In this article, we’ll look at how to use AI in HR data management. We'll explore challenges HR teams face when it comes to managing data, as well as how they can save valuable time with the help of AI tools.
The future of HR and why AI is in the picture
The future is looking highly promising for businesses looking to invest in AI technology in Canada. It currently ranks fourth on the Global AI index, behind the US, China, and the UK. Canada has been an early supporter of AI, with the Government of Canada calling upon CIFAR, a global research organization, to design and lead the Pan-Canadian AI Strategy in 2017.
The investment has created a favourable ecosystem for AI companies in Canada—there are now more than 1,200 AI startup companies, which is more than double the number since 2018. The AI market in Canada is already projected to be worth more than US$6 billion in 2023 and to grow to more than US$16 billion by 2030.
Now in its second phase, the Government of Canada has also invested millions of dollars in funding across the three pillars of the strategy: commercialization, standards, and talent and research. There’s plenty of support and backing for companies not only looking to invest in AI, but also for creating their own AI tools that could solve many issues within their own industry — and of course — developing further their human resources data management solutions.
So why should human resources teams be investing in AI? According to Indeed & Glassdoor's Hiring and Workplace Trends Report 2023, the Canadian workforce is shrinking, leaving behind a 'post-pandemic participation gap.' HR teams need to look at finding new ways of hiring and retaining staff in what is a competitive jobs market for recruiters. With the help of AI, they might be able to develop more compelling interview questions, or save time that would otherwise be spent on manual tasks.
To understand how to develop AI-based solutions to human resources data management, let’s look at what AI could do when it comes to data analysis, management, and streamlining of workflows.
Automation and AI can save HR teams valuable time
Automation and AI can help make it easier for your HR team to save time on repetitive manual tasks. As Indeed’s leading AI expert Matt Burney points out, recruiters are always finding new ways of streamlining tasks with automation.
Matt found, through an Indeed survey of Talent Acquisition leaders, that respondents spent on average 14 hours per person per week on manual tasks and processes. With language model GPT tools — that is, text creation and analysis tools — you could already save about 35% of time otherwise attributed to manual tasks.
HR teams are then free to work on tasks that require a more human, hands-on approach — dealing with staff face-to-face and addressing their concerns in a personable way.
Some other uses for AI in HR
As Capterra found, the most popular software uses in AI HR management include employee engagement, analytics, and performance. These softwares can help identify when staff require additional training, or support interventions – which could be identified far more quickly by AI than human input. It also saves managers time. A survey from Telus Health found that, of the Canadian workers considering a job change, the most common reason was that they wanted better career opportunities (20% of respondents). Rather than spend time peering through data, HR teams can focus on performance management, one of their top priorities.
One of the biggest pain points for HR is allocating time to managing unstructured data, because of just how difficult it is to manage.
What is unstructured data?
So what is unstructured data exactly? According to MondoDB, unstructured data is any data which is: ‘Not arranged according to a preset data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS.’ It’s also any type of data that falls under text or multimedia, including formats like emails, videos, webpages, audio files, and photos. MondoDB claims that 80% to 90% of data collected by organizations is unstructured.
While much of the data that HR teams deal with is structured, like an employee or candidate’s name or date of birth for instance, other types of data such as candidate interviews, emails, identification documentation, employee feedback survey responses, or PDFs will be unstructured. AI can create solutions to managing the above types of unstructured data, and potentially free up time for HR teams.
Let’s look at how to process unstructured data — which is somewhat more complex than structured data management.
Building AI tools to manage unstructured data
AI expert Matt Burney expresses that it’s inadvisable to input sensitive company or customer data into ChatGPT type models (which is likely the predominant kind of data that your HR team will be working with), because it’s a data confidentiality issue. It might also run the risk of going against PIPEDA rules, but at the moment it’s somewhat of a legal grey area.
Matt also explains that one solution for companies is to develop AI tools to manage their own unstructured data without having to input data into AI tools owned by other companies. The data stays within the hands of the company, and doesn’t run the risk of being leaked. This makes AI a useful solution to HR recruiters who are dealing with plenty of unstructured data. He says that you can use these tools to better organize interviews and learn about what did and didn't work from each one. Recruiters can also use this information to look at which behaviours make the most successful interviewee and why.
How do you make unstructured data actionable?
Unstructured data processing involves taking data from formats like emails, videos, webpages and the like, and turning the data available into quantifiable data. HR teams planning on doing this will need the right data extraction tools to extract unstructured data from its original file type. It involves having to 'clean up' the data that you’ve extracted, before storing it in a management system for employee use. Datasets extracted from unstructured data will usually need to be cleaned as they can often include errors like spelling mistakes or incorrect HTML tags.
Managing unstructured data with AI
Once you have actionable unstructured data, you can then manage it more effectively with the help of AI tools. You might create or use a text analysis tool similar to that of ChatGPT, bearing in mind our points above about PIPEDA and handling sensitive data. You might use this to organize text by topic. For example, you might use AI to organize survey responses by specific phrases or words. This is particularly useful for HR teams which are dealing with qualitative rather than quantitative survey results, and need to better organize participant responses.
As VentureBeat found, there are currently tools on the market which enable you not just to analyze text with AI, but to also use optical character recognition, voice recognition, and more.
AI can help your HR teams focus on the more important issues
With the help of AI, HR teams can better manage unstructured data which is otherwise difficult to quantify. Streamlining HR processes can help free up time on manual tasks, so that company teams have more availability to deal with responsibilities that need a more human approach.
