Elie Bechara (Talent Insights, LinkedIn), Theodoros Evgeniou (INSEAD), Gaël Gioux (NoesysAI)
Introduction – A Quiet Revolution at the Heart of the Enterprise
AI is no longer a side project. It is reshaping how companies operate, compete, and grow. Beyond customer-facing or productivity gains, a deeper shift is underway: the reinvention of work and organizations. At the center is the workforce. How people are recruited, developed, deployed, and supported is being redefined, and with it, the role of HR. Far from a support function, HR now has the mandate to design and continually adapt the organization for an AI-enabled future.
Waiting is not a strategy. Microsoft’s 2024 Work Trend Index reports that 75% of global knowledge workers already use generative AI on the job, and 78% bring their own tools (“BYOAI”). This adoption is outpacing corporate change programs, reshaping employee experience and productivity, whether companies are ready or not. Business leaders are aware: 82% said 2024 was a “make or break” year to adapt strategy and operations for an AI-driven world.
For CHROs, the task goes beyond digitizing HR operations. The real challenge is leading the organization through cultural change, accelerated learning, team redesign, and new governance. AI doesn’t just improve work, it changes what work is, who does it, and how it’s done.
This article outlines four imperatives for CHROs to lead this shift:
- Transform HR’s core activities
- Enable AI adoption enterprise-wide
- Redefine culture, capabilities, and the shape of work
- Rebuild governance from the ground up
Done well, HR can become the architect of a new kind of organization, where humans and AI work together, responsibly and creatively, to deliver on the mission.
1. HR’s Core Activities Will Be Transformed—and Enriched
AI is reshaping the core responsibilities of HR. Not only by automating tasks, but by enriching them with insights, speed, and scale that were previously impossible. The opportunity is not just to do traditional HR faster, but to elevate its strategic value.
Take recruiting. Generative AI can now assist in crafting job descriptions, parsing resumes, ranking candidates, generating personalized outreach messages, and optimizing employer branding, from refining career site content to enhancing value proposition messaging. A BCG analysis estimates recruiting tasks offer between 25% and 50% efficiency potential with GenAI. And LinkedIn’s ‘Future of Recruiting 2025’ reports companies already seeing up to 20% time savings per recruiter by automating the early stages of sourcing and screening. These efficiencies do not replace recruiters, they free them to focus on candidate experience, strategic advising, and talent relationship management.
Beyond recruiting, AI transforms internal mobility and workforce planning. Tools powered by large language models (LLMs) and organizational data can now identify skill adjacencies, suggest career paths, and match employees with new roles or projects. This supports a shift from jobs to skills, a move backed by the World Economic Forum Future of Jobs report, that predicts that 40% of core skills will be changing by 2030. AI systems can also simulate the impact of business changes, like shifting market needs or automation, on workforce supply and demand. Scenario modeling, predictive attrition risk, and talent heat maps all become more accessible.
AI is also transforming learning and development by making it more personalized, dynamic, and embedded into the flow of work. Modern learning experience platforms (LXPs) leverage AI to curate individualized learning paths, recommend relevant content based on role and skill gaps, and adapt in real time to employee progress. These platforms shift learning from a static, course-based model to a continuous, skills-first experience. In parallel, organizations are beginning to deploy on-demand microlearning agents or AI copilots that act as real-time learning companions. These agents can answer questions, surface just-in-time resources, and coach employees through new tools or workflows. Together, these innovations create a responsive learning ecosystem, one that evolves with the needs of the workforce and supports scalable, self-directed development.
In performance management, AI enables more continuous and objective evaluation. Sentiment analysis, feedback loops, and behavioral signals (e.g., collaboration data, project timelines) provide early indicators of burnout, disengagement, or untapped potential. AI doesn’t replace the manager’s role, but it provides signals that help them intervene more effectively. This shift supports a more humane and data-informed approach to performance.
Taken together, these advances point to a new model for HR operations. AI is foremost augmenting the HR function. CHROs must lead this transition, not as a technology deployment, but as a rethinking of how people strategy is defined and delivered.
Ultimately, the enrichment of HR’s core functions through AI creates the conditions for HR to play a more central role in the business. Freed from unnecessary overload, equipped with better data, and empowered with new tools, HR can shift its focus toward enabling performance, guiding transformation, and elevating the employee experience at scale. Companies with AI-enabled HR are also more appealing to top candidates who value and actively seek out workplaces that embrace intelligent tools.
2. HR Is Now an Enabler of AI for the Rest of the Organization
While AI is transforming HR’s own operating model, its broader and arguably more important impact lies in how HR enables AI adoption across the organization. In most companies, every function, from marketing to operations, is experimenting with AI tools. But tools alone do not drive transformation. People do. Research by Erik Brynjolfsson and colleagues (NBER Working Paper No. 31161, 2023) demonstrates that AI tools can increase individual productivity by 14%–35%, but only when workers receive targeted training and workflows are redesigned to integrate AI effectively. Without this enablement, adoption lags and much of the potential value remains untapped. Success hinges on capability-building, trust, and behavior change, territory where HR is central.
The first pillar is AI related upskilling. Microsoft’s Work Trend Index reports that while 75% of employees are using generative AI, 60% say they lack the right training to use it effectively. Meanwhile, 79% of leaders expect employees to develop AI capabilities on their own, creating a significant mismatch between expectations and enablement.
The urgency is visible in the data: AI-related skills are being added to LinkedIn profiles at five times the rate of other skills. Non-technical professionals, too, are stepping up. To match this grassroots momentum, HR needs to formalize and scale training programs. That can include live bootcamps, curated learning paths, certification tracks, and internal coaching networks. AI-powered learning platforms can also help tailor development paths to individual roles while anchoring them in organizational goals.
Building AI capability at the leadership level is one of the fastest accelerators of enterprise adoption. Research finds that firms embedding AI fluency into leadership development, and encouraging cross-functional experimentation, scale adoption significantly faster. Indeed, executive education programs on AI are currently in very high demand. One of us has been directing and teaching multiple such programs, both tailored for specific organizations and open to participants across organizations (e.g., AI for Business, Transform Your Business with AI). Hundreds of senior leaders a year, from CEOs and board directors to senior managers, attend both in person and online these programs, often with full C-suites of large companies spending days on campus learning, debating, and working on AI and what it means for them, their business, and their industry. Such programs prepare leaders to best leverage AI to create business value, develop their AI strategy and AI governance, rethink their organizations as they build teams of humans and AI, and in the process demystify AI and gain confidence in this space which is necessary for them to successfully navigate this fast evolving area and to pick up the signal from the noise – critical capabilities for ensuring that AI adoption is strategic, responsible, and impactful.
Trust, cultural acceptance, and employee experience are all critical to successful adoption. Microsoft found that 52% of employees are reluctant to admit they’re using AI at work, often fearing that doing so may signal their job is automatable. HR must tackle this fear head-on, by clarifying that AI is meant to augment, not replace, human contribution. Research reinforces this approach, showing that the organizations extracting the greatest performance gains from AI are those that deliberately design work for human–AI complementarity. In these setups, AI’s speed and data-handling capabilities can amplify human judgment, creativity, and relational skills. But done wrongly, they can also hurt. Careful adoption is essential to capture its value and avoid its risks. At the same time, HR can shape the employee experience by helping teams navigate uncertainty, build confidence, and cultivate curiosity and adaptability, anchoring the transition in ethical, inclusive, and human-centered innovation.
In short, HR’s influence extends beyond its own function. By equipping employees with the skills, support, confidence and psychological safety to embrace AI, HR becomes the orchestrator of a transformation that is not just technological but deeply human.
3. Culture, Capabilities, and the Shape of Work Are Being Redefined
AI is not only transforming how work gets done, it is redefining the nature of work itself. Fixed roles are giving way to fluid capabilities. Traditional job architectures are being replaced by more modular, project-driven configurations. Agents are increasingly performing tasks once assigned to humans. These shifts demand urgent reflection from HR leaders.
