How AI in People Analytics Is Transforming HR

Updated April 10th, 2026

AI in people analytics is no longer a competitive advantage — it's fast becoming a baseline expectation. According to SHRM's 2026 State of AI in HR report, 87% of CHROs now forecast greater AI adoption within HR processes this year, up from 83% in 2025. Yet most organizations still use AI for isolated tasks rather than as an integrated, predictive intelligence layer across the entire talent lifecycle.

The performance gap between analytics leaders and laggards is widening fast. HR.com's State of People Analytics 2024–2025 report found that organizations with mature people analytics practices are 5x more likely to act constructively on data insights than those without. And Insight222's 2025/26 global study — covering 372 organizations employing more than 20 million people across 180 countries — found that sustained investment in people analytics directly improves AI adoption rates and drives measurable business outcomes. The cost of not investing is no longer theoretical.

But capitalizing on people analytics requires more than just awareness. Learn more about why AI is increasingly important for HR, the benefits of AI in HR, and common use cases.

What Is AI in People Analytics?

People analytics is the discipline of applying data science, machine learning, and AI to workforce data — enabling HR leaders to move from intuition-based decisions to evidence-based ones. Where traditional HR reporting tells you what happened, AI-powered people analytics tells you why it happened, what will happen next, and what you should do about it.

The four levels of people analytics maturity:

• Descriptive: What happened? (e.g., turnover rate last quarter)

• Diagnostic: Why did it happen? (e.g., which teams or managers drove it?)

• Predictive: What will happen? (e.g., who is at risk of leaving in the next 90 days?)

• Prescriptive: What should we do? (e.g., which retention interventions are most cost-effective for which employee segments?)

A fifth level is now emerging: agentic analytics, in which AI systems don't just recommend actions — they take them autonomously, within defined guardrails. AIHR's 2026 Workforce Analytics Trends report describes this as HR moving from asking "What does the data show?" to "What action should we take, and what is the business impact?"

Use of artificial intelligence in HR is trending up

Adoption has accelerated dramatically. SHRM's December 2025 survey of 1,908 HR professionals found that 39% currently have AI deployed within their HR functions specifically, and 62% are using AI somewhere in their organization. At the leadership level, 73% of HR directors and above have already adopted AI — making senior HR leaders the primary change agents in this shift.

The size divide is real but narrowing: 60% of organizations with 5,000+ employees have implemented AI tools in HR, compared to 33–35% of small and midsize organizations. Forrester projects SME adoption of AI-driven HR tools will reach 55% by 2025, up from roughly 30% just a few years ago.

Critically, the type of AI being adopted is evolving. The first wave brought standalone tools — resume screeners, pulse survey platforms, onboarding chatbots. The current wave centers on agentic AI: systems that autonomously execute multi-step HR workflows, monitor workforce signals continuously, and surface recommendations without being prompted. According to Mercer's 2025 HR technology research, 84% of HR leaders predict their function will become more automated and tech-enabled as agentic systems mature.

TriNet's 2025 State of the Workplace survey adds a workforce perspective: 38% of employees now prefer interacting with an AI assistant over a human HR admin for routine questions — up nearly 27% from the prior year — with Gen Z leading adoption and Baby Boomers remaining the most skeptical. As digital-native generations dominate the workforce, this preference gap will continue to widen.

8 benefits of AI in HR

AI isn't a replacement for HR teams. What AI does best is augment existing capabilities, by automating routine tasks and identifying insights that would otherwise be too time-consuming for a human alone to uncover. AI helps transform your HR with people analytics by adding predictive capabilities that drive data-driven decisions instead of relying on intuition or surface-level analysis. Here are eight ways AI helps HR departments.

Become a tactical and strategic asset

By providing actionable insights at scale, AI empowers HR teams to drive business results through a comprehensive talent strategy. For example, with predictive analytics, you can look at performance, learning and development, hiring, and internal mobility data to project whether you’ll have skills and people in place to hit goals five years out.

In an increasingly data-driven world, HR needs to be able to gather large amounts of data, analyze them quickly, and make sound decisions. AI helps HR leaders understand their workforce better and create strategies to increase engagement, productivity, and innovation.

Reduce human bias

Unconscious bias shapes decisions at every level of an organization. In theory, AI can reduce this effect by guiding decisions based on large datasets rather than individual preferences — and it can detect patterns pointing to bias in performance reviews, promotions, and calibration sessions. Research cited in SHRM's 2026 analytics report notes that AI-driven tools can evaluate factors like employee sentiment, workload patterns, and organizational communications to give HR a real-time view of cultural and equity issues.

However, the data that powers AI algorithms is rarely neutral. If historical promotion data reflects systemic exclusion — of women, or people of color, or disabled employees — the model will encode that exclusion. Deloitte's 2025 HR Tech Marketplace report recommends AI-enabled skills validation — using objective, real-time competency data rather than manager perception — as one of the most effective structural interventions for this problem. As long as you audit inputs and outputs regularly, AI can be a net positive for equity — but it requires active governance, not passive deployment.

Improve relationships between employees

‌AI can identify and analyze patterns in employee data to help HR leaders better understand employee behavior. This information can be used to create programs and policies that encourage positive employee relationships and improve productivity.

It may also be used to help employees provide thoughtful and properly stated feedback to peers when requested, such as through a performance management system.

Improve efficiency and insight in candidate assessment

Modern applicant tracking systems already use AI to help recruiters. AI is useful for screening resumes, cover letters, and candidate communication for indications of the person’s potential in the role they’re seeking.

AI tools collect data collected across the hiring process (from initial assessments to interview responses). They compare that data against industry benchmarks or historical data to project each candidate’s chance of success in a role. If a candidate submits a video interview or a personal resume website, for example, hiring managers can apply AI tools to the transcripts to glean insights that humans alone might have overlooked.

Supercharge strategic decision-making based on predictive data

People analytics, powered by AI, is a form of data-driven decision-making that focuses on uncovering meaningful insights from employee data to improve organizational performance, what Paul Rubenstein, the chief people officer at Visier, refers to as the “precursors to productivity.” You likely have a wealth of historical data in your organization that can help create AI-based models.

The best part about people analytics is that you aren’t limited to one analysis. You can change variables to project the various potential impacts of a decision. If the data shows that high performers are disengaging, for example, you can evaluate the impact of increasing promotions or learning opportunities on metrics such as engagement or turnover.

Using AI in people analytics also helps your managers to make better decisions about their work and how they can support their teams. 

When managers have data-driven insights about their teams, they can think more strategically about improving performance, building skills, and mapping out career paths. Of course, AI is only a tool. Managers should also be having regular check-ins and goal conversations to keep employees informed and engaged.

Prioritize your time and resources 

HR is increasingly automating routine and repetitive tasks. This was happening even before the latest wave of AI, and AI use cases are only increasing.

AI-driven people analytics solutions, for example, make it easy to slice and dice workforce performance in a few clicks, providing insights that would have otherwise taken hours to generate..  Now, you can jump right into solutions — saving time and increasing your impact on the business. For example, Betterworks’ partnership with Visier will enable data visualizations that will make it easier for HR and others in the organization to understand the data AI generates.

Automate repetitive tasks 

ChatGPT and other large language models can help HR practitioners, managers, and employees save time by automating repetitive tasks, and help create consistently high-quality content, such as:

• Helping HR create company policies or job descriptions

• Assisting employees with defining  good goals and milestones that are aligned with company strategy and with their career ambitions

Remain open to new ideas and trends

There’s a reason tools like ChatGPT and other large language models have gained traction so quickly. Business leaders recognize how AI can save time and reduce costs in many areas without sacrificing quality. While HR professionals should view AI tools critically, don’t ignore this trend. Look at how AI and other technologies can improve HR’s impact and efficiency. Push back on use cases that don’t make sense, as well as legal and ethical violations. But don’t fight AI itself.

ChatGPT’s explosive growth highlights another trend in AI: the democratization of AI capabilities. Today, in-house developers can build proprietary algorithms without particular training or expertise. This opens up new and exciting possibilities for people analytics.  

Agentic AI in HR: The Shift from Insight to Action

Until recently, AI in HR was reactive: you queried it, it responded. Agentic AI changes this entirely. An AI agent is an autonomous system that perceives its environment, sets goals, plans a sequence of actions, and executes them with minimal human prompting at each step. In HR, this means moving from AI as a reporting dashboard to AI as a proactive participant in workforce management.

AIHR's 2026 Workforce Analytics Trends report describes common agentic HR use cases already in production: simulating workforce what-if scenarios and recommending hiring or redeployment responses; acting as policy-aware agents that apply compliance rules automatically during payroll validation or leave approvals; and monitoring engagement signals to alert managers when an employee shows early signs of disengagement.

Josh Bersin's January 2026 analysis estimates that 30–40% of existing HR job tasks can be automated using currently available agentic tools. His projection isn't that HR shrinks — HR job postings have grown 60% over the past five years — but that the mix of work shifts toward judgment, strategy, and AI governance. ADP's 2026 HR Trends Guide frames this as a new HR-IT partnership imperative: as agentic systems coordinate multi-step work across platforms, HR needs IT's implementation expertise while IT needs HR's insight into human adoption and impact.

The key distinction: generative AI helps HR professionals work faster as individuals. Agentic AI helps HR systems operate autonomously at scale. Organizations that understand this difference — and build governance structures to match — will build a durable talent intelligence advantage.

