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.
Trust, Social Proof, and the Future of Hiring
The way hiring works is changing rapidly. At the heart of this shift? Trust.
In today’s market, trust is no longer just an advantage—it is a necessity. Candidates, like consumers, rely on social proof—the psychological principle that people look to others to validate their decisions. This is why employee referrals, alumni rehires, and internal mobility are becoming the most effective hiring strategies.
The evidence supports this. Referred candidates are not only hired faster—they stay longer as well (LinkedIn Talent Trends). When someone recommends an organisation, new hires already have a level of trust in the culture and expectations.
There is also a growing boomerang effect, where former employees are returning in record numbers. Organisations are recognising that when an individual chooses to come back, it is a strong endorsement of the company’s credibility (Gartner Research). A great workplace is not just one that attracts new talent—it is one that people actively want to return to.
Industry thought leaders, including Josh Bersin, have noted this trend. The most effective hiring teams are no longer solely focused on sourcing external candidates—they are investing in high-trust networks because referrals, alumni hires, and internal mobility lead to stronger hiring outcomes at a lower cost.
What is driving this shift? Two key factors: the power of social proof and a declining trust in traditional hiring methods.
The Social Proof Effect: Why People Trust People More Than Brands
We are living in an age of influence, but not in the way social media suggests. Influence today is not just about follower counts or carefully curated employer branding campaigns—it is about authentic, human credibility.
People trust people, not brands – A referral from a trusted colleague is not just a recommendation; it is proof that an organisation is worth considering.
Referrals reduce uncertainty – Changing jobs is a major decision. When someone you respect endorses an opportunity, it immediately feels more credible.
Boomerang hires strengthen employer reputation – When former employees return, it sends a strong signal: this is an organisation worth working for. Gartner research shows that organisations investing in alumni engagement achieve higher rehire rates and stronger reputations.
AI-generated outreach lacks social proof – Candidates can easily identify when a LinkedIn message has been rinsed and repeated by a bot after receiving the same template 10 times. Without human connection, it feels impersonal and unconvincing.
And this is not just theory—organisations are seeing tangible results.
One global technology company recently overhauled its alumni hiring strategy and experienced a 40% increase in rehires over two years. Why? Because trust was already established. These former employees were not taking a risk—they had direct experience with the culture, leadership, and business operations. That trust led to faster onboarding, higher engagement, and a stronger commitment to success.
This is the power of social proof—it builds trust, accelerates hiring, and improves retention.
Why Offboarding and Redeployment Are Essential to a Strong Talent Ecosystem
However, alumni networks and boomerang hiring only succeed if organisations handle offboarding and redeployment effectively.
Poor offboarding damages brand loyalty – If an employee has a negative departure experience, they are unlikely to refer others or return. Research from Workday shows that organisations with structured, positive offboarding programmes are twice as likely to rehire former employees.
Negative offboarding experiences harm employer brand – A poorly managed exit process often results in negative Glassdoor reviews and reputational damage, which discourages future referrals.
Redeployment prevents unnecessary talent loss – Organisations that actively support employees in transitioning to new internal roles rather than defaulting to redundancies retain institutional knowledge and sustain trust.
Best and Worst Practices in Offboarding
Some organisations treat offboarding as a transaction rather than an opportunity. Impersonal redundancies, delivered through mass emails or pre-recorded video messages, with no transition support or career assistance, leave departing employees feeling undervalued. This approach damages trust, erodes employer reputation, and often leads top talent to join competitors instead of returning later.
In contrast, leading organisations take a long-term view of offboarding. Rather than severing ties completely, they provide structured alumni programmes, networking events, and even career coaching for departing employees, ensuring that relationships remain strong. Organisations that adopt well-managed exit strategies experience higher alumni engagement, stronger employer branding, and an increase in boomerang hires.
Organisations investing in structured offboarding and alumni engagement see up to a 20% increase in rehires (Harvard Business Review).
Redeployment strategies that prioritise internal mobility reduce turnover by 41% and strengthen employee trust (LinkedIn Workplace Learning Report).
The Pitfalls of High-Trust Networks: The Risk of Reinforcing Bias
While referrals and alumni hiring can be highly effective, there is an important risk to address: they can reduce diversity and reinforce bias if not actively managed.
The “Like-Me” Effect – Research shows that employees tend to refer individuals who share similar backgrounds, education, and demographics (Harvard Business Review). If organisations are not intentional, referral programmes can result in homogeneous talent pools.
Alumni Networks Reflect Past Hiring Gaps – If an organisation’s workforce lacked diversity in previous years, its alumni pool will mirror those representation gaps. Over-reliance on alumni hiring can reinforce historical imbalances.
Over-Reliance on Known Talent Can Limit Innovation – While trust is crucial, excessive dependence on referrals and alumni hires can reduce fresh perspectives and new ideas.
AI Bias in Referral Matching – If AI-driven talent recommendations are not designed with diversity safeguards, they can replicate existing hiring patterns rather than broaden talent pools (Gartner).
How to Mitigate These Risks
Forward-thinking organisations are already implementing solutions to ensure high-trust hiring networks remain inclusive and diverse:
Diversifying Referral Incentives – Some organisations now offer increased referral bonuses for underrepresented candidates, ensuring that referrals contribute to a broader talent pool (LinkedIn Hiring Trends).
Expanding Alumni Networks Beyond Full-Time Employees – Progressive organisations include former interns, contractors, and contingent workers in alumni networks, widening the scope of potential rehires.
Balancing Referrals with Inclusive Hiring Strategies – Industry leaders have embedded structured diversity hiring initiatives alongside referral programmes to create more balanced sourcing strategies.
The Bottom Line: The Social Proof Revolution Is Here
Hiring in 2025 will not be about volume-based recruiting or relying solely on AI-generated outreach. The most successful organisations will strike the right balance—using technology to enhance trust-driven hiring, not replace human relationships.
The future belongs to companies that integrate AI intelligently—leveraging automation for efficiency, predictive analytics for smarter decision-making, and digital platforms to scale high-trust networks—while ensuring that human engagement remains at the centre of hiring.
Some organisations are already ahead of the curve. They are moving beyond transactional recruitment models and instead building dynamic, trust-based talent ecosystems where AI supports, rather than substitutes, authentic human connections. These companies are strengthening employee advocacy, deepening alumni engagement, and expanding high-trust hiring channels to secure the best talent.
The real question is: Will your organisation use technology to reinforce trust—or allow automation to dilute it?
In the future of hiring, social proof will be the strongest currency—trust built through referrals, alumni networks, and human connections will outperform cold outreach and AI-driven automation on its own
Even though Halloween has come and gone, a spooky problem persists in the world of Talent Acquisition: ghosting.
While candidate ghosting has been a pervasive issue for many years now, it’s clearly no longer just a problem for employers. Recruiter ghosting is a serious frustration for candidates in today’s already challenging job market—as many a Reddit, Glassdoor, Fishbowl and LinkedIn post can attest. That should be a concern for all employers in today’s competitive marketplace where a company’s reputation is critical in the race for talent attraction, and this phenomenon may be skirting even the most robust recruitment processes to wreaking havoc.
Fortunately, we live in the time of AI. Where human intervention and manual process is well-known to fail, AI tools can provide consistency and streamline communication, with timely updates and deliver personalized feedback to drive a positive candidate experience.
Why is Closing the Loop So Important?
Candidate experience is nothing new but continues to be an area where many employers have room for improvement, especially around candidate communication. When employers ask so much of candidates for the privilege of consideration, letting them know where they stand, what’s next and how they did in return should be fundamental.
It should be no surprise that a recent study shared by HRMagazine found nearly nine in 10 (88%) of candidates expect to hear from a potential employer within weeks of submitting an application. Despite this well understood expectation, according to Greenhouse’s 2024 Candidate Experience Report, “just under half (45%) of job candidates have been ghosted after an initial conversation with a recruiter,” with an even more dismal response rate for candidates from underrepresented backgrounds.
When candidates engaging with your company and brand are left feeling disrespected, undervalued and frustrated that can spell big trouble. In a time when candidates are no longer shy about “naming and shaming” employers online, your company’s reputation can take a hit – both as a corporate brand and as a potential employer.
Your hiring practices are more visible and matter now more than ever and are viewed as a reflection of corporate culture that candidates and customers alike are choosing to support.
How is Your Team Doing?
Before you can address any potential issues, you’ll need to understand the nature and scale of the problem as it applies to your company’s recruitment practices. You may have a best-in-class candidate communication strategy, but how do you know whether it’s breaking down in practice?
