Decentralized recruitment, while enabling larger companies to hire efficiently, suffers in a labor-short market.
Under ordinary circumstances – like, say, the world before COVID and the Great Resignation – it’s ideal to let local hiring managers build their own workforces. Generally speaking, the decentralized approach is better for productivity, candidate experience, and the overall satisfaction of hiring managers, who look favourably on the trust and autonomy they get from head office.
However, when good candidates are hard to come by, the dearth of talent puts stress on the joints of such a sprawling network. We hear this frequently from companies who come to us to help improve efficiency, diversity, and quality of hire.
Here are the common problems companies are having with decentralized hiring in 2022:
- Hiring managers are frustrated, because they have a trickle of applicants and little control over employer branding and recruitment marketing.
- Consistency is hampered by inconsistent processes and rogue hiring managers, who frequently abandon workflows and ATS protocols in order to acquire warm bodies by any means necessary.
- Job advertising budgets are distributed unevenly, resulting in consternation for already-strained teams.
- Diversity is put on the backburner, both because hiring managers have the final say, and because they have little-to-no accountability over decisions.
- The company’s recruitment centre (i.e. head office) is unable to collect and analyze sufficient data to diagnose and fix recruitment problems across its decentralized network.
- The company is using an ATS with which either some (or all) of hiring managers are unhappy. Head office may know this, but in any case, it decides that the process of researching, purchasing, and implementing a new ATS is not worth the pain.
- A staunch desire to stick to the status quo, or ‘the way we’ve always done things’, because the company assumes that this period of hiring difficulty will soon pass.
These challenges (and others) have effected a drop in confidence in the way companies interview and process candidates. An Aptitude Research and Sapia.ai report from earlier this year found that 33% of companies aren’t confident in the way they interview, and 50% have lost talent due to poor processes. Meanwhile, 22% of the average talent pool is drained at the application stage.
Statistically speaking, roughly one in five people, at minimum, are bailing out of your application process at the very beginning.
How to improve efficiencies across a strained decentralized hiring network
As with many things in business, the answer to alleviating organizational pain lies in small, iterative improvements. Our recommendations do not include haphazard technological upgrades, nor do we advocate for widespread process changes. These will more than likely cause your decentralized hiring network to fall apart.
Here are some good places to start.
Look at removing time-wasting entry barriers, like resumes and cover letters
This is particularly important for the retail and hospitality industries, but certainly applies to any companies that hire entry-level team members at volume. Given the average level of job experience at this level of employment, most resumes and cover letters aren’t useful in gauging candidate quality. On the contrary – they take up precious hiring manager hours, are cumbersome for candidates to write, and are the main cause of the 22-24% candidate drop out rate we mentioned above. That’s not even accounting for the fact that anywhere between 60-80% of resumes contain falsifications.
Implement a simple, standardized process for capturing a candidate experience NPS baseline
Decentralization, almost by definition, makes capturing useful information difficult. But if you use an ATS as a tool for centralization, consider adding a candidate NPS measurement step to your application process. It can be as simple as a Net Promoter Score scale (1 to 10). If you hire at volume across multiple localities or regions, asking this one simple question can help you produce meaningful insights about how candidates find your process. What gets measured, gets managed, and though there are many other data points you might want to collect, this is a good (and relatively easy) place to start. If you’re keen to learn more about this, check out our podcast episode on candidate experience with Lars van Wieren, CEO at Starred.
Speak to your hiring managers regularly
Quantitative data is gold, but qualitative data is platinum. Make a habit of interviewing (not surveying, interviewing) your hiring managers on the ground. You’ll uncover invaluable insights that may enable you to make fast changes at scale. We help our clients collect qualitative feedback from hiring managers as a matter of course, leading to increases in productivity and hiring manager satisfaction.
Here are some useful questions to ask your hiring managers:
- Take me through how you run your local (e.g. instore) hiring process, from start to finish.
- Explain your process for interviewing candidates.
- Where do you think you waste the most time?
- What doesn’t work as well as it should?
- What kinds of candidates are you seeing, and how would you rate the overall quality?
- How might we support you in hiring more effectively?
This kind of bottom-up research aims to understand how hiring managers are actually behaving and interacting with systems. Some may be breaking from established protocols, but if you ask them why and how, you might uncover tactics and efficiencies that can be brought back to the rest of the organization, thereby improving the way all hiring managers operate. Two adages apply here: ‘Necessity is the mother of invention’, and ‘People will always find the path of least resistance’.
