AI opportunities for Human Resources

AI is set to revolutionize the way we work, and will impact every part of our organizations, including both front and back-office functions. Whilst it’s essential to develop an overall strategy for AI that’s closely linked to your business objectives, it’s just as important to consider the opportunities for each business function. This can help AI feel less conceptual and overwhelming, and more tangible and usable in practice.

For human resources (HR), AI offers significant possibilities, but be aware of the limitations and determine your priorities carefully. 

Identifying potential AI use cases for HR

Clearly defining use cases is vital for the successful adoption of AI. Breaking it down into these bite-sized chunks will bring focus, help delivery and allow you to track the benefits more easily. Each use case should be assessed for how difficult they are to achieve, and the return on investment that they deliver.

We believe organizations should choose the 4-6 that are most relevant for their specific circumstances and focus on getting them right. Some of the most interesting opportunities include:

1. Talent acquisition: Machine learning algorithms can analyze datasets to identify patterns in successful hires, enabling HR to make data-driven decisions. AI can help create job descriptions with more compelling and inclusive text to better attract diverse talent with in-demand skills. Automated CV screening can streamline the early stages of recruitment, freeing up valuable time for HR teams to focus on the more strategic aspects of the hiring process.
2. HR operations: HR chatbots or virtual assistants can handle routine employee requests using human-like text, providing a better user experience at a low cost. It can also help automate the drafting of policies and other documents, such as helping to explain complex regulations in a simple Q&A format, or tailoring documents to a particular audience. The technology can even be used for tasks such as creating consistent corporate headshots from people’s personal photos.
3. Learning and development: AI can help to personalize training and other interactive experiences to drive better learning outcomes. AI-driven analytics can track learning progress, providing HR with valuable insights into the effectiveness of training initiatives.
4. Employee engagement: AI tools can provide real-time insights into employee sentiment, allowing HR to proactively address concerns and enhance overall engagement. It can analyze a myriad of data sources to build a broader picture than staff survey alone.

Evaluating feasibility and implementation risks

Getting AI right is not straightforward, and certain use cases are not achievable right now. For example, using AI for flight risk analysis or sentiment analysis is far more easily achieved than using AI to generate training materials. Limitations will not only be down to the AI technology itself, but also data, organizational factors, and the markets you operate in.

Technical feasibility challenges include:

• Ease of access to the data required
• Complexity involved in building the models
• Level of customization required vs off the shelf solutions
• Risk of biases.

Organizational feasibility factors include:

• Change management capability and employee adoption challenges
• Readiness of the infrastructure
• Overall organizational maturity and capacity for change
• Resource capacity and capability to deliver and support.

External factors can also pose limits, such as restrictive agreements with unions or works councils, or country-level regulatory constraints. Although the UK is taking a particularly light touch approach so far, other countries are stricter at present.

Ethical considerations must also be at the forefront of implementation. Guardrails should be established to ensure fairness, transparency, and accountability in AI-driven decision-making. You will need to actively monitor and address any biases embedded in algorithms, promoting diversity and preventing discrimination. Clear communication with employees about the role of AI in HR processes is essential to build trust and alleviate concerns.

Calculating the return on investment

Companies are increasing their investment in AI and with significant levels of hype around the technology, it is easy to get carried away. Whilst some overall investment in capabilities may be considered a strategic overhead, best practice business case principles should still be followed for each use case. Do the benefits outweigh the costs? Is there a reasonable payback period?

For HR uses cases, the key categories of benefits are: 

Employee sentiment

Improve employees’ perceptions and feelings towards their employer through better interactions with processes, policies and tools. Enhance your ability to measure sentiment.

Recruitment and retention

Improve your ability to attract the best talent and react to your competitor’s actions in the competitive jobs market and enhance your employee value proposition.

Operational efficiency 

Reduce the time HR teams and line managers spend on undertaking HR processes. The majority of uses cases will still require a level of human oversight, reducing overall effort but not removing it, so it is important to consider whether you might restructure to realize efficiencies.

The approach to technology is a key driver of costs. HCM platforms are rapidly integrating AI into their products and services, but there are also standalone options. You may be tempted to stick with simple browser-based platforms, which are easiest to roll out and can be very effective for certain use cases such as job descriptions. However, most of these solutions scale linearly with minimal volume discounts and may be more suitable for experimentation than a long-term solution.

In addition, limitations in exposing sensitive data may render cheaper solutions unsuitable when employee details are required. Organizations should prioritize data security and compliance with privacy regulations to safeguard employee information.

A pragmatic approach for AI in HR

When rolling out AI, mistakes will be made, and lessons will be learnt – this is a new discipline for many organizations. Starting inside the organization, such as in HR, can be a good bet. It will allow you to test extensively with internal stakeholders and employee use cases, before using AI to create external-facing content where there is a bigger risk of harm to your business. Whilst the opportunities for AI in HR may not be as significant as some other business functions, there are several viable use cases that are relatively simple to progress. 

The important thing is to treat an HR AI project like any other investment and ask the right questions. You need to weigh up the benefits, the costs and the risks. For many organizations, there may be a broader portfolio of change so you need to assess how it all fits together. Ultimately, you must not lose sight of the big picture and why you’re undertaking the investment, ensuring it aligns with your overall business strategy.