Consider how you evaluate and monitor candidate communication and experience. Utilizing or implementing post-interview surveys with tools such as Survale can be an invaluable tool for understanding candidate sentiment, as is monitoring social media. Crunching your recruitment performance metrics can also shed some light on potential communication breakdowns. You may also consider a mystery shopper technique togain a firsthand understanding of the candidate experience.
Once you know what to needs to be addressed, you can create clear objectives for an AI strategy and develop targets for improvement.
How AI Can Help
AI-powered tools can revolutionize candidate communication by automating tasks, personalizing messages and providing timely feedback to close the gaps.
Automated Communication AI-powered chatbots can enhance candidate communication by providing instant responses to inquiries, answering common questions and application status updates. AI can also automate distribution of personalized email updates tailored to the specific stage of the recruitment process, ensuring candidates stay informed and engaged.
Personalized Feedback AI can analyze candidate data, such as resumes, cover letters and interview performance, helping provide candidates with specific and actionable recommendations for improvement to develop skills and improve their job search, while demonstrating that the company values their time and effort.
Efficient Tracking and Follow-up AI tools, when implemented alongside an ATS or candidate experience platform, can support candidate progress tracking and automate timely follow-up communications by email and/or SMS, ensuring candidates remain engaged and informed throughout the hiring process.
Proactive Communication AI can also be deployed to proactively address common candidate questions and concerns through automated messages and FAQs and analyze candidate questions to identify areas of the process that are unclear or potential bottlenecks, enabling the company to mitigate these issues proactively.
By implementing AI-powered tools, recruiters can significantly improve the candidate experience and reduce the risk of ghosting – by both parties. Remember, a happy candidate is a loyal advocate.
Just under half (45%) of job candidates have been ghosted after an initial conversation with a recruiter.
The Bureau of Labor Statistics (BLS) anticipates that 23.1% of all new jobs projected from 2021 to 2031 will be in the hospitality and leisure sector, with the largest increase expected in food preparation and service. Given that there are already an estimated 2 million open jobs in the sector in the US and an aging workforce, this will leave a significant gap in workers to meet demand. The labor market challenges are compounded by the sector having the highest quit rates and the greatest need for in-person work, with over 80% of workers fully on-site.
Battling over the same workers using the same approaches for restaurants, hotels, and retailers will not change the results. In fact, it is risky because while competitors act, those that do not will face higher quit rates and less engaged employees.
Three Strategies to Attract, Hire, and Retain the Best Frontline Employees
Invest in Responsible AI and Thoughtful Automation According to the National Restaurant Association’s Restaurant Technology Landscape Report 2024, 64% of restaurant operators consider their use of technology to be mainstream, yet only 13% think their restaurant is on the leading edge compared to peers. Hiring technology can be a great starting point for investment that will enhance or even transform the experience. Given that in this industry, your candidates are often your customers, it is a great way to embrace and lead the way while also increasing speed and efficiency.
Amplify Worker Voice Constantly seek feedback from your frontline workers, even if it’s hard to hear. Staffing your restaurants, stores, and properties might be step one, but to sustain operations, it is critical to continuously listen and reduce friction so team members are best equipped to deliver on the quality that is most important to guests – happy, friendly, and attentive staff.
Blend Technology with Human-Centered Design 82% of US and 74% of non-US consumers want more human interaction as technology improves. It’s not that humans are resisting technology; quite the opposite. Rather, there are key moments where a human provides connectivity, empathy, and compassion that only human interaction and kindness can deliver. The human touch fosters warm connections and empathy in recruitment, meaning understanding candidates’ aspirations, anxieties, and motivations. By incorporating one or two key touchpoints in a technology-led recruitment experience, candidates have the chance to feel valued and heard as they make a life-altering decision about joining and staying with a company.
In an industry as dynamic and people-centric as retail, restaurants, hotels, and consumer goods the blend of technology and human interaction is not just beneficial – it’s essential. By investing in responsible AI and automation, amplifying the voices of your frontline workers, and ensuring a human-centered recruitment experience, you can create a more efficient, empathetic, and engaging environment that ultimately leads to achieving business outcomes.
