Technology has the attention of human resources. What’s next? The majority of businesses and HR departments now recognise the strategic importance of using technology in their operations. Many, however, are still figuring out how.
A thumb rule of successfully using new-age technologies like AI in HR is – improving your data. Technology can only be as strong as the information that powers it. Banking on the right information not as simple as pushing a button.
Sourcing the data and managing it has to become a core function of human resources, for HR tech to work its magic on company processes. Workforce analytics isn’t new. Great line HR mangers have always analysed employee data to inform their decisions. Technology tools have expedited, streamlined and automated the process.
What then are the roadblocks to adoption of AI, Machine Learning and Blockchain in HR? Do you have quality data?
The benefit of taking a technology approach can be highly limited by the quantity and quality of data available.
Say, you want to predict which of your present employees would make a good salesperson. In no circumstance, can you answer that without ample data on employees’ performance, their preferences and capabilities, and the characteristics of a salesperson that benefit your business? Add to it that you need significant data points to come up with a universal answer (that applies to all employees and your business needs).
Common roadblocks to bringing about HR transformation through technology are limited data, hesitant leaders, shortage of resources to work on data, and regulatory restrictions. However, the need for quality data trumps it all. Without it, no real change is possible.
Toward HR Technology: Busting the limitations
Efficient data management should primarily include three practices:
- pinpointing right sourcing points
- consistent data definitions, and
- data entry and cleaning culture (a feat in itself).
The better these practices are engraved in your functioning, the more data you will generate and its quality will be rich to be used for informing decisions and automating processes with accuracy.
As a starting point, we explore the data points three of the much talked about new-age technologies – Artificial Intelligence, Machine Learning, and Blockchain – need to improve your productivity. They are not all-inclusive, but indicators to get HR managers started.
Must-have Data Points for HR: In AI, Machine Learning & Blockchain
AI in HR
Artificial Intelligence is complex technology. It feeds on data, and get smarter as more data is given to it. AI can learn and based on the learning; it can act independently without the need for human intervention. The only condition being: it needs data. More and more people should submit their data for AI to perform tasks with accuracy.
Greater and ‘relevant’ data are the foundations to make AI systems smarter and seamless. It applies to HR tech too.
When can you use AI? When there is a task that can be automated.
Exploring the objectives human resource professionals can fulfil with AI; and essential data points.
Objectives of AI in HR:
- Employee Learning. Based on employee’s preferences and performance, AI system can give training (online or offline) recommendations.
- Enrolling for benefits. AI can check eligibility and approve one for the benefits.
- Initiating a retirement. An Exit process can be made easier and automated with AI.
- Managing leaves. AI can decrease leaves, adjust them, and resolve related queries, based on embedded algorithms.
- Source candidates. Based on what talent works and what doesn’t for a business, the process of targeting and inviting the right candidates can be automated.
- Resume screening and detecting anomalies. AI can sift through the barrage of resumes with high accuracy and less bias.
- Detecting employee attrition. AI HR tech can detect patterns of even the slow withdrawal of employees from the business process.
Data points to look out for:
- Performance reviews
- Experiences, skills, and qualifications desired
- Employee exit metrics
Machine learning in HR
Machine learning is under AI umbrella. AI can create intelligent machines that can think like humans, machine learning allows machines to learn from data.
ML can help human resources draw conclusions and show a correlation between data points.
For instance, OLX Group identified flight risk markers with machine learning. Aggregating data from previous years, it reached the conclusion that completing a 12-month mark, and working in a unit that’s below-average satisfaction levels are two prominent flight risk markers.
When is machine learning used? Drawing conclusions from myriad data points. The use of AI and ML, however, is bleeding into each other.
Objectives of Machine Learning in HR:
- Employee engagement
- Finding out if higher training benefits are preferred over average spike in salary by employees?
- Employee retention
Data Points to look out for:
- Round the season sales. Figure out the shopping season for your business. Use this to augment your staffing needs and scheduling processes
- Foot traffic. Can figure if a particular event (road closure or other externalities) led to ore footfall, and how you fared with respect to your competitor
- Others mentioned in the section on AI in HR
Blockchain in HR
Blockchain is yet to scale in HR tech. Less than five per cent of organisations presently use blockchain in human resources. The tech can, however, gain prominence in recruiting and data protection, as the talent war and calls for the privacy of data becomes louder.
Objectives of Blockchain in HR:
- Data privacy and security
- Resume verification
- Financial management
- Protecting the intellectual property of the business
Data Points to look out for: It’s not data point as much as creating an environment for confidentiality that’s important to implement blockchain tech in HR. Proofing recruiting, and maintaining data confidentiality is where blockchain can give confidence to companies and HR managers.
HR Transformation: How do you collect data?
Other than KPIs there are many valuable metrics that can be collected by HR to improve processes.
Pulse surveys, discussion forums, suggestion boxes, thinking workshops, candidate resumes, performance reviews, and other such sources can be used to collect desired data. Instead of undertaking data collection when the need arises, the data tap should remain open. Much better if you can place a team to structure and clean it (the overarching goals and objectives for which will have to be provided by you).
Gamification is another novel method. Crunchr in Netherlands uses 16 question games to find what employees’ value most in their workplace – covering salary, benefits, career growth, the security of job, engagement, and other aspects. The game is anonymous (except for collecting basic information such as academic level, experience, gender, etc.) that ensures participation. Preference groups based on different sub-groups are then formed from the results.
Asking the right questions is key to unravel the potential of HR Technology. The best questions are those that led to insights!
Ariaa Reeds, writer, Top CHRO