By Barb Hyman
The Social Dilemma tells a story of data gone mad, of it being used to personalise ‘the truth’ so that everyone’s truth is their own. The idea of ‘objective truth’ doesn’t exist anymore, it argues.
The combination of hyper-connectivity at scale that comes from social media, the addictive habits of engaging with it, and the incredible ability to personalise what we see, listen to, and believe, creates a feeling of satisfaction at best (think Spotify) and at its worst, a fractured society.
HR has been on a journey to do the opposite of unchecked social media – to introduce an objective standard of truth. This is especially important given the risks that come from personalised decision making when it comes to hiring. The risk of making hiring decisions based on personal views means we see hiring being influenced by unconscious biases – something that can be easier to identify than fix. ‘Mirror hiring’, and companies that hire for “culture-fit”.
Do you think Kodak and its ilk would have crashed as quickly if they all had a genuinely diverse set of opinions and experiences at their leadership level?
It’s no coincidence that in The Social Dilemma most of the characters sharing their regrets and insights on “How the heck did we get here?” were mostly young white men.
From my own experience of being HRD at a leading digital tech company, engineers were hired based on two data inputs: their coding ability, and their ‘fit’ with the team.
The former is readily tested using objective tools, but the latter is largely tested through having coffee chats with the team.
Is it any wonder then that you end up with more of the same when you use the personal opinions of humans to drive these decisions? People are so scared of data amplifying bias, and humans can be pretty good at it too.
Bias in the recruiting process has been an issue for as long as modern-day hiring practices existed. In order to address some concerns, the idea of “blind applications” became popular a few years ago, with companies simply removing names on applications and thinking that it would remove any gender or racial profiling.
It made a difference, but bias still existed though the schools that people attended, as well as past experience they might have had. Interestingly, these are two things that have now been shown to have no impact on a person’s ability to do a job.
It has to ensure that there is objective truth on every candidate. It has to do this for every new hire, every promotion.
Ironically, it is what social media weaponised – ‘data’ that can only, truly help us achieve this. I talk often about “objective data” – that is data that has been collected without input bias – and it is only this data that helps us disrupt bias that comes from putting humans in the decision in making seat. This objective data is more builds a truly holistic picture about an individual when helping inform hiring decisions, decisions that will shape a company’s culture, and its future.
The data seeks to understand who you are, not the school you went to, or the degree you hold, but how you think and behave and most of all your intrinsic traits. It was Facebook’s homogenous culture that encouraged technical brilliance over ethical thinking that ultimately created the issues discussed in The Social Dilemma. If only they’d used their skills to invest in objective data that set aside its technical bias and hired for humanity, we might not be questioning it in the way we are.
Retail leaders have embraced AI to improve supply chains, automate checkout, and enhance customer experience. But what about finding the people who deliver that customer experience?
AI brings incredible possibilities to supercharge how retailers hire, develop, and retain talent.
At Sapia.ai, we helped iconic retailers like Woolworths, Starbucks, Holland & Barrett, and David Jones reimagine hiring from the ground up – replacing resumes, ghosting, and gut feel with structured, ethical AI that delivers performance and fairness at scale.
The Retail Problem: Volume, Turnover, and Ghosting
Retail is high volume. It’s high churn. And it’s high stakes for candidate experience:
And yet, most hiring still relies on broken tools: resumes, forms, manual processes, and outdated systems.
Sapia.ai: The AI-Native Hiring Engine Built for Retail
Our platform automates the entire “apply to decide” journey, leveraging AI & automation to streamline the hiring process & bring intelligence into retail hiring.
Smart Interviewer™: Mobile-first, chat-based, structured interviews for a holistic candidate assessment.
Live Interview™: AI-driven bulk interview scheduling without calendar chaos.
InterviewAssist™: Instant interview guide generation.
Discover Insights: Embedded analytics to track hiring health in real-time.
Phai: GenAI coach for career and leadership potential.
Unlike resume parsing or generic chatbots, Sapia.ai assesses soft skills, communication, and culture fit using natural language processing and validated psychometrics. It’s ethical AI built in, not bolted on.
From Application to Interview in Under 24 Hours
Candidates don’t want to wait. They don’t want to be ghosted. And they don’t want resumes to define them.
> 80% of Sapia.ai chat interviews are completed in under 24 hours.
We see consistently high completion across categories: grocery, merchandising, home improvement, and luxury retail.
“It was fast, fair, and I actually got feedback. That never happens.” – Retail Candidate Feedback
Real Impact, Across Every Retail Category
Sapia.ai powers hiring for millions of candidates across diverse retail environments:
Impact of Sapia.ai on Retail Hiring in 2024 | |||
Category | Hours Saved | FTEs Saved | Cost Saved |
Grocery | 272k | 131 | $6.5m |
General Merchandise | 193k | 93 | $4.6m |
Specialty Retail | 133k | 64 | $3.2m |
Home Improvements | 103k | 50 | $2.5m |
Merchandising | 22k | 11 | $0.5m |
Luxury | 9k | 4 | $0.2m |
The savings created by intelligent, AI-native automation have unlocked team capacity, impacted retailers’ P&L, and improved store readiness.
