I spent 13 years working as an agency recruitment consultant but my customer-facing jobs started a lot sooner – at the age of 12, collecting monthly charity raffle contributions for the local hospital. Paper rounds and retail jobs through school were followed by contact centres and bar work at uni, where I first learned about recruitment. It just seemed to fit with my previous experiences as well as my mindset so I figured that’s what I’d do when I graduated.
Actually, that’s a lie. It’s what I decided to do once I’d graduated and decided I hated the idea of being an employee number within a grad scheme but knew it was about time to lock in a career.
I remember my first round of recruitment interviews – I just couldn’t understand why recruiters didn’t understand that when I said “this is what I want to do” i really meant it. I explained I’d done my research. I knew that if I worked harder, longer and smarter than my competitors I would find the best candidates, I’d place them and I’d be rewarded for doing my job
But I just couldn’t get past those infernal recruitment industry group balloon debates/assessment days of the early 2000s that principally involved a white male in his early 20s talking more loudly than the rest despite not really having any substance to his bellowing. I couldn’t understand why Timmy from Surrey’s slightly shouty, verging on passive-aggressive bullying tone always got him progressed to the next stage while the more insightful, reflective comments from others around the table went unnoticed?
I persevered nonetheless and I eventually joined a recruitment process that involved one-on-one interviews followed by a group presentation from the MD. No fake debates, no pitting people against each other – just truth and honesty from the company owner.
I called my recruiter as I walked out the door to tell him I really wanted to work there. And I did, for 8 years.
Now I wonder how much more quickly I could have found a job if those balloon debating sessions had instead been replaced by a tool that helped the recruiters understand my propensity to succeed within recruitment, leveraging my personality and behaviours, my competitive nature, my desire and drive to succeed and then the recruiters combined that with my demonstrable passion for technology…
I’m pretty confident I articulated them during my interviews and backed up my answers with my life experience (at the ripe age of 22!). Alongside my early start in the world of work, I was in the first team for all sports for my entirety of senior school (I even gave Fives ago but it really wasn’t for me). I started played the piano at 4, violin at 7 and self-taught the saxophone as a teenager. I’m a classical pianist (seeing as you didn’t really ask, Shostakovich’s 2nd piano concerto with the school orchestra was my proudest musical moment) and finally I graduated with a 2:1 from a Redbrick University.
An outstanding childhood? No, I don’t think so. But I know I was well above the average for a candidate applying for a graduate recruitment career. I know there was enough about my school and working history to show my commitment to learning, dedication to working hard individually and collectively and displaying a consistent understanding of work = reward. And until those recruitment interviews I had a 100% interview to job-offer ratio. And so I wonder, how many of those companies said “no” to me because they weren’t aware of their biases?
And look, I get it. There were no AI crystal balls back then. Recruiters had to make judgement calls on candidates without the benefit of technology tools to guide them towards the right talent. But I wonder how many of those money-hungry agencies would have paid more attention to candidates like me if a recruitment tool had helped them look beyond their biases and told them I was an applicant worthy of closer attention?
My guess is pretty much all of them.
Barb Hyman, CEO & Founder, Sapia.ai
Every CHRO I speak to wants clarity on skills:
What skills do we have today?
What skills do we need tomorrow?
How do we close the gap?
The skills-based organisation has become HR’s holy grail. But not all skills data is created equal. The way you capture it has ethical consequences.
Some vendors mine employees’ “digital exhaust” by scanning emails, CRM activity, project tickets and Slack messages to guess what skills someone has.
It is broad and fast, but fairness is a real concern.
The alternative is to measure skills directly. Structured, science-backed conversations reveal behaviours, competencies and potential. This data is transparent, explainable and given with consent.
It takes longer to build, but it is grounded in reality.
Surveillance and trust: Do your people know their digital trails are being mined? What happens when they find out?
Bias: Who writes more Slack updates, introverts or extroverts? Who logs more Jira tickets, engineers or managers? Behaviour is not the same as skills.
Explainability: If an algorithm says, “You are good at negotiation” because you sent lots of emails, how can you validate that?
Agency: If a system builds a skills profile without consent, do employees have control over their own career data?
Skills define careers. They shape mobility, pay and opportunity. That makes how you measure them an ethical choice as well as a technical one.
At Sapia.ai, we have shown that structured, untimed, conversational AI interviews restore dignity in hiring and skills measurement. Over 8 million interviews across 50+ languages prove that candidates prefer transparent and fair processes that let them share who they are, in their own words.
Skills measurement is about trust, fairness and people’s futures.
When evaluating skills solutions, ask:
Is this system measuring real skills, or only inferring them from proxies?
Would I be comfortable if employees knew exactly how their skills profile was created?
Does this process give people agency over their data, or take it away?
The choice is between skills data that is guessed from digital traces and skills data that is earned through evidence, reflection and dialogue.
If you want trust in your people decisions, choose measurement over inference.
To see how candidates really feel about ethical skills measurement, check out our latest research report: Humanising Hiring, the largest scale analysis of candidate experience of AI interviews – ever.
What is the most ethical way to measure skills?
The most ethical method is to use structured, science-backed conversations that assess behaviours, competencies and potential with consent and transparency.
Why is skills inference problematic?
Skills inference relies on digital traces such as emails or Slack activity, which can introduce bias, raise privacy concerns and reduce employee trust.
How does ethical AI help with skills measurement?
Ethical AI, such as structured conversational interviews, ensures fairness by using consistent data, removing demographic bias and giving every candidate or employee a voice.
What should HR leaders look for in a skills platform?
Look for transparency, explainability, inclusivity and evidence that the platform measures skills directly rather than guessing from digital behaviour.
How does Sapia.ai support ethical skills measurement?
Sapia.ai uses structured, untimed chat interviews in over 50 languages. Every candidate receives
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.
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.
When retailers achieve mirrored diversity, their teams look like their customers:
Customers buy where they feel seen – making this a commercial imperative.
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.
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.
David Jones, Australia’s premium department store, put this into practice:
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
As you prepare for festive hiring in the UK and Europe, ask yourself:
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.
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
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.
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:
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
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.
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.