Navigating HR tech products as a CHRO
Having been a CHRO of a listed company in my last role, I can empathize with the confusion and exhaustion that comes from navigating the myriad of HR tech products, especially those involving AI in HR tech, flooding the market while trying to manage many ongoing HR change initiatives.
Last year, as CEO of an HR tech start-up, a key player among companies using AI in HR, I did what most do in that role — I spent a whole lot of time talking to customers, CHROs, heads of talent, recruiters, and business owners, listening to their challenges to build a product that effectively integrates AI use cases in HR. There are a few themes I picked up on through these conversations.
- Improving procedural fairness — ensuring the most important processes (recruitment, promotion and REM ) is fair in both perception & application
- Solutions which deliver integrity of both recruitment and promotion decisions (as more companies aim to promote from within)
- A strong desire to leverage automation for low value, manual and/or repetitive HR tasks
- Make everything simpler for the HR team and employees
- More insights to make better, fairer decisions
- The challenges of knowing what data matters and building the internal capability to make use of it
What is the ‘right’ tech stack?
‘What’s the right tech stack for my team and our company?’ and ‘how do I integrate all these technologies?’ are questions every CHRO of any sizeable company is grappling with. And the answer is more complicated than committing to a new HRIS.
Whilst I am not a tech expert, I spend many hours a week thinking about one critical part of the HR function that is ripe for technology innovation — recruitment. In that vein, I am sharing some things I have learnt which I hope will be useful to your investments in your tech stack in 2019.
1. Decision-making transparency to enhance organisational trust
There are HR tech products that give you insights on engagement hot spots, employee sentiment, and screen applicants for roles by scraping and analysing people’s personal profiles or communications. If you believe (as I do) that transparency enhances trust, especially when it comes to anything coming out of HR, these tech products could undermine organisational trust and maybe even your employer brand. Look beneath the hood of a tech product to validate how it works. AI and the concern of algorithmic bias is one every CHRO needs to be ready to talk about. Understand the source data and how it will be used in the solution. For candidate selection, any front end testing needs to not only be valid but feel valid to the user. That’s why we use relatable and valid questions to assess candidates in building our predictive models. No CVs, no video and no games.
Any extra discretionary effort by employees is going to be heavily influenced by how much trust your people have in you. Better to invest in tech solutions that allow for more transparency around how decisions are being made, that use reliable, objective and valid data.
2. Create and source forward-looking data for prediction
Think of the people analytics generated by HR today — turnover reports, engagements stats, culture diagnostics, exit survey analysis, 9 box talent management. All of it is backward-looking reporting on the past performance of talent. Much of it also subject to the vagaries of human analysis, therefore biased insights. How many of your organisations use data to validate the placement of people on the ‘potential axis’ of a 9 box? Or use NLP to extrapolate the key themes from engagement surveys and exit survey verbatim?
A bigger challenge for all of this backwards analytics is connecting the dots — how does a culture survey actually move you towards and predict a different culture? My colleague who spent his early years building up the data science team for a leading engagement survey platform and led the benchmarking analysis for their clients observed that year after year the same companies were in the top and the bottom quartile of engagement.
Changing culture is hard unless you change the people — the people you hire and the people you promote.
Predictions versus Psychometrics
The best investment you can make to change the culture and help the organisation move towards forward-looking predictive analytics is to start to capture data from the outset — from your applicant pool, through to the people you hire.
Having a data DNA profile of your applicant and hired pool means you can better target your employer branding, you can identify with high accuracy the profile of the stronger performers, the people who are high flight risk in the early months, the talent that moves fastest to productivity. Knowing these profiles means you can seamlessly feedback into your recruitment a better hiring profile.
This is the power of predictive analytics over psychometric testing which has no feedback loop back to the business on whether the person with the high OPQ test was any good in the role.
3. Data authenticity matters
‘Garbage in garbage out. This is usually a reference to a data quality issue.
Data can take many forms- it’s not always hard numbers (more on that later), it can be data that is structured and regulated by you vs data that is unstructured and not regulated by you, such as CV’s. The former is always better — closer to the objective source of truth, usually owned by you, and less prone to gaming.
CVs are a source of bias
CVs are a poor man’s data substitute and rarely indicative of anything. A CV is a highly gameable type of data and relying on CV data to select talent exacerbates the risk of bias, as was experienced by Amazon when they built their hiring models around a 10-year database of CVs (mostly male).
I won’t spend time on the risks of bias in CV screening as enough has been written about that, other than to share this from a blog post which quotes academic research that ‘both men and women think men are more competent and hirable than women, even when they have identical qualifications ‘, and that ‘resumes with white-sounding names received 50% more calls for interviews than identical resumes with ethnic-sounding names’. https://www.lever.co/blog/where-unconscious-bias-creeps-into-the-recruitment-process.
Removing bias in the screening process is no longer about social justice, now it’s about commercial outcomes — McKinsey has documented each year since 2014 that companies with top quartile diversity experience outsized profitability growth https://www.mckinsey.com/business-functions/organization/our-insights/delivering-through-diversity
4. Think broadly about ‘what is data’
There are a plethora of surveys that make the point that HR functions are starting to invest in the power of people analytics.
