A recent report found that 76% of HR leaders view the adoption of AI (Artificial Intelligence) as necessary to stay competitive in the coming years.
"AI is becoming increasingly prevalent and, due to its relatively unbiased nature, it can review data in a way that’s especially useful in the workplace and for hiring practices," says Jonathan Best, International Business Development Director at Benify.
As AI plays a vital role in transforming HR practices within organizations, HR leaders must adopt a data-driven approach to harness AI’s potential.
How can AI help organizations succeed?
In recent years, AI has rapidly advanced, providing organizations with unprecedented opportunities. A recent whitepaper by McKinsey, studied 63 companies using AI and found that the annual economic benefits could range from $2.6 trillion to $4.4 trillion.
Data-driven AI practices have already been adopted by a range of departments. AI can significantly improve the software development process, with tools like Microsoft GitHub Co-Pilot leading to efficiency gains of up to 55%. Within customer support operations, AI-enabled support chatbots have also proven successful in enhancing efficiency.
Now, it’s time for HR teams to start capitalizing on the potential that AI offers.
AI tools have the potential to increase productivity by up to 30%, providing the opportunity to reshape HR workflows, boost efficiency, streamline unnecessary admin work, and create more personalized experiences. When AI is combined with a data-driven approach, the HR possibilities are seemingly endless.
What are the different types of AI?
AI can be divided broadly into two categories: traditional AI and generative AI.
Traditional AI uses established algorithms, rules, and data to execute specific actions. Examples of traditional AI include voice assistants like Siri, recommendation systems on streaming services, and various search algorithms. Traditional AI is typically trained on basic, labeled datasets and is only as effective as the data it’s based on — further emphasizing the importance of collecting and harnessing quality data.
Generative AI (otherwise known as GenAI) represents the next evolution of AI. Unlike traditional AI — which can execute tasks — generative AI has the ability to generate content. ChatGPT and DALL-E exemplify the power of generative AI, being able to produce materials based on users’ requests. Like traditional AI, generative AI is also trained on datasets. However, generative AI requires more extensive datasets that allow it to identify underlying patterns and produce responsive content.
Regardless of which type you’re using, if you want to integrate AI into your HR practices, you must follow a data-driven approach.
What is data-driven HR?
Data-driven HR refers to the process of using meaningful data to make strategic HR decisions. This data can come from different sources, such as employee feedback, benefit enrollment, and engagement levels. While traditional HR decision-making may rely on intuition and outdated methods, the rise of AI is making it vital for HR teams to harness data to stay competitive and thrive.
“Data-driven HR refers to when an organization uses employee information for internal analytics to improve the decision-making processes. This takes the guesswork out of the equation and enables fact-based, strategic decision-making,” explained Linnéa Melin, Senior Product Marketing Manager at Benify.
What kind of HR data do you need?
Over 50% of CEOs expect cost savings and significant productivity gains in operations thanks to AI. But AI's effectiveness depends on the quality of data that's provided. As AI expert Bernard Maar noted, “Inaccurate or unclean data can lead to flawed AI-driven decisions, negating the benefits AI could otherwise bring to HR management.” Maar outlined the metrics that HR leaders can use to evaluate whether their data is ready for AI processing. These are as follows:
- Consistency: All data should be recorded consistently.
- Accuracy: Data must be error-free to ensure reliable AI-driven decisions.
- Uniqueness: Duplicate data should be eliminated to maintain data integrity.
- Validity: Each piece of data should be fit for its intended purpose.
- Timeliness: Data should remain relevant concerning the time it was collected.
- Completeness: Data should capture as much relevant information as possible.
As HR teams integrate data and AI, understanding how to approach this process ethically is crucial for future-proof success.
How to ensure ethical AI use in HR Processes
While AI offers immense potential for HR transformation, organizations must use it responsibly to avoid unintended consequences or ethical concerns. For example, there have been instances of companies implementing AI algorithms to monitor workplace performance, resulting in the AI automatically ending employee contracts.
When collecting data to be used by AI tools, HR teams must ensure transparency. To foster trust and reduce employees’ concerns, it’s crucial to safeguard data privacy and address any potential misuse of data head-on.
As HR leaders navigate the integration of AI into the workforce, these kinds of ethical considerations must be prioritized. Jonathan suggests, "Companies should consider establishing an ethics council to oversee how decisions influenced by AI could impact employees."
Embracing AI with Data-Driven HR
By leveraging data and AI responsibly, HR teams can unlock new opportunities for growth, innovation, and success in today's competitive market for talent.
At Benify, we recognize the power AI has to transform HR practices. We believe that by educating organizations on the power of data, we can help companies around the world unlock new possibilities for HR optimization and employee engagement.
To learn more about this kind of data-driven HR approach, download our guide "Data-Driven HR: The Secret to Effective Benefit Programs" to discover practical insights, best practices, and real-world examples designed to help you harness your organization’s data and unlock the full potential of your workforce.
