“People are not your most important asset. The right people are.” – Jim Collins, author and lecturer on company sustainability and growth.
“Are the right people in the right roles?” [This is] the single most important factor for leadership success and for organisational success.” – Gail Kelly, former CEO of Westpac
Adam Bryant who pens the terrific weekly column, The Corner Office, for the NYT has interviewed a diverse pool of leaders, and a common theme from 99 % of his interviews with CEOs is that success correlates with hiring the best team.
My former boss Tracey Fellows, CEO of the REA Group, was also fond of saying that it is ‘people’ that keeps her up at night more than any other business challenge.
Most hiring in most organisations relies 100 % on people to make those most important decisions. Yet we do so with little objective data. Instead, we have layers upon layers of bias! And to give you an idea of how many there are, here is a whooping full Wikipedia list of cognitive biases for you to check out. This article lays out in great detail a plethora of cloudy, smeary and hazy biases I didn’t know could exist.
It concludes that they are mostly unalterable and fixed, regardless of how much unconscious bias training you attend in your lifetime.
Read: Why a Lack of Diversity is Costing Your Business
In some functions/ departments, your “Hiring for Diversity” may be going very well. However, diversity training and hiring isn’t repeatable, where humans are involved. And, if humans could be trained out of their biases, we may get more diversity in our new hires. But then, do we know that we are getting the ‘better’ hire from the applicant pool? How CAN you tell if you have no method of reliably testing for what matters for success?
You might say we rely on CVs to give us that ‘insight’ but did you know CVs are usually crafted, designed, worded and reworded to ‘best-light’ the applicant. Ever appointed an Excel whizz, who on hire doesn’t know a pivot from a concatenate? Or even worse, who cannot apply logic, reasoning and critical thought?
Because biases appear to be so hardwired and inalterable, it is more straightforward to remove bias from algorithms than from people.
This gives AI the potential to create a future where important insights underpinning decisions such as hiring, are made more fairly.
Read: Why Machines make better decisions than humans (oh and why I hate Simon Sinek)
Algorithmic bias is not the elephant in the room. Some argue that algorithms themselves have bias. The reality is that machine learning, by its very definition, is aiming to find patterns in large volumes of data, mostly latent, to support decisive actions. Removing bias is driven by what bits of training data you use to feed the machine.
If you build models from the profile of your talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self-reinforcing hiring.
If you are using data which looks at age, gender, ethnicity and all those visible markers of bias, then, sure enough, you will amplify that bias in your machine learning. Relying on internal performance data to make people decisions, that is like layering bias-upon-bias. Similar to building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.
So instead of lumping all AI and ML into one big bucket of ‘bias’, look beneath the surface to understand what’s going into the machine as that is where amplification risks loom large.
Unlike trying to solve human bias, machine learning is repeatable, stable, consistent and most importantly, testable. The value to the organisation is of course, immense.
Now that is ticking all the right boxes. It makes the possibility of objective and valid decisions available at scale, a probability.
The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.
Soon (maybe already) you will be putting yours, and your loved ones live in the hands of algorithms when you ride in that self-driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data, even in hiring.
You can try out Sapia’s Chat Interview right now – HERE. Else leave us your details to get a personalised demo
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.