2026 Core HR Trends Forecast: What to Expect in the Future of Human Resources

Five keywords

In September 2025, Aberdeen released a survey showing that among organizations with over 200 employees, human resources leaders are primarily concerned with five major issues, including:

Employee productivity & engagement, accounting for 24%;
Quality / reliability of products or services, accounting for 23%;
Data quality, accounting for 20%;
Financial planning & management, accounting for 19%;
Cross - functional collaboration & communication, accounting for 14%

Corresponding to these issues, we've extracted five keywords to predict the core trends in human resources in 2026, namely: AI and AI agents, the implementation of the value of human resources business partners, data - driven decision - making, quality improvement and efficiency enhancement, and the innovation of organizational collaboration mechanisms.

First: "Employee productivity and engagement" corresponds to "AI and AI agents"

In the past, employee effectiveness depended largely on individual abilities in "time management and energy management", with significant differences among individuals. However, these differences are likely to be minimized today, and the decisive factor behind this is the "rapid growth of technological power", especially AI and AI agents. They have greatly helped employees optimize their "time and energy allocation". Meanwhile, at the functional level, they have also integrated the entire human resources process, strengthening collaboration and engagement. With the maturity of large - language models and multi - modal AI, AI agents in human resources have evolved from "single tools" to "strategic systems". They can understand complex employee needs, anticipate organizational risks, and generate precise decision - making suggestions. The core value of human resources has also shifted to "defining agent rules based on the talent lifecycle and building an AI strategy for the entire human resources process"; rather than simply using AI and AI agents as tools for single - purpose applications.

Let's take IBM's "HR AI strategy" launched in 2018 as an example. IBM's "HR AI strategy" centers around AI technology to build a comprehensive talent management system covering the entire process, including three core modules: "attracting new talents, defining the'success' criteria, and developing existing talents", ultimately forming a closed - loop talent management ecosystem:

For the "attracting new talents" stage: IBM Watson Candidate Assistant focuses on "social listening + real - time recruitment insights". It follows the process of "identifying required job skills, building a data - driven talent success profile, prioritizing recruitment needs, and intelligent and precise matching" to efficiently connect with high - quality talents, establish stable candidate relationships, and build a dynamic talent reserve.

For the stage of defining the "success" criteria: IBM Watson Recruitment relies on an intelligent recommendation engine, focusing on "precise screening" and "experience optimization". It uses a multi - dimensional model to screen out "best - fit" talents in terms of skills, career, and culture. At the same time, it optimizes the entire application process experience, such as response speed and feedback efficiency, to attract more high - quality candidates.

"Developing existing talents" is the core of value: IBM Watson Career Coach focuses on personalized learning to improve the ROI of talents. That is, it activates employees' career growth motivation, promotes internal job mobility and optimal allocation, builds a customized learning system to drive dynamic improvement of capabilities, and implements industry - leading talent management practices to ensure the alignment of employee growth and organizational development.

Second: "Quality and reliability of products or services" corresponds to "the implementation of the value of human resources business partners"

One important way to implement the value of human resources business partners is to provide strong support for the "quality and reliability of products or services". How is this manifested? For example, the "job position" is the core link between "human capabilities" and "business outcomes". Therefore, the rationality and precision of "job design" directly determine whether the key "quality checkpoints" in the entire business process can be effectively covered, thus transforming human resource allocation into a "solid guarantee" for "business outcomes". How to determine "whether to create a position and what kind of position to create"? Let's take an example:

In the automotive manufacturing industry, there is a step called "paint inspection". Most automakers have automated inspection systems. However, these systems have blind spots in detecting "slight deviations in gloss" and "slight unevenness in local flatness". These details about "paint texture" are precisely some of the "core qualities" that many consumers can intuitively perceive. Therefore, many automakers still conduct "manual inspections". Under specific lighting conditions, they observe the gloss and flatness of the car body paint from different angles and touch the paint with their hands to assess its smoothness. The reason for this is to ensure that each car is delivered to consumers with high - quality paint. Although the "manual inspection" step may increase the "complexity" of production process management, it ensures the "craftsmanship requirements" and is a value - creation process centered on the customer. Therefore, from a human resources perspective, designing the "manual inspection" position plays a crucial role in determining the success of the organization and is a significant manifestation of the implementation of the value of human resources business partners. From this example, we can derive three core logics for "job design":

The first is: adhering to the business orientation, which is the "starting point" of job design. Human resources departments should not create positions based solely on experience. Instead, they should be deeply integrated into the business process and co - create with key departments. First, sort out the "core checkpoints" of product quality, and then analyze the coverage gaps of these "checkpoints" in the existing process. For example, if the automated inspection system cannot detect defects such as "slight deviations in gloss" that are highly perceptible to customers, it is necessary to determine the necessity of "creating a full - time position" to ensure that the "position" is aimed at "solving business quality problems" from the very beginning, rather than simply increasing the headcount.

