McKinsey - HR Monitor 2026: HR at a Turning Point
Source: McKinsey & Company, HR Monitor 2026, June 2026
Key takeaways
HR is moving from functional excellence towards system level transformation. AI is changing roles, skills and workflows across the enterprise, giving HR a dual role as an architect of workforce transformation and a lighthouse that demonstrates the change inside its own function.
Workforce planning remains too short term for the pace of AI change. Only 11% of organisations take a strategic workforce planning perspective of three years or longer, while nearly two thirds plan no more than 12 months ahead.
Skills are changing even where jobs remain. McKinsey estimates that up to 30% of current work hours in Europe and the United States could be automated by 2030 under moderate AI adoption, while more than 70% of today’s skills remain relevant. The larger shift is how those skills are combined as tasks move between people and intelligent systems.
The skills organisations value are moving towards judgement and interpretation. Problem solving remains the most frequently cited future skill, creativity has risen sharply, and digital literacy recorded the largest increase in importance. Skills required to guide, interpret and apply AI outputs are becoming more prominent.
Learning activity remains far below the pace of workforce change. Employees report only 3.4 training days per year on average, while HR believes the figure is 6.2. Almost a quarter of employees report no training participation at all during the previous 12 months.
AI training remains highly uneven. Formal Gen AI training reaches 25% of surveyed employees in Europe, 31% in the United States and 49% in China, even though 71% expect AI to affect their work and change the skills they require.
Performance management is not operating at the cadence required for continuous development. One in five employees report having had no career development or feedback meeting during the previous year, while 58% receive formal feedback only once or twice annually.
The labour market is more employer driven, but hiring effectiveness still matters. Offer acceptance and overall hiring success have improved, yet the median hiring cycle remains 70 days compared with 49 days among top quartile organisations.
Employee retention is increasingly influenced by tangible economic factors. Compensation and benefits are now the leading retention driver at 52%, followed by work life balance at 46% and job security at 45%.
Falling attrition should not automatically be read as stronger engagement. Voluntary attrition has declined and fewer employees intend to change employer, but McKinsey attributes part of this to economic caution and constrained external opportunities rather than a major improvement in employee experience.
HR itself remains early in AI adoption. Companies report operational AI solutions across only 28% of HR processes, while another 37% remain in pilot phase. Progress has been incremental despite materially larger theoretical automation potential.
Agentic AI changes the HR operating model rather than simply automating more tasks. McKinsey argues that HR needs to move from static role based structures towards models where agents perform more transactional and analytical work and HR professionals concentrate on orchestration, judgement and value creation.
HR has two jobs in the AI transformation. It must act as a copilot for enterprise AI transformation by redesigning work, workforce planning, skills and incentives, while simultaneously acting as a lighthouse by transforming its own processes and operating model.
Executive summary
McKinsey’s HR Monitor 2026 argues that the people function has reached a turning point. Economic pressure, rapid AI adoption and changing workforce expectations are forcing HR to move beyond improving individual processes and towards reshaping the broader system through which people, skills, technology and work interact.
The report draws on surveys of approximately 1,300 HR professionals and 5,500 employees across ten countries, covering Belgium, China, France, Germany, Italy, the Netherlands, Poland, Spain, the United Kingdom and the United States. McKinsey uses the research to examine workforce planning, talent acquisition, learning and development, employee experience and HR’s own operating model.
The first major gap is workforce planning. AI is changing the allocation of tasks between humans and machines, but most organisations continue to plan mainly through headcount and short term capacity. Only 11% use a strategic planning horizon of three years or more. Nearly two thirds focus on the coming 12 months.
That matters because job titles may remain while the underlying skill composition changes substantially. McKinsey expects nearly every occupation to experience skill shifts by 2030. Problem solving, creativity, AI and data capabilities, digital literacy and reasoning are rising in importance as more routine technical execution can be supported by AI.
The report therefore argues for skills intelligence: the ability to continuously understand skill supply and demand across the organisation, identify adjacencies between current and future skills and translate that information into workforce interventions.
