Artificial intelligence is rapidly emerging as a critical technology for organizations and is now widely accessible to HR functions, both large and small. HR functions today do not need data scientists to start using AI—many HR technologies already have it embedded as part of their core functionalities.

As HR leaders work to make the case for investments in AI and early adopters look to assess its impact, many want to know what kind of return on investment they can reasonably expect and how it might compare to other HR functions.

To learn more about the ROI of AI in HR, we carried out survey research with 600 HR leaders and qualitative interviews with 20, all of whom are at least piloting AI-driven tools and technology for HR. A quarter of respondents (25%) are in this phase, while 40% said they are implementing AI, 25% are actively operating it and 10% are optimizing its use (the highest level of maturity).

Drawing from our findings, we share key insights about the ROI of AI for HR, provide guidance for how you can maximize ROI and reflect on the importance of storytelling for your ability to make a strong business case for AI in HR.

The benefits of AI—and the challenge of ROI

While AI is still relatively new in the HR space, it is already reshaping how work is carried out in many HR functions. Generative AI is a case in point. When we asked HR leaders where they see this technology meaningfully impacting HR processes, the top 10 applications and use cases included:

• Providing self-service for answering HR questions (81% of respondents)
• Generating personalized learning content (80%)
• Generating HR reports and visualizations (77%)
• Conducting data analysis and creating forecasts (74%)
• Mitigating bias in the hiring process (71%)
• Providing skills gap assessments and recommendations (68%)
• Analyzing and coordinating candidate sourcing and matching (65%)
• Creating job requisitions and building job profiles (59%)
• Analyzing employee sentiment (56%)
• Providing personalized benefits recommendations (50%)

As part of the survey, we asked HR leaders to share the ROI that their HR function has achieved from the start of its investments in AI until the time of the survey. Respondents reported a median ROI of 15%. HR functions at the 25th percentile have the lowest ROI at 5% or lower, while those in the 75th percentile are faring substantially better with an ROI of 55% or higher.

 

Why the traditional ROI formula is challenging (for now)

The traditional way to calculate ROI is to divide the net benefits by the cost of investment and multiply the result by 100 to express ROI as a percentage. However, there are several reasons why benchmarking with this formula will prove challenging for many HR functions right now:

• We’re still in the early days of applying AI to HR. Even for early adopters, it’s hard to draw conclusions about what a “good” ROI looks like relative to others.
• Returns from AI may be indirect and not readily quantifiable.
• Even directly measurable returns may need extra storytelling and context to be intelligible to leaders, especially since AI often works behind the scenes in a way that is not fully visible to end users.
• It can be difficult to get a handle on AI cost in HR, especially in the early stages as many organizations are still integrating technology and establishing processes to collect clean and consistent data.
• AI often requires significant upfront investments, while the returns can come over numerous use cases and multiple fiscal years. As one HR leader told us, “To get to a point where you have ROI, you need to be in the journey for at least three to five years.”

These factors make it challenging to determine an expected ROI for AI in HR, especially if you are early in your AI journey.

How organizations are responding—and what it means

Given the challenges that many HR leaders face in calculating the ROI of AI, many told us that they are instead asking: “What are the pain points we are trying to solve? What are the areas or measures that we are trying to improve by implementing AI?” For example, one interviewee shared that “We had to set KPIs based on each specific use case, and the point of the pilot was to achieve that use case in order to measure and achieve ROI.”

Some common measures that we’ve seen HR leaders use to estimate ROI for AI include:

• Speed/cycle time measures
• Customer satisfaction measures
• Accuracy
• Time saved
• Number or percentage of deflections (i.e., queries answered fully by AI so human HR representatives can focus elsewhere)
• Costs saved (including labor costs through lower headcount)

HR leaders were also cognizant of the need to put context behind their measures and benchmarks for AI. While this is always a best practice when communicating about HR measures and benchmarks, it is especially important when it comes to those used to assess the ROI of AI in HR. Stakeholders may be less familiar with AI applications in HR, unaware of what it takes to effectively implement AI in this area, and unsure what to realistically expect in the form of returns.

10 tips for maximizing returns on AI investment

Regardless of how you measure the ROI of AI in HR, we’ve discovered strategies through our research and conversations with HR leaders that can help you to maximize it.

1. Take a big-picture, long-term view of the impact that AI can have across the entire employee lifecycle. This is necessary to reap the full benefits of AI in HR so you can put your investments/potential investments in context.
2. Standardize your HR processes as much as possible. Doing so is often a prerequisite for effective automation and for quality data to which you can apply AI.
3. Work for high-quality, consistent HR data. Even the best technology can’t work effectively if data is dirty, missing or inaccurate.
4. Align technology with HR processes. The processes built into HR technologies can be far different than those at the end-user organization. Some level of customization is often required.
5. Ensure that HR end-users are involved in the creation and development of AI applications to ensure that AI fits a use case that is worthy of your time, money and other resources.
6. Set up an ethics committee and involve your legal team from the beginning to ensure you stay current with shifting regulations and concerns.
7. Push for transparency from actual or potential HR technology vendors about their true AI capabilities, ease of use, what benefits to expect and in what timeframe.
8. Start your AI journey with small pilot projects to show proof of concept and minimize losses if things go in the wrong direction.
9. Gather qualitative success stories from end-users in addition to quantitative data. This helps leaders put real faces, names and teams behind the value that AI delivers for HR.
10. Use tailored messaging to explain the benefits and directly address common concerns around AI to each stakeholder group. Where possible, demonstrate the benefits that have come from other parts of the business that were early adopters of AI.

Craft the story of AI’s value for HR

Perhaps in the not-so-distant future, AI will be able to predict and then measure its own ROI within HR. In the meantime, ROI estimates have both value and purpose for building the business case for deeper investments.

Whether you’re just starting your AI journey or are working toward more advanced applications, it’s important to continually gather data and sharpen your storytelling around AI. Even if your current leaders are convinced of its value, priorities can quickly shift as organizations merge, change structure, change leaders or hit hard times financially. You will be much better equipped to make the business case if you can support your argument with a good story built on solid data.

While plenty of stakeholders will want to see charts and graphs with numerical data, some will naturally be drawn to narrative stories about how AI is helping HR and the business more broadly. In cases like these, it’s helpful to have compelling stories of AI’s value among its end users.

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Data in this content was accurate at the time of publication. For the most current data, visit apqc.org.

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