The second is: conducting in - depth horizontal benchmarking, which is a "guide to avoid pitfalls" in job design. Human resources departments need to investigate the practices of similar "positions" in leading companies in the same industry. For example, in the automotive manufacturing industry, most high - end automakers retain the "paint inspection position". However, in some companies, due to vague position requirements, the accuracy of "manual inspection" in identifying defects is less than 80%. At this time, human resources departments should not only refer to "whether to create a position" but also analyze the efficiency and core competence requirements of similar positions in peer companies, avoid their loopholes, and optimize job design based on industry practices.

The third is: implementing customized planning, which is the "key to adaptation" in job design. Benchmarking is only a reference. Human resources departments need to make adjustments according to their own business characteristics. For example, if the quality standard is "luxury - car - level paint", the competence requirements should include "differentiated inspection skills for different processes such as metallic paint and matte paint"; the salary design should be strongly linked to "inspection accuracy and monthly zero - defect delivery rate". This can attract experienced practitioners and encourage them to maintain high - quality standards, ensuring that the position's capabilities and salary are closely tied to the actual business situation.

Third: "Data quality" corresponds to "data - driven decision - making"

The essence of AI is "data alchemy": only with high - quality data as the "raw material" can accurate decision - making insights be extracted through algorithms, injecting "intelligent power" into human resources work. Without standardized, complete, and consistent data, even the most advanced AI is like "cooking without rice". In the entire human resources process, "data assets" have penetrated into every key link, forming a "five - category core data system" that provides "hard - science" support for human resources work, including:

First, recruitment - stage data: This includes not only resume information, interview evaluations, and skill assessment results but also full - process data such as channel conversion rates, on - boarding cycles, and candidate retention rates, which directly reflect recruitment efficiency and talent fit;

Second, performance - stage data: In addition to KPI completion, 360 - degree feedback, and salary benchmarks, it also includes performance improvement trajectories, cross - departmental collaboration scores, etc., which are the core basis for evaluating employee value;

Third, training - stage data: From course completion rates and skill assessment scores to post - training behavior changes and performance improvement correlations, it can accurately measure the return on training investment;

Fourth, development - stage data: This covers promotion paths, leadership potential assessments, mental health status, internal rotation records, etc., supporting talent pipeline construction;

Fifth, departure - stage data: It includes the turnover rate of core talents, in - depth analysis of departure reasons, post - departure career directions, etc., providing reverse references for organizational optimization.

However, these valuable "data assets" are often scattered across different systems in the organization, forming "data silos" that are difficult to break. A Deloitte survey showed that 73% of surveyed companies admitted that due to the scattered and isolated nature of human resources data, they were unable to conduct effective linkage analysis. For example, when trying to determine "whether a certain type of training can improve performance", they had to abandon quantitative verification because the training data and performance data had inconsistent formats and could not be connected. When trying to optimize recruitment strategies through correlation analysis of "recruitment channels, on - boarding performance, and retention duration", the conclusions were inaccurate due to scattered data. This fragmented situation severely restricts the implementation of "data - driven decision - making".

The core value of AI agents has far exceeded single - process automation and has become the "core engine" for human resources data governance. It can automatically collect fragmented data from different systems through "algorithms", standardize the format, remove duplicates, correct errors, and align the data according to a unified standard. For example, it can unify the performance evaluation criteria of different departments into quantitative indicators and integrate scattered candidate information into complete talent profiles. At the same time, AI agents can establish a standardized, high - quality data storage repository, enabling centralized data management and hierarchical authorization, ensuring data accessibility while meeting compliance requirements through permission control and data anonymization.

Fourth: "Financial planning and management" corresponds to "quality improvement and efficiency enhancement"

From a human resources perspective, "financial planning and management" serves as the core for "quality improvement and efficiency enhancement". The key lies in the logical shift from "cost reduction and efficiency improvement" to "quality improvement and efficiency enhancement". In the past, human resources focused on "cost reduction and efficiency improvement", which was about "subtraction", achieved through layoffs, headcount reduction, and budget cuts to compress labor costs. In the future, human resources will focus on "quality improvement and efficiency enhancement", which is about "addition". Through precise investment in "data and technology", "human and financial resources" will be transformed from "cost items" to "value - added items", leveraging the "effective combination" of resources to drive both quality and efficiency improvements.

