The Rise of AI in HR: A Guide to Empowering Employees

Did you know that by 2025, our world will produce 175 zettabytes of data? That’s the mind-blowing equivalent of a billion terabytes every year. We’ve reached the point where data is practically a limitless and renewable commodity. It’s crazy to think that we generate it as a byproduct of everyday life.

I’m not going to expound on the arguments about data privacy, since most people have just accepted data collection as a necessary requirement of living in a connected world. We understand that companies are using big data to gain insights into our behavior as consumers. We’ve essentially traded confidentiality for convenience.

What’s less obvious, at least to most people, is how companies use big data to manage their most valuable asset: their employees.

You may have heard the term “people analytics”, which refers to a field of human resources called HR analytics (or HR science). HR analytics has been around since the start of the digital era.

Until recently, Human Resources were primarily viewed as administrative or support departments. HR analytics introduced a more evidence-based approach to managing people, moving away from intuition and anecdotal decision-making towards a more data-driven model. This involved collecting and analyzing basic HR data like employee turnover rates, absenteeism, and staff demographics to understand historical patterns and inform basic decision-making in human resource management.

Over time, especially with the introduction of advanced data analytics and AI-powered tools, the role of HR evolved. HR departments now use data more strategically to make informed decisions, predict future trends, optimize workforce management, and contribute directly to organizational performance and growth.

As a result, organizations now recognize HR as a strategic partner that helps to shape business strategy and provide a competitive advantage.

Revolutionizing HR: How Top Companies Leverage AI

I can’t overstate just how much of an impact AI is having in the field of human resources. With the ability to accurately analyze large amounts of data, automate reporting, and expedite decision making, it’s not hyperbole to say that HR departments are considered trailblazers of business innovation.

The best way to highlight this is through real-world examples. There are plenty of incredibly innovative case-studies out there, and I encourage you to do your own research. I’ve chosen these particular examples because they could be considered blueprints for future AI-driven HR.

Over the past six years, IBM‘s HR team has been diligently enhancing the abilities of its AI-driven digital assistant, AskHR. It has automated over a hundred processes and now manages more than 1.5 million employee conversations annually. AskHR can proactively send travel tips, weather alerts, and helping with everyday tasks. With AskHR, IBM found that it frees up human staff from repetitive duties, allowing them to concentrate on more intricate and challenging work.

Unilever has replaced conventional hiring processes with digital interviews and online assessments powered by AI. This innovative approach expands the talent pool by embracing diversity and inclusivity. More importantly, it mitigates unconscious biases, ensuring a fair and objective selection process. The result has been a more efficient hiring process that aligns with the demands of a modern workforce.

Would it surprise you to know that Google, known for their ability to attract top talent, had an employee attribution problem? At one point, they realized only 30% of their employees stayed with the company beyond two years. In response, Google leveraged predictive analytics to help guide their hiring decisions. By analyzing an applicant’s job history, educational background, skills, and personality traits they are able to forecast the candidate’s potential success within the company. It had a huge impact, and they’ve continued using this approach to hire over 100,000 employees.

Even AI-driven learning platforms are changing how organizations manage professional development. Accenture embraces this approach by customizing their training material to match the individual learning styles and career goals of their workforce. With a more engaging and effective learning experience, employees are equipped with skills that are relevant to their current roles while aligning with their future career paths.

Many organizations are using AI for strategic workforce planning. Siemens uses AI to analyze a wide range of data points about its workforce — from job performance metrics to individual learning progress and industry trends. It uses these insights to align employee development with the evolving demands of the industry, helping Siemens stay agile and competitive.

These real-world applications demonstrate a new era of AI-powered innovation in HR and workforce management. Big data has become an important commodity and strategic asset in modern HR, significantly altering the dynamic between organizations and their employees.

It means employers can now make more empirical, evidence-based HR decisions – from how employees are recruited and retained to how their performance is evaluated and their career paths are shaped.

Employees, on the other hand, are increasingly aware of their data footprint and its implications.

Every organization should strive towards a more balanced power dynamic, where both employers and employees have a stake in how data is used to shape the workplace. It’s going to require transparency, fairness, and personalization in how employee data is used.

Using Data for Employee Empowerment

From the moment an employee joins an organization to the day they leave, every interaction, performance metric, and feedback session generates valuable data. Even before they are hired, it’s likely that employers are collecting pre-employment data – through the recruitment process, candidate interviews, and even their social media or online presence.

This data can give employers profound, personal insights into an employee. However, it’s also a unique opportunity for employees to adopt a proactive stance.

First and foremost, employees should educate themselves on the basics of data analytics and its implications in HR. By understanding the language of data, they can interpret how their performance metrics, engagement levels, and other data points are used. This knowledge can help them to engage in more informed conversations with HR and management.

In my article “The Imperative for Reskilling and Adaptation in an AI-Enhanced Workforce“, I provide a comprehensive list of resources for learning about AI, especially for those with only a foundational knowledge of the subject.

At a minimum, every employee should be familiar with these high-level terms to help them understand and contribute to conversations about data analytics in the context of HR:

• Data Analytics: The process of examining data sets to draw conclusions about the information they contain. It involves using specialized software and systems.
• Metrics and KPIs (Key Performance Indicators): Metrics are quantitative measures used to track performance or production. KPIs are a subset of metrics that are pivotal in achieving organizational goals.
• Predictive Analytics: A branch of analytics used to make predictions about future outcomes based on historical data and analytics techniques like statistical modeling.
• Data Mining: The practice of examining large pre-existing databases to generate new information and identify patterns, trends, and relationships.
• Machine Learning: An AI application that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
• Data Privacy: Refers to handling sensitive data, such as personal employee information, in a manner that is compliant with relevant legal and ethical standards.

In addition to those general terms, there are a few specific terms that are especially relevant to the employer-employee relationship within the context of HR analytics. Understanding these terms can help employees better comprehend how data influences their work life and relationship with their employer:

• Employee Satisfaction Index: A measure that gauges the level of satisfaction and contentment employees feel towards their job and work environment. Most often used in surveys and feedback tools and used in broader analytics for workplace improvement.
• Turnover Rate: The rate at which employees leave a company and are replaced. It’s crucial for understanding employee retention and satisfaction. AI tools use this data to understand employee retention and identify patterns or reasons for turnover, which can inform retention strategies.
• Performance Management System: The tools and processes used by an organization to evaluate and monitor employees’ performance. AI can enhance these systems by analyzing performance data to provide more nuanced feedback and development suggestions.
• 360-Degree Feedback: A feedback process where employees receive confidential, anonymous feedback from the people who work around them. AI tools can help in aggregating and analyzing this feedback to provide a comprehensive view of an employee’s performance.
• Workforce Analytics: The use of statistical models and employee data to optimize employee management and make better workforce decisions. AI analyzes trends and patterns in this workforce data to aid in decision-making around hiring, training, and deployment of human resources.
• Engagement Analytics: Data-driven insights related to employee engagement levels, often derived from surveys and other feedback mechanisms. AI can analyze this data for measuring and improving employee engagement levels.
• Cultural Fit: Refers to how well an employee’s attitudes and behaviors align with the core beliefs, attitudes, and behaviors of the organization. AI can be used to assess compatibility between an employee’s values and those of the organization, often during the recruitment process.
• Employee Lifecycle Analytics: This refers to the analysis of an employee’s data throughout the entire period of their tenure at an organization, from hiring to exit. AI tools track and analyze this data to provide insights for improving the employee experience.
• Employee Value Proposition (EVP): The unique set of benefits and experiences an employee receives in return for the skills, capabilities, and experience they bring to a company. While more qualitative, AI can assist in analyzing feedback to understand and enhance the EVP.
• Succession Planning: The process of identifying and developing new leaders who can replace old leaders when they leave, retire or otherwise become unavailable. AI is able to identify potential leaders by analyzing performance, competencies, and career trajectory data.
• Employee Onboarding Analytics: The measurement and analysis of data as it relates to the process of integrating a new employee into an organization. AI tools analyze onboarding processes and their effectiveness, helping to streamline and improve the integration of new hires.

Armed with a better understanding of these concepts, employees have the ability to strategically use their data footprint for career advancement.

They could identify opportunities for promotions or special projects that align with their strengths, using their performance data to make their case. Similarly, by identifying skill gaps or areas for development, employees could proactively ask for training or mentorship.

Even setting goals becomes more tangible when it’s data-driven. Employees can look at their past performance, then set realistic goals and methodically work towards them.

Employee data can also be a powerful tool during performance reviews or salary negotiations. Having objective data give employees strong, evidence-based arguments about their value and contributions to the company. It also helps them understand how they are perceived by colleagues and management.

An often-overlooked aspect is work-life balance. By analyzing work hours and stress periods, employees can make informed decisions about time management and advocate for a healthier balance between work and personal life.

For employees, having a holistic view of their data footprint is akin to having a roadmap or compass. I helps guide them when making strategic decisions for their career progression.

Working Around Limited Data Access

Of course, all of this depends on the level of data access and transparency their employers provide.

In organizations where data accessibility is limited, employees can advocate for more transparency by requesting access to their own performance data, learning and development records, and other relevant metrics.

During performance reviews or one-on-one meetings with managers, employees can ask for specific feedback and data insights. This can include discussions about performance metrics, project outcomes, and areas of improvement.

Even in the absence of any formal data, employees can still keep track of their own achievements, projects they’ve contributed to, skills they’ve developed, and feedback they’ve received. Furthermore, publicly available data on market trends, salary benchmarks, and skill demands can be used to inform career decisions and discussions around compensation and role advancement.

As the primary stakeholders in data collection, employees have a vested interest in advocating for transparency and privacy. They should feel empowered to ask questions about how their data is used and be assured of its confidentiality and security. By doing so, employees not only protect their own interests but also contribute to a culture of openness and ethical responsibility within the organization.

Conclusion

As I draw this article to a close, I want to emphasize the incredible power that data holds in the hands of employees, especially in this AI-driven landscape. The use of AI in human resources isn’t just a boon for companies. Employees have a golden opportunity to leverage the same data to take charge of their careers, advocate for better compensation, or influence data transparency within their company.

Our individual data footprint can be a rich source of personal insights that highlights our strengths, identify areas we can improve, and reveal possibilities we might not have considered. We have a unique chance to use this data to advocate for ourselves, identify areas for professional development, and choose a career path that aligns with our aspirations and personal values.

Looking ahead, the future of work with AI isn’t solely about organizational efficiency and growth. It’s just as much about how we, as employees, can use this technology to build careers that are rewarding and dynamic. AI isn’t just a corporate tool; it’s a catalyst for our own empowerment and a companion on our professional journey.