This fact-finding method is better than surveys because surveys impose a limited scope in which potential problem areas are preset. “We’re asking you about these things,” you’re saying, “and therefore, we’re suggesting they’re most important.” As a result, other problems and possible solutions are likely to be excluded from discovery. You’ll always learn more by having real conversations, because they can go in any conceivable direction.
Look for novel ways to encourage applications from otherwise passive candidates
Again, incredibly useful for retail, but applicable in a wide range of industries and contexts. Think about the universal touchpoints you have with customers (a.k.a candidates) across your decentralized network. In retail, some good examples might be your receipts and carry bags. These provide you invaluable real estate to advertise your jobs and employer brand. Consider putting a URL or QR code on these assets, and you might drastically increase the amount of people who know about and apply for the jobs you advertise. This tactic has the added benefit of capitalizing on active and loyal customers; after all, if they’re buying from you, they’re a prime target for recruitment marketing.
The best part about this manner of advertising? You already own the space, and the design can be centralized and rolled out at scale.
We’d be remiss if we didn’t point out that Sapia’s Ai Smart Interviewer is a dynamite solution for the inevitable pain points of decentralised recruitment. Our technology can be rolled out across your entire company, and takes care of the application, screening, interviewing, and assessment stages of your process.
Hiring managers save time – as much as 1,600 hours per month, for some of our customers – but they still get the option to approve and interact with short-listed candidates. Better still, our platform captures vital data on diversity and candidate experience, enabling you to see exactly how your network is performing, individually and collectively.
Best of all, Sapia tech integrates directly with the leading ATS platforms, and can be rolled out in as little as four weeks.
Woolworths Group, Australia’s largest private employer, uses Sapia to hire more than 50,000 candidates per year, nationwide. To see how they flourish in a labor-short market, check out our case study here.
Related
Blog
Reinventing the Competency Framework: A Data-Driven Approach for the AI Era
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.
Why Rethink Competency Frameworks?
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.)
Our Approach: Where AI Meets I/O Psychology
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
- Can we make competency discovery agile, scalable, and evidence-based?
- Can we use AI to automate the process without losing the rigour of traditional psychology?
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:
- Behavioural Descriptor Extraction
- Clustering and Labeling
- Cluster Analysis by I/O Psychologists
- Thematic Categorisation and Definition of Competencies
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.
Built to Scale. Built to Adapt.
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:
- Job Analyser – Starting with a job description, it creates a unique competency profile for each role to build tailored structured interviews in seconds.
- Structured Chat-based Interviews that assess candidates’ responses according to the competency profile for consistent candidate assessment.
- Talent Insights Reports from every interview with deep reasoning and explainability for fair and objective hiring decisions.
- Phai Career Coach for internal mobility and employee growth that considers their competency strengths and career aspirations.
The Future of Talent Acquisition & Development is Competency-First
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.
Want to see how it works? Download the full framework.
Blog
It’s Time to Stop Hiring for Skills, and Start Hiring for Competencies
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.
Skills vs Competencies: The Crucial Distinction
- Skills are task-specific capabilities. Think Python programming, Excel, or even negotiation.
- Soft skills refer to interpersonal or behavioural qualities like adaptability, communication, and resilience.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
- Competencies are clusters of skills, knowledge, behaviours and abilities that are observable, measurable, and context-specific.
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?
Why Competencies Matter More Than Ever
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:
- Roles are changing faster than static skill frameworks can keep up
- Job candidates may have non-linear, cross-functional backgrounds
- The shelf-life of technical skills is shrinking rapidly
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.
Adaptive Talent: The New Competitive Advantage
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:
- Learning agility
- Change resilience
- Cross-functional collaboration
- Problem-solving in ambiguous contexts
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.
Building a Competency-Based Talent Framework
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
- Define Role-Specific Competencies: Move beyond job descriptions based on qualifications or vague skill sets. Break roles down into measurable competencies that reflect current and emerging performance expectations. This step is crucial for organisations to be able to accurately assess role-fit in the next stages. Sapia.ai does this automatically, taking job descriptions and building role-specific competency models in seconds.
- Assess Competencies Fairly and Objectively: Use structured behavioural interviews, ideally at scale. These provide a much more accurate picture of a candidate’s readiness than self-reported skills or credentials. Sapia.ai’s AI powered interviews enable competency assessment, at scale.
- Build Pathways for Development and Internal Mobility: A competency framework makes it easier to identify transferable strengths, development gaps, and future-fit potential. It gives employees clarity on how to grow within the business. Using an AI-powered coach can help ensure that talent is being continuously developed against the organisation’s competency framework.
The Future of Work Requires Depth, Not Just Breadth
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.
Keen to Shift to Competencies, but Lacking a Framework?
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.
Blog
The AGC Debate: Are AI-Written Interview Answers a Red Flag or Smart Strategy?
Every day, we read stories of increased fake or AI-assisted applications. Tools like LazyApply are just one of many flooding the market, driving up applicant volumes to never-before-seen levels.
As an overwhelmed hiring function, how do you find the needle in the haystack without using an army of recruiters to filter through the maze?
At Sapia.ai, we help global enterprises do just that. Many of the world’s most trusted brands, such as Qantas Group, have relied on our hiring platform as a co-pilot for better hiring since 2020.
Our Chat Interview has given millions of candidates a voice they wouldn’t have had – enabling them to share in their own words why they’re the best fit for the role. To find the people who belong with their brands, our customers must trust that their candidates represent themselves. Thus, they want to trust that our AI is analysing real human answers—not answers from a machine.
The Rise of GPT
When ChatGPT went viral in November 2022, we immediately adopted a defensive strategy. We had long been flagging plagiarised candidate responses, but then, we needed to act fast to flag responses using artificially generated content (‘AGC’).
Many companies were in the same position, but Sapia.ai was the only company with a large proprietary data set of interview answers that pre-dated GPT and similar tools: 2.5 billion words written by real humans.
That data enabled us to build a world-first:- an LLM-based AGC detector for text-based interviews, recently upgraded to v2.0 with 99% accuracy and a false positive rate of 1%. An NLP classification model built on Sapia.ai proprietary data that operates across all Sapia.ai chat interviews.
Full Transparency with Candidates
Because we value candidate trust as much as customer trust, we wanted to be transparent with candidates about our ability to detect artificially generated content (AGC). As an LLM, we could identify AGC in real time and warn candidates that we had detected it.
This has had a powerful impact on candidate behaviour. Since our AGC detector went live, we have seen that the real-time flagging acts as a real-time disincentive to use tools like ChatGPT to generate interview responses.
The detector generates a warning if 3 or more answers are flagged as having artificially generated content. The Sapia.ai Chat Interview uses 5 open-ended interview questions for volume hiring roles, such as retail, contact centre, and customer service, and 6 questions for professional roles, such as engineers, data scientists, graduates, etc.
Let’s Take a Closer Look at the Data…
We see that using our AGC detector LLM to communicate live with candidates in the interview flow when artificial content has been detected has a positive effect on deterring candidates from using AI tools to generate their answers.
The rate of AGC use declines from 1 question flagged to 5 questions – raising the flag on one question is generally enough to deter candidates from trying again.
The graph below shows the number of candidates, from a total of almost 2.7m, that used artificially generated content in their answers.
Differences in AGC Usage Rate by Groups
We see no meaningful differences in candidate behaviour based on the job they are applying for or based on geography.
However, we have found differences by gender and ethnicity – for example, men use artificially generated content more than women. The graph below shows the overall completion ratios by gender – for all interviews on the left and for interviews where the number of questions with AGC detected is 5 or more on the right.
Perception of Artificially Generated Content by Hirers.
We’re curious to understand how hirers perceive the use of these tools to assist candidates in a written interview. The creation of the detector was based on the majority of Sapia.ai customers wanting transparency & explainability around the use of these tools by candidates, often because they want to ensure that candidates are using their own words to complete their interviews and they want to avoid wasting time progressing candidates who are not as capable as their chat interview suggests.
However, some of our customers feel that it’s a positive reflection of the candidate, showing that they are using the tools available to them to put their best foot forward.
It’s a mix of perspectives.
Our detector labels it as the use of artificially generated content. It’s up to our customers how they use that information in their decision-making processes.
This concept of having a human in the loop is one of the key dimensions of ethical AI, and we ensure that it is used in every AI-related hiring product we build.
Interested in the science behind it all? Download our published research on developing the AGC detector 👇