As noted earlier, 40% of the skills required for today’s jobs will change by 2030 according to the World Economic Forum. This means organizations must move away from static role definitions toward dynamic skills-based architectures. Rather than matching people to jobs, HR will increasingly match people to problems, projects, and missions, adapting in real time to shifting market conditions and technological capabilities.
Microsoft’s Work Trend Index anticipates that companies will evolve from traditional org charts to ‘work charts’: team structures built around AI-human collaboration. In these environments, AI agents may autonomously perform data gathering, first drafts, and monitoring tasks, while humans focus on judgment, creativity, and relational work. As one of us noted in a recent article these AI coworkers will be embedded directly into collaborative platforms, capable of interpreting context, adapting dynamically, and partnering with humans to solve complex tasks, not merely augmenting work, but becoming autonomous, decision-making team members.
This hybrid approach already exists in a few pioneering companies like Moderna. In 2024, Moderna merged its HR and IT departments to create a People and Digital function led by a Chief People and Digital Officer. This reorganization allowed them to redeploy over 3,000 internal AI agents across research, HR, and operations. These agents serve not only to reduce repetitive work but to augment the performance of their human colleagues. The HR team, for example, uses a virtual HR agent to handle routine employee queries, enabling HR professionals to concentrate on strategic support.
This transformation requires more than just redesigning structures. It demands a cultural pivot toward learning, experimentation, and adaptability. Trust and safety must be preserved while encouraging employees to take calculated risks with new tools. Managers play a critical role here: their openness and behavior directly impact team adoption of AI. Training for managers must now include how to lead hybrid human–AI teams, assess performance with new indicators, and foster curiosity rather than fear.
As jobs become less linear and more blended with automation, career paths must also evolve. HR should rethink advancement frameworks, job leveling, and role evolution in the context of augmented roles. For example, how should one assess the contribution of an employee who delegates 50% of their work to AI tools but consistently delivers better outcomes? HR should introduce new KPIs and performance narratives to reflect the contributions of hybrid work configurations.
Inclusion and equity also require new attention. Without intervention, AI-based transformations risk amplifying existing inequalities. Not all employees have equal access to digital literacy or to emerging AI tools. It is essential for HR to ensure inclusive access to learning and tools, monitor disparities in AI deployment across functions and geographies, and protect underrepresented groups from exclusion.
Finally, HR will be tasked with helping employees find meaning and identity in a world where their traditional job titles or roles may evolve rapidly. This calls for an investment, for example, in purpose-driven work, values-based culture, and storytelling. The human side of transformation must not be lost amid technical efficiency.
The bottom line is that AI is reshaping not just tasks, but teams, relationships, and careers. CHROs must design adaptive work systems that allow both humans and machines to thrive. That requires courage, experimentation, and above all, a willingness to reimagine what organizations are for, not just how they function.
4. HR Must Help Rebuild the Foundations: Governance and Dilemmas
As generative AI becomes embedded in core organizational processes, from hiring and performance reviews to promotions and terminations, the question is no longer whether governance is needed, but how it is executed. AI governance refers to the structures, processes, and norms that ensure AI systems are used safely, ethically, and transparently. It encompasses everything from algorithmic bias mitigation and explainability to usage rights and human accountability. As noted in an article from one of us, AI governance is not a one-time compliance task, but an ongoing system of controls, human oversight, and adaptive decision-making that evolves with the technology.
While governance frameworks are typically defined at the enterprise level, led by a combination of legal, data, risk, and technology functions, HR plays a critical role in implementing them wherever people and algorithms intersect. This includes not only ensuring compliance with governance standards in talent practices, but also embedding those principles into everyday workflows, tools, and behaviors across the workforce.
In the talent domain, that means ensuring AI systems used in hiring, evaluation, or development align with ethical expectations. AI tools that screen résumés, assess interviews, or predict attrition must undergo systematic bias audits, explainability assessments, and cross-functional reviews. LinkedIn’s recent report on bias in AI-powered hiring highlights the risks of automated systems that penalize candidates for irrelevant traits like accents or eye contact. HR must work with compliance, legal, and data science teams to ensure only approved systems, tested against governance standards, are deployed in people’s decisions.
Transparency is another cornerstone. Employees and candidates need to know when and how AI is involved in decisions. HR can help operationalize governance by providing clear communication protocols, disclosures, and consent mechanisms that align with both ethical and legal standards. In doing so, HR acts as the translator between abstract principles (fairness, accountability, transparency) and day-to-day decisions affecting people’s careers and compensation.
HR is also instrumental in translating enterprise-level AI policies into role-specific guardrails. For example, as AI agents begin to support decision-making across teams, someone must define which decisions can be delegated, which require human sign-off, and how AI input should be documented. While HR does not unilaterally own these policies, it must ensure they are usable, understood, and enforced across the organization, especially in performance evaluation, leadership expectations, and employee engagement. This implementation role is particularly important as AI systems become more autonomous, influencing workflows in ways that may be invisible unless clearly governed.
Accountability is perhaps the most complex dimension. If an AI system produces a flawed recommendation or executes an action with unintended consequences, who is responsible? Conversely, if AI contributes to success, who receives recognition? These questions are not theoretical. They influence incentives, culture, and risk exposure. HR must help ensure that performance management systems reflect the new blended nature of work, where outcomes are often the product of human–AI collaboration. That includes defining new accountability models, contribution metrics, and career paths that align with this hybrid reality.
Organizational norms are also key elements for the success of any governance, and here, HR’s influence is central. Codes of conduct, training programs, and behavioral expectations must evolve to reflect a digital workplace where ethical AI usage is not just a compliance matter but a leadership competency. This could include education on data privacy and algorithmic bias, norms for feedback on AI outputs, or policies for experimenting with new tools responsibly. As an HBR article notes, firms that embed governance into culture, through shared values, peer accountability, and leader modeling, are better equipped to manage AI’s long-tail risks.
Looking ahead, one of the least mature areas of governance is digital labor management. As AI agents increasingly perform tasks once handled by junior employees, drafting content, answering employee queries, summarizing reports, HR must begin to co-develop models for how these agents are integrated, supervised, and iterated. Who “manages” the agent? How is its output audited? Do agents have usage logs, behavioral expectations, or role documentation? These questions will become increasingly urgent. Moderna’s early move to merge HR and IT into a single People and Digital function illustrates one potential approach, but it remains a bold outlier. Most organizations have yet to build the cross-functional infrastructure needed to govern digital labor effectively.
In short, governance is not an abstract technical concern, it is the backbone of responsible adoption. And HR is indispensable to its execution. By implementing ethical and inclusive frameworks, ensuring transparency and accountability, and helping the organization adapt to a new category of digital labor, HR reinforces the trust, fairness, and integrity that AI transformation depends on.
Conclusion – From Function to Architect
AI is not just another wave of automation. It is a platform shift, a fundamental reconfiguration of how work gets done, by whom, and under what rules. For HR, this shift is both an invitation and a mandate. CHROs who embrace AI not only future-proof their organizations, they also reposition HR as a strategic architect of transformation.
The stakes are high. Generative AI is already redefining workforce expectations, team design, and productivity. Microsoft reports that 81% of leaders expect AI agents to be embedded in strategy within 18 months. At the same time, many employees are racing ahead of their organizations, learning new skills, adopting new tools, and redefining their own roles in the process.
To lead this change, HR has to evolve. That means:
- Reimagining recruiting, learning, performance, and planning with AI at the core
- Enabling the rest of the business through skill-building and adoption frameworks
- Building a “Human-Agent Operating model” that links governance principles with day-to-day workflows
- Redesigning culture and structure for human–agent collaboration
- Operationalizing fair, transparent, and ethical standards for AI at work, in alignment with enterprise AI governance.
The organizations that succeed in this transformation will not be those with the most advanced algorithms, but those with both AI and the most adaptive people. HR holds the key to unlocking that adaptability, by equipping teams, enabling leaders, and anchoring change in purpose. The future of work is not something to be predicted. It is something to be built. And in that construction, HR is not a bystander. It is the architect.