7 use cases for applying artificial intelligence in the workforce

Here are seven specific use cases for using AI to build a better workforce.

Sentiment analysis

Feedback is a combination of the quantitative and qualitative. AI-based text analysis systems can help you interpret and understand worker sentiments by categorizing feedback as positive, neutral, or negative. This framework provides a window into how your workforce feels on a day-to-day basis. 

While sentiment analysis isn't new, AI is turbocharging this approach. By combining survey data with other information, such as historical data and industry trends, AI unlocks predictive insights into workforce engagement, risk of turnover, and more.

AI-enabled sentiment analysis works well with many existing HR activities. The annual employee engagement survey can move from a static snapshot of worker sentiment into a vital, real-time analysis of the social framework that supports the entire organization. 

Feedback response

Organizing and dividing feedback into insightful slices is incredibly time-consuming. Machine learning in HR can automatically categorize feedback data into predefined HR categories such as work-life integration, compensation and benefits, or employee experience. With this data sorted, HR has an easier time examining the findings and acting on them

You can also measure employee satisfaction by using AI to analyze natural language in communication across the business to quantify a speaker’s underlying emotions, not just the message itself. Such technologies also show promise for analyzing conversations across the organization, including in email and chat tools. 

Instead of addressing negative events (e.g., a disgruntled employee, bad behavior) reactively, AI-powered text analysis could flag potential issues while there’s still time to address them.

Smart assistants

AI-based assistance can drive relevant and contextual alerts to managers based on conversations occurring within an organization. An AI-driven workflow tool, for example, could alert managers when an employee has leveled up their skills and is ready for new career opportunities within the organization. 

AI as a smart assistant helps HR see opportunities to improve the organization in real time. Instead of HR pulling reports manually, AI can push important information to HR.

Talent selection

Smart AI mechanisms can scan, read, and evaluate job applicants against the criteria you set for each role. Using AI to identify the top candidates reduces the time spent narrowing down the candidate slate and remains more objective than human recruiters. Meanwhile, candidates may use AI job appliers to assist in creating well-structured resumes and automatically applying to job openings. This dual use of AI enhances the hiring process by improving decision-making for recruiters while also increasing candidates' chances of getting noticed.

Skills intelligence and workforce planning

As the skills required for jobs evolve rapidly — executives project that required skill sets will have changed by 50% between 2015 and 2027 — AI-powered skills analysis has become a critical people analytics application. Skills intelligence platforms scan internal job descriptions, employee profiles, project participation records, and external labor market data to build a real-time skills inventory, surface capability gaps, and generate personalized learning recommendations. Deloitte's 2025 HR Tech Marketplace research finds that AI-enabled, skills-based organizations are 79% more likely to drive positive workforce experiences and 63% more likely to achieve their organizational outcomes.

Agentic workflow automation

The most transformative current use case isn't a single AI-powered task — it's the autonomous orchestration of entire HR processes. Agentic systems can manage end-to-end onboarding administration, compliance routing, document collection, and eligibility reviews without step-by-step human prompting. AIHR documents Salesforce's deployment of agentic AI trained on nearly 740,000 content items, resolving 84%+ of routed queries autonomously with clear human escalation paths for exceptions — a model HR teams are now replicating for high-volume, low-judgment requests like policy questions, leave calculations, and benefits queries.

Chatbot interactions

AI-powered chatbots can create streamlined and unique employee experiences based on personal interaction with every employee and historical data. While onboarding, for example, remains a deeply personal experience, chatbots can easily and accurately answer standard new-hire questions at any time, not just when a manager or other co-worker is available.

The future of AI in people analytics

The near-term roadmap is now concrete enough to plan against. Based on current research from SHRM, Deloitte, Visier, and Josh Bersin, here is where enterprise HR is heading.

Through 2026, the majority of large enterprises will operationalize agentic AI within at least one HR process. Real-time skills inventories — rather than annual performance snapshots — will become the standard for talent management. HR and IT will increasingly co-own AI implementation decisions.

By 2027–2028, the boundary between HR software and workforce intelligence platforms will dissolve. AI agents will monitor, recommend, and in many cases execute HR actions continuously, with humans retaining authority over high-stakes or legally sensitive decisions.

The persistent barrier is organizational, not technological. SHRM research finds that 58% of HR executives report insufficient resources for upskilling HR professionals in data literacy, and 56% cite inadequate data infrastructure as a barrier to effective people analytics. The organizations that close this gap — not just by purchasing better tools, but by building genuine AI fluency across HR teams — will be the ones that turn data into durable competitive advantage.

The question is no longer whether AI will transform HR. It's whether your HR function will lead that transformation or spend the next five years catching up.