Talent acquisition presents a different picture. Labour markets have stabilised and offer acceptance has improved, but recruitment processes remain slow. Across the surveyed markets, the median hiring cycle is 70 days, while top quartile organisations complete the process in 49 days. AI offers considerable potential across screening, assessment, coordination and candidate engagement, but McKinsey cautions that technology needs to be incorporated into well designed processes rather than layered onto existing complexity.
The employee development findings show another gap between ambition and execution. Performance management and skill building are again rising on executive agendas, yet feedback remains infrequent and training participation low. Employees report about half as many training days as HR believes they receive.
AI learning is similarly uneven. Most employees expect AI to alter their work, but formal preparation varies sharply across regions. McKinsey therefore recommends moving away from measuring training activity towards measuring whether learning actually produces skill progression and improved performance.
Employee experience is increasingly shaped by economic uncertainty. Voluntary mobility is declining and employees are becoming more cautious, but satisfaction has not materially increased. Compensation, work life balance, security and flexibility dominate retention decisions.
The final section turns the same scrutiny towards HR itself. The traditional operating model is gradually giving way to more agile and technology enabled configurations, but most organisations remain in transition. AI adoption is still dominated by isolated use cases and administrative processes.
McKinsey argues that this needs to change. HR must become both more digital and more human: automating and redesigning core workflows while strengthening the judgement, coaching, culture and governance required in increasingly hybrid human agent organisations.
The result is a new strategic role. HR becomes both an architect of the enterprise’s AI workforce transition and an example of what that transition should look like when executed inside a function.
Key stats and quotable claims
McKinsey surveyed approximately 1,300 HR professionals and 5,500 employees across ten countries.
Under moderate AI adoption, up to 30% of current work hours in Europe and the United States could be automated by 2030.
More than 70% of today’s skills are expected to remain relevant, but nearly every occupation is expected to experience skill shifts.
HR professionals estimate that 23% of employees lack at least some skills required for their current roles.
22% of employees doubt they will have the skills needed to remain in their roles over the next five years.
Problem solving is cited as a top future skill by 44% of HR professionals.
Digital literacy recorded the largest rise in the future skills ranking, moving from 20th to sixth.
Only 11% of organisations conduct strategic workforce planning three or more years ahead.
Nearly two thirds of organisations plan their workforce no more than 12 months ahead.
85% report having a skills taxonomy, but only 57% combine organisation wide workforce planning with comprehensive skills documentation.
Only 22% use skill based workforce forecasting.
HR professionals report being unable to fill 11.6% of vacancies.
The average job posting attracts approximately 30 applications, compared with 165 in Spain and ten in the Netherlands.
Global offer acceptance has increased to 59%, from 56%.
Overall hiring success has risen to 50%, from 46%.
Median time to hire is 70 days, compared with 49 days among top quartile organisations.
McKinsey estimates core recruiting activities such as screening, assessment and interviewing have automation potential of at least 60%.
20% of employees report having had no career development or feedback meeting during the previous year.
58% receive formal feedback only once or twice per year.
Employees report an average of 3.4 training days per year, while HR estimates 6.2.
24% of employees report no training participation during the previous year.
71% of employees expect AI to affect their work and change required skills.
Formal Gen AI training reaches 25% of employees in Europe, 31% in the United States and 49% in China.
Frequent AI use reaches 36% in Europe, 47% in the United States and 77% in China.
Voluntary attrition stands at 4% globally and 3.7% in Europe.
17% of employees plan to change employer within six months, down from 23% one year earlier.
52% identify remuneration and benefits as a leading retention driver.
46% identify work life balance and 45% job security.
57% of employees report no salary change during the previous 12 months.
Approximately 11% of working hours, equivalent to 27 working days, are lost to absenteeism.
McKinsey estimates that roughly two thirds of current HR tasks can be automated using existing and emerging technologies.
Only about 5% of companies successfully and routinely incorporate new technologies into HR.
34% of HR professionals say they operate under at least a partially agile HR model.
15% report a strongly machine powered operating model.
Companies report operational AI solutions across 28% of HR processes, with another 37% in pilot phase.
McKinsey modelling suggests AI could automate an additional 30% of HR activities beyond approximately 50% that can already be addressed through traditional technologies.
In selected areas such as learning, analytics and compliance, incremental AI automation potential may reach 40%.
Overall summary
McKinsey’s central argument is that the people function cannot manage an AI driven workforce using planning, learning and operating systems built for a slower environment.