Making data more visual has been a big driver behind the success of engagement analytics companies such as a Culture Amp, Glint and Peakon, transforming ugly engagement decks and the traditional circumplexes into insights-driven real-time dashboards. Visualisation offered by tools like Tableau is table stakes these days for HR.
Data doesn’t always look like data in a traditional sense. Take textual search data, human behavioural tracking data for example. Google has been making money off that data strategy for years and there are now books written about how google search terms are the most accurate mirror to our true beliefs and values (Read Everybody Lies for a fascinating insight into the power of text).
Tracking human behaviour has been mainstream in marketing teams for years, but has been slower to be leveraged in HR. In consumer marketing, no one cares why a person is more likely to buy an item, they are only interested in optimising for the outcome. There has been some interesting research applying consumer behaviour analysis to HR with fascinating insights, for example, that your choice of browser in completing an online assessment is a strong predictor of your performance in the role.
5. Provide data-driven actionable insights for the business
In consulting there is an often-used accusation of consultants ‘boiling the ocean’, which usually refers to those 100-page decks with chart after chart, visualising every data point possible as if the sheer weight of the deck is somehow testament to its accuracy.
Most junior consultants aspire to write the ‘killer slide’, the elusive one slide that crystallises the strategy in one data visual that will transform the company’s trajectory.
As HR teams start to produce more output on people analytics, there is a risk of ‘boiling the ocean’ on people analytics — quarterly engagement surveys, monthly churn data, diversity reporting. Figuring out the ‘so what’ of the data and using those insights to move the needle on business metrics that matter is harder, but also necessary. For HR integrating non-HR owned data is also important to get a fuller picture, especially for sales led businesses. For example, if sales drop off at the 2-year mark, what can HR do about that? What HR processes change as a result of seeing high correlations between sales trajectory in the first 6 weeks and tenure greater than 6 months.
In building predictive models for our clients, this is a sample of some of the actionable insights that were revealed through analysing both HR and sales performance data:
- We found that female sales recruiters had a faster performance trajectory, but with a noticeable slow down at 2 years — this led the client to change their targets at recruitment to hire more women and embed new development programs for women at the critical 2-year mark
- We observed a strong statistical correlation between outbound inside sales performance in the first 6 weeks and retention beyond 6 months, which led to changes in their onboarding but also more stringent probation processes earlier on to avoid holding onto people for too long
- We identified better team leaders based on the relative performance of new starters by team leaders, where performance is defined as speed to sales productivity and retention at the 1-year mark
- We reduced the complexity and time invested in KPI tracking for both recruiters and the operations team through knowing which KPIs truly mattered as lead indicators of performance
6. Think creatively about what business data can help in HR
HR’s role is very much one of building bridges across the organisation — taking a helicopter view of talent, ensuring that the needs of the business will be met in 3 years, 5 years by the people in the business, in enabling communication and collaboration channels across teams and geographies.
Building a single source of truth about their employee base often justifies HR’s biggest tech investment in helping achieve those objectives — the so called ‘one size fits all’ HR system. Yet it’s a big step to assume that even with the HRIS in place that HR has all the data it needs to do its job. Every function is making similar investments — sales & marketing into CRMs, operations teams into rostering systems, LTI and OHS data that might sit in the BU or a separate OHS team.
Last century, HRs accountability might have ended when they filled a role. Today, HR is accountable for ‘talent optimisation’ and that means ensuring people’s success through their career with the organisation, and often even beyond. Knowing how that talent is performing on the job– roster adherence, injury patterns, call centre metrics, sales performance — are integral to optimising that talent pool.
Capitalise on these various streams of data!
I encourage HR leaders to be expansive about what is performance data, especially objective performance data, and being relentless in sourcing that data from their non-HR colleagues internally.
7. HR as a data generator for the business
Data generated within HR can help drive broader organisation decisions. B2C companies with large volumes of sales and marketing applicants can leverage the power of those volumes for the benefit of the rest of the business.
Big brand companies can receive half a million-plus applications in one year, often engaging meaningfully with just a fraction. Technology allows you to test and engage meaningfully with every one of those applicants. Instead of thinking of that pool only as a candidate pool relevant to recruitment, for a B2C business, that pool is most likely also your consumer base and a rich source of data for your business.
Customer acquisition cost (CAC) for product and services like travel, retail, software, financial products range from $7 to $400, with companies committing substantial advertising budgets to reach that kind of audience, yet over in recruitment, they are engaging with them for free, at a point where the candidate/consumer is at their most willing and motivated to engage with you.
Imagine what consumer data you could capture from that applicant pool for the benefit of the business?
Transparency and authenticity, forward-looking predictive data, business impact first, think creatively and broadly, and HR as a data generator. These are 7 themes that can transform your organisation in by leveraging the data hidden within HR through the efficient use of technology.
You can try out Sapia’s Chat Interview right now, or leave us your details to book 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: