Whether it’s a revolution or evolution, technology touches our lives in ways that few could even imagine just a few years ago. There are few, if any, industries that have not been transformed by the digital age.
With the rise of new technologies and automation, there were some that feared the robot apocalypse was upon us. Who needs actual people when machines, algorithms, software and programs can do the job faster, smarter and more cost-effectively?
In the recruitment and HR industries, as job boards, social networking and websites like LinkedIn began their relentless rise, many believed that the human touch was about to stripped from an industry that only exists for human capital.
Of course, the very opposite is true. While there’s no denying some roles and industries have changed forever, advancing technology means new industries are emerging, new roles are being created and new skills are being sought by candidates and employers alike.
In the hiring industry, recruitment software is here to stay. This article explains how it can be working hard for you and providing a seamless and rewarding experience for your team, your clients and for every candidate.
The technology landscape in recruitment and HR
In this article we’re specifically looking at recruitment software and exploring its role and impact in the hiring process. Before we do, it’s important to understand its context and connection to the technology that can support the complete employee lifecycle. This extends from talent acquisition right through to the ongoing management and development of people through HR teams. The recruitment strategy and candidate management technology pipeline covers:
- Customer Relationship Management (CRM) – tools use to manage and analyse interactions with past, current and potential candidates. It enables a proactive and ‘always on’ hiring strategy, by tagging, categorising and growing deep talent pools.
- Applicant Tracking System (ATS) – an online platform that simplifies and streamlines the entire recruitment process from sourcing to selection. We’ll explore ATS in this article.
- Vendor Management System (VMS) – tools used by specialist recruiters and companies to manage the end-end process of contingent labour – freelancers, contractors, sub-contractors, statements of work (SOW)
- Human Resources Information Systems (HRIS) – the core people system underpinning an organisation’s HR department with capabilities that may cover: onboarding, human capital, leave management, learning, recruitment, performance management and feedback, payroll and benefits.
- Learning Management System (LMS) – Learning and development is critical for organisations to evolve workforce skills and knowledge to meet skills gaps and evolving technologies. These tools administer, track, report and deliver
e-learning, training and development programs
So what is recruitment software?
Recruiting software is not one thing. It’s an umbrella term for different tools that address different stages of the recruitment process. From creating job requisitions and conducting candidate screening to scheduling interviews and even sending out job offers, recruitment software can automate every step of the hiring process.
Generally, most types of recruitment software can be categorised into four categories and will address some or all of these key functions of the recruitment process:
- Sourcing – this is all about identifying, finding and attracting potential candidates.
- Engagement – how recruiters, candidates and organsations stay connected throughout the hiring process. It’s about nurturing relationships.
- Selection – a range of tools and technologies to help accelerate the process of identifying candidates with the best or appropriate qualifications from the pool of applicants
- Hiring – software tools that can be used to manage and coordinate the practicalities and processes of hiring and even onboarding candidates
Just as there are many types of recruitment agencies, there are many types of recruiting software and every solution will look different. Some recruitment agencies may also manage work placements so their recruiting software technology stack may include a Vendor management System as described above.
An agency placing technology professionals into permanent positions will have very different sourcing, database and engagement needs than an agency working to high volume briefs for customer-facing service roles. An agency retained for search and recruitment at the highest executive levels will have different needs again.
Why do you need recruitment software?
It’s rare to find anyone in the recruitment business that hasn’t begun to automate at least some of the hiring process. Job seekers – and not just those tech-savvy millennials – have been quick to embrace and engage with mobile apps, social media, job boards and more to find their next job. However, there are still many organisations relying on outdated and labour-intensive recruitment methods.
Recruiting software has been developed and continues to evolve to address the universal challenges and experiences of recruiters the world over:
- Time – Filling any role could take weeks or months, much of it spent reviewing and screening CVs and candidates, communicating with candidates and hirers, scheduling and coordinating interviews, making job offers and more. Recruiting software can help automate these processes and give you back your time to concentrate on higher value tasks.
- Costs – As much an issue for recruiters as it is for the businesses engaging them, traditional manual and paper-based processes can be expensive. Recruiting software can dramatically lower costs to hire, repaying technology investment quickly.