The mismatch starts with workforce planning. Companies broadly understand that skills are changing, but the majority still forecast headcount using short planning horizons. AI makes that increasingly unreliable because it changes tasks within roles before it necessarily removes entire roles.
The relevant unit of analysis therefore shifts. Rather than asking only which positions the organisation will need, leaders increasingly need to understand which tasks will remain human, which will be automated, which will be shared with agents and what capabilities the resulting work requires.
Learning needs to operate on the same basis. Annual training calendars are poorly matched to skills that may change continuously. Performance feedback delivered once or twice a year is similarly ill suited to employees whose workflows, technology and expectations are changing much faster.
The employee side of the research also provides an important warning. Lower mobility does not necessarily mean stronger loyalty. Employees are becoming more cautious in an uncertain labour market, and practical factors such as pay, security and sustainable workloads have become more important.
The largest transformation is reserved for HR itself.
AI has considerable theoretical potential across recruiting, learning, workforce planning, analytics and employee service. But most organisations have not yet translated that potential into end to end transformation. Technology landscapes remain fragmented, data is inconsistent and adoption is concentrated in administrative use cases.
The report therefore positions HR at the centre of a much larger transition. The function must redesign itself while simultaneously helping the wider organisation redesign work.
That is why McKinsey describes HR as both copilot and lighthouse.
Deep dive
HR at a turning point
Economic pressures, AI disruptions, and shifting workforce expectations are redefining effective people management. While many HR functions have strengthened processes in recent years, McKinsey’s data reveals a need for broader progress. Structural gaps persist between operational planning and strategic foresight, training and skills growth, employee expectations and organizational responses, and AI experimentation and scaled impact.
The report identifies five priority changes that leaders will need to embrace in the age of human AI collaboration. Workforce planning must move beyond operational capacity planning to strategic capability planning. Recruiting is becoming less critical in employer driven labour markets, but hiring effectiveness remains essential. Performance management and employee development are back on leaders’ agendas but remain fragmented in execution. Employees are becoming more cautious amid economic uncertainty. And agentic HR operating models are emerging while large scale AI adoption remains limited.
Automation and AI are rapidly reshaping how work gets done and which skills are required. Yet workforce planning remains predominantly focused on short term headcount planning, with only 11% of organisations adopting a long term perspective.
At the same time, skills gaps persist and demand is shifting towards more people centred and AI related capabilities as the need for routine task based skills declines.
Organisations therefore need to evolve towards forward looking, task and capability based planning or risk underestimating the scale of the workforce transformation ahead.
Strategic workforce planning in the age of human AI collaboration
Advances in AI are expected to substantially shift how and where skills are applied as work evolves into a partnership between people and agents, combining human judgement with the power of automation.
If AI is adopted at a moderate pace through 2030, up to 30% of current work hours in Europe and the United States could be automated. Over the same period, more than 70% of today’s skills are expected to remain relevant and could be applied in both automatable and non automatable work.
But this does not mean roles themselves will remain unchanged.
Nearly every occupation will experience skill shifts by 2030 as tasks are reallocated between humans and intelligent systems.
In periods of rapid technological change, the organisations best positioned to respond are those that treat talent with the same strategic rigour as financial capital. Top performers use strategic workforce planning to stay ahead, taking a three to five year view and anticipating multiple scenarios so that they have the right number of people with the right skills at the right time.
Yet the uncertainty introduced by Gen AI makes this forward looking view harder and more critical than ever.
Gen AI is not just another technological advancement affecting specific tasks. It is a catalyst for organisations to rewire how they operate and generate value, fundamentally altering the ratio of humans to technology.
Strategic workforce planning provides the analytical foundation to model these shifts explicitly, helping organisations move from reactive headcount adjustments to capability and scenario based workforce decisions.
Skills are changing faster than roles
Problem solving remains the most frequently cited future skill, with 44% of HR professionals ranking it among their top five. Creativity is now cited second most often, while data analytics and AI remains one of the three most frequently cited skill areas.
By contrast, the importance of software development and other highly specialised technical execution skills is declining as AI systems increasingly take over parts of those tasks.
At the same time, the skills required to guide, interpret and apply AI outputs are becoming more prominent.
Digital literacy is the fastest rising skill, moving from 20th place in 2025 to sixth in 2026. Ability to reason has also increased in importance.
These shifts illustrate the need for skills intelligence: the ability to systematically capture, analyse and forecast skill supply and demand across the organisation.
Without a granular and continuously updated understanding of which skills the organisation holds and which are emerging as critical, workforce planning cannot keep pace with the rate at which AI is reshaping task structures.
Organisations that build robust skills intelligence, combining comprehensive skills taxonomies with AI powered analytics, can detect these compositional shifts early, identify skill adjacencies that enable talent to be redeployed and translate emerging requirements into targeted workforce interventions.
Workforce planning remains operational
On average, 62% of HR professionals say their organisations conduct company wide workforce planning, while another 34% apply it to at least part of the workforce.
However, only 11% conduct strategic workforce planning, defined as forecasting needs three or more years into the future.
Nearly two thirds concentrate on a horizon of no more than 12 months.
Eighty five percent of organisations report having a skills taxonomy, up from 78% in 2025. However, only 57% combine organisation wide workforce planning with comprehensive skills documentation.
This indicates that many organisations do not yet use skills taxonomies to inform workforce planning, missing an opportunity to identify skill adjacencies that enable faster internal redeployment and to use granular people data to make more informed, evidence based talent decisions.
Forecasting practices reveal the same issue. Forty six percent plan at the level of specific roles or positions, around one third at the level of job families and only 22% use a skill based forecasting approach.
Given the growing importance of task level transformation, even skill based approaches may not be sufficient.
The pace and uncertainty of AI adoption make point forecasts increasingly unreliable because the range of plausible workforce scenarios is wider than ever.
This makes scenario based planning critical.
What workforce planning now requires
McKinsey recommends beginning with a clear North Star and pragmatic design. Strategic workforce planning should specify the decisions it will inform, the stakeholders it serves and the key business questions it needs to answer.
Organisations can initially focus on a limited number of high impact use cases such as supply and demand for critical job families over a three to five year horizon.
The next move is from headcount based role planning towards a task and capability based view of the workforce. Leaders need to identify how activities will evolve through automation and which capabilities will still be required.
AI can support that shift by analysing tasks, forecasting capability demand and identifying emerging gaps at scale.
Finally, organisations need large scale reskilling and upskilling engines capable of systematically closing existing gaps while building capabilities required in the future. Continuous skill development increasingly needs to become part of day to day work as human AI collaboration evolves.
Talent acquisition in a steadier labour market
Amid persistent geopolitical and economic uncertainty, the global labour market has largely steadied and remains an employer driven market.
Recruitment outcomes have improved moderately. The global offer acceptance rate has increased to 59% from 56% in the previous year, while the hiring success rate has risen to 50% from 46%.
But hiring effectiveness remains a strategic advantage.
Across the surveyed markets, median time to hire from position approval to offer acceptance stands at 70 days, with a mean above 90 days. Top quartile organisations complete the same process in 49 days.
This gap has consequences.
Candidates with in demand skills can participate in several processes simultaneously, and delays increase the likelihood of losing preferred candidates to faster moving employers. Long processes can also weaken candidate experience and increase recruiting cost.
Internal mobility remains stable at roughly one third of positions filled globally, while 66% continue to be filled externally.
AI in recruiting
Within HR, talent acquisition is one of the areas with the highest automation potential through AI.
McKinsey analysis estimates that core elements such as screening, assessment and interviewing have automation potential of at least 60%.
AI applications typically concentrate in three areas.
The first is the creation of job requisitions and vacancy postings. AI tools can support drafting, refinement and internal approval.
The second is screening and selection. Automated CV screening, skill based matching and shortlisting can reduce manual workload, while tools can synthesise interview feedback and improve consistency.
The third is candidate engagement. Chatbots and assistants can support scheduling, answer questions and maintain communication throughout the recruitment journey.
But recruiting also involves sensitive personal data. This makes regulation, transparency, bias testing and meaningful human oversight central design principles.
The opportunity is therefore not simply to automate the old hiring process.
Leading organisations reduce time to hire by eliminating unnecessary steps, clarifying decision rights, enforcing accountability and using technology to improve process discipline.
Employee development is not keeping pace
Performance management has returned to the leadership agenda as organisations strengthen their focus on measurable productivity and performance.
At the same time, rapid AI and technology disruption increases the importance of continuous learning.
Previous McKinsey research finds that companies combining effective performance management with strong opportunities for skill building are four times more likely to outperform peers.
Effective employee development requires understanding future skill needs, delivering regular and credible performance assessments and providing continuous feedback with clear developmental direction.
Learning needs to be linked to role requirements and career progression, while producing measurable skill development.
Yet current practice remains much more traditional.
One in five employees say they had no career development meeting or feedback session during the previous 12 months. HR professionals believe only 3% received no evaluation.
A further 58% of employees say they receive formal feedback only once or twice a year.
Feedback also remains highly hierarchical. Line managers and senior managers provide most formal feedback, while structured peer feedback remains uncommon.
The learning gap
At a time when skill requirements are changing rapidly, learning activity remains low and is often overestimated by organisations.
Employees report an average of 3.4 training days each year.
HR professionals estimate almost twice as much, at 6.2 days.
Twenty four percent of employees report no training participation at all during the previous 12 months.
This suggests that learning may be happening much less than organisations believe, potentially because it is deprioritised during day to day work.
Satisfaction with learning opportunities is also mixed. Overall, 38% of employees report being dissatisfied or neutral. Among blue collar employees, that figure rises to 46%.
The report therefore argues that organisations need to move beyond measuring attendance or training days and towards measuring return on learning.
Role specific skill expectations should be defined, and learning outcomes linked directly to performance, career progression and business outcomes.
AI learning is uneven
On average, 71% of employees expect AI to affect their work and anticipate changes in the skills required.
Employees identify both risks, such as inaccuracies and data protection concerns, and opportunities including productivity improvements and stronger problem solving.
Formal training remains uneven.
Only 25% of employees in Europe report having received formal Gen AI training, compared with 31% in the United States and 49% in China.
Usage also differs significantly. Thirty six percent of European employees use AI daily or several times per week, compared with 47% in the United States and 77% in China.
McKinsey recommends embedding AI skill development directly into employee development systems rather than treating it as a standalone technology training programme.
AI skill development and demonstrated use can increasingly be connected to performance expectations and progression.
A cautious workforce
Employee experience is a strategic performance lever, but the 2026 findings show a workforce becoming more cautious.
Employee mobility is declining without a meaningful improvement in satisfaction or the underlying employee value proposition.
That suggests retention is being influenced partly by economic caution and constrained external opportunities rather than rising engagement.
Overall voluntary attrition stands at 4% globally and 3.7% in Europe, down by around two percentage points.
Only 17% of employees now say they plan to change employer within six months, down from 23% a year earlier.
At the same time, 17% remain concerned about losing their jobs.
Taken together, the data points towards a more risk averse workforce in an uncertain environment.
Satisfaction and workload
Satisfaction remains broadly stable.
HR professionals report that 63% of employees are satisfied based on company engagement surveys, while McKinsey’s direct employee survey records a higher 75%.
Absenteeism has declined, but remains significant. On average, 11% of working hours, equivalent to 27 working days, are lost to absenteeism, down from 15% a year earlier.
Health issues account for the largest share of lost hours, followed by care responsibilities.
The data also suggests that countries with fewer paid vacation days tend to record greater absenteeism and longer working hours, highlighting broader structural workload pressures.
What employees now value
Compensation and benefits have become the largest retention driver, cited by 52% of employees and rising sharply from the previous year.
Work life balance follows at 46%.
Job security is cited by 45%.
Flexibility remains important at 33%, while relationships with colleagues continue to matter for 27%.
The shift reflects a more cautious workforce responding to economic pressure and uncertainty.
McKinsey also challenges simplistic generational narratives. Compensation and benefits rank first across all generations, followed broadly by work life balance and job security.
Meaningfulness of work ranks lower and is not uniquely dominant among younger workers.
Flexible work reaches a new equilibrium
The data points to a moderate return to office movement rather than a wholesale abandonment of hybrid work.
Employees who are eligible to work remotely are permitted an average of 2.8 remote days per week under company policy, approximating a 60/40 hybrid model.
In practice, employees use only 1.9 remote days each week, 0.6 fewer than in 2025.
At the same time, satisfaction with existing arrangements is increasing.
Only 30% would prefer to work remotely more often, down eight percentage points, while 46% say they are satisfied with their current arrangement, up 12 points.
The findings suggest that many hybrid models may be settling into a perceived equilibrium.
HR’s operating model is now part of the transformation
The HR operating model defines how an organisation structures, governs and delivers services to execute its people strategy.
It is the backbone for translating strategic priorities into measurable impact across workforce planning, talent, learning and employee experience.
But the environment in which HR operates is changing rapidly.
McKinsey argues that HR operating models now need to evolve along two dimensions: becoming more agile and creating greater value through technology.
More than three quarters of HR functions were historically organised around the classic Ulrich model of business partners, shared services and centres of excellence.
The future requires leaner and more human centred models where expertise can be flexibly deployed to the highest value priorities.
People strategists, people scientists and people technologists can increasingly collaborate through product teams rather than remain inside static functional boundaries.
HR must become more digital and more human
McKinsey estimates that two thirds of current HR tasks can be automated.
Those efficiency gains can allow HR teams to concentrate on higher value work while reducing administrative overhead.
AI can also support more personalised and frictionless employee experiences.
Yet only about 5% of companies successfully and routinely incorporate new technologies into their HR function.
This creates a dual imperative.
HR must become more digital and more human at the same time: embedding AI into core workflows, integrating fragmented systems and using data at scale, while strengthening leadership, coaching, culture and ethical guardrails for hybrid human agent work.
Shared services and self service are changing
Shared service centres have long centralised and standardised transactional HR activity.
But their role is changing as a large share of routine activity becomes automatable.
This is forcing CHROs to reassess whether dedicated processing units remain necessary in their current form or whether much of the activity can be absorbed by AI enabled platforms with limited manual intervention.
Nearly every organisation already provides at least one HR self service solution, but the average organisation offers only around 40% of relevant processes in self service form.
AI could materially expand this.
Intelligent assistants and agentic workflows can broaden the scope, availability and personalisation of employee services, allowing more complex queries to be handled autonomously and continuously.
AI potential remains far ahead of adoption
AI can automate parts of HR’s transactional backbone while improving employee experience through more intelligent and personalised interactions.
It can act as a catalyst for operating model transformation by freeing capacity, augmenting expertise and reshaping service delivery across workforce planning, learning, performance and employee services.
But HR professionals have become more conservative about the near term automation potential.
They currently estimate that approximately 20% of core HR functions can be automated, down from more than 30% in the previous year.
McKinsey’s modelling suggests substantially greater technical potential. Beyond roughly 50% of HR activities that can be addressed through traditional automation, AI could automate an additional 30% across the function.
In learning and development, analytics and compliance, incremental potential may reach 40%.
Actual adoption remains much lower.
Operational AI solutions are present in only 28% of HR processes globally, while another 37% remain in pilot phase.
Operational use remains concentrated in administrative areas such as time and absence management, process optimisation and employee data management.
Agentic AI is the next frontier
Agentic AI systems that can autonomously plan, decide and execute multistep workflows represent the next stage of innovation in HR.
Awareness and adoption, however, remain limited.
In some European countries, as many as one third of surveyed HR professionals are not yet familiar with the concept.
China and the United States report the largest shares of HR professionals who already perceive a transformative impact from agentic AI.
The bigger point is that the transition requires an operating model redesign rather than another isolated technology deployment.
Agents can take on transactional and analytical activity while HR professionals increasingly focus on orchestration, judgement and value creation.
HR as copilot and lighthouse
McKinsey describes the AI transformation as a defining moment for people functions and gives HR a dual mandate.
The first role is HR as copilot of the AI enterprise.
HR should help leaders redesign work itself rather than simply replacing people with technology. This means rethinking how tasks are distributed across humans and AI agents and how that mix changes over time.
It includes workforce planning for human agent environments, organisational redesign, large scale AI fluency, changes to incentives and performance systems and support for leaders managing workforce transitions.
As work increasingly becomes a partnership between people and agents, HR can shape the cultural, structural and governance foundations that determine whether AI generates sustainable value.
The second role is HR as lighthouse.
HR must lead by example by reimagining its own processes and operating model around expertise and technology.
Doing so demonstrates what successful transformation looks like and creates the credibility needed to influence the broader enterprise transition.
Different organisations will take different paths
The value potential of AI in HR is widely acknowledged, but structural and capability barriers continue to prevent scaling.
Many organisations remain stuck in pilot mode because of uncertainty around governance, integration and capability building.
Some organisations begin from the bottom up by agentifying individual pain points to build confidence.
Others redesign complete end to end processes such as payroll or onboarding as human plus agent systems that can become templates for further change.
The most transformative organisations begin from a defined target state for the whole people function, then work backwards to redesign individual HR domains.
The distinction is not simply where they start.
It is whether each experiment is deliberately building towards a coherent whole.
The agentic HR operating model
McKinsey’s recommendation is to redesign HR for human agent environments.
AI agents can take on more transactional and analytical tasks while HR professionals focus on orchestration, judgement and value creation.
This requires moving from static, role based structures towards expertise driven, technology enabled models.
People strategists, subject matter experts and HR technologists can be deployed more flexibly against strategic priorities through flow to work pools.
The technology foundation needs to change at the same time.
Organisations need a harmonised architecture connecting fragmented systems and enabling clean, interoperable and accessible data across the employee lifecycle.
Without that foundation, comprehensive self service, responsible AI deployment and a scalable intelligent employee experience remain difficult to achieve.
HR also needs much stronger data and AI capability within the function itself.
That means targeted reskilling for current staff alongside recruitment of new talent profiles.
Finally, McKinsey argues organisations need to move faster from experimentation into scaled deployment and increasingly towards end to end process redesign.
Methodology
The HR Monitor 2026 combines two surveys conducted in January 2026.
The HR Professionals Survey included more than 1,300 managers and decision makers responsible for HR topics in organisations with at least 100 employees.
The Employee Survey included more than 5,000 full time and part time employees between the ages of 18 and 66.
Respondents came from Belgium, China, France, Germany, Italy, the Netherlands, Poland, Spain, the United Kingdom and the United States and represented more than 18 industries.
The research therefore combines HR’s assessment of organisational practice with the direct experience of employees, allowing McKinsey to highlight gaps between the two perspectives.
Conclusion
McKinsey describes the HR function as standing at a defining moment.
The shift towards AI enabled and increasingly agentic organisations represents one of the largest opportunities the people function has had in decades, but also one of its most demanding challenges.
The scope of the change requires HR to rethink how work is structured, how capabilities are built and how value is delivered across the enterprise.
The report describes two possible paths.
In one, technological change and organisational complexity move faster than HR can respond. A growing share of responsibilities then migrates towards IT and other digital functions.
In the other, HR rises to the challenge and takes a leadership role in shaping the future of work, potentially working much more closely with technology functions while retaining a clear mandate to define how humans and agentic systems operate together.
The dividing line is execution.
HR needs an accurate view of its current maturity, a clear target state and the ability to translate priorities into transformation.
The opportunity is therefore not simply for HR to become more efficient through AI.
It is for HR to become one of the principal architects of the human AI operating model of the enterprise.
Key takeaways
The people function is moving from managing HR processes towards shaping the system through which people, skills and intelligent systems create value.
Strategic workforce planning remains a significant weakness. Only 11% of organisations plan three or more years ahead even as AI makes task level workforce change increasingly important.
Skills intelligence becomes foundational because roles can remain while the mix of work inside them changes materially.
Learning systems also need to move faster. Employees report limited training and infrequent feedback even though most expect AI to change their skill requirements.
Lower attrition should not automatically be interpreted as stronger engagement. The workforce is increasingly cautious, and compensation, job security and sustainable workloads are becoming more important.
HR’s own AI adoption remains early despite significant technical potential. Most organisations still operate through pilots and isolated administrative use cases rather than end to end redesign.
Agentic AI therefore creates an operating model challenge. Technology, data, roles, workflows and human accountability need to be redesigned together.
McKinsey’s central message is that HR now has two responsibilities: help architect the AI transformation across the enterprise and demonstrate that transformation inside the people function itself.
