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Candidate Experience Strategies: How to Improve, Hiring Playbook

To find out how to improve candidate experience using Recruitment Automation, we have a great eBook on candidate experience.


Hiring with heart is good for business: candidate experience in C-19 times. Sapia launches its Candidate Experience eBook. This book provides an insight into the changing face of the candidate experience and sheds light on the candidate experience meaning in today’s context.

If there was ever a time for our profession to show humanity for the job searchers, that time is now. Unemployment in Australia has passed a two-decade high. The trend is similar for other countries. That means there are a lot more candidates in the market looking for work.

With so many more candidates, the experience of a recruiting process matters more. What are candidates experiencing? Are they respected, regardless of whether they got the job or not? Is their application appreciated? Are they acknowledged for that?

This is where improve candidate experience initiatives come into play.

 

Here are two big reasons to prioritise improving candidates’ experience:

 

There is a much higher value attached to it – both for candidates and your organisation.
This story won’t be unfamiliar to you: An Australian based consulting firm, possibly in need of a candidate experience consultancy, advertised for a Management Consultant and decided to withdraw the advert after 298 candidates had applied. That was during their candidate experience day in the first week of advertising.
When candidate supply outstrips demand, that is bound to happen. Inundation of your Talent Acquisition team becomes an everyday thing. Employers are feeling swamped with job applications. Being effective is much harder when there are more candidates to get through every day.

High-volume recruiting issues become further aggravated when two additional dynamics come into play:

 

>> When the role for which you are hiring requires a relatively low skill level.

In the example provided above, the Management Consultant role had several essential requirements that should have limited applications in the context of high-volume hiring. Included in the applicant list were hoteliers, baristas, waiting staff, and cabin crew (it’s heartbreaking). So, when it comes to roles with a much lower barrier to entry, the application numbers can quadruple.

The traditional ‘high-volume low-skill role’ has now become excruciatingly high-volume. This trend is being seen across recruitment for roles like customer service staff, retail assistants and contact centre staff.

>>When your organisation is a (well-loved) consumer brand. 

Frequently, candidates will apply to work for brands that they love. Fans of Apple products, work for Apple. They also apply to work and get rejected in their millions. So, how do you keep people as fans of your brand when around 98% of them will be rejected in the recruiting process? That’s not only a recruiting issue – it’s a marketing issue too.

Thousands of organisations and their Talent Acquisition teams are grappling with both dynamics right now.

The combination of unemployment and being in Covid-19 lockdown means that consumer buying is being impacted. Their confidence is down. Buying is also down. With people applying for more jobs and spending less as consumers, the hat has somewhat switched. For many who were consumers, they have now become candidates. That may be how they are currently experiencing your brand. As candidates first, customers second.

If customers are candidates and candidates are customers, is there a reason for their experience to be fundamentally different? 

Candidate experience is defined as the perception of a job seeker about an organisation and their brand based on their interactions during the recruiting process. Customer experience is the impression your customers have of your brand as a whole throughout all aspects of the buyer’s journey.

Is there a difference? It’s all about how the human feels when interacting with your brand. A person is a person, regardless of the hat they are wearing at the time!

All about the human experience.

Millions, even billions, of dollars are spent each year by organisations crafting a positive brand presence and customer experience. Organisations have flipped 180 degrees to become passionately customer-centric. It makes sense to do so. Put your customers first, and that goes straight to the bottom line.

What is perhaps less recognised is the loss of revenue and customer loyalty which is directly attributed to negative candidate experiences.

How about those loyal customers who want to work for your brand? They eagerly apply for a job only to get rejected.

2.Candidate Experience improvements have become super easy to implement.

For those who have tried in the past, you may well know that it can take an extraordinarily long time to ‘define’ a Candidate Experience strategy, create its metrics, find a budget and then execute on it.

Have a look inside the ‘too hard’ basket and there you may well find many thousands of well-meaning ‘candidate experience’ initiatives, that are still lying dormant! So many want to focus on candidate experience, but may shy away from doing so. This is because it’s perceived as time-consuming and expensive.

Plus, right now there is so much on which CHROs need to focus. From ensuring workers’ wellbeing to enabling remote working. Who has the time to also worry about the experiences of candidates?

However, that has changed. Boosting candidate experience is no longer too hard, too expensive, nor too time-consuming. Technology becomes more manageable, quicker and cheaper over time. Also (borrowing from Moore’s law), its value to users grows exponentially.


Candidate Experience Playbook: Hire with Heart

The good news is that for those organisations who genuinely want to improve candidate experience, it has become much easier to do so. Finally, it is possible to give great experiences at scale while also driving down costs and improving efficiencies.

Win-win is easily attainable. In the Sapia Candidate Experience Playbook, read how organisations are hiring with heart. All by creating positive experiences for candidates while also decreasing the workload for the hiring team.


Blog

New Research Proves the Value of AI Hiring

A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.

Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.

We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.

The problem with traditional psychometric tests

While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.

For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.

This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.

Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.

The Rise of Chat-Based Interviews

Enter conversational AI.

Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach. 

Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.

The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.

Importantly, every candidate receives the same questions, evaluated against the same objective, explainable frameworkThese interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.

Key Findings from the Latest Research

The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.

One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.

The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.

And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.

Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.

Why Trust and Candidate Agency Win

Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.

Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.

This signals respect from the employer. It says: We trust you to show us who you are.

Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.

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AI Maturity in the Enterprise

Barb Hyman, CEO & Founder, Sapia.ai

 

It’s time for a new way to map progress in AI adoption, and pilots are not it. 

Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.

And here’s what struck me:

Despite the hype, we still don’t have a shared language for AI maturity in business.

Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.

You don’t need more pilots. You need a maturity model.

The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.

It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.

Here’s an early sketch based on what I’ve seen:

The 5 Stages of AI Maturity (for real enterprises)
  1. Curious
    • Awareness is growing across leadership
    • Experimentation led by innovation teams
    • Risk is unclear, appetite is cautious
    • AI is seen as “tech”
  2. Reactive
    • Gen AI introduced via vendors or tools (e.g., copilots, agents)
    • Some pilots show promise, but with limited scale or guardrails
    • Data privacy and sovereignty questions begin to surface
    • Risk is siloed in legal/IT
  3. Capable
    • Clear policies on privