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.
Join industry experts from AMS and SAP Fieldglass at a roundtable on Tuesday 14th May in our own offices at London Wall as we dive into challenges and opportunities facing the Transport, Engineering and Construction industries in accessing the skills and talent they need for the short and longer term.
Whether you’re just starting out on your journey , or looking to take your established contingent labour program to the next level, we’ll share the latest insights and industry specific innovation designed to help you optimise your supply chain to:
Access critical skills at speed
Diversify talent pipelines
Achieve workforce compliance, visibility and spend control.
Spaces are limited, so register here to secure your seat!
Our recent roundtable discussion covered how technology can be leveraged to help optimise your non-permanent workforce.
From AI to the evolving role of MSPs, and the importance of presenting a unified business case for tech change within your organisation, here are our 6 key takeaways:
1. AI: A Game-Changer Across the Talent Lifecycle
AI is actively streamlining sourcing and delivery processes, offering opportunities to enhance efficiency and precision. Its adoption, however, has proven much easier within Contingent Workforce Solutions (CWS) compared to permanent hiring scenarios.
AI should and is being deployed in contracting, writing Statements of Work (SoW), defining milestones, and managing supplier performance.
Possible resistance, stemming from job insecurity and uncertainty about AI’s role, was acknowledged. Participants agreed that addressing this resistance through change management and clear communication is essential.
2. Future of MSP and Workforce Ownership
The ownership of non-permanent workforces remains blurred, with Procurement traditionally owning Services Procurement/SoW while HR increasingly seeks visibility and influence in this domain.
Unified governance, combining input from HR, Procurement, and Finance, was seen as the solution to enforcing meaningful change.
3. Vendor Management Systems (VMS): Opportunities and Challenges
Smaller businesses often face difficulties with VMS implementation due to the complexities of vendor relationships and lack of accountability for results. While larger solutions offer robust governance, start-ups can be a cost-effective first step away from basic spreadsheets.
The importance of identifying a strong tech owner and fostering real accountability emerged as critical to successful VMS management.
It was agreed that despite impressive demos, many VMS platform implementations and adoption falter in real-world scenarios without the right partner to ensure success.
4. Technology Maturity and Incremental Change
Discussions on the technology maturity model revealed variations among businesses. Most participants identified themselves at levels 1 and 2.
Over-reliance on incremental change was flagged as a potential risk, leading to inconsistencies and complexity, a strategic partner is vital to help businesses navigate this.
5. Building a Robust Business Case
CFO alignment and early Finance involvement are critical when it comes to obtaining buy-in for technology change and implementation. Market insights and ROI analysis can further strengthen the case for investment.
Being clear on the key business drivers for the change, the benefits it will enable and the roadmap to implementation are all crucial factors to consider. Improvement in the time-to-hire metric may be a component along with an emphasis on achieving “more for less”, enhancing efficiency Linking strategic objectives to measurable outcomes will also foster stakeholder support.
6. AI-Powered Tools and the Road Ahead
Generative AI was recognised as a powerful ally in reducing the time-intensive burden of administrative tasks. Many VMS providers are releasing tools, supplementing their platforms such as SAP Fieldglass Joule which assist in:
Automating the creation of business cases, significantly minimizing the manual effort required from managers.
Generating standardised documents such as Job Descriptions (JDs) with precision and speed, allowing HR teams to focus on strategic initiatives rather than repetitive tasks.
This ability to simplify routine processes is not only streamlining operations but also creating opportunities to redirect human talent towards more value-adding activities. In addition, tools that support with the visibility of the total work force are increasingly popular for key hiring approaches such as skills-based hiring and determining the best route to market, empowering organisations to align talent strategies with business goals effectively.
The next roundtable in our series will be held in May 2025. You can also read our last article in this series, 6 smart strategies for reducing costs through your non-permanent workforce here.
The Tech & Digital Contractor market is an ever evolving one, much like the skills required to work within it.
Recently it has been a challenging environment with all the ups and downs of the fairground, culminating in the last 12 months with a scarcity of opportunity and stagnant day rates. KPMG’s CEO said hirers face a “fiscally restrained” Spring Statement 2025, but there are some aptly timed ‘green shoots’ appearing.
ContractorUk.com states “For the first time since August 2024, the numbers on the REC’s index for temporary tech roles last month pointed upwards… The IT contractor jobs market carved out a potential foothold for growth in February 2025.”
Changes to the National Living Wage, Employer’s National Insurance and subsequently, The Employment Rights Bill are contributing to a cautious outlook, but technical advancements aren’t waiting around for anybody.
Organisations are increasingly under pressure to adopt AI functionality to remain competitive and the UK Government has clearly set out their ambition under the AI Opportunities Action Plan. This aims to harness the power of AI to transform various sectors and improve the quality of life for citizens.
Many employers do not currently have the internal talent to scope, lead and deliver in this space and they are likely to look to the contractor population.
Talent in Demand
Unsurprisingly AI skills top the list of those most in demand in the contingent market, closely followed by (and likely in conjunction with) cyber security, all-things data, cloud computing and python development.
Artificial Intelligence (AI) and Machine Learning: These skills are crucial for developing intelligent algorithms and models that drive automation and predictive analytics. The technology is moving so quickly that there are few true experts in the field; all and any commercial exposure to AI will be in demand.
Data Science and Analytics: With the increasing amount of data being generated, professionals who can analyse and derive insights from data are in high demand.
Cybersecurity: As cyber threats continue to evolve, skills in intrusion detection, risk assessment, and data protection are essential for safeguarding digital assets.
Cloud Computing: Expertise in cloud platforms and services is vital as more companies migrate to cloud-based systems.
DevOps and Automation: These skills help bridge the gap between development and operations, improving efficiency and collaboration.
Blockchain: Beyond cryptocurrencies, blockchain technology is being used in various industries for secure and transparent transactions.
In the last year many organisations have evolved to hybrid working models. This has been mandated to permanent employees and therefore frequently includes contractor populations. There will still be some fully remote opportunities, or potential exceptions based on skills v needs – but realistically, most contract opportunities moving forward will require some onsite presence.
Soft Skills Revolution
One of the most interesting aspects of the GenAI ‘revolution’ is the recognised 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.
Non-Traditional Role Parameters
In the last 12-18 months within the UK Tech & Digital market, there has been an increased demand for candidates with blended skill sets—roles that now often combine expertise in multiple disciplines.
For example, there is an upward trend in full stack development as opposed to front or back-end disciplines; DevOps processes (such as CI/CD, Kubernetes) added to support or development roles; Data aligned roles requiring significant Python or R coding; and most needs requiring diverse levels of cloud storage or security capabilities – stand-alone Cloud Engineers are now a rarity.
Advancements in using AI to streamline hiring processes have also driven a ‘skills-first hiring” trend, led by the Tech Sector and including companies such as Google and Apple. Approximately 50% of technology job postings no longer require degrees and 80% of employers prioritise demonstrated abilities over academic credentials.
Forbes writes the “These organizations recognize that conventional degree requirements often exclude qualified candidates who’ve developed valuable skills—particularly in high-demand areas like machine learning, data science, and automation—through alternative means.”
Legacy Alive & Well
The headlines will always focus on the shiny new toys (not taking away from the leaps forward GenAI has brought to the world) but organisations can’t just wipe their tech estate slate clean and start again.
Financial Services and Public Sector bodies offer contracting opportunities for those underpinning and therefore critical legacy tech stacks, on which new functionality is built. New arrivals into the contracting market will not have these skills, and expertise will become a commodity in demand.
IT Contracting as an Opportunity
Robert Half stipulates that “Contract work will become a significant employment model in 2025, encompassing freelancing, right-to-hire positions, and on-call work. Companies increasingly use contractors to fill critical skill gaps, especially in AI, technology, and marketing, with about 40% of managers planning to use contract professionals for key projects.”
Contingent Tech & Digital offers scope to broaden expertise – no client has the same tech stack – and gain valuable knowledge and differing industry experience. Contractors have always needed to stay relevant and therefore employable: with the speed of technical advancement this is now more common in permanent roles and therefore even more critical. An appetite to evolve, a curiosity to learn, and a willingness to step outside traditional role parameters to gain new skills, will make you stand out from the crowd.
And on that final note (with a nod to the volume of AI generated CVs and applications), to maximise your success, ensure your online persona and/ or CV are representative of skills and clear on capability; if they are technical, include the hobbies and online hangouts evidencing your interests; and build credibility with TA, Recruiters and Hiring Managers and leverage your professional network.
So, the roller coaster may be stomach churning at times, but it is fast, and it is thrilling, and few really want it to end!
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.
