How AI Is Reshaping Hourly Jobs—and the Skills That Now Matter
The rise of artificial intelligence (AI) is transforming the labor market in ways that are both profound and nuanced. While much of the public discourse has focused on white-collar automation, the impact on hourly and traditionally “non-skilled” jobs is equally significant—and often overlooked.
The Two Paths of AI: Automation vs. Augmentation
AI’s influence on hourly work is unfolding along two distinct trajectories. One path emphasizes automation—replacing human labor with machines. This is already visible in sectors like retail, logistics, and food service, where AI-driven kiosks, robotic fulfillment systems, and predictive scheduling tools are becoming commonplace1.
The other path, more hopeful but less traveled, focuses on augmentation—using AI to enhance human capabilities. For example, AI can provide real-time data to warehouse workers to optimize inventory handling or assist healthcare aides with patient monitoring1. This approach not only preserves jobs but can also elevate them, making them more engaging and better compensated.
What Skills Are Emerging in Hourly Roles?
Contrary to the term “non-skilled,” many hourly jobs now require a blend of digital literacy, adaptability, and soft skills. According to research from MIT, companies are increasingly using AI to identify and close skills gaps, even in frontline roles2. Skills like:
Basic data interpretation (e.g., reading dashboards or using mobile apps)
Problem-solving and decision-making in dynamic environments
Communication and collaboration with both humans and machines
These are becoming essential. For instance, Johnson & Johnson implemented an AI-driven “skills inference” system to map out future-ready capabilities across its workforce, including roles not traditionally seen as tech-centric2.
The Reality: AI Is Not Replacing All Jobs—Yet
Despite fears, there is little evidence that AI is eliminating hourly jobs at scale—at least not yet. A study by the Federal Reserve Bank of Dallas found that while AI exposure is increasing, it hasn’t strongly correlated with job losses in most sectors3. Instead, the technology is shifting the nature of work, requiring workers to adapt rather than exit.
What This Means for Employers and Workers
For employers, the message is clear: investing in training and upskilling is not optional. Workers are more likely to stay with companies that offer continuous learning opportunities2. For workers, embracing AI as a tool—not a threat—can open doors to new roles and responsibilities.
Conclusion: A Call for Proactive Adaptation
The future of hourly work in the age of AI is not predetermined. It hinges on the choices made by employers, policymakers, and workers themselves. By focusing on augmentation over automation and investing in human potential, we can ensure that AI becomes a force for inclusion and opportunity—not displacement.
In 2025, the frontline and hourly hiring landscape is more complex—and more critical—than ever. Retailers, hospitality operators, and consumer brands are navigating a perfect storm of high turnover, rising candidate expectations, and shrinking talent acquisition (TA) resources. Yet, amidst the chaos, one truth is emerging: the organizations that win are those that start small, iterate fast, and build a culture of continuous change.
The Reality on the Ground
Turnover remains sky-high: Hospitality turnover exceeds 100% annually in some segments, while retail hovers between 60–80%. Many frontline workers leave within 30–90 days, creating a costly cycle of backfilling and burnout.
Candidate ghosting is rampant: From application to Day 1, drop-offs and no-shows are disrupting workforce planning and inflating time-to-fill.
TA teams are stretched thin: In 2024, TA teams met just 47.9% of their hiring goals1. In 2025, they’re being asked to do more with less—fewer recruiters, tighter budgets, and higher expectations.
Technology is underutilized: Despite widespread adoption of platforms and the capabilities of AI, many organizations struggle to unlock the full potential due to competing priorities and lack of in-house expertise.
What’s Working in 2025
The most successful organizations are not the ones with the flashiest tools or biggest budgets—they’re the ones that are willing to evolve:
AI and automation are gaining traction: 93% of TA leaders plan to increase tech investments this year, focusing on reducing bottlenecks and improving candidate experience1.
Experience is the differentiator: Top-performing TA teams are 55% more likely to prioritize candidate experience1, recognizing that a seamless, human-centered journey reduces ghosting and boosts retention.
Flexibility is key: In hospitality, where labor shortages persist despite record-high employment, employers are rethinking roles, offering career pathways, and embracing more supportive, flexible environments.
The Call to Action
You don’t need to overhaul everything at once. But you do need to start with a few basics:
Audit your hiring journey—where are candidates dropping off?
Empower your TA team—what’s slowing them down?
Optimize what you already have—are your tools configured for today’s needs?
Test, learn, and adapt—what worked last quarter or last season may not work next.
The frontline hiring challenge isn’t going away. But with the right mindset and a commitment to ongoing improvement, it’s one you can meet—one iteration at a time.
The past decade has been defined by acceleration. Organisations have embraced digital transformation at speed and scale, driven by a desire to be leaner, faster and more productive. From early automation to today’s generative and agentic AI, workforces have been equipped with smarter tools and increasingly autonomous systems. These technologies are not only changing how work gets done, but also how decisions are made, how people interact and what is expected of them. On nearly every front, operational efficiency has improved.
But beneath that surface, something else is shifting. As output is optimised, the quieter elements of work such as empathy, trust, etiquette and human connection are becoming harder to maintain. Not gone but stretched. And in some cases, quietly deprioritised.
This is not a crisis. It is a pattern. The kind that does not cause headlines but quietly reshapes culture if ignored.
This piece explores that pattern. Not as a rejection of innovation, but as a reminder that presence, engagement and cultural consistency must evolve alongside performance. If organisations want sustainable, resilient cultures, they cannot afford to let efficiency dilute the experience of the people delivering it.
Efficiency as a Cultural Force
Technology has delivered meaningful improvements to how work is structured, measured and executed. Automated workflows, intelligent systems and AI-driven optimisation have changed how organisations hire, communicate, manage and scale. These systems are effective and often essential. But they also shape behaviour, expectations and culture in subtle ways.
The more efficient work becomes, the more transactional it can feel. Conversations are shorter. Feedback loops become templated. Systems remove friction but also reduce the context and texture that create belonging and trust.
As agentic AI becomes more embedded in workflows, taking actions on behalf of users or systems, the pace and expectations of work are shifting again. Routine decisions are now handled autonomously. Communications are drafted and sent without direct input. This creates new efficiencies, but also raises questions.
When fewer human moments are required, are the right human signals still being sent?
These developments are not inherently negative. But they highlight the need to design culture as deliberately as systems. Without that balance, organisations risk optimising for delivery while quietly eroding connection.
Candidate Experience: A Signal Worth Watching
One of the most visible early indicators of this shift is candidate ghosting. A 2023 SHRM study found that 42 percent of candidates had been dropped from hiring processes without any follow-up. In the UK, CIPD research shows nearly one in three candidates report similar treatment.
In most cases, this is not due to poor intent. It often reflects overloaded systems, ambiguous process ownership or automation designed without human closure points. But to a candidate, it sends a clear message. Their time and effort did not warrant a response.
Candidate experience is more than a hiring issue. It reflects how communication, feedback and responsibility are handled more broadly. The way an organisation treats people it chooses not to hire can often signal what it values in the people it does.
I previously wrote about the emergence of hyper-personalisation in hiring, where the candidate journey is becoming far more ‘choose your own adventure’ and consumer-inspired. But even the most curated experiences can fall short when communication and closure are absent. These foundational signals still matter.
Small cultural breakdowns often surface at the margins, in hiring, onboarding and exits, before they appear in performance metrics. These are moments worth paying attention to.
Etiquette as Cultural Infrastructure
Professional etiquette still matters. It is not about formality. It is about signalling consideration and reinforcing mutual respect. Responding to messages. Following through on commitments. Acknowledging effort. These are the habits that make people feel seen and valued. In high-output environments, these behaviours are often the first to be deprioritised.
Efficiency becomes the justification for silence. Courtesy becomes optional.
Where etiquette slips, clarity suffers. People begin to fill gaps with assumptions. Silence is misread as disinterest or avoidance. Over time, this creates emotional distance, weakens collaboration and erodes confidence in leadership. These are not major incidents. But they accumulate. And when they are not addressed, they become norms.
Leaders who consistently model responsiveness, transparency and care help embed a culture that feels accountable and considered, even under pressure.
It is ironic that in 2023, Sam Altman noted that people being overly polite to ChatGPT was costing OpenAI millions in unnecessary energy. Yet in many workplaces, the opposite trend is taking hold. Human interactions are being stripped of basic courtesy in the name of speed.
Politeness to machines may be optional. Politeness to people is not.
Empathy in Leadership: Intentional, Not Assumed
Empathy has always been a core leadership trait. But it now requires a level of intentionality that many organisations are still adapting to.
In digital-first and hybrid environments, the signals that once guided leaders are less visible. Casual check-ins, hallway conversations and off-the-record comments no longer happen as naturally. This makes it easy for leaders to drift into operational oversight without maintaining emotional connection.
Empathy in this context is not just about personal warmth. It is about being attuned to what is happening in the spaces between meetings. It is about noticing when tone changes, when contributions drop or when feedback slows. These are often the early signs that someone does not feel safe or supported.
A 2024 study by MIT Sloan Management Review found that leaders rated highly for empathy were nearly three times more likely to lead psychologically safe teams. However, the same study also found a significant drop-in informal one-to-one time since hybrid models became the norm. Empathy does not scale in the way technology does. But it can be embedded through leadership habits.
Checking in without an agenda. Listening without fixing. Noticing, and then acting.
Psychological Safety: A Barometer, Not a Backstop
Psychological safety is a known driver of innovation, retention and performance. But it can be difficult to measure accurately, especially in systemised environments.
Many organisations rely on engagement scores or survey data as indicators. While useful, these tools may only capture what people feel safe to share, not what they truly experience.
