As AI transforms work at an unprecedented pace, forward-thinking CHROs are seizing the opportunity to fundamentally redesign how their organizations hire, develop, and deploy talent. In our recent webinar, The CHRO as Workforce Architect: Leading AI Transformation in Talent Acquisition with The Josh Bersin Company, Radancy’s Jahkedda Akbar, SVP of Strategy, Insights & Innovation, and Nathan Perrott, SVP of Global Customer Strategy & Advisory, were joined by Josh Bersin, CEO and Founder of The Josh Bersin Company, to discuss how this shift is redefining talent acquisition and workforce strategy in an AI-driven environment.
Why the CHRO Is Now Leading AI Transformation
Investment in AI-driven technology has accelerated, but progress in applying it to impact real change on the workforce often depends less on technology selection and more on whether organizations can support workforce change at scale.
When workflows shift and roles evolve, CHROs are uniquely positioned to guide how work is redesigned and how employees adapt over time. As Josh Bersin explained, “The reason it’s falling into HR is it’s all about change management. It isn’t really about technology.”
This is about shaping how capability, accountability, and productivity are structured across the business as AI becomes embedded in daily work. That’s always been HR’s domain. What’s changed is the pace and scale.
Workforce Architecture Requires a Broader Planning Lens
As AI changes how work operates, workforce planning must expand beyond headcount and static roles. Jahkedda Akbar introduced a three-layer model for workforce capacity that reflects how AI reshapes work and where leadership accountability sits. As she emphasized, “This isn’t just an HR or CHRO problem. It requires cross-functional design of how work will evolve. When you try to make transformation happen by isolating it to one team, things fall apart.”
- System capacity evaluates what AI and the technology stack can support in terms of scale and quality.
- Activation capacity considers the short-term talent needed to implement, train and operationalize AI.
- Operational capacity focuses on who runs, governs and evolves AI-enabled workflows once they are in place.
This model helps organizations plan for AI as a permanent operating condition rather than a temporary initiative. As Josh noted, “This is not like implementing a new ATS or implementing a new ERP. You’re essentially building … a new type of automation infrastructure that you’re going to be living with for a long time.”
Josh described how many organizations were historically designed by breaking work into steps and creating jobs to support them. When AI removes or combines steps, fewer roles remain, but those roles often require broader scope and accountability. “That means each of those people have to have greater levels of capability and responsibilities because they’re using an AI that might be doing things that five other people did before,” he added. With workforce design orchestrating capability, organizations must support roles that expand over time.
When Strategy Outpaces Systems and Skills
As organizations align on workforce architecture at a strategic level, execution of those plans often struggles to keep pace. Many hiring systems were designed for a different environment and are poorly equipped to support roles that continue to evolve after hire.
Nathan Perrott described the execution gap: “Most TA systems were designed for requisition management, and not necessarily capability orchestration.” As a result, workforce strategy advances faster than the tools used to operationalize it.
AI’s impact is most visible at the task level. When AI takes on portions of work, roles do not disappear. Instead, remaining responsibilities shift toward judgment, oversight, collaboration and decision making. These changes frequently occur faster than job descriptions and hiring criteria are updated, creating misalignment between how work is performed and how candidates are assessed.
Jahkedda pointed to research from the World Economic Forum showing that professional skills now have a half-life of around 5 years, with many technical skills changing in as little as two. As roles continue to evolve after a hire is made, past experience becomes a weaker indicator of future success. She framed this as a fundamental shift in how work is organized: “We’ve been talking about skills-based hiring for some time now, but as AI augments workflows, what’s really happening is it’s changing who owns which task. The skills that are necessary for a job today will shift.”
Skill fit still matters, but it no longer provides a complete picture. The ability to learn, adapt and take on new responsibilities plays a growing role in long-term performance.
The Confidence Paradox in Screening
Talent acquisition teams express high confidence in their ability to hire for an AI-augmented future, but their methods haven’t caught up.
Jahkedda shared Radancy Labs research that revealed a striking disconnect: 75% of talent acquisition practitioners are confident they can identify future-fit candidates, yet 47% rely primarily on gut feel and informal judgment. When asked what causes bad hires, 45% traced failures to adaptability gaps, not skills mismatches.
The implication: Organizations know they need to screen differently. They just haven’t operationalized it yet.
Screening processes have been largely unchanged, even as roles shift more rapidly after hire. Resumes, static criteria and informal assessment methods continue to anchor early decisions, limiting the ability to evaluate how candidates will grow as work evolves.
Why Workflow Redesign Creates Momentum
The gap between confidence and practice raises a deeper question: What does screening for adaptability actually require? Traditional screening methods, resumes, static criteria, and informal assessment weren’t designed to answer these questions. This is where workflow redesign becomes essential.
Nathan described what happens when AI is layered onto traditional screening workflows without rethinking how decisions are made, saying, “If you overlay AI screening tools on top of that process, but they are still primarily evaluating and weighing the resume against the job requisition, the outcomes don’t really change. In fact, what you’re doing is you’re amplifying the problem.”
Progress emerges when leaders rethink how hiring decisions are made, allowing AI to support stronger evidence, reduce manual work and help recruiters focus on best-fit candidates.
Screening and Scheduling as Decision-Shaping Moments
When hiring processes change, the moments that most strongly shape outcomes are often early and operational, where clarity at key decision points is essential. Screening determines which candidates move forward, while scheduling influences whether candidates stay engaged long enough to convert.
Scheduling is frequently treated as an administrative task, but Nathan framed it as something more consequential, saying, “Interview scheduling is not just an admin task. It’s a key moment where candidates either convert or get lost, depending on things like time lag and how complicated it is to schedule the interview.”
Together, screening and scheduling illustrate how workflows directly influence outcomes long before final decisions are made. Improving these steps helps reduce friction early, surface stronger candidates more reliably and set the rest of the hiring process up for better future decisions.
In Practice: Rethinking Screening for a UK Professional Services Firm
Nathan shared a real-world example from an organization that redesigned their screening workflow entirely. Rather than filtering resumes against job descriptions, they built structured screening questions delivered through a conversational AI that assessed:
- How candidates approach unfamiliar problems
- How they’ve adapted to role changes in the past
- How they think about working alongside AI tools
The organization’s recruiters now spend less time drowning in AI-optimized resumes and more time evaluating candidates who demonstrate genuine adaptability, a valuable quality as roles continue to evolve.
From Automation Toward Orchestrated Intelligence
As AI adoption accelerates across talent acquisition, both Josh and Nathan warned of a growing risk when tools are deployed in isolation. Engagement, screening, scheduling and hiring may each improve independently, but the intelligence generated at one stage often fails to inform decisions across the rest of the process.
Josh framed this as an architectural challenge rather than a tooling issue. As organizations add more AI-driven capabilities across HR and talent acquisition, the question becomes how those systems interact. Without coordination, insights remain trapped within individual steps, limiting their value and increasing complexity.
Nathan noted that when workflows are disconnected, early signals captured during attraction or screening are often missed or lost as candidates move forward. While AI may speed up individual tasks, decision quality suffers when those early indicators are not carried through the process. Jahkedda distinguished this shift from traditional data management: “It’s no longer just about accessing data points. Now we’re talking about intelligence — the ability to stitch together answers that would have previously required searching across multiple systems. This allows TA practitioners to be much faster and more strategic.” A connected hiring system powered by orchestrated intelligence closes this gap by ensuring early signals continue to inform decisions as candidates move through the hiring journey.
Trust and Oversight as Enablers of Scale
As intelligence is coordinated across hiring workflows, trust and human oversight become critical to sustained adoption. Both Josh and Nathan emphasized that scale depends on clarity around how AI supports decisions and where human accountability sits.
Josh cautioned that organizations lose confidence when recommendation logic is opaque or difficult to interrogate. Hiring teams need visibility into how AI generates recommendations and applies signals to trust the outcomes and defend them internally and externally. Trust emerges when recruiters and leaders understand how AI contributes to decisions rather than replacing judgment.
Nathan reinforced that this is not about slowing progress. Clear oversight enables consistent use by reducing uncertainty and risk. When scoring, recommendations and workflow logic are explainable and auditable, and teams are more willing to integrate AI into everyday hiring decisions.
In this way, oversight functions as an enabler. It allows organizations to expand AI-driven hiring with confidence, rather than limiting adoption to isolated experiments.
What Talent Leaders Can Take Away
The webinar concluded with three practical takeaways for CHROs and talent acquisition leaders navigating AI-driven change:
- Workforce architecture requires a multi-layer approach. Planning must account for systems, activation and ongoing operations, not just headcount.
- Screening for adaptability is becoming increasingly important. Skill fit alone provides an incomplete picture in roles that continue to evolve after hire.
- Coordinated intelligence delivers stronger outcomes than isolated automation. When hiring workflows are connected, signals compound across the journey, rather than fragment across tools.
These reinforce the expanding role of HR leaders in shaping how organizations hire and retain talent in an AI-enabled organization.
Moving from Insight to Action
The shifts described in this session — from headcount planning to workforce architecture, from credential screening to adaptability assessment, from isolated tools to orchestrated intelligence — require both strategic clarity and operational infrastructure.
Organizations leading this transformation aren’t starting from scratch. They’re working with partners who understand both the strategic imperative and the practical challenges of executing at scale.
Radancy: Delivering the Future of Talent Acquisition
As organizations rethink workforce design in an AI-enabled environment, the ability to coordinate intelligence across workflows becomes essential.
Radancy is a trusted technology partner for global leaders, delivering the future of talent acquisition through a single, Agentic AI-powered platform. Designed to simplify hiring and deliver proven results at scale, the Radancy Talent Acquisition Cloud enables teams to surface stronger signals earlier to increase recruiter efficiency, reduce costs and maximize ROI, helping organizations build the workforce of tomorrow. To explore these ideas in more detail and hear directly from industry leaders on how talent acquisition is evolving, watch the full webinar here.
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