AI people analytics: Unleash human potential

Unlock the potential of AI people analytics with a focus on data. Learn how it can transform HR, boost productivity, and navigate today's remote work landscape.

Alfie Young

Data speaks volumes - Adopting AI people analytics is crucial for businesses, especially with the rise of remote and digital work.

The evolution of people management - HR has shifted from an administrative role to a vital function in modern businesses, boosting productivity and revenue, but many face skill gaps in AI and people analytics.

Covid-19 impact on HR - HR transitioned from crisis mode during the pandemic to facing diverse challenges, including new issues, while utilising internal data for insights.

What is people analytics? - The practice of gathering and analysing data about business teams to make insightful decisions and strategies.

What is AI? - Artificial Intelligence is a machine's ability to perform the cognitive functions we usually associate with human minds.

Why is data so important? - Used in conjunction with AI it can inform decision making, predict trends and enhance personalisation

This history of people analytics - People analytics, from Taylor's 1911 work to recent crises, people analytics has evolved into a central element in HR and business strategy.

The need for people analytics? - People analytics is essential for success in today's work landscape; embrace people analytics to thrive, leveraging its benefits to enhance productivity, employee experience, and resource allocation.

How has AI developed people analytics? - AI-enhanced people analytics elevates HR by automating data analysis, ensuring evidence-based decisions, and transforming HR practices. 

Why is people analytics important? - Benefits include efficiency gains, better employee wellbeing, reduced employee turnover, improved engagement and enhanced talent acquisition.

How to get started? - Diagnose, create, iterate. Get started with people analytics in three steps, get access here

Our take? - Leveraging data and AI is essential to remain competitive in the workplace today. Omnifia handles data collection to ensure your business thrives, adapts, and excels.

Data speaks volumes 

Typically organisations excel more at monitoring externally focussed than metrics of internal team dynamics and productivity. With the spike in remote and digital work over the past few years, adopting AI people analytics is vital to sustain your business.

From admin to strategy: the evolution of people management

Before the digital era, HR was considered “the boring admin bit”, where HR executives were paid to handle employee grievances and pay rolls. The function was frequently viewed more as a cost centre than a source of revenue generation  across many corporations. 

Due to the growth of remote work, we have seen an increased emphasis on emotional health, diversity, and the digital boom. As a result, HR now plays a significant role in many firms. An effective, data-led HR strategy, can transform how a company develops and raises productivity among employees. 

For example, a notable 88% of employees in top-ranking companies willingly exert extra effort, directly boosting revenue-per-employee. This demonstrates that HR now goes beyond paperwork, becoming a key player in boosting both employee happiness, productivity and company profits. 

Some HR departments have already undergone transformations as a result of recent advances in AI and people data. According to one study, 78% of HR teams believe that AI and people metrics are essential to establishing a future in HR that is driven by data. However, the same report also showed that 32% of HR teams believe there are serious skills gaps in people analytics, technology, and digital adoption. 

At Omnifia, we understand that while everyone is aware of the enormous possibility presented by AI People Analytics, they often are unsure of where to begin or how to apply it. This blog will serve as an introduction and a clear roadmap to unlock its true potential.

Human evolution graphic, showing HR evolution from humans to AI people analytics.

The influence of the Pandemic on the role of HR

After operating in crisis mode in the peak pandemic era of 2020-2021, HR executives were having to figure out how people could work from home while providing the necessary support they needed. 

The responsibilities placed on the HR function today are more varied than ever. According to a Gartner survey, 80% of HR leaders are dealing with "different challenges" than they were before the pandemic. 

Internal data can now be accessed and analysed more easily than ever before. In addition to being in charge of performance, productivity, and employee wellbeing, human resources must now comprehend the issues facing the workforce and innovative working practises.

Therefore, we firmly believe that harnessing data for a deeper understanding of your workforce is now essential. 

What is data? 

Data is more than just numbers or words on paper; it is a collection of facts, stats or information used to generate insights and inform decisions. Whether it takes the form of written words, bytes and bits in the memory of technological equipment, or even knowledge stored in a person's head, its significance is undeniably vast.

Data serves as the foundation for decision-making engines and forms the basis to implement people analytics.

What are people analytics? 

People analytics is the process of acquiring and evaluating information on the workforce in order to inform strategic and tactical decisions. 

People analytics enables the HR division to play a significant role in forming company strategy, expanding upon its traditional function of managing administrative activities. 

For instance, the information gathered can be used to increase employee engagement, keep talent, and deal with problems that hinder organisational effectiveness, like inefficient staffing and management techniques.

What is AI?

Artificial Intelligence is a machine's ability to perform cognitive functions typically associated with human minds. This capability allows machines not only to perceive and reason but also to learn, interact with the environment, problem-solve, and even showcase creativity. Currently, AI has found its footing in various industries. In the UK, for instance, around 16% of organisations have adopted at least one AI technology, a figure projected to climb to 22.7% by 2025. 

Based primarily on machine learning and deep learning, uses of AI include data analytics, predictions and forecasting, object categorisation, natural language processing, recommendations, intelligent data retrieval, and more. Given that 54% of executives assert that AI solutions have bolstered their business productivity, the expansive integration of AI across all business sectors seems not just probable, but inevitable.

Understanding AI and its components?

Despite its recent upsurge, AI isn’t as novel as it is so often portrayed. Having originated as an academic discipline in 1956, AI has since branched out into specific areas of study.

Machine learning (ML)

A subset of AI, ML revolves around algorithms designed to recognise patterns, learn, and decide based on data. It’s the reason why machines can detect specific features, like identifying a bird in an image. However, ML’s accuracy often requires human validation. Think of it as a machine making an educated guess, with humans verifying its accuracy. 

Deep Learning

Nested within ML is deep learning. This subset of AI leverages neural networks to let machines make accurate decisions without human intervention. For example, self-driving cars are becoming increasingly common, rely on deep learning. But, unlike ML, deep learning can independently reach precise conclusions. 

Generative pre-trained transformers (GPT)

Part of the deep learning category, GPT facilitates machines in generating human-like text and engaging in fluid conversations. For example, you ask ChatGPT, “draft a team-building activity plan” and it will instantly craft a detailed agenda, including icebreakers, collaborative tasks, and reflection sessions tailored to your company culture.

Young professional using ChatGPT software on laptop, highlighting use of AI people analytics and data strategies.

But, it is pivotal to remember, for AI to walk, let alone to run, you need data to nourish and inform its algorithims. Just like a plant needs nutritive soil to grow, AI needs quality data to develop and work effectively. 

Data in AI-powered people analytics

Within AI people analytics, data comprises a wide array of information related to employees and organisational processes. This can include: 

HR data 

This is data that can be found in your Applicant Tracking System (ATS), Google Sheets and HRIS systems to better identify what draws in new applicants. HR data also includes survey data that comes from popular tools like Culture Amp and Work Buzz

Finance data

Tools like Xero system, Zoho books, Workday offer insights into your financial operations. With this you can see how decisions impact commercial outcomes and how your talent strategy aligns with business objectives. This aids in understanding key metrics like Cost Per Hire (CPH) or Revenue  Per Employee (RPE).

Workspace data

To get a handle on team productivity and workplace habits, data from workspace tools is invaluable. For tech teams, Jira and repositories like GitLab reveal engineering efficiency. Meanwhile, tools like Hubspot or sales CRMs shed light on sales and marketing outputs. For a holistic view, platforms like google workspace and microsoft 365 offer insights into meeting trends, potential burnout risks, and many more drivers that influence employee engagement.

Public data 

Want to know where you stand against rivals? Public data offers a lens. Say you're part of a 200-strong software firm and wonder about the engineering team size at peer companies. Public data sets offer such benchmarks to help you understand how you stack up and potential areas of focus.

The pivotal role of data

Used in conjunction with AI, data is not just a collection of numbers or facts but is a strategic advantage for your business:

Informs decision-making:

Data aids in making evidence-based decisions, minimising biases, and ensuring your strategies are grounded in reality.

Predicts trends

By analysing historical data, AI can predict future trends and enable your organisation to be proactive rather than reactive.

Enhances personalisation

Data allows for a more tailored approach in managing and supporting your employees, enhancing their experiences and improving retention.

Brief history of people analytics:

The earliest developments in people analytics can be traced back to Frederick Taylor's famous book “The Principles of Scientific Management '' all the way back in 1911. In this book, Frederick presents the goal of increasing employee productivity by measuring everything they do. Taylor made notable use of these theories to scientifically analyse and automate operations in the vehicle manufacturing facility at Ford.

In the 1940s, mass industrialisation gave rise to industrial-organisational psychology, which is now essential to contemporary people analytics. 

Human resources departments expanded beyond simple administration in the 1980s and 1990s to include performance management, development, and recruitment. This increased the need for strategies to measure process efficiencies and workforce development. 

People analytics saw a substantial transformation in the middle of the 2010s, going from a supporting function to a core element in HR and corporate planning. These innovations gave rise to new models, technologies, and procedures. As a result, people analytics became widespread and acknowledged in businesses of all sizes and industries. 

People analytics gained even more prominence in the 2020s as a result of its importance in providing data throughout numerous crises, such as racial inequality, financial instability, and the COVID-19 pandemic. 

The need for people analytics?

"Moneyball" not only stands as an infamous baseball film but also serves as a compelling illustration of the indispensable need for people analytics. The Oakland A's, despite their modest $41 million salary budget in 2002, ably navigated competition with the Yankees and their towering $125 million payroll, all by strategically employing analytics. This underdog story underscores the principle that implementing people analytics can maximise output even when resources are limited. 

The Moneyball methodology can be seamlessly transitioned into today's corporate world and your business.  Leveraging HR metrics provides managers with vital data, enabling them to optimise staff management and enhance business efficiency. 

In the sphere of today's evolving work landscape, the workforce has become increasingly remote and complex, causing increased data fragmentation whilst employee experience and business outcomes decline. 

Companies that are able to leverage people analytics are 5.6 times more likely to achieve their employee experience and business outcome objectives. Thus the need for people analytics is becoming more prevalent.

Economic challenges have caused businesses to cut spending, making it even more crucial for HR leaders to wisely use people analytics to navigate through tough times and keep teams stable and engaged.

Through the use of AI people analytics, you can automate routine tasks and identify insights that would otherwise be too time-consuming for a human alone to uncover.

The new wave: the rise of AI people analytics 

AI-enhanced people analytics takes the foundational concepts of people analytics and integrates artificial intelligence to elevate its impact. While traditional people analytics revolves around data collection and manual analysis, AI people analytics leverages artificial intelligence to automate data analysis and draw insights, transforming HR practices and decisions. 

This means decisions about team members are not no longer guesses or quick judgements made on emotions or unreliable survey data, now they are backed by thorough objective data analysis. 

Incorporating AI into your people strategy ensures smooth decision making whilst making sure these decisions are solid, evidence-backed, and beneficial for the business and individuals alike. 

Modern workspace with professionals collaborating amid vibrant data visualisations, showcasing AI People Analytics in contemporary business.

Benefits of AI-Powered People Analytics

Efficiency gains

Insights into the entire employee lifecycle can be achieved using people analytics. Some key metrics discoverable through people analytics include onboarding time, ramp up time or whether teams are engaged. By equipping your organisation with the ability to identify inefficiencies whilst recommending solutions, your business can develop and implement new processes quickly, optimise workflows and improve collaboration. Overall, enhancing organisational efficiency

Enhance employee wellbeing

Employees experiencing wellbeing difficulties can significantly impact the wider workforce. However, the objective isn't to pinpoint the "bad guys," but to leverage people analytics to identify detrimental situations. Burnout, a prevalent issue, often results in disengaged employees, costing employers as much as 34% of the affected individual's annual salary. By discerning the causes of such burnout, companies can take proactive steps to address the core issues. Employing people analytics provides  your business with invaluable insights into these negative trends, enabling you to strategise and enhance overall employee wellbeing. 

Reduce turnover: 

Utilising AI-driven people analytics is an effective way to identify underlying causes of employee attrition, such as disengagement, which significantly drives employees away. With AI people analytics, you can detect early signs of disengagement or whether your workforce is becoming unstable. By diagnosing these problems earlier you are able to understand the impact it is having on your business, then you can determine fixes to solve the issues. 

Attract talent: 

According to a Corporate Responsibility Magazine / Allegis Group Services survey, a staggering 69% of job seekers wouldn't accept a job from a company with a poor reputation, even if they were out of work. Highlighting a positive company culture, high engagement levels, and the efficacy of collaboration and teamwork emerges as a compelling strategy to attract top-tier talent. People analytics offers a valuable tool, allowing hiring managers to substantiate their claims about a flourishing organisational culture. Moreover, by leveraging this data, you can discern the distinct traits of top performers and tailor your hiring processes to seek out those particular attributes.

In general, AI people analytics helps organisations make ethical and well-informed strategic decisions that enhance outcomes for both people and the organisation. By utilising this technology,  you can monitor employee demands and maintain a positive work environment. 

How to get started

As we defined earlier, data is the foundation for which to build your business strategy. But, bringing the data together that AI can be used on top of to support your decision making is the main challenge of most organisations. 

To get started on a project like this using your own internal resources, this is how it usually works:

1. Define Objectives 

Begin with the end in mind. Identify the hurdles preventing your company from reaching its objectives. To ensure maximum impact, it's crucial to be discerning in this phase. 

Perhaps you're trying to pinpoint why productivity is lagging or why there's an uptick in employee turnover. Select a pressing issue, delve into its implications, and correlate it to broader organisational goals. Always anchor your objectives in reality.

2. Identify data sources

Then you will need to identify the data sources you need, such as survey data, HRIS data (like absences), and workspace data, to gain insights, especially when considering engagement or demographic filtering.

3. Collect and store data

This is very technically challenging, as you will be dealing with high volumes of data where you must ensure the data collected is of good quality, whilst guaranteeing data security and privacy.  You will likely need data experts for this, particularly when collecting multiple data sources.

4. Clean Data

Again, this is also technically challenging, including multiple steps, such as inspecting and profiling your data, cleaning it, verifying its cleanliness and then reporting to your business executives. 

Often you will encounter challenges such as missing values, data silos and data quality issues in big data systems.Therefore, this will also require data professionals.

5. Gather Insights

Now, you can dig into the data to find new insights and validate your hypothesis. 

For instance, if you suspect low engagement, check for manager-related issues or infrequent 1-1 meetings for certain employee segments. Address concerns like diversity by validating these hypotheses through data analysis. 

If we take the engagement example, perhaps there is a lack of activity across one segment of employees due to manager issues, or perhaps are there barely any 1-1s going on for employees that have 1-2 years tenure? Or are some employees concerned about a lack of diversity in the team. This is where you can validate your hypothesis by getting into the data

6. Communicate Insights

Once your hypothesis has been verified, you can share the new insights with your team. Here you will need to tailor your message to your different team members and communicate insights via written reports, slide decks, dashboards, videos, infographics, and so on.

7. Take Action

Come up with an action plan with clear KPIs to measure success and implement effective tactics. This demands a deep understanding of data, expertise in business strategy, strong analytical skills to derive insights, and collaborative engagement with business owners to drive actionable outcomes."

8. Share Results

Share results with your team based on organisational impact of the KPIs you chose. Afterward, you iterate based on what you've learned and consider using this strategy to solve the next problem. 

Our Steps to Launching Your AI People Analytics Strategy

Using Omnifia, you can streamline this internal process from 8 to 3, saving you time and money:

1. Diagnose

Engage with one of our team members for a complimentary diagnosis aimed at uncovering any obstacles hindering you from achieving your objectives. Are you grappling with absenteeism within a specific employee segment? Are disengaged teams slipping under the radar? Is employee well-being contributing to higher turnover rates? Reach out to the Omnifia team to gain your complimentary diagnosis report to automatically reveal these issues.

2. Create

Handpick your focal points and brainstorm strategies to address the uncovered challenges. Visualise your strategies on a graph, assessing both their feasibility and significance. Opt for the most effective tactics. Establish the data points and metrics that will be instrumental in tracking the success of your devised strategy, and set it into motion.

3. Iterate

Evaluate the impact of your strategy on the designated metrics. What strides have your tactics made? Could there be room for enhancement, areas to refine? Utilise these findings to disseminate insights, fine-tune your strategy, and showcase its triumphs with your colleagues.

With great insight comes great responsibility: 

In order to ensure that you make the most of your data it is important to be responsible with it too. Using data-driven insight you can make valuable changes within your organisation, but you must also make sure that this is recognised by your workforce.

Communicate the value of AI people analytics to stakeholders

Discuss with executives, managers and leadership from different departments on the power of using people analytics. Explain how this technology can provide insights into organisational performance. Then collaborate with departmental leaders to identify key analytics questions and the data needed to address them.

Create fairness and transparency

People analytics is a powerful tool that can provide you insight into your employees. However, that may be more insight than your staff would be comfortable with. Employee resistance may result from data collection without consideration for the effects. Nobody wants to believe they are being watched, after all.

By being transparent about how you’re using your people's data and why, you can also empower employees to keep you accountable and provide feedback. This will fine-tune and improve your people analytics strategy.

Data protection and GDPR in the workplace

Although data is held in many places in an organisation, it should ideally be managed by a specific data owner. The data owner is responsible for ensuring that data is maintained and kept secure according to your organisation’s data protection policy.

When employing people analytics, you should follow the ICO guide on the DPA and the GDPR as it applies in the UK. This includes principles regarding consent, lawful processing, accountability, among others.

Our perspective - the need to change: 

Many companies aspire to leverage AI and pledge more data driven approaches. However, they often overlook the foundational step of utilising their complete data set. Ignoring the importance of structuring your data will make generating the best outcomes impossible. 

Investing in a tool like Omnifia where we do the data collecting for you isn’t just a smart move - It’s a necessity. By harnessing the power of data and AI you can ensure your business is robust, adaptable and readily meets the challenges of the future. 

This isn’t just about surviving the changes in the business world, but thriving, growing and harnessing AI People Analytics to transform productivity and business outcomes.  

Want to leverage the power of AI people analytics for your business? Contact us today!