Speed That Delivers Real ROI
Every candidate gets interviewed instantly. No waiting. No bias. Just fast, fair, data-backed decisions. This generates real impact for retailers who previously relied on slow, outdated processes to handle thousands of applicants.
DEI by Design, Not by Mandate
With Sapia.ai:
DEI Fairness Scores (based on actual hiring data):
Gender: 1.03 (vs customer baseline of 1.01)
Ethnicity: 1.15 (vs customer baseline of 0.74)
Why? Because ethical AI removes what humans can’t unlearn: bias. With a candidate experience that is inclusive by design, retailers can ensure fairness in screening, and measure it in hiring.
Candidate Experience = Brand Experience
Retail candidates are your customers. And the experience you give them matters. We have built a brand advocacy engine that delights candidates and gives you the data to prove it.
Responsible, Explainable AI Built for Retail
Not all AI is created equally. Since 2018, Sapia.ai has been built on a foundation of responsible AI:
“We can’t go back to life before Sapia.ai. We used to spend half the day reading resumes.”
— Talent Lead, Starbucks AU
What’s at Stake: Time, Brand, and Revenue
Every day spent using outdated hiring methods costs retailers:
With Sapia.ai, you get the productivity unlock retail hiring demands, and the intelligence your talent deserves.
Want to see how fast, fair, and human retail hiring can be?
We can’t hide from reality anymore. Talent needs are shifting overnight, and AI is redefining what it means to work. Traditional talent frameworks are no longer fit for purpose. At Sapia.ai, we believe the future of talent strategy lies in a smarter, fairer, and more adaptive way of defining what great looks like.
Our AI hiring platform is built on the largest proprietary dataset of interview answers globally – we’re a data company at heart, and we’ve seen the power of data-driven people methodology in transforming how organisations hire and retain good talent.
So, when it came to building a new Competency Framework that could be leveraged globally for hiring for any role at any scale, of course, we used a ground-up, data-led methodology that bridges the gap between organisational psychology and AI.
Conventional frameworks are typically crafted through expert interviews and focus groups. While valuable, they tend to be subjective, static, and too slow to keep pace with evolving job demands. As roles become more fluid and technology augments or replaces task-based skills, organisations need a new way to understand the human capabilities that genuinely matter for performance.
We wanted to identify enduring, job-agnostic competencies that reflect what drives success in a modern workplace – capabilities like adaptability, resilience, learning agility, and customer orientation.
(Why competencies and not just skills? Read why here.)
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
The answer to both: yes.
We began with a rich dataset of over 37,000 job descriptions across industries and role types. Using large language models (LLMs) and advanced NLP techniques, we extracted over 200,000 behavioural descriptors. These were distilled down through a four-step process:
This resulted in a refined list of 25 human-centric competencies, each with clear behavioural indicators and practical relevance across a wide range of roles.
Our framework is intelligent, but importantly, it’s adaptive. Organisations can apply this methodology to their own job descriptions to discover custom competencies. This bottom-up, role-data-led approach ensures alignment to real work, not just theoretical models.
And because the framework integrates directly with our AI-powered hiring tools, you get a connected system that brings your talent strategy to life.
Our framework comes to life in the following tools:
Skills alone cannot predict success. Competencies do. As AI continues transforming how we work, Sapia.ai’s Competency Framework offers a scalable, scientific, and fair foundation for hiring and developing the talent of tomorrow.
If you’re a CHRO or Head of Recruitment at an enterprise today, chances are you’ve been inundated with messages about the importance of “skills-based hiring.” LinkedIn’s recent Work Change Report (2025) is full of compelling data: a 140% increase in the rate at which professionals are adding new skills to their profiles since 2022, and a projection that by 2030, 70% of the skills used in most jobs today will have changed.
This is essential reading. But there’s a missed opportunity: the singular focus on “skills” fails to acknowledge the real metric that talent leaders need to be using to future-proof their workforce — competencies.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
Put simply, competencies answer the all-important question: Can this person apply the right skills, in the right way, at the right time, to deliver results in our environment?
The Work Change Report outlines a future where job titles are fluid, roles evolve quickly, and AI is a constant disruptor. This creates three massive challenges for hiring at scale:
Skills alone don’t tell us whether someone can succeed in a role that will look different 12 months from now. But competencies can. Because they measure not just what a person knows, but how they apply it.
The LinkedIn report highlights a critical insight: organisations now prioritise agility in entry-level hiring. And there’s a good reason for that. With professionals expected to hold twice as many jobs over their careers compared to 15 years ago, adaptability is not just a nice-to-have. It’s core to success.
But you can’t measure agility with a keyword on a CV. You measure it by looking at competencies like:
When you shift the focus away from skills to behavioural competencies that can be defined, observed, and assessed in structured ways, you open yourself up to a much more dynamic and more useful way of managing talent.
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
LinkedIn’s data shows that people are learning more skills more quickly than ever. But the real question for talent leaders like you is: Are those skills being applied in ways that drive value? Are we hiring for task proficiency or performance?
The truth is that the organisations that will thrive in an AI-driven, skills-fluid economy aren’t the ones chasing the next hot skill. They’re the ones designing systems to identify, develop and scale competence.
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.