Three cutting-edge ways we’re seeing AI in HR 


We tend to fear robots taking over our jobs, but oftentimes, AI is actually helping us to perform our work in far more discreet ways. HRM explores three examples of AI in HR.

In the aftermath of a cataclysmic event, many disaster-hit zones are too dangerous for humans to enter. That’s when AI can step in to help.

Following the Fukushima nuclear disaster in 2011, robotic objects were sent into a meltdown reactor to aid in the rescue effort.

In another cutting-edge example, the US fire patrol is using drones to spot fire patterns and patrol exposed grounds that are vulnerable to fire.

AI has also been utilised in the fight against COVID-19, with robots having been used as additional security in hotel quarantine, or to stack supermarket shelves and thereby minimise human-to-human contact.

While these groundbreaking inventions are delivering solutions to pressing global problems, machine learning is often used in far more subtle, even invisible, ways.

This is a key sentiment expressed by distinguished professor Genevieve Bell of the 3A Institute and School of Cybernetics, who will unpack human capabilities for an AI-enabled world at a masterclass being held next week at AHRI’s Conference Transform 2021. (Registrations close 9 August).

Invisible AI

Some age-old inventions are being repurposed with AI, changing humans’ preconceived ideas about how these systems are expected to behave, says Bell.

Take Canberra’s light rail system, for instance.

Most of us would think the traffic lights turn red, yellow or green as a result of a human pressing a button. But in actuality, it’s the train telling the traffic light when to go red or green.

“There’s a whole lot of AI like that around us that we don’t even notice,” says Bell. “I’m particularly interested in the places where AI is a lot less visible.”

Many HR-related AI systems are also less visible. Predictive algorithms and machine learning are often utilised behind the scenes in the workplace. HRM explores three ways that it’s being harnessed by HR professionals to build more engaged, productive and efficient workplaces.

1. Retaining employees in a scarce market

As leaders are currently experiencing high levels of emotional exhaustion, there’s a risk that they may overlook signs of burnout in their teams – and we know from a litany of research that the rate of burnout has skyrocketed during COVID-19.

AI can help to ensure leaders experiencing emotional exhaustion don’t overlook signs of burnout in their teams.

Nick Bailey, senior vice president Asia Pacific and Japan for WorkForce Software, argues that technology can help to “proactively identify signs of employee burnout, distress and lower job satisfaction… by surfacing insights on attendance, rostering preferences and employee sentiment, so HR teams can make data-informed decisions that improve the employee experience”.

He gives the example of an employee who receives an automatically generated message, which notes that they’ve been working more than their regular amount of overtime shifts in the past week. The text message asks the employee how they’re is coping with these overtime shifts.

Based on the employee’s response, their manager is then able to follow up with appropriate support.

“Time, attendance and rostering data can indicate if an employee is dissatisfied at work – say, if the data shows they’re repeatedly showing up to work late with no previous pattern of tardiness, or if their roster is frequently changed by management less than two days before a shift, or if they’re being asked to work additional unwanted weekends,” says Bailey.

This kind of comprehensive data enables employers to identify signs of dissatisfaction early on, before they snowball into a bigger problem. 

Bailey notes that pairing this information with additional data from HR systems – such as when an employee last received a pay rise or completed a peer review – enables them to develop a detailed understanding of an employee’s overall experience.

In light of the current skills shortage, taking time to implement these strategies and methodically collect data about employee sentiment could be one potential safeguard against losing staff.

“Employers who don’t invest in technology to enhance the employee experience and use every data-informed opportunity to boost job satisfaction simply risk losing more staff – and stoking the flames of national burnout,” says Bailey. “The Great Resignation can stop with better AI.”

2. Personalising learning

It’s well-documented that individuals learn differently. Some of your employees might be visual learners, while others perform at their peak by engaging in hands-on demonstrations. Other employees might rely on reading and writing to improve their recall.

By bringing in an AI system, it’s possible to roll out learning and development programs tailored to each employee’s individual needs.

AI powered training systems can be personalised so  employees are able to engage with content through formats such as a demonstration, gamification or presentation. 

Brooke Jamieson, head of enablement, AI/ML and data at Blackbook,an automation consulting company, says AI is “often thought of as something that ‘finds solutions’ but it can also be used to perceive and understand the systems that lead to an outcome, and help to optimise this process”.

Machine learning can direct employees into different learning categories, thereby “tailoring learning experiences to these groups to enhance completion and progression” and helping “people to reach their learning goals”.

Since AI can also predict how likely users are to complete a course, employers can then identify and provide support to learners who might need extra support, says Jamieson.

3. Finding the right hire

AI is increasingly being used to accelerate and streamline the hiring process.

AI-powered skill testing platforms can assess a candidate’s skills to predict their job performance.

Liam Potter, recruiter at NewyTechPeople, develops machine learning solutions for companies in Newcastle’s technology community to assess candidate fit.

He says that while recruitment will always be about “strong, consultative relationships with hiring managers and candidates alike”, efficiency has become “more important than ever in the wake of candidate shortages”.

“This is where AI shines. It’s able to handle a lot of the sourcing/evaluation grunt-work that would normally require hiring a full-time resource,” says Potter. 

“It’s never going to replace the role of a genuine recruitment expert, and it’s never going to magically solve the scarcity problem a lot of organisations are facing, but it will allow good people to do more with their time.”

Potter’s recruitment process normally follows a similar process:

  • Stage 1: When a candidate applies for a role, NewyTechPeople’s applicant tracking system scans their resume and inputs key elements (i.e. applicable skills, university/degree, contact information) onto the system.

    “It will then assign a rating to the candidate based on the job posting they applied for, and pull the more relevant results to the top of the list (similar to how Google tries to put the ‘best’ result on the first page),” says Potter.

    As HRM has reported before, AI recruiting tools aren’t necessarily free of bias, so it’s important to keep this consideration front of mind when leaning on machine learning for your hiring efforts.

  • Stage 2: The company will run an initial video or in-person interview.

    “We keep this stage intentionally low-tech to ensure the comfort of the candidate.”

  • Stage 3: Subsequent stages of interviews are client-dependent.

    “We encourage more unstructured, conversational interviews. AI can’t help you sell people into your company’s culture and teams, and it can’t substitute meeting a senior figure and being able to ask them anything and everything.”
  • Stage 4: An automated system conducts reference checks.

    “There are some edge cases in which we won’t use this platform (the referee has limited technical literacy, for example),” says Potter, but in 95 per cent of cases, referees are inputting their data into this system.

AI can also be used to keep candidates informed at each stage of the application process, helping to increase the likelihood of turning candidates into employees. 

Creating meaningful work with AI in HR

With AI often used to complete tedious and repetitive tasks that typically fall within an HR department’s gambit, machine learning can enable human employees to dedicate more time towards human-centred work.

This could include higher-order creative problem-solving and everyday decision-making, says Bailey.

“HR teams and managers have more opportunities to connect with and support employees in meaningful ways.

“By surfacing insights on attendance, rostering preferences and employee sentiment, [HR can] assess whether an employee is dissatisfied, overworked, or ready to quit, [and] future-proof their organisation against further churn.”

As we rebuild our workplaces and look towards a post-COVID world, these technologies can help to build a more robust foundation, if we approach challenges with curiosity and ask the right questions.

“One of the things that the pandemic has made clear to us is that the system is really important,” says Bell. “How do we want to think about lockdowns, vaccines and supply chains? How do we think differently about an existing system if we insert AI into it? How do you establish a new branch of engineering to take AI to scale? AI is about working out how to build better systems and fix the ones that we have.”

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Three cutting-edge ways we’re seeing AI in HR 


We tend to fear robots taking over our jobs, but oftentimes, AI is actually helping us to perform our work in far more discreet ways. HRM explores three examples of AI in HR.

In the aftermath of a cataclysmic event, many disaster-hit zones are too dangerous for humans to enter. That’s when AI can step in to help.

Following the Fukushima nuclear disaster in 2011, robotic objects were sent into a meltdown reactor to aid in the rescue effort.

In another cutting-edge example, the US fire patrol is using drones to spot fire patterns and patrol exposed grounds that are vulnerable to fire.

AI has also been utilised in the fight against COVID-19, with robots having been used as additional security in hotel quarantine, or to stack supermarket shelves and thereby minimise human-to-human contact.

While these groundbreaking inventions are delivering solutions to pressing global problems, machine learning is often used in far more subtle, even invisible, ways.

This is a key sentiment expressed by distinguished professor Genevieve Bell of the 3A Institute and School of Cybernetics, who will unpack human capabilities for an AI-enabled world at a masterclass being held next week at AHRI’s Conference Transform 2021. (Registrations close 9 August).

Invisible AI

Some age-old inventions are being repurposed with AI, changing humans’ preconceived ideas about how these systems are expected to behave, says Bell.

Take Canberra’s light rail system, for instance.

Most of us would think the traffic lights turn red, yellow or green as a result of a human pressing a button. But in actuality, it’s the train telling the traffic light when to go red or green.

“There’s a whole lot of AI like that around us that we don’t even notice,” says Bell. “I’m particularly interested in the places where AI is a lot less visible.”

Many HR-related AI systems are also less visible. Predictive algorithms and machine learning are often utilised behind the scenes in the workplace. HRM explores three ways that it’s being harnessed by HR professionals to build more engaged, productive and efficient workplaces.

1. Retaining employees in a scarce market

As leaders are currently experiencing high levels of emotional exhaustion, there’s a risk that they may overlook signs of burnout in their teams – and we know from a litany of research that the rate of burnout has skyrocketed during COVID-19.

AI can help to ensure leaders experiencing emotional exhaustion don’t overlook signs of burnout in their teams.

Nick Bailey, senior vice president Asia Pacific and Japan for WorkForce Software, argues that technology can help to “proactively identify signs of employee burnout, distress and lower job satisfaction… by surfacing insights on attendance, rostering preferences and employee sentiment, so HR teams can make data-informed decisions that improve the employee experience”.