First, the "addition" of data "precipitation, integration, and analysis" is the "decision - making anchor point" for "quality improvement and efficiency enhancement". For example, in the past, the financial budget allocation for human resources was mostly in a rough mode. Now, the budget should be directed towards building data capabilities: precipitating data on employee skills, performance, and business needs, integrating them to form a matching model of "talent capabilities and business gaps". Then, by analyzing the correlation between "human input and output contribution" of different positions, the recruitment and training budgets should be tilted towards areas with "urgent business needs and capability shortages". This "addition" does not mean increasing the total budget but rather ensuring that every budget dollar is targeted at clear quality - improvement goals, replacing the previous "blind budget - cutting" subtraction logic.

Second, the "addition" of technology application "introduction, upgrade, and iteration" is the "efficiency lever" for "quality improvement and efficiency enhancement". For example, in the past, financial budget control in human resources relied on manual account reconciliation and salary calculation, which was costly and error - prone. Now, the budget should be directed towards the implementation of technology tools: introducing intelligent recruitment systems to precipitate candidate data and automatically match positions, significantly improving recruitment efficiency; upgrading digital performance platforms to automatically integrate employee output data and reduce manual statistics costs; iterating AI salary analysis tools to calculate salary competitiveness and cost - controllable ranges in real - time. These technology investments may seem like "increasing the budget", but in fact, they eliminate repetitive work and improve decision - making accuracy. This can free up human resources to focus on strategic work, leveraging the "technology lever" to amplify the value of financial resources.

The "financial budget planning and management" of human resources should reconstruct the resource logic with an "addition mindset": instead of reducing investment, the budget should be directed towards the two "value amplifiers" of "data and technology", enabling human resources to shift from "cost control" to "value creation" and ultimately achieving the goal of "quality upgrade and efficiency multiplication".

Fifth: "Cross - functional collaboration and communication" corresponds to "innovation of organizational collaboration mechanisms"

"The implementation of the value of human resources business partners" means "reconstructing the operation mode of human resources with business results as the orientation and data and intelligence as the core". What kind of evolution will the human resources operation model undergo in the future AI era? Applaud Solutions has outlined a clear roadmap for us:

First stage - 1990s, the launch of David Ulrich's "Three - Pillar" model: This was the starting point of the modern human resources operation framework, introducing the three - pillar structure of "business partners + centers of excellence + shared services", shifting human resources from scattered administrative work to a specialized system with clear divisions of labor.

Second stage - 2000s, the wave of shared services and outsourcing: To improve efficiency and reduce costs, companies began to centralize repetitive human resources work, such as salary calculation and attendance management, in "shared service centers" or outsource them to professional institutions. This was a crucial stage for the scaling and standardization of human resources.

Third stage - 2010s, the upgrade of the "Three - Pillar" model + the popularization of digital self - service: Based on the "Three - Pillar" model, the concept of "global business services" was incorporated, and digital self - service tools were promoted, such as systems for employees to independently check their salaries and submit leave applications, making human resources services more efficient and convenient.

Fourth stage - early 2020s, driven by employee experience, the emergence of "product teams": Treating "employees as users" and applying Internet product thinking to human resources services: special "human resources product teams" were formed, focusing on designing services for employee experience, such as a smoother onboarding process and a more user - friendly benefits system, shifting human resources services from "meeting compliance requirements" to "enhancing experience".

Fifth stage - after 2025, dominated by AI agents, the era of "human - in - the - loop": Human resources operations enter the stage of "AI agents + human collaboration": AI tools, such as intelligent Q&A and automatic scheduling systems, handle most administrative work. However, human resources and managers will control core decision - making and adjust AI rules, combining the efficiency of AI with human judgment and strategic thinking.

The core trend of this timeline is that human resources operations shift from "standardized efficiency" to "personalized experience" and ultimately enter a new stage of "AI - enabled, human - machine collaboration". This upgrade of the human resources operation model is the underlying guarantee for cross - functional collaboration: a unified human resources system, efficient self - service, and precise talent allocation enable departments to reach a consensus on talent needs and capability building, reducing collaboration costs.

Conclusion

With the deep integration of AI, data, and business, the core of human resources is shifting from "management support" to "strategic drive". The trends in 2026 point towards a more intelligent, precise, and collaborative future: reconstructing productivity with AI agents; solidifying the decision - making foundation with high - quality data; safeguarding value output with business - oriented job design; reshaping the resource logic with "quality improvement and efficiency enhancement"; and lubricating the collaboration network with an evolving human resources operation model. Only by actively embracing these changes and evolving from rule - executors to system designers can human resources departments lead organizations to continuous success in a complex environment.

This article is from the WeChat official account "HR Voice". Author: HR Voice. Republished by 36Kr with permission.