Big decisions on ‘gut feel’
It scares me sometimes when I think about the big decisions I’ve made on gut feel and will probably continue to make relying on my instincts.
Personally, I would love to be armed with meaningful data and insights whenever I make important life decisions. Such as what’s the maximum price I should pay for that house on the weekend, who to partner with, who to work for, and who to hire into my team. Data that helped me see a bigger picture or another perspective would be very valuable. For most of those decisions there is so much information asymmetry which makes it feel even riskier. For sure I could check out glassdoor when choosing my next job but it comes with huge sample bias and not much science behind it.
So why is there still an (almost) universal blind acceptance that these decisions are best entrusted to gut feel? Especially given the facts show we are pretty crap at making good ‘gut’ based decisions.
Dialling down subjectivity
I’m one of those people that believe in the power of AI — to remove that asymmetry, to dial down the bias, to empower me with data to make smarter!
At a recent HR conference, a quick pulse around the room confirmed there is high curiosity and appetite to understand AI. What we’re missing is the clarity about the opportunities and what success looks like from using it. The concern about how to navigate the change management exercise that comes with introducing data and technology into a previously entirely human-driven process is daunting.
The best human resources AI is not about taking the human out of hiring and culture decisions. Far from it. It’s about providing meaningful data to help us make better decisions faster.
Having worked in the ‘People and Culture’ space for a while, I know building trust in how the organisation makes decisions — especially people decisions — is hard in the absence of data. Yet we all know that transparency builds trust. So how can you build that trust through transparency when the decision-maker is a human — and the humans make decisions in closed rooms and private discussions.
Human decision making is the ultimate black box.
Remember that feeling when the recruiters call up and say you weren’t a good fit — who feels great about that call? A total black box cop-out response!
It doesn’t have to be this way, and the faster we can get to better decision making the better. Seven months ago, I joined a team of data scientists who had spent the prior three years building technology that relies on AI to work its magic and equip recruiters with meaningful and actionable insights when hiring.
I’m no data scientist. I have had to learn the ins and outs of our AI pretty fast. And because our technology is at work in the people space, I’m learning how to ensure the AI is safe, fair and our customers trust it and us to do the right thing with it.
If we reduce it to its core process, a machine learning algorithm is trying to improve the performance of an outcome based on the input data it receives. In some instances, such as in deep learning algorithms, it’s trying to simulate the functioning of the human brain’s neural networks, to figure out the patterns between the data inputs and data outputs.
A machine is infinitely more obliging and easier to understand than a human in responding to the direction.
Because it has no feelings, it’s going to be free of the biases humans bring to these critical decisions. Plus machines are more malleable to learning and way faster at it. This is more critical these days when roles are changing dynamically and swiftly as industries are disrupted.
Our team plays in predictive analytics for recruitment space. What this means is our AI seeks out the lead indicators of job success: the correlating factors between values, personality and job performance. We all intuitively know that behaviours drive leading indicators. But we struggle to assess for those consistently well.
Our job is to augment your intelligence and ability to make the right decision. By knowing how people treat others, what drives them, and their values, you become better informed about the real DNA of a person and how they might function in your team.
All of our customers are looking for a slightly different kind of worker and use AI to help.
A powerful motivator to use AI is to build confidence and trust in the process from both candidates and people leaders by dialling down the human element (getting rid of the bias) and revealing the patterns for success. Less room for bias = more fairness for candidates = more diverse hiring. Key to this is we don’t look at any personal information — the machine doesn’t know or care about your age, gender, colour or educational background.
For our customers having this data is empowering and helps them make smart decisions. For all the people who are affected by those decisions, they can feel relieved that they were considered on their merits, not based on someone’s gut feel.
There is no perfect method for sure to make the right decision.
But if I have to choose between trusting biased humans and (a sometimes) biased machine they create, I know which one I would trust more. At least with a machine, you can actually test for the bias, remove it, and re-train it.
Suggested reading:
https://sapia.ai/blog/hr-job-manage-risk/
Related
Blog
Mirrored diversity: why retail teams should look like their customers
Walk into any store this festive season and you’ll see it instantly. The lights, the displays, the products are all crafted to draw people in. Retailers spend millions on campaigns to bring customers through the door.
But the real moment of truth isn’t the emotional TV ad, or the shimmering window display. It’s the human standing behind the counter. That person is the brand.
The missing link in retail hiring
Most retailers know this, yet their hiring processes tell a different story. Candidates are often screened by rigid CV reviews or psychometric tests that force them into boxes. Neurodiverse candidates, career changers, and people from different cultural or educational backgrounds are often the ones who fall through the cracks.
And yet, these are the very people who may best understand your customers. If your store colleagues don’t reflect the diversity of the communities you serve, you create distance where there should be connection. You lose loyalty. You lose growth.
We call this gap the diversity mirror.
What mirrored diversity looks like
When retailers achieve mirrored diversity, their teams look like their customers:
- A grocery store team that reflects the cultural mix of its neighbourhood.
- A fashion store with colleagues who understand both style and accessibility.
- A beauty retailer whose teams reflect every skin tone, gender, and background that walks through the door.
Customers buy where they feel seen – making this a commercial imperative.
How to recruit seasonal employees with mirrored diversity
The challenge for HR leaders is that most hiring systems are biased by design. CVs privilege pedigree over potential. Multiple-choice tests reduce people to stereotypes. And rushed festive hiring campaigns only compound the problem.
That’s where Sapia.ai changes the equation: Every candidate is interviewed automatically, fairly, and in their own words.
- Bias is measured and monitored using Sapia.ai’s FAIR™ framework.
- Outcomes are validated at scale: 7+ million candidates, 52 countries, average candidate satisfaction 9.2/10.
- Diversity can be measured: with the Diversity Dashboard, you can track DEI capture rates, candidate engagement, and diversity hiring outcomes across every stage of the funnel.
With the right HR hiring tools, mirrored diversity becomes a data point you can track, prove, and deliver on. It’s no longer just a slogan.
Retail recruiting strategies in action: the David Jones example
David Jones, Australia’s premium department store, put this into practice:
- 40,000 festive applicants screened automatically
- 80% of final hires recommended by Sapia.ai
- Recruiters freed up 4,000 hours in screening time
- Candidate experience rated 9.1/10
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
Recruiting ideas for retail leaders this festive season
As you prepare for festive hiring in the UK and Europe, ask yourself:
- How much will you spend on marketing this Christmas?
- And how much will you invest in ensuring the colleagues who deliver that brand promise reflect the people you want in your stores?
Because when your colleagues mirror your customers, you achieve growth, and by design, you’ll achieve inclusion.
See how Sapia.ai can help you achieve mirrored diversity this festive season. Book a demo with our team here.
FAQs on retail recruitment and mirrored diversity
What is mirrored diversity in retail?
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Why is diversity important in seasonal retail hiring?
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
How can retailers improve their hiring strategies?
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
Blog
The Diversity Dashboard: Proving your DEI strategy is working
Why measuring diversity matters
Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.
Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.
Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.
What is the Diversity Dashboard?
The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.
Key features include:
- Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
- Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
- Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
- Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
- Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.
From insight to action
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
- See if your EASL candidates are applying in high numbers but not progressing to live interview.
- Spot if candidates with a disability report high satisfaction but have lower offer rates.
- Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.
Built on science, backed by trust
Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.
Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.
Book a demo to see the Diversity Dashboard in action.
Blog
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.