He gives the example of an employee who receives an automatically generated message, which notes that they’ve been working more than their regular amount of overtime shifts in the past week. The text message asks the employee how they’re is coping with these overtime shifts.

Based on the employee’s response, their manager is then able to follow up with appropriate support.

“Time, attendance and rostering data can indicate if an employee is dissatisfied at work – say, if the data shows they’re repeatedly showing up to work late with no previous pattern of tardiness, or if their roster is frequently changed by management less than two days before a shift, or if they’re being asked to work additional unwanted weekends,” says Bailey.

This kind of comprehensive data enables employers to identify signs of dissatisfaction early on, before they snowball into a bigger problem. 

Bailey notes that pairing this information with additional data from HR systems – such as when an employee last received a pay rise or completed a peer review – enables them to develop a detailed understanding of an employee’s overall experience.

In light of the current skills shortage, taking time to implement these strategies and methodically collect data about employee sentiment could be one potential safeguard against losing staff.

“Employers who don’t invest in technology to enhance the employee experience and use every data-informed opportunity to boost job satisfaction simply risk losing more staff – and stoking the flames of national burnout,” says Bailey. “The Great Resignation can stop with better AI.”

2. Personalising learning

It’s well-documented that individuals learn differently. Some of your employees might be visual learners, while others perform at their peak by engaging in hands-on demonstrations. Other employees might rely on reading and writing to improve their recall.

By bringing in an AI system, it’s possible to roll out learning and development programs tailored to each employee’s individual needs.

AI powered training systems can be personalised so  employees are able to engage with content through formats such as a demonstration, gamification or presentation. 

Brooke Jamieson, head of enablement, AI/ML and data at Blackbook,an automation consulting company, says AI is “often thought of as something that ‘finds solutions’ but it can also be used to perceive and understand the systems that lead to an outcome, and help to optimise this process”.

Machine learning can direct employees into different learning categories, thereby “tailoring learning experiences to these groups to enhance completion and progression” and helping “people to reach their learning goals”.

Since AI can also predict how likely users are to complete a course, employers can then identify and provide support to learners who might need extra support, says Jamieson.

3. Finding the right hire

AI is increasingly being used to accelerate and streamline the hiring process.

AI-powered skill testing platforms can assess a candidate’s skills to predict their job performance.

Liam Potter, recruiter at NewyTechPeople, develops machine learning solutions for companies in Newcastle’s technology community to assess candidate fit.

He says that while recruitment will always be about “strong, consultative relationships with hiring managers and candidates alike”, efficiency has become “more important than ever in the wake of candidate shortages”.

“This is where AI shines. It’s able to handle a lot of the sourcing/evaluation grunt-work that would normally require hiring a full-time resource,” says Potter. 

“It’s never going to replace the role of a genuine recruitment expert, and it’s never going to magically solve the scarcity problem a lot of organisations are facing, but it will allow good people to do more with their time.”

Potter’s recruitment process normally follows a similar process:

  • Stage 1: When a candidate applies for a role, NewyTechPeople’s applicant tracking system scans their resume and inputs key elements (i.e. applicable skills, university/degree, contact information) onto the system.

    “It will then assign a rating to the candidate based on the job posting they applied for, and pull the more relevant results to the top of the list (similar to how Google tries to put the ‘best’ result on the first page),” says Potter.

    As HRM has reported before, AI recruiting tools aren’t necessarily free of bias, so it’s important to keep this consideration front of mind when leaning on machine learning for your hiring efforts.

  • Stage 2: The company will run an initial video or in-person interview.

    “We keep this stage intentionally low-tech to ensure the comfort of the candidate.”

  • Stage 3: Subsequent stages of interviews are client-dependent.

    “We encourage more unstructured, conversational interviews. AI can’t help you sell people into your company’s culture and teams, and it can’t substitute meeting a senior figure and being able to ask them anything and everything.”
  • Stage 4: An automated system conducts reference checks.

    “There are some edge cases in which we won’t use this platform (the referee has limited technical literacy, for example),” says Potter, but in 95 per cent of cases, referees are inputting their data into this system.

AI can also be used to keep candidates informed at each stage of the application process, helping to increase the likelihood of turning candidates into employees. 

Creating meaningful work with AI in HR

With AI often used to complete tedious and repetitive tasks that typically fall within an HR department’s gambit, machine learning can enable human employees to dedicate more time towards human-centred work.

This could include higher-order creative problem-solving and everyday decision-making, says Bailey.

“HR teams and managers have more opportunities to connect with and support employees in meaningful ways.

“By surfacing insights on attendance, rostering preferences and employee sentiment, [HR can] assess whether an employee is dissatisfied, overworked, or ready to quit, [and] future-proof their organisation against further churn.”

As we rebuild our workplaces and look towards a post-COVID world, these technologies can help to build a more robust foundation, if we approach challenges with curiosity and ask the right questions.

“One of the things that the pandemic has made clear to us is that the system is really important,” says Bell. “How do we want to think about lockdowns, vaccines and supply chains? How do we think differently about an existing system if we insert AI into it? How do you establish a new branch of engineering to take AI to scale? AI is about working out how to build better systems and fix the ones that we have.”

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