High scores may suggest alignment. They can also suggest caution. In some contexts, people provide answers that feel appropriate rather than reflective. This is especially true where AI-driven feedback tools or performance analytics create pressure to perform visibly at all times.
The more efficient the system, the more important it becomes to validate sentiment in human ways. Conversations. Observation. Follow-up.
Psychological safety cannot be assumed. It must be reinforced through action. Leaders who create space for disagreement, who follow through on difficult feedback and who model vulnerability set the tone for others to do the same.
Presence as a Strategic Leadership Skill
Presence is often misunderstood as proximity. In reality, it is about consistency, attentiveness and relevance, especially in distributed teams and tech-driven environments.
Presence means responding quickly when it matters. Being visible in decision-making. Showing up not just in crises or high-stakes moments, but regularly and reliably. It connects performance expectations with emotional commitment.
As agentic systems handle more decisions autonomously, leadership presence becomes more critical, not less. Employees still want to feel that their experience is being considered. That their effort is recognised. That their concerns are heard by someone who can do something about them.
Organisations that develop presence as a leadership competency are not resisting automation. They are complementing it. They are creating cultures where people still feel that human judgement matters, even when machines are making suggestions or taking action.
Reimagining Culture as Work Evolves
As AI redefines the nature of work itself, including how it is structured, who performs it and which decisions are delegated to machines, organisations have an opportunity to reimagine culture in parallel.
Culture can no longer be thought of as a fixed layer beneath operations. It must be adaptive, intentional and designed to evolve alongside new models of collaboration, automation and scale. If AI is changing what work looks like, then culture must change how work feels.
At AMS, we firmly believe in keeping a human in the loop when it comes to AI enablement. As work continues to be reimagined, our goal is not just to optimise tasks, but to create more space for meaningful engagement, better decision-making and human-centric interactions.
Automation should elevate the work experience, not erase the human presence from it.
Five Questions for Leadership Reflection
1. Are our systems improving connection as well as performance? 2. Do our cultural indicators reflect real sentiment or reported behaviour? 3. Where are small breakdowns in etiquette or feedback becoming normalised? 4. Are leaders equipped to recognise and respond to signals of disengagement? 5. How are we designing for presence in an increasingly autonomous workplace?
Final Thought: Balancing Automation with Human Connection
Culture does not unravel in obvious ways. It wears thin in the spaces we stop paying attention to. It shifts in tone, in pace and in the cues, people receive from leadership and systems. The next wave of change will be shaped not just by technology, but by how leaders respond to what technology makes possible. Generative and agentic AI will continue to create new efficiencies, but those efficiencies cannot come at the cost of connection.
Organisations that thrive in the future will be those that integrate automation without losing humanity. That move fast but stay close. That understand presence, empathy and trust are not nostalgic values. They are strategic imperatives.
Progress and presence are not trade-offs. They are partners. And leaders who balance them well will shape cultures built to last.
If AI is changing what work looks like, then culture must change how work feels.
One of the most interesting aspects of the GenAI ‘revolution’ is the recognized requirement for a range of soft skills in employees within the field. These skills include critical thinking, problem-solving and collaboration alongside the ability to communicate the strengths and weaknesses of using artificial intelligence, as well as when not to use it.
Qualities like creativity, persistence and decision-making will grow more and more important as AI and the very nature of the professional world continues to evolve. While technical skills will always prove important, intangibles like these can often make the difference between two equally skilled candidates.
Forbes writes; “Last year, Indeed ranked generative AI as the hottest tech skill of the year….however, what many people tend to miss is that AI is only as effective as the professional behind it. The AWS study, which surveyed over 1,300 employers, noted that 73% of respondents agree that they’re “not solely focused on workers with technical skills such as coding. In fact, critical and creative thinking are even more in demand by employers.”
Another thread in this story is the future workforce demographics and the adoption of age inclusivity as a strategic advantage. With the global 60+ population expected to double by 2050 (WHO), age-inclusive hiring is essential for building resilient teams and future-ready talent strategies.
As Lindsay Simpson of 55/Redefined said recently in a fire-side chat – “Who better to play the role of the storyteller than those with the most life-experience?”.
A good storyteller can take us on a journey and help us to imagine new possibilities. In a world where AI is now so functionally adept to give us access to unthinkable quantities of information, the creative skills are even more important. By translating that to us, our teams and our clients and by sharing that vision and ‘telling the story’, we have the option to stand out from crowd.
And so, the moral of our story is – absolutely use the AI to act as assistant and to scale and augment your work; but also, be creative, authentic and use your style and tone to set the scene of whatever you want to portray. Great communication is key and will always be in demand.
“Technology changes what we do, but not who we are. The human touch will always matter.” – Tim Cook – CEO of Apple
In a world where generative AI takes on the heavy lifting, storytelling emerges as the ultimate superpower. Grab your cape!
In case you missed my other post, ‘AI Storytellers: Using AI in Talent Acquisition – Part 1’ click here to read it.
Good storytelling is a highly sought-after skill. The ability to bring to life a rounded, measured, and exciting vision, taking your customers on a journey; it’s ultimately about personality, relatability, credibility, communication, and opportunity – and it’s all enhanced, but not created, by the capability of AI.
We are entering the Era of the Storyteller.
As we take steps to adapt GenAI into our working processes and advance our use of prompt engineering, ‘storytelling’ is becoming the new must-have skill. We are encouraged to progress to a more stylized and unique flavour to our outputs, essentially creating a memorable voice.
Matt Poole, Head of Service Development at AMS has shared some guidance on creating content that feels authentically human and results in engaging, thought-provoking work:
“The Storyteller approach is the most creative and distinctive focusing on voice and style rather than just structure and information. This approach treats AI prompting as a collaborative creative process, resulting in content that feels like it has a unique perspective and personality.”
This type of prompt has multi-faceted instructions, targeted audience needs, instruction on how to say it, not just what to say, and has layered requirements.
In a recent article, Craig Hunter, AMS Global Head of Sourcing – Centre of Excellence takes it further:
“…Talent Acquisition isn’t just about hiring anymore—it’s about navigating the future.
And yes, that means hiring differently. The most agile teams are recruiting for curiosity. For humility. For learning velocity. They’re embedding AI fluency across departments—not just in tech teams. They’re working closely with L&D to make upskilling part of the everyday employee experience.
Now, let’s bring it back to the humans. Because even with all this talk of tech, they’re still the centre of the story. But the bar is shifting. The future doesn’t need humans who can repeat tasks. It needs humans who can reimagine them.”
AI models are trained on vast amounts of data, including books, articles, and scripts written by humans. This training helps AI understand context, tone, and style, enabling it to generate text that mimics human writing. Thereby, analysing user data and preferences, AI can generate personalized narratives that resonate deeply…but it is humans that guide this process to ensure the content is engaging and relevant.
“Too often, the conversation around AI is framed as “AI vs. humans.” It shouldn’t be. The real opportunity lies in AI for humans—technology that amplifies our creativity, sharpens our insights, and accelerates collaboration” – Bernard Marr, Author & Thought Leader
AI serves as a tool to enhance human creativity, rather than replace it.
When change happens due to technological advancements or the introduction of change to legal or regulatory frameworks, the world around that change adjusts, and Risk and Compliance functions build out the necessary governance and controls to manage that change.
With GDPR, we saw investment in governance, resources, and in technology to deliver compliance, alongside a shift in the way businesses operate. In the grand scheme of things those organisations that already had a strong respect for and approach to privacy didn’t feel significant disruption to their overall business model.
This time with Artificial Intelligence (AI), it’s different—everything is changing. Throughout the supply chain, the internal and external technology environment, threats, risks, client or prospective client requirements and expectations, and the way talent acquisition interacts with AI is fundamentally shifting from industry norms.
With such significant change, building out robust, scalable, efficient, and effective AI risk governance is a challenge. So, how do we accomplish this task when we have nothing static to anchor our governance activity?
Our answer is flexibility and focus, with one eye always on the future.
Systems and processes that allow for rapid change to accommodate the changing environment are essential. We must do something, but we also know that whatever we do will need to rapidly shift to keep up to date with the change around us.
Targeting our resources based on risk is essential, but this can only be done if we understand not just our risks, but also our client’s risks. Industry law, or sectoral regulatory guidance is in some cases moving much faster than comprehensive national AI law. Applying our effort to deliver a service that enables our customers to achieve their objectives is not just desirable, but a key part of what drives us and allows us to succeed.
Being aware of what might “be next” helps, but even better is preparing for it by building a framework that is scalable, and sufficiently robust so when changes are required—and they definitely will be required—then they won’t need a full redesign of the overall program.
Finally, people powered partnership isn’t just a company slogan. Ensuring you have the right people with the right skills to work in this new world is not optional—it’s essential.
Prepare all your people for this change well before it’s needed, because if you don’t, you may be playing catch up for a long time to come.
I read a book last weekend. It was How to Think About AI by Richard Susskind, and, together with others that I have read, it left me feeling a little clearer on the excitement that surrounds AI (Artificial Intelligence), with its known and unknown potential. I continue to feel more than a little uncomfortable about the enormity of the challenges we face with AI – of relevance, ethics and energy consumption when it comes to how it is developed and operates. In this article I am sticking to my lane and reflecting on the implications when it comes to AI with a neurodiversity lens, and about the relevance that can be achieved with inclusive and thoughtful intent when thinking about talent.
AI is likely to transform how we hire, evaluate, and engage talent; it’s happening already. From algorithmic resume screening to automated video interviews and productivity tools, AI offers powerful opportunities to enhance workplace inclusion, especially when designed with a broad range of human experiences in mind. And for its full potential to be realized and optimal results achieved, we need to ensure these tools also support neurodivergent talent.
Neurodivergent individuals—those who think and process information differently, including people with autism, ADHD, dyslexia, and more—bring unique strengths to the workplace. AI can play a pivotal role in enabling more equitable access to opportunities and tailoring environments that allow diverse minds to thrive. To do this, we must consciously design systems that are inclusive by default.
Many AI-driven hiring tools rely on patterns based on past candidates. Without careful attention, this can risk replicating narrow definitions of success. But the good news is that AI, when thoughtfully applied, can help break these molds, and thinking with inclusivity in mind will ensure organizations are making choice that are both effective and ethical.
For example, tools can be configured to prioritize skills over traditional career trajectories or offer asynchronous, written alternatives to video interviews—benefiting not only neurodivergent candidates but many others. Productivity platforms can evolve to value outcomes over activity tracking, recognizing that focus, creativity, and problem-solving don’t always follow linear patterns.
When inclusivity is built into AI, it becomes a force multiplier: reducing bias, expanding access, and enhancing talent discovery. Rather than reinforcing old norms, it can usher in a more adaptive and human-centered era of work.
AI has the potential to dismantle barriers, not build them—if we design with intention.
The best tech is shaped by those who use it. Engaging neurodivergent people in the design and testing of AI tools ensures that systems reflect a variety of needs and working styles. This isn’t just inclusive—it’s smart design.
Neurodivergent employees and candidates can be keen adopters of technology. Many may embrace automation, clarity, and asynchronous communication—tools that minimize ambiguity and allow individuals to operate at their best. By incorporating their insights, organizations can create systems that are not only fairer but also more intuitive and effective for all users.
Including these perspectives from the ground up helps avoid unintended consequences and makes inclusion a feature— rather than a retrofit.
When we reimagine AI through a neuroinclusive lens, the workplace becomes more flexible, humane, and productive for everyone. Inclusive AI includes:
Offering choices in how people apply and engage, such as video or written formats
Creating interfaces that reduce sensory overload, with customizable layouts and quiet modes
Auditing systems regularly to ensure they support equity and access
Designing for flexibility, allowing individuals to showcase their strengths in ways that suit them best
Embedding transparency and explainability, so users understand how decisions are made
These aren’t just nice-to-haves—they’re features that make work more inclusive, resilient, and future-proof.
Forward-thinking organizations are already leading the way. Some companies now allow applicants to opt out of video assessments and complete written challenges instead. Others have built platforms that offer custom onboarding experiences, adaptive learning pathways, and interface personalization—all of which support neurodivergent success.
Vendors, too, are starting to see inclusion as a product differentiator. AI solutions that are more transparent, customizable, and sensitive to cognitive diversity are gaining traction in the marketplace. These innovations aren’t fringe—they’re fast becoming essential to ethical, scalable talent solutions.
The opportunity ahead
Neurodiversity is a wellspring of innovation, insight, and creativity. When AI is built with inclusivity in mind, organizations gain a deeper, more diverse talent pool and tools that reflect the richness of human potential.
AI isn’t inherently biased—it reflects the intentions behind its design. By embedding neuroinclusive thinking from the outset, we move beyond accommodation toward environments where all kinds of minds can excel. This shift can spark a broader transformation, where difference is not just accepted, but valued as a driver of success.
As we integrate AI more deeply into our workplaces, we have a tremendous opportunity: to build systems that elevate everyone’s contributions, especially those who have traditionally been misunderstood or overlooked.
It’s not about lowering standards—it’s about redefining excellence in ways that capture the full spectrum of human ability. Neurodivergent individuals have long been underrepresented in the workplace not due to lack of talent, but due to systems that fail to see or support their strengths. With AI, we can change that.
By listening, learning, and designing with intention, we can ensure AI doesn’t just reflect the world as it is—but helps shape a more inclusive and empowered future of work. For neurodivergent talent and beyond, that’s a future well worth building.
Let’s harness AI that is relevant, not just as a tool for efficiency, but as a catalyst for equity and innovation—where every mind has a place, and every contribution counts.
Leaders who invest in inclusive AI are investing in smarter systems, broader talent pipelines, and stronger business performance.
Why AI Literacy Is the Next Strategic Skill for TA
As artificial intelligence becomes increasingly embedded in the hiring process, many organisations are asking the same questions: What role will AI play in recruitment, and what does it mean for the people behind the process?
While headlines often focus on automation replacing human effort, the reality is more nuanced. The next chapter of talent acquisition isn’t about replacing people, it’s about redefining their contribution. Those who understand how to leverage AI as a tool, rather than view it as a threat, will be the ones who continue to create value.
But AI literacy in TA doesn’t happen by accident. It requires new skills, new mindsets, and a clear understanding of where AI can meaningfully support the recruiting lifecycle. It also demands an honest look at how different roles, sourcers, coordinators, advisors, and strategic partners, will be impacted differently.
AI Has Entered the TA Workflow, But Capability Gaps Remain
Recent data from LinkedIn shows that 74% of talent professionals are optimistic about AI’s impact on recruitment, yet only a small percentage feel equipped to use these tools effectively. Many organisations are still navigating early-stage experimentation, often lacking a framework for how to roll out AI responsibly and practically.
The challenge isn’t just technology, it’s people readiness. Adoption is uneven, often slowed by fear of redundancy, tool fatigue, or a lack of clarity on where AI actually adds value.
That’s why leading TA teams are shifting their focus from surface-level adoption to deeper capability-building. TA professionals need to understand how to use AI tools not just functionally, but strategically. That means asking smarter questions, engaging with data more fluently, and knowing when to apply AI-generated insights versus when to rely on experience and judgment.
From Tool Usage to Strategic Enablement: The AI Maturity Curve
A growing number of TA leaders are mapping out an AI capability journey that moves through several stages:
Exploration – Piloting tools in isolated workflows, often with individual enthusiasm leading the charge.
Enablement – Upskilling teams in prompt engineering and basic data interpretation, often with measurable time savings.
Integration – Embedding AI into core systems (ATS, CRM, sourcing stacks) to support consistent workflows.
Augmentation – Using AI to inform strategic decisions, shape job architecture, and advise hiring managers at a consultative level.
Where a TA function sits on this curve should inform its investment priorities. Skipping stages leads to poor adoption, fragmented workflows, and wasted spend.
What Skills Are Emerging for the AI-Enabled TA Professional?
Forward-thinking talent teams are investing in capability development that goes well beyond basic tool adoption. Some of the key skills being prioritised include:
1. Prompt Engineering
Learning how to write effective, targeted prompts has quickly become essential. This skill allows TA professionals to extract better results from generative AI tools, whether it’s drafting a job description, building Boolean search logic, or personalising outreach messages based on candidate motivations.
Training in prompt engineering is already underway in several enterprise environments. These programmes focus on secure platforms like Microsoft Copilot and ChatGPT Enterprise, teaching TA teams how to apply AI in daily workflows while remaining compliant with data and privacy standards.
2. Predictive Analytics for Strategic Demand Planning
As organisations mature their workforce planning efforts, AI offers an opportunity to improve how TA professionals anticipate and prepare for complex hiring needs. Predictive analytics helps teams interpret demand plans with greater precision, identifying potential bottlenecks, forecasting sourcing difficulty, and prioritising critical roles before requisitions hit the system.
Rather than reacting to intake meetings, AI-enabled TA professionals can proactively partner with talent intelligence and workforce planning teams. By surfacing patterns in hiring volume, geography, and skill clustering, they help design sourcing strategies that are more aligned to business timing, risk tolerance, and labour market constraints.
This shift moves TA from execution to orchestration.
3. Advanced Market and Role Research
In parallel, TA professionals are using AI to enhance their ability to conduct strategic market research. This includes analysing adjacent skill sets, identifying alternative career paths into hard-to-fill roles, or benchmarking similar positions across peer organisations and industries.
These insights help reshape job design, adjust expectations, and open up more inclusive or innovative talent pipelines. When combined with recruiter experience and hiring manager consultation, it enables more agile and data-informed decision-making.
Used well, these research capabilities strengthen the TA team’s role as an advisor, not just a delivery function.
4. Experimentation and Peer Learning
Perhaps most powerful is the rise of shared experimentation. A growing number of talent functions are creating internal “AI labs” or learning communities where teams test new workflows, explore niche sourcing challenges, and share what works (and what doesn’t). These environments are critical for building capability and trust.
A common use case emerging from these labs is forensic sourcing: using AI tools to convert vague job specs into structured search logic, sometimes across multiple geographies or languages. Over time, these experiments build institutional knowledge that scales beyond individuals.
Infrastructure Still Matters: Data and Integration Are Make-or-Break
One of the most overlooked blockers to AI impact is infrastructure. Even the best AI tools won’t deliver value if the underlying systems, ATS, CRM, and talent data, are fragmented or outdated. TA teams need to partner closely with HRIT and data governance to ensure they have a stable foundation for scale.
What Should TA Leaders Be Doing Now?
For TA leaders and CHROs, the focus should be on structured readiness, not reactive adoption. That doesn’t mean rolling out every new tool or jumping on hype trends. It means thinking strategically about where AI can support core goals like improving workflow efficiency, enhancing candidate experience, or surfacing underrepresented talent.
Here are a few actions that progressive leaders are already taking:
Define clear use cases where AI can add value, starting with sourcing, scheduling, and candidate communications.
Invest in TA professional upskilling, especially around prompt engineering, predictive analytics, and ethical reasoning.
Encourage safe experimentation through structured learning spaces, team jams, or AI hackathons.
Choose secure platforms that support responsible use and align with company risk policies.
Track outcomes like time savings, response rates, and TA professional satisfaction, not just cost reduction.
Procurement with Purpose: Avoiding the Shiny Tool Trap
With so many AI vendors flooding the market, discernment is critical. Teams should look past flashy demos and ask tougher questions:
What data is the model trained on?
Is the algorithm explainable and auditable?
How does it integrate into existing TA workflows?
Can we govern this tool in alignment with company risk policies?
The most sophisticated teams aren’t just buying tools, they’re evaluating partners.
Responsible AI: From Ethics to Governance
As AI tools evolve, so do the risks. Algorithms trained on biased data can reinforce inequity. Black-box models may produce impressive outputs without transparency. The responsibility for maintaining fairness, inclusivity, and data security still sits with humans.
TA teams should implement clear policies on responsible AI use, including:
Oversight committees involving TA, Legal, DEI, and Data Governance
Review checkpoints in the workflow for all AI-generated recommendations
Documentation of how decisions were made, especially in high-impact hiring situations
Final Thought: A More Human, More Strategic TA Function
The best TA professionals will always be those who build trust, influence hiring decisions, and spot potential others might miss. AI doesn’t replace those qualities, it amplifies them. It gives professionals back the time and insight they need to operate at a higher level.
As a partner to many organisations navigating this shift, we’re seeing that AI success doesn’t come from tools alone. It comes from mindset change, capability building, and cultural integration. There’s no one-size-fits-all playbook, but there is a clear opportunity to rethink what great recruitment looks like in the age of AI.
Reframing workforce disruption in the age of AI
No one really knows what the future of work looks like right now. Not with certainty. Not really.
We don’t know what jobs will exist five years from now, what skills will define success, or what careers our kids will be preparing for. Roles are dissolving, industries are mutating, and the whole idea of a ‘career path’ is being rewritten in real time.
It’s unsettling—and if we’re honest, a bit disorienting. But it’s also wide open and so, so exciting!
And that’s the bit we sometimes forget: the future isn’t just happening to us—it’s something we get to help shape.
That’s the opportunity. It’s right there, hiding in plain sight. Ours to influence—as teams, as talent professionals, as humans.
“If you’re waiting for clarity, you’re already behind.”
It’s a line I’ve caught myself repeating lately—to clients, in team calls, and honestly, in my own head. Because let’s face it, the AI conversation is messy. There’s excitement, confusion, panic. Every other headline feels like it’s predicting the end of work as we know it.
But here’s the uncomfortable truth that no one’s really saying out loud: this isn’t an AI problem—it’s a wake-up call for all of us.
We’ve been talking about disruption for years. Digital transformation. Agile. Remote work. The metaverse. Take your pick. But AI feels different, doesn’t it? Not because it’s more dangerous—but because it’s exposing things we’ve maybe avoided for a while. The reality that our org structures, hiring habits, and a lot of our business logic were built for a different era.
This isn’t a moment of replacement—it’s a moment of recalibration. Treat it like a threat and you’ll stall. Treat it like an opening and you might just help shape what’s next.
Let’s bust a myth right up front: AI is not here to wipe out the workforce.
According to LinkedIn’s 2024 Workforce Report, while 80% of jobs globally will be impacted by AI in some way, only 7% are at risk of being fully automated. That’s not an extinction event—it’s a shift in how work gets done.
And if we zoom in, it’s actually pretty exciting. What’s going away isn’t human value—it’s repetition. Redundancy. The stuff no one really enjoyed doing in the first place.
Josh Bersin’s research hits the nail on the head: AI is accelerating the shift away from rigid job titles and towards capability-based thinking. The question is no longer “What role do we need to fill?” but “What outcomes do we need to drive—and what human strengths will get us there?”
It’s less about someone’s CV, and more about how fast they can learn. Less about where they’ve been, more about how they adapt.
So what’s being disrupted here? Not people. Not even work, really.
It’s how we frame value. And that requires a different kind of leadership—from all of us.
Gartner recently shared that only 24% of HR leaders believe their organisations are truly ready for a workforce that blends AI and human capability. That’s not a failure—it’s a signal. One that tells us we’re in a moment of leadership transition, not crisis.
And honestly? That’s fair. For years, transformation was something we planned for. We mapped it out, scoped the budget, ran the comms plan. But AI doesn’t play by those rules—it’s unpredictable, evolving daily. Which means we need to show up differently.
Leadership now isn’t about control—it’s about curiosity. It’s about asking better questions, being okay with ambiguity, and rethinking how we define performance and potential.
The shift is already happening. Now it’s about how we choose to respond.
The organisations getting this right aren’t scrambling. They’re designing.
They’re moving beyond job titles and investing in dynamic skill architectures. Everest Group highlights this in its research—high-performing businesses are prioritising ecosystems of capability over static roles.
They’re also recognising that Talent Acquisition isn’t just about hiring anymore—it’s about navigating the future. TA leaders are getting pulled into conversations around workforce design, internal mobility, and AI literacy—because how we find and grow people is business adaptability.
And yes, that means hiring differently. The most agile teams are recruiting for curiosity. For humility. For learning velocity.
They’re embedding AI fluency across departments—not just in tech teams. They’re working closely with L&D to make upskilling part of the everyday employee experience.
LinkedIn’s latest Talent Trends report backs this up—internal talent marketplaces are gaining traction, helping match people to projects in real time. It’s not just smart retention—it’s smart risk management. A way to build capability that actually sticks.
Now, let’s bring it back to the humans. Because even with all this talk of tech, they’re still the centre of the story.
But the bar is shifting. The future doesn’t need humans who can repeat tasks. It needs humans who can reimagine them.
People who ask “what if?” more than “what now?” People who are endlessly curious. Who get comfortable with discomfort. Who adapt—not because they have to, but because they want to.
This next chapter belongs to the fast-learners. The open-minded. The ones who move before the roadmap is printed. Who are okay with not having all the answers—but aren’t afraid to start asking better questions than the machine can answer.
Being human is no longer the default advantage. It’s a differentiator. But only if we’re willing to evolve.
And for TA leaders?
This really is the moment.
You’ve spent years proving talent isn’t just about filling roles—it’s about building futures. Now, the table has moved—and you’re already sitting at it.
Because when skills are the new currency, the people who understand talent are the people who understand business.
This is also a moment to lead differently.
To partner more boldly. To speak up more often. To help shape—not just support—the future of work.
Because AI isn’t a cost-cutting tool. It’s a spark. And what it lights up will depend on the people—and principles—guiding the change.
We’re not facing a workforce apocalypse. We’re facing a wake-up call.
AI won’t replace people. But it will replace mediocrity. It’ll ask us to think harder about how we lead, how we hire, how we learn—and how we measure value.
The ones waiting for certainty might get left behind. But the ones who embrace a bit of discomfort? They’ll be the ones who build the future.
AI won’t replace people. But it will replace mediocrity.
It’ll force us to rethink how we lead, how we hire, how we learn—and how we measure value.
The conversations at Workday’s FY26 SKO in Las Vegas made one thing evident: AI is no longer just a tool for optimization—it is becoming an autonomous force reshaping the enterprise.
While artificial intelligence has been embedded in HR technology for years, the discussion has evolved. The focus is shifting from AI as a support mechanism to AI as an independent agent capable of executing tasks, making decisions, and orchestrating workflows.
At the center of this transformation is Agentic AI, a departure from traditional automation. Rather than augmenting human effort, Agentic AI fundamentally redefines roles, workflows, and decision-making structures.
The Shifting Landscape of Hiring
Talent acquisition has long been characterized by inefficiencies. Recruiters manage administrative burdens, hiring managers navigate approval bottlenecks, and candidates expect seamless, personalized experiences that many organizations struggle to deliver. AI-powered automation has addressed some of these pain points. Agentic AI introduces a different paradigm.
By deploying autonomous AI agents, organizations can move beyond task automation to true orchestration of the hiring process. These agents do not wait for human input, rather they can:
Identify what needs to be done
Make decisions based on real-time data
Execute tasks across multiple systems
Learn and adapt over time
This represents a shift from AI as a passive assistant to AI as an active agent capable of managing hiring workflows with reduced human intervention. The implications are significant. Instead of recruiters focusing on process execution, their roles can evolve to emphasize strategy, relationship-building, and candidate engagement. Hiring managers can spend less time navigating approvals and more time making informed talent decisions.
Challenges of Scaling Agentic AI
The adoption of Agentic AI presents challenges that organizations must address to ensure effective deployments.
Key considerations include:
Accountability: As AI agents take on decision-making responsibilities, defining ownership and oversight becomes critical.
Transparency: Organizations must establish mechanisms for tracking and auditing AI-driven actions to maintain compliance and trust.
Integration: Many HR technology ecosystems remain fragmented, raising questions about how AI agents will operate across disconnected systems.
EthicalConsiderations: AI-driven decision-making introduces risks related to bias, fairness, and regulatory compliance.
Governance: The spread of AI agents requires organizations to establish frameworks for monitoring their scope, actions, and impact.
Striking the right balance between innovation and control will determine the success of Agentic AI adoption.
Workday’s Vision: The Agent System of Record
A key takeaway from Workday’s SKO was its strategic commitment to an enterprise-wide AI, with the Agent System of Record at the core. This concept is designed to provide organizations with visibility, governance, and control over autonomous AI agents as they become embedded in business operations.
Just as Workday redefined how companies manage financial and workforce data, the Agent System of Record will serve as the foundation for managing, deploying, orchestrating, and measuring AI-driven agents across the enterprise.
Closing Thoughts
AI agents represent a new category of enterprise resource. Organizations must manage, track, and optimize to fully realize its value. As businesses integrate these autonomous systems, governance and strategic oversight will be essential.
Workday has positioned itself at the center of this transformation, envisioning a future where AI agents operate alongside human employees and financial systems to drive business outcomes. This shift is not just about automation—it is about fundamentally redefining how work gets done. Organizations that embrace this new model will be better equipped to navigate the evolving AI landscape and unlock new levels of efficiency, decision-making, and innovation.