Without a doubt, one of my favourite milestones during the year is bringing our leadership team together, in one room, for two days of thought-provoking discussions. Thanks to a combination of outstanding speakers and an extremely engaged and collaborative team, earlier this month we had a phenomenal time together.
So, which topics came up again and again and really left me thinking about their impact on the future of TA and what we need to focus on for the rest of this year and beyond?
Skills-based hiring is essential to business success
Skills shortages continue to be a significant challenge for organisations globally and a move to skills-based hiring and innovative approaches are essential to business success. Companies must put emphasis on skills and abilities over qualifications, in turn resulting in more efficient and inclusive hiring.
Bridging the green skills gap, in particular, is of critical importance as 82% of talent leaders feel that hiring talent with green skills is moderately or extremely challenging. We address these issues in detail in our recent whitepaper on tackling the skills crisis for a sustainable future.
Data, data, data
If you’ve read our Talent Climate Series, you’ll know that everything comes back to data. Being able to analyse and interpret data and translating our findings with our clients will allow us to elevate conversations and ensure our solutions are relevant to their specific needs and solving their pain points.
And this goes hand in hand with my next point..
Tech advancements are a key disruptor for how we do things
The buzzword of the moment – AI. It’s no surprise that generative AI is fundamentally revolutionising the talent acquisition world, from improving candidate experience to reducing time to hire.
We must stay ahead of the curve in every aspect of our talent acquisition lifecycle by continuing to implement more efficient and effective AI powered solutions for a competitive edge.
Incorporating a social value strategy is no longer a ‘nice to have’
We were delighted to be joined by our Crown Commercial Services client who brought to life how we’ve stayed ahead of the curve to stand out from our competitors, overcome challenges, and drive outcomes for our clients.
Not only did our client discuss the importance of technology in our framework, but they highlighted the significance of working together to create an impactful social value strategy and how this is essential to attract and hire diverse talent.
Our Diversity & Inclusion Alliance has been instrumental in supporting the social value agenda for our clients as we’ve tapped into the expertise and insights of our hub of DEIB (Diversity, Equity, Inclusion & Belonging) focused partners to inform talent strategies, weave this into company culture, and in turn improve diverse representation across our clients’ workforces.
At the end of the two days, I asked the team to describe the offsite and these critical conversations in one word – inspired, insightful, excited and collaborative are just some of the words that stood out to me. I used the word ‘proud’ after something that our client said during the event really resonated with me: ‘it’s a real honour to be part of the AMS agreement, and that’s down to the people’.
With key market insights underpinning everythingwe do, our ‘One AMS’ mentality and the unrivalled expertise and passion of our people, I truly believe we are unstoppable.
According to a 2023 report by the McKinsey Global Institute (MGI), Artificial Intelligence could generate $60 billion to $110 billion annually in economic value for the pharmaceutical and medical-product industries. A key driver of this potential growth is the rapid advancement of artificial intelligence (AI), particularly Generative AI, which creates opportunities and drives change from operational efficiencies to talent management strategies.
To explore AI’s transformation of talent acquisition (TA) and management in the Pharma, Life Sciences and Healthcare sectors, AMS Singapore recently hosted a breakfast event featuring a fireside chat with Sam Hannaway, Lead Solutions Consulting, APAC from The Smart Recruiters, and Leon Kwang, General Manager, APAC from The Udder Group. We delved into the impact of AI on TA and management, emphasising the importance of acquiring skills, as well as upskilling, reskilling and change management. These practices are increasingly essential in today’s workplaces, as strategic skills-based hiring begins to replace traditional role-based hiring.
Strategic opportunities with AI
While AI has existed for years, such as in the form of Siri, recent innovations such as ChatGPT have accelerated the adoption of AI across industries around the world, including the APAC region.
Despite the growing focus on AI, a quick poll I conducted at the start of the event revealed varied levels of AI adoption among the participants. While many are early adopters who were still navigating its complexities, some are cautiously exploring AI’s potential.
Although TA professionals advocate for AI in recruiting and retaining talent in this competitive market, Sam cautioned against jumping on the AI bandwagon without careful consideration. He advised business leaders to first identify their specific challenges and assess how automation can streamline processes for increased productivity and cost-efficiency.
Leon echoed this sentiment, highlighting that each company has unique needs and goals, necessitating a customised approach to AI implementation. Given the rapid evolution of AI, he recommended starting with small improvements – such as aiming for a one percent improvement before scaling up for a multiplier effect. This approach can minimise errors, reduce operational costs, and improve AI integration in TA.
Working with the HR and TA leaders within AMS’s clients, we have witnessed firsthand the transformative impact of AI on Talent Acquisition. It is crucial not to view AI in isolation. It is essential to integrate AI thoughtfully within the broader ecosystem of mindsets, values, and strategies. TA leaders must ensure AI is embedded in work processes and company cultures for a holistic approach to solutions.
Sam highlighted the importance of balancing AI integration with human interaction. AI should serve to enable, not replace, human workers. By automating routine tasks, AI allows recruiters to focus on strategic decision-making and meaningful human interactions.
He further shared a recent project involving the implementation of a chatbot for a healthcare organisation in Australia. This chatbot handles screening questions and schedules appointments with potential candidates around the clock, freeing up time for the hiring manager and recruiter to conduct in-depth interviews and assess their suitability for the roles more effectively.
Ethical dilemmas involving AI
Despite AI’s benefits, concerns about its ethical risks remain.
One participant concurred that AI is unlikely to replace their recruitment staff but can enhance their work. She was, however, particularly interested in understanding how AI could support diverse hiring – such as gender, LGBT groups, disability groups and groups with a diversity of thoughts – to ensure a non-discriminatory hiring process.
Another participant raised concerns about navigating challenges, such as job candidates relying heavily on ChatGPT to prepare their resumes while job recruiters use AI to detect skill sets from resumes. Sam suggested the potential of “defensive AI” that can identify AI-generated applications. When complemented by human judgement, this approach could prevent technology from limiting the search for the right candidate or introducing bias.
Leon addressed the participants’ concerns about data privacy, acknowledging that AI is still an unchartered territory for many organisations regarding data integrity. He expressed optimism that with the appropriate measures in place, such as conducting regular AI audits, the future could see AI agents tailored to our individual preferences and skill sets.
By addressing these issues early in the AI integration process, TA leaders in the Pharma, Healthcare and Life Sciences sectors can effectively tackle such challenges and leverage strategic opportunities for greater success in talent acquisition.
AI and the future of hiring
Leon highlighted that in the past technological advancements often depended on standardisation of recruitment and HR processes and the challenge was integrating various enterprise systems and data sources into a cohesive platform. Application of AI and the new age Talent Intelligence platforms break the barriers and support in creating a unified system where all data and skills are standardised and accessible.
We are just beginning to explore the full potential of AI in TA. AI is adept at handling extensive recruitment data, from applications to feedback. We should expect significant improvements in efficiency and more targeted support for skill – based hiring and experience focussed hiring. For instance, AI might alert us when a job is about to close or highlight diversity metrics, recommending actions like extending job postings or exploring specific sourcing channels.
The potential for AI to evolve and make an impact is substantial, and its sophistication and influence will continue to grow. The possibilities are exciting, and I am eager to see how AI will shape the future of TA.
As a leading global talent acquisition provider, AMS harnesses technology and innovation to explore how digital advancements, such as AI and data analytics, can help employers meet increasing demands efficiently. We are committed to striking the right balance between productivity, cost-effectiveness and meaningful human connection.
Did you know that there is a significant gender divide when it comes to AI usage and adoption?
Forbes writes: “Artificial intelligence has a gender issue, and it’s not just about the images it creates or the biases that models may include”.
Overwhelming statistical research shows that women use generative artificial intelligence tools less than men do. Surprisingly the gap is biggest among the youngest workers, a new survey from Slack finds. It includes results from a survey of more than 10,000 “desk workers” and found that Gen Z men are 25% more likely to have tried AI tools compared to Gen Z women.
The Kenan Institute has established that nearly 80 per cent of today’s female workers are in jobs exposed to automation via generative AI, compared with 58 per cent of men. These jobs will not be replaced by artificial intelligence, but by people who have mastered AI. And in the current landscape, that means men.
Lack of diversity in AI development
Another factor gives cause for even greater concern. According to a training expert on the Coursera platform, women are underrepresented in the development of AI-related skills. In fact, three times as many men as women sign up for the most popular AI training courses on this platform.
This is not breaking news – a BBC article at the end of 2023 addressed this very issue. They interviewed AI expert Jodie Cook who says there are deeper, more ingrained reasons why women are not embracing the technology as much as men.
“Stem fields have traditionally been dominated by males,” says Ms Cook, who is the founder of Coachvox.ai, an app that allows business leaders to create AI clones of themselves.
“The current trend in the adoption of AI tools appears to mirror this disparity, as the skills required for AI are rooted in Stem disciplines.”
And then there’s the matter of the low representation of women in senior management positions in the fields of science, technology, engineering, and mathematics, and consequently, in AI.
“This is something that we should be keeping an eye on,” says Christina Janzer, head of Slack’s Workforce Lab. “My hypothesis is that the people who are using it today are the people who are going to help shape the future of it. We want those people to be representative of our entire population. That’s not what we’re seeing today. This is a big opportunity for leaders to understand that and to course-correct.”
Psychologist Lee Chambers says that typically female thinking and behaviour may be holding some women back from embracing AI.
“It’s the confidence gap – women tend to want to have a high level of competence in something before they start using it, ” he says. “Whereas men tend to be happy to go into something without much competence.”
Mr Chambers also says that women may fear having their ability questioned, if they use AI tools.
“Women are already discredited, and have their ideas taken by men and passed off as their own, so having people knowing that you use an AI might also play into that narrative that you’re not qualified enough. It’s just another thing that’s debasing your skills, your competence, your value.”
I was interested to see what AI itself had to say on the matter. I asked Copilot “why are women less likely to use AI”? This was the response:
Trust and Reliability Concerns: Some women may find AI tools less reliable or trustworthy. For instance, errors in AI outputs can lead to skepticism about their accuracy.
Perception of Competence: Women often feel the need to emphasize their credentials more to demonstrate their expertise. Using AI might be perceived as undermining their qualifications.
Safety and Ethical Concerns: Women tend to be more concerned about the ethical implications and safety issues related to AI, such as biases in AI algorithms and the potential misuse of AI-generated content.
Personalization and Authenticity: Many women prefer to maintain a personal touch in their work, feeling that AI might strip away the authenticity and personalization they value.
Lack of Awareness or Training: There might be less exposure or training opportunities for women in AI, leading to lower adoption rates.
Then I asked, “What can be done to encourage more women to use AI”? Its response was:
Encouraging more women to use AI involves a multifaceted approach. Here are some effective strategies:
Early Education and Outreach: Introducing AI concepts and technology to girls at a young age through educational programs, workshops, and coding classes can ignite interest and build confidence.
Role Models and Mentorship: Highlighting successful women in AI and providing mentorship programs can inspire and guide aspiring female AI professionals. Seeing relatable role models can make a significant difference.
Inclusive Work Environments: Promoting inclusive and supportive work environments where women feel valued and respected is crucial. This includes addressing biases and ensuring equal opportunities for advancement.
Upskilling and Training Programs: Offering upskilling programs, internships, and gamified challenges can help women develop the necessary skills and confidence to succeed in AI.
Community and Networking: Creating communities and networks for women in AI can provide support, resources, and opportunities for collaboration and growth.
Addressing Stereotypes and Bias: Challenging stereotypes and biases that discourage women from pursuing AI is essential. This can be done through awareness campaigns and by promoting positive narratives about women in tech.
There are several successful initiatives aimed at promoting women in AI – AI4ALL; IBM’s Women Leaders in AI; Women in AI (WAI); Algorithmic Justice League; and Women in Machine Learning (WiML) but more must be done.
So why is it important?
Who develops AI, and what kind of data it is trained on, has gender implications for AI-powered solutions. It mirrors the biases that are present in our society and that manifest in AI training data. In a rapidly advancing AI industry, the lack of gender perspectives, data, and decision-making can perpetuate profound inequality for years to come.
Sola Mahfouz, a quantum computing researcher at Tufts University – “When technology is developed with just one perspective, it’s like looking at the world half-blind,”
As an opportunity, one of the most interesting aspects of the gen-Ai ‘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.
In conclusion, the AI field needs more women, and that requires enabling and increasing girls’ and women’s access to and leadership in STEM and ICT education and careers. Understanding and bridging the AI gender gap is essential for ensuring equitable access and representation in the burgeoning AI landscape.
AI itself says “By implementing these strategies, we can create a more inclusive and diverse AI ecosystem”.