- Hard work – Any recruiter will tell you there are simply not enough hours in the day (or dollars in the budget) to stick with traditional labour-intensive, manual processes, especially in high volume recruitment briefs. Is there really anyone who will manually read and screen the CVs of 5,000 applicants?
- Free the people – For most hiring and talent acquisition teams, human resources are stretched thin already. Recruiting software can give your people more time to deliver a positive experience for every candidate.
- Skills shortages – In the history of work, there has always been and always will be industries and times when it’s challenging to find appropriately skilled candidates. Recruiting software can help recruiters find more candidates, with the right skills to fill roles sooner.
What does recruitment software do?
The hiring process has many steps. From promoting job opportunities to screening CVs, tracking candidates to making job offers, there are many pressure points. The process can be costly and time-consuming and if things go sideways, you’re not just burning hours and dollars, you could be burning candidates too by making poor hiring decisions.
Automated recruiting software can do all the heavy lifting for you. It organises all the tools and all the data in one place to provide end-to-end functionality through the complete recruitment process. Simplifying and enhancing that process ensures a better experience for everyone – recruiters, hirers, HR departments… and job candidates alike.
By streamlining process, recruiting software can help you significantly reduce hiring costs and fill roles faster.
What are the benefits of recruitment software?
Here’s how your organisation can benefit from recruitment software:
- Automation – It’s not about software doing your job, it’s about software helping you to do your job better. From managing client databases to scheduling interviews, automation of many daily tasks can give you back time to focus on higher value briefs and enhancing the candidate and client experience.
- Hire better, faster – Through the one platform, recruiting software can help improve hiring metrics including time to hire, cost per hire, employee retention, turnover rate and quality of hire. An easy, pain-free application process means more candidates are likely to apply for your jobs.
- Finding, attracting and cultivating candidates – Candidates are the fuel in the engine of the recruitment business. Most recruiting software solutions will include capabilities to implement a CRM system to build deep talent tools of ‘ready to go’ prospective candidates.
A CRM platform may stand alone or integrate with an Applicant Tracking System (ATS) that can streamline the entire recruitment process. At its simplest, an ATS is a data-driven system that eliminates the paper chase of traditional recruiting. There are fewer opportunities for data-entry errors and as data is digitised and can even be stored in the cloud, say goodbye to physical files and unwieldy paper charts.
Every ATS is different, but most will include integration with online job boards, careers pages and resumé databases, automated hiring workflows, communication capabilities and reporting tools.
- Evaluating candidates – As reading and screening CVs and job applications can be the most time-consuming part of any recruiter’s job, this is one of the areas that many recruiters prioritise for automation. Driven by artificial intelligence and powered by machine learning, Ai recruitment tools are used for 3 key functions in the hiring process: sourcing, screening and interviewing of candidates.
Ai tools can use text, voice and even video to automate part or all of the evaluation and interviewing process. Making the recruitment process up to 90% faster, it’s especially useful for high volume recruitment briefs such as frontline retail or customer service roles.
- Support diversity goals – Ai-enabled interviewing and screening can help reduce the effects of unconscious bias – the inherently human prejudices, personal preferences, beliefs and world-views that shape our assessment of others. Our biases can easily have a negative impact on candidates and mean you’re potentially missing out on the best candidates for the job. It can also mean employers are missing the opportunity to cultivate workplace diversity and all the benefits it delivers.
PredictiveHire’s Ai recruiting tool is a text-based, mobile-first interview offering blind-screening at its best, with no gender, age or ethnicity revealed. Candidates rate the experience highly and appreciate personal feedback and coaching tips.
- Reporting and insight – Most recruitment software solutions will provide easy, extensive and accurate reporting so you can gain valuable insights into candidates, trends, costs of recruiting and more.
Good reporting and centralised information can also enhance communications and collaboration between all stakeholders in the hiring process – candidates, recruiters, hiring teams and employers.
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Ready to continue your exploration of recruitment software and the benefits it can bring to your business? Find out more about PredictiveHire’s Ai-powered recruitment tool and how we can support your recruitment needs today.
You can try out PredictiveHire’s FirstInterview right now, or leave us your details to get a personalised demo
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Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: