions, feel more satisfied with their work, and, in turn, become more loyal and committed to their organizations.

How Can You Start Taking Advantage of AI in HR?

With so many potential use cases for AI, from recruiting to employee engagement and retention, knowing where to start can be daunting. This was a topic during a recent panel we hosted with leaders from DHL, Bain and Unifi.

Their advice? Begin with solutions that deliver high impact without requiring large upfront investments. Additionally, launching pilot projects before scaling them across the organization can minimize risk, streamline implementation and ensure real value.

To drive the success of these initiatives, promoting AI literacy and strengthening confidence in its use among employees will make a huge difference. Educating workers about the limitations of the technology and reminding them that it isn’t always truthful can also help by mitigating the risk of costly errors.

However, the success of AI in employee engagement, retention and any other HR application will ultimately depend on a well-defined strategy that considers the particularities of each organization: its culture, its values, its objectives and the specific challenges it faces.

Artificial intelligence represents the greatest technological leap we have ever experienced, and we are still discovering how to maximize it. Adopting AI effectively requires not only an investment in technology but also a change of mindset within organizations.

To achieve this, HR departments and business leaders would be wise to foster a culture of learning and help demystify AI among employees. This will empower them to leverage AI’s full potential to build more efficient, engaging and personalized work environments.

AI is already showing how it can transform employee engagement, retention and skills development, but turning this potential into measurable outcomes requires the right approach. At Avature, we help organizations start with practical, high-impact use cases, pilot them safely, and scale them to deliver lasting value. If you’re ready to explore how AI can strengthen engagement and loyalty in your workforce, get in touch with us today.

Discussions about AI in HR are widespread, but many organizations still face a disconnect between lofty expectations and tangible results. In fact, our recent survey revealed that while an encouraging 42 percent of respondents using AI have seen an increase in productivity, 27 percent have yet to experience AI’s impact.

While opinions vary on how best to harness AI in the HR space, there is almost unanimous agreement across the industry that artificial intelligence offers immense potential and equally significant challenges. In this context, we wanted to hear how several industry leaders are navigating this leap at their organizations.

Neha Sharma, Head of HR at Unifi, Ramesh Razdan, CIO at Bain, and Ralph Wiechers, EVP of Digital HR & People Operations at DHL, recently came together for a dynamic panel discussion moderated by Avature CEO Dimitri Boylan. They shared real-world insights on how companies can use AI to address the challenge of creating a competitive advantage.

Everyone needs a game plan, and we hope these takeaways help to inform yours. Let’s jump in.

The Pursuit of Opportunities for AI in HR

According to our survey, 95 percent of respondents intend to implement AI and machine learning in their HR processes in the coming year. Conscious of some of the challenges around this, Boylan was keen to talk about how our panel intended to do so. After an animated exchange of opinions, four key takeaways emerged.

1. Define a Prioritization Framework

In the face of so much opportunity, deciding where to invest and deploy AI can feel overwhelming. Razdan shared a failsafe prioritization framework that he recommends for determining the feasibility and value of AI initiatives by balancing data complexity with potential impact.

We have a framework. We build this 2×2 matrix that considers what value is at stake, whether the value at stake would be operational efficiency or whether you can build a new product or create new value for the company. But also, how is the ease of implementation, which is the availability of the data, how cross-functional it is.”

Ramesh Razdan
CIO at Bain

Progress is near-impossible until you master complex data management. But those able to deal with the data effectively will soon grasp whether a project is worth pursuing. Once you’ve taken the time to plot your potential use cases on the matrix, it becomes easier to focus on those high-value, easy-to-execute projects.

Boylan was quick to agree with Razdan: “Frameworks is an area that HR has to focus on to make sure they don’t end up just with a lot of AI scattered throughout a tech stack… Put that in a framework so that you have a benchmarking capability, you have guardrails so that people know what they need to know about what other people are doing, and so that the whole thing hangs together at some point in the future.”

2. Focus On Low-Hanging Fruit

Sixty-one percent of the audience shared that they have already deployed an HR-specific artificial intelligence use case at scale. The result is promising, though there’s a whole lot more to do.

From Sharma’s perspective, there is plenty of low-hanging fruit that will help the HR organization go from good to great with AI. For example, she believes that AI will help her solve real HR pain points, like improving how to conduct employee engagement surveys. Instead of forcing employees into boxes through rigid rating scales that do not reflect how people truly think or feel, she envisages letting them express themselves in their own words, before using Natural Language Processing to analyze sentiment at scale.

What are the low-hanging fruits that can truly give you a lot of impact with less effort? That’s in the short term. I think that’s where all of us are trying to leverage AI to really change the way we operate and create value for different stakeholders.”

Neha Sharma
Head of HR at Unifi

Retention is another major focus area at Unifi, and an AI-driven attrition-prediction model using multiple data points is already providing valuable insights for Sharma’s team. She shared that this model currently runs at 50 percent accuracy. Though she would like that number to be much higher, it’s good enough to start using and tweaking and for incremental improvements to be integrated over time. In this regard, she advocates for progress over perfection.

3. Balance a Top-Down Mandate with Grassroots Innovation

According to our panelists, AI-driven HR transformation should involve both grassroots efforts and strategic top-down guidance. Each shared that their organization is embarking on different levels of controlled experiments within different parts of the business to truly assess the real value that AI can bring them.
Razdan shared that Bain’s innovative culture lends itself to this approach, and bottom-up innovation in the form of hackathons has uncovered unexpected, high-value AI innovations while generating employee engagement. He acknowledged that failure is part of the process—successful organizations test quickly, iterate and move forward. At the same time, when it comes to setting an overarching strategy and working on “those big hairy problems,” top-down guidance is best. DHL shares a similar perspective.

The approach that we have taken is balanced between grassroots on the one side and a kind of top-down guidance, but not too strict, on the other hand. What do I mean by this? We quite quickly came up with our own set of compliant ways to use large language models… But, on the other hand, to not limit use cases too much. We have grassroots activity to tap into the exploration and the knowledge of our advanced teams that want to work with it.”

Ralph Wiechers
EVP of Digital HR & People Operations at DHL

4. Build Flexibility into Your Strategy

We are at the dawn of the AI revolution, with advances coming at us on an almost weekly cadence. In this context, Razdan stressed the importance of investing in a flexible AI architecture that will allow for changes in direction. Boylan agreed, sharing that this was the motivation for designing Avature’s Models-as-a-Service architecture for AI.

We are in the early innings, and technology and architecture are evolving so rapidly. So, building a flexible architecture that you are able to adjust as the needs arise is also important.”

Ramesh Razdan
CIO at Bain

AI Literacy: The Right Amount for the Right People

The conversation swiftly moved on to the theme of AI literacy. When it comes to educating the workforce on AI, our panelists were clear that a one-size-fits-all approach won’t suffice.
For both DHL and Unifi, whose workforces are made up of between 80 to 90 percent frontline staff, there is a clear distinction between those that really need to know about AI and those that don’t.

Blue-collar workers are increasingly exposed to AI in their workflow, such as the routing tools that support DHL drivers in their delivery routes. However, Wiechers highlighted that often, these employees might not even know that they are interacting with AI. In this regard, he shared a pertinent observation. Ensuring that AI is intuitive for this group of the workforce is far more important than educating them on AI. Such is their design that many of the AI tools now emerging are actually more intuitive than those that they are replacing.

A good example is picking robots that help people in a warehouse. In the past, you might have had onboarding training for those who wanted to co-work with the robots. The new technologies that are building on AI and Gen AI are so super intuitive that there’s even less need to train because it’s very personalized and very intuitive to work with, which is a bit counterintuitive. Gen AI or AI can even reduce the training needs.”

Ralph Wiechers
EVP of Digital HR & People Operations at DHL

Sharma echoed this perspective, highlighting that she is more focused on change management. If the solutions she and her team develop are simple enough, frontline employees will seamlessly adopt them as part of their daily lives. That being said, there are other user groups that require a much higher level of literacy, given the nature of their work – HR being one of them.

Boylan cautioned that some basic training might be a good idea for everyone to reduce operational risk, for example, ensuring employees aren’t putting intellectual property into ChatGPT. Furthermore, recent headlines provide a stark reminder that AI doesn’t value the truth, and it might be good to educate the workforce on that.

I stress that it’s not an oracle of the truth. It’s a statistical engine that gives you the most popular combination of words in a sequence, and it can use the truth, but it’s not obligated to produce the truth. So, there are some fundamental things that people need to understand, based on exactly how they’re interacting with it.”

Dimitri Boylan
CEO at Avature

At the technical helm of a very different kind of organization, Razdan shared that Bain offers training to 100 percent of its employees and now includes Gen AI training as part of their onboarding programs. As a management consultancy that supports clients with their own responsible and ethical artificial intelligence strategies, this level of education for its workforce is something that differentiates Bain from a value proposition perspective.

With every organization falling somewhere on the scale between Bain and DHL or Unifi, defining how much time and effort to invest in increasing AI literacy across the workforce will depend on the nature of your business. However, increasing AI literacy across the HR function is non-negotiable.

There is a dire need for all of us to really understand the pros and cons, challenges and risks that AI presents and see how best to leverage it. So, there is a level of understanding that is needed as the function, and that holds true for some of the other corporate functions as well.”

Neha Sharma
Head of HR at Unifi

The Future of AI in HR

AI represents a sea change in technology. It’s a prediction engine, a consumer of a huge amount of context. No system has ever been able to incorporate this degree of context when producing an answer. It’s also the most powerful knowledge transfer tool that mankind has ever created. With these fundamental components to draw on, how does the talent function use this technology to elevate its value inside the organization? We sought to answer this question with our panelists.

As technology itself is evolving, the role of talent function is to enable everybody to operate at their full potential. That’s to me, what the talent function of the future is in partnership with technology.”

Ramesh Razdan
CIO at Bain

Transforming Strategic Workforce Planning

A lack of robust, real-time data has handicapped HR when it comes to strategic workforce planning. As Wiechers explained, “[Workforce planning is] so granular and so dependent on local talent markets that a company can’t do it as a one-size-fits-all model. So, from my point of view, the reason why it hasn’t been cracked yet is with respect to the availability and the structure of data.”

With the right architecture in place, HR teams could leverage AI for comprehensive analysis of historical and market data, as well as predictive analytics. But alone, it’s no silver bullet. Sharma highlighted that “There is a need to upgrade the way HR systems work so that they are a lot more intuitive and cognitive in terms of the way they capture information.” Instead of requiring employees or teams to enter information manually, she envisages a near future where data can be captured on the go as workflows progress or the employee lifecycle moves forward.

When data is collected in the flow of work, some organizations will probably figure out how to conduct workforce planning in real time. This would be a game-changer for Unifi, operating as it does in such a dynamic industry.

Maybe twenty people didn’t show up and I have five more people leaving. I have X number of new hires walking in today. Then I have maybe five possible flight delays that I can anticipate today because of the weather. It’s just too much to keep up with. We are managing it today, but I think this is truly one area where AI can exponentially help.”

Neha Sharma
Head of HR at Unifi

Wiechers is serious about the need for HR to get its house in order in terms of available data. Only then can a redefinition of the business model take place.

Focus on people data as a product. If [data] is not done right and not available and not accessible, prediction models won’t work. Use cases will come by themselves; models will become commodities. That’s all easy; but if you don’t have fuel, your engine will not start.”

Ralph Wiechers
EVP of Digital HR & People Operations at DHL

Realizing the Skills Opportunity

Until now, technology has been unable to handle the complexity of people data necessary to empower a skills-based approach at scale. Addressing the recent leaps in machine learning and AI that now enable us to consider the role of skills as a currency, Wiechers and Razdan emphasized this as a key area of opportunity for HR. Of course, this is intrinsically linked to the first opportunity we identified. Enhanced visibility of skills is an essential element of successful workforce planning.

Every organization I look at has a skills gap. We have always tried to figure out how to infer the skills. We don’t really know what skills are needed. How do we leverage this technology to infer the skills and build them automatically? We human beings are generally lazy. We don’t want to do anything manually. Anything we can do automatically, systematically inferring the information is the way to go.”

Ramesh Razdan
CIO at Bain

Improving the Employee Experience

Beyond helping HR shift from service provider to driver of strategic value, our panelists also highlighted some areas in which AI could help the function better serve its employees. On the one hand, Sharma shared her vision for introducing hyper-personalization in the context of career pathing, rewards, recognition and benefits. Razdan also sees the opportunity for AI to help with information overload, a common pain point amongst enterprise employees.

There is so much information, but people can’t find information anywhere. We have information silos and how do you bring information with context? There’s a fantastic opportunity between the talent and technology functions to build that, to give the right information to the right people.”

Ramesh Razdan
CIO at Bain

People are nuanced, and that complexity has long challenged talent teams and HR technology. Boylan closed the session on an optimistic note, suggesting that AI in HR might be the gift that finally allows talent teams to really get a handle on the human.

The conversation with our panelists made one thing clear: the future of HR will be defined by how effectively organizations can align AI with their people strategy. From prioritizing the right use cases to driving AI literacy and capturing real-time workforce insights, success requires both a strong framework and a flexible mindset. At Avature, we’re helping leading enterprises put these principles into practice with an AI-powered platform designed for strategic HR transformation. If you’re ready to turn opportunity into measurable impact, get in touch with us today.

Agentic AI is quickly becoming one of the most talked-about developments in enterprise technology. As AI agents begin to interact with employees, influence decisions and reshape workflows, HR has a central role to play in understanding where this technology can bring real value, and ensuring it’s introduced with transparency, care and strategic alignment.

But where to get started? This article explores the most promising agentic AI use cases in HR, as well as key strategic actions needed to ensure a successful, people-centered rollout.

But first, a quick recap: What exactly is agentic AI?

What Is Agentic AI, and How Is It Different From Traditional AI?

Agentic AI refers to artificial intelligence that goes beyond providing AI-driven insights and recommendations to actually taking action. Unlike traditional AI, which requires human intervention to complete tasks, agentic AI can operate autonomously to execute and delegate tasks, make decisions within predefined parameters and optimize workflows based on context to achieve a predefined goal.

As agentic AI evolves, it’s emerging in two powerful forms. The first is AI copilots: interactive assistants that guide users through tasks via natural, conversational interfaces. The second is autonomous agents: AI systems embedded into workflows that operate behind the scenes to carry out complex, multi-step actions without human prompts.

Rather than a mere tool, agentic AI acts more like a highly skilled virtual employee who needs oversight and guidance, but can also learn from outcomes to improve performance over time with minimal intervention.

To bring this contrast to life, let’s look at a common HR scenario: handling Paid Time Off (PTO) requests.

• A traditional AI chatbot can retrieve PTO policies or check available dates on a calendar. This is valuable information, but it doesn’t save a lot of time.
• An agentic AI assistant can go further—it can check internal policies, verify PTO balances, submit a request, update the calendar and even notify relevant team members.

This chaining capability—where AI autonomously breaks down complex tasks into smaller, manageable steps—makes agentic AI a game-changer for HR. By moving beyond simply surfacing information to accomplishing end-to-end tasks, agentic AI can save hours when multiplied across thousands of employees.
While productivity gains are significant, agentic AI also opens the door to smarter decision-making, better experiences and greater strategic impact. Let’s take a look at agentic AI in action with some real-world HR use cases.

Agentic AI Use Cases for HR

End-to-End Talent Acquisition

While traditional automation is already streamlining many talent acquisition processes, agentic AI has the potential to take this one step further. This could include initial candidate screening based on the requirements for a particular requisition, scheduling an interview with a recruiter, summarizing interviewers’ feedback and then taking appropriate action, such as dispositioning a candidate or progressing them to the next stage with a personalized congratulations and scheduling link.

By automating these multi-step, time-consuming processes, your team can shift its focus from repetitive admin to more strategic endeavors, such as relationship building, all while improving candidate engagement and helping you keep pace in a competitive talent environment.

Onboarding

While many organizations have used workflow automation and configurable portal frameworks to streamline onboarding—enabling new hires to complete paperwork, access key resources and connect with their teams—it remains an area of opportunity for most. Agentic AI in HR practices offers a new level of service delivery, enhancing the new hire experience by acting as a dynamic onboarding assistant.

From the moment a new hire signs their contract, an agentic AI assistant can guide them through preboarding—helping them complete necessary documentation, share it with the appropriate internal teams and ensure tasks like setting up IT permissions are triggered automatically. It can also recommend relevant training based on the role and schedule introductions with key team members. By handling these steps and coordinating behind the scenes, agentic AI not only accelerates integration but also frees HR to focus on higher-impact initiatives instead of resolving onboarding bottlenecks.

Hyper-Personalized Career Pathing

Despite growing demand for clear career development opportunities, HR teams often lack the resources to provide tailored guidance at scale. Agentic AI can help bridge that gap by synthesizing vast amounts of data—from employee profiles and performance metrics to market trends—to identify skill gaps and recommend hyper-personalized career paths.

What’s more, it can go beyond simply recommending courses to enrolling employees in relevant training, nudging them to complete key milestones and adapting suggestions over time as their skills and interests evolve.

By providing highly targeted, ongoing career guidance at scale, agentic AI boosts your employer value proposition while empowering HR teams to build a more agile, future-ready workforce.

Now that we’ve seen a couple of examples of where agentic AI can help HR teams, let’s examine what it takes for human resources to lead this transformation responsibly and strategically.

Agentic AI in HR: Steering the Shift

While HR hasn’t always been synonymous with technological innovation, Seán Morris, Principal and US Talent Transformation Leader at Deloitte US, believes there is “no better place in the enterprise structure” to start exploring and integrating AI than HR. After all, HR’s decisions around AI don’t just shape its own function—they influence the entire organization.

With agentic AI in particular, the implications run deeper. As AI agents begin performing tasks once handled by people—and in some cases, augmenting or replacing roles—there are clear ethical, cultural and workforce considerations. That’s why it’s essential for HR to help guide how this technology is introduced and managed. From strategy and structure to oversight and governance, HR has a critical role to play in ensuring adoption is both effective and responsible.

How HR Can Prepare for the Shift to Agentic AI

1. Create a Responsible Agentic AI Framework

As the custodians of organizational culture, HR leaders have a vital role in overseeing how agentic AI aligns with organizational values. In the same way that human employees need to adhere to company policies concerning legal issues, data privacy and ethics, organizations also need a clear framework and guardrails for responsible agentic AI use, not just within HR, but across the entire organization.

Frameworks is an area that HR has to focus on in order to make sure they don’t just end up with a lot of AI scattered throughout the tech stack. […] We live inside frameworks. It’s not something that just people in technology can use. It’s great for when things are highly dynamic and changing and you’re allowing lots of people to do lots of different things, but that somehow have to work together at the end of the day.”

Dimitri Boylan
Avature Founder & CEO

This isn’t the sole responsibility of HR and tight cross-functional collaborations with IT, legal and other departments will be crucial. Here are some key questions to get you started:

• Where are you comfortable with Agentic AI acting autonomously? Clear guidelines should be established for AI-human collaboration, outlining which tasks are appropriate for AI agents and which are better left to human judgment. For example, while agentic AI might be well-suited to scheduling interviews or drafting candidate communications, final hiring decisions should remain firmly in human hands.
• Which data will AI agents be able to access and how can they use that data? By aligning your AI framework with international standards, you can remain compliant with regulations like the EU AI Act and the NYC AI regulation.

There are a lot of guardrails and guidelines that we’re putting in place around AI, just to make sure that we’re legally compliant, to make sure that we’re not letting it make decisions for us.”

Erica Rutherford
Director of Technology, Bain & Company

2. Evaluate Your Tech Stack and AI Partners

As HR continues to take on a more strategic role—often operating like a product owner—its influence on technology decisions is growing. In fact, our State of the HR Landscape 2025 survey found that 60 percent of HR leaders now consider HR technology and systems a core part of their role. To successfully leverage agentic AI in HR, that strategic lens must include a thorough evaluation of your existing tech stack. 

Legacy tech stacks with overly siloed systems can severely limit your ability to extract meaningful value from AI. When data is fragmented across platforms, AI lacks the holistic view needed to generate insights or drive effective action. For agentic AI specifically, these silos pose an even greater challenge. Agents are designed to carry out multi-step processes across workflows, but if they can’t access the necessary data or systems at each step, their ability to deliver impact is significantly reduced.

Whenever you’re exploring the use of AI agents in HR operations, it’s essential to assess whether your underlying tech stack can support it. It’s not just about adding new capabilities—it’s about ensuring your existing tech stack offers the right architectural foundation to enable seamless data flow and integration across platforms. Without that, even the most advanced agents will be unable to deliver.

Next, assess your technology providers carefully. What’s on their roadmap concerning agentic AI? Do their AI solutions align with your organization’s unique goals and strategic vision? Choose vendors whose AI offerings are transparent, flexible and built to scale—so your organization can grow with the technology, not around it.

3. Address Concerns and Build AI Confidence

We conducted an AI survey across our entire associate population to get a sense of how people are feeling about it. What’s the sentiment? How are they using it? What tools are they using? Just to kind of get a baseline. And it was all over the place. It was 50/50, ‘I use it’ or ‘it’s evil, it’s going to take my job.’”

Rachel Raymond
Director of Talent Acquisition, People Technology & Analytics, Jack Henry

While the long-term impact of agentic AI on roles is still unfolding, it’s natural for employees to feel uncertain, especially as AI agents begin performing tasks that overlap with their own. Rather than downplaying those concerns, HR can play a key role in guiding the conversation, focusing on where agentic AI can enhance work, create new opportunities and open doors to growth.

To do so, HR will need to adopt a range of measures, including:

• Creating safe spaces for discussion and addressing fears of job displacement openly.
• Clearly communicating how AI is used to inform decisions, especially in high-stakes areas such as hiring, promotions or performance evaluations.
• Sharing employee success stories, highlighting how AI has positively impacted their daily work. By demonstrating AI’s practical benefits — how it saves time, reduces administrative burden and unlocks opportunities for professional development — employees are much more likely to see AI as a powerful tool for growth rather than a threat to their roles.

4. Invest in AI Training and Workforce Resilience

Beyond addressing fears of job displacement, the most forward-thinking HR leaders will go a step further by proactively anticipating how agentic AI might reshape roles, skills and workforce composition. Preparing employees to thrive amid this transformation demands deliberate investment in AI-related training and continuous learning.

This investment is essential for future-proofing workforce capabilities and a strategic lever for talent attraction and retention. A recent survey from global staffing giant Adecco found that 57 percent of workers want AI training, signaling a clear expectation: employees want to grow alongside the technology, not be left behind by it.

The depth of AI training, however, should match each employee’s role. While it might be wise to deliver foundational training to every employee to mitigate risk (for example, you wouldn’t want someone unwittingly uploading personally identifiable information to ChatGPT), extensive training won’t be necessary for everyone. In fact, many agentic AI tools are so intuitive that they can even reduce training requirements.

Yet, certain roles, particularly within HR or other corporate functions, will require deeper knowledge. For instance, talent professionals must develop expertise in data analytics, AI fundamentals and emerging trends. Continuous learning, whether that’s through industry publications, webinars, podcasts or events will be essential in this regard. Additionally, hands-on experimentation will help HR teams better understand what agentic AI can and can’t do, allowing them to confidently evaluate the technology and craft well-informed policies about how to best leverage it.

While the responsibility for organizing AI training and upskilling initiatives typically rests with HR and L&D teams, agentic AI can be an invaluable ally. For example, agentic AI could support upskilling efforts by recommending relevant learning opportunities, identifying skill gaps and offering timely, role-specific guidance to keep employees progressing with confidence. In short, agentic AI doesn’t just require training; it can power the training too.

5. Prioritize High-Impact, Low-Risk Use Cases

Don’t create the big bang straight away, but find the right use case and find the right opportunities to test these hypotheses before you go out and make a bigger investment.”

Salma Rashad
Talent Thought Leader

Last but not least, just because you can use agentic AI doesn’t mean you should. Fully autonomous agents remain aspirational in many business contexts, so starting small and staying grounded is key. To get started, HR leaders should focus first on low-hanging fruit—non-controversial, high-impact use cases closely aligned with strategic business goals. Demonstrating clear ROI through these quick wins will build confidence, driving greater buy-in and paving the way for broader adoption.

Large enterprises are leveraging Avature’s agentic AI to significantly enhance user experiences across key touchpoints in the talent lifecycle. For example, knowledge agents reduce HR’s workload by responding to employee queries based on company-specific documentation and routing more complex issues to the right people when needed, giving teams more time for strategic work.

Content and code generation are other promising areas for quick wins. Avature writing agents help global recruiting teams rapidly craft clear, consistent job descriptions and emails aligned with their organization’s unique tone of voice and style, while coding agents enable non-technical users to effortlessly build branded portals and landing pages using simple, natural language prompts.

By strategically selecting such high-impact, practical applications, you can quickly showcase agentic AI’s value, strengthening support across your organization and laying the groundwork for broader, more ambitious initiatives in the future.

Leading HR Into the Agentic AI Era

While the road ahead may be complex, with both opportunity and disruption on the horizon, HR is uniquely positioned to help shape how agentic AI is introduced into the workplace. As AI agents take on more responsibility across workflows, some roles will inevitably change, making it all the more important for HR to guide this transformation with clarity and care.

Doing so means helping organizations understand where agentic AI can bring real value, how it should be governed and what it means for the people whose work will evolve alongside it. By stepping into this role, HR can ensure that adoption is thoughtful, inclusive and aligned with long-term workforce resilience.

If you’re ready to explore how agentic AI can add real value to your organization, get in touch today to discover how Avature’s AI-powered platform for strategic HR can help you bring your AI vision to life.