How AI is Starting to Change the Face of HR
Artificial Intelligence is starting to make its mark in HR, but the picture is more nuanced than the headlines suggest. Yes, it is being used in more areas than ever before, but it is far from universal. In reality, most businesses are still exploring where it fits, how it helps, and what risks or limitations come with it.
Some HR teams are actively integrating AI into day-to-day processes. Others are watching and waiting. Many professionals are not yet aware that the platforms they use already have AI capabilities built in.
Below are the main areas where AI is either already in use or starting to gain traction, and a more honest look at what is actually happening on the ground.
Learning and Development
Some larger businesses have started using AI to personalise learning pathways, flag skills gaps, and recommend tailored development content. However, in most organisations, this is still quite basic. Learning platforms may use data to suggest relevant training, but genuine customisation is rare unless the business has invested heavily. What we are seeing more of is the use of AI to track course completion or prompt line managers when someone is overdue on key development milestones.
Compensation and Benefits
AI can support compensation modelling and benefits forecasting, especially when linked to broader HR analytics platforms. That said, most of this work is still being done manually or through traditional data tools like Excel. Where AI is being used, it tends to be in the background, helping HR teams benchmark pay or assess benefit uptake across regions or roles. This is still a growing area, and adoption tends to be limited to large or highly centralised organisations.
Employee Engagement
This is one of the most sensitive areas, and while the potential is there, actual use of AI is still limited. Some organisations use text analysis tools to track sentiment from surveys or open feedback, but most are doing this as part of a broader engagement strategy rather than fully automating any decisions. The idea of using AI to monitor internal communications for mood or morale is still quite experimental, and understandably raises questions around privacy and trust.
Talent Acquisition
Recruitment is where AI has made the most noticeable impact. Applicant tracking systems now regularly include features like automated CV screening, skills matching, and interview scheduling. Larger businesses and agencies are also experimenting with video screening platforms that assess tone and communication style. While not every business uses these tools, they are becoming more common, especially where the volume of hiring is high.
Workforce Planning
Some organisations are exploring AI-driven tools to model future workforce needs and identify skills shortages ahead of time. These tools rely heavily on having strong data already in place, which is a challenge for many. As a result, while the theory is there, the actual execution is often limited to high-level modelling in large organisations with a mature HR function.
Diversity, Equity and Inclusion
AI is being tested to support DEI by anonymising applications and flagging biased language in job adverts. This is one of the more positive use cases, but again, adoption is still at an early stage. Most of the progress in this area is driven by the HR software providers rather than internal AI expertise. Businesses are benefiting passively from what the tools offer, rather than actively designing AI-led DEI strategies.
HR Analytics and Insight
AI has the potential to support real-time HR analytics and performance dashboards. A few organisations are using these tools to monitor engagement, spot retention risks, or assess productivity patterns. For the majority though, analytics is still a developing area. Reports are often retrospective, and few teams have the internal capability to take full advantage of AI-led insights.
So What Does This Mean if You Want to Progress in HR?
If you are a specialist in one of these areas, this is the time to build deeper expertise. Whether that is in analytics, learning and development, recruitment, or reward, AI is going to shape the way these functions evolve. The more comfortable you are with how the tools work, the more valuable you will become.
If you are more of a generalist, the pressure is not to become an expert in everything. That is not realistic, nor is it expected. Instead, focus on building a working understanding of where AI is being applied, what the tools are capable of, and how they could affect your role or your team. You do not need to run the data models, but you should know what questions to ask.
The key is to stay curious and keep learning. The pace of change may not be as fast as some make out, but it is happening. And those who understand the direction things are heading will be better placed to lead the change rather than react to it.