It’s a cliché, but nonetheless true, that as time passes all processes become dated.
Some might need to be thrown out completely. Many more need to be adjusted and refined to keep up as workplaces and ways of working change.
I’m not old enough to remember the recruitment days of Rolodex and faxed documents. But I’ve heard the stories. Paper mountains of resumes teetering on desks. Consultants queuing at the one office fax machine to send their applicants’ profiles to clients.
Who knew that today we’d be communicating almost instantly by email, on our own computers, or sifting through resumes using Applicant Tracking Systems? In the 1980s that would have sounded like something from Doctor Who.
Since then, it’s all slowed down a bit.
Sure, ATSs take a lot of the legwork out of choosing who to interview. But they’ve also led to Resume Optimisation tools to help applicants beat our filters.
How can we avoid picking only the people who are best at gaming the system? How do we know we’re not missing our perfect applicants?
Now AI is taking the hiring process another leap forward. It’s speeding up the more process-driven elements and helping us select interviewees who are more likely to fit into our businesses.
And that means we need to re-examine two elements of that hiring process – the resume and the interview.
First, let’s tackle the resume.
Here’s a challenge for you. Find five well-known businesses that don’t ask for a resume on their careers page. Difficult, isn’t it?
Now think about the resumes you’ve seen recently.
I’ve seen resumes that are well-constructed, professionally crafted prose. And others that are complete works of fiction.
You’re as likely to find glaring spelling mistakes, a messy layout, and a shameless plea to be considered as you are a concise summary, an attractive photo and carefully chosen keywords. If you’re really unlucky you get all of these in one “super-resume”.
A quick search on “How are resumes used?” reveals the astounding advice that applicants should “know the facts in detail, as they may be questioned” about them. That just confirms my suspicion that these documents are more like scripts than records of facts.
And, there’s one more thing that recruiters know about resumes, even if they don’t all admit it …
According to research by the Cambridge Network, some recruiters give CVs a six-second speed-read and many recruiters spend just under 20% of their time on a profile … looking at the picture!
Resumes are rarely used correctly or understood properly, by applicants or recruiters. They most certainly do not predict how successful an applicant is likely to be in a role. Instead, they’re a minefield of potential bias: year of graduation (age bias), name (racial / gender/identity bias), experience in a similar business (confirmation bias), and so on.
So isn’t it better to put some truly intelligent AI for HR to work instead?
I was astonished to see that 96 per cent of senior HR leaders understand the benefits of using artificial intelligence in their HR and talent functions. But there’s a big gap between recognising the benefits and reaping them.
The canny HR leaders who are already adopting AI techniques will have a head start on their slower rivals.
Some more traditional HR tech providers have evolved their recruitment tools, presenting them as predictive. However, they’re more likely to be creating profiles of your better staff and matching these profiles to the external candidate market, not predicting how they will perform.
Instead, the new wave of HR tech uses well-constructed algorithms, created using a business’s performance data, to provide an unbiased shortlist of candidates far more likely to succeed within the business once hired.
The algorithm can’t be misled by optimisation techniques, personal feelings or prejudice. Instead, it uses objective data, science and evidence to find the people who are most likely to be a good fit and perform. For this role, in this business. And it will help uncover applicants we might have otherwise overlooked when their resume didn’t match our expectations.
The better solutions work by identifying the defining characteristics of the whole performance group within a business (superstars through to under-performers) and then predicts where external applicants will sit on your performance scale once/if hired.
These advanced solutions then go further via validation reports to prove their better predictions are turning into better new hires. They then use Machine Learning to ensure each unique model continues to learn more about the performance of each business, further improving its predictive power over time.
These two additional steps mean that whilst us humans are still required to make the final hiring decision, we will get better results for our applicants and our businesses. Maybe that’s where the resume might still have a role – as the frame for some reasonable high-level questions to help us understand the person in front of us in more depth, once they’ve got through the first stage.
The most sophisticated algorithms are already outperforming humans in the selection and identification of suitable candidates – and by that I mean candidates who go on to become productive, valuable and loyal employees.
So, what would you rather have?
– A shortlist of candidates chosen because of what they’ve selected to include in (and omit from) their resume?
Or
– A shortlist of candidates you know are likely to do well in your workforce, because they’ve been chosen using statistically-proven, company-specific performance drivers validated by behavioural science?
Not that tricky a question, is it?
And very easy to see how, with the advent of AI for HR, resumes will soon be as much a part of recruitment as faxes and Rolodex.
Suggested Reading:
https://sapia.ai/blog/cv-tells-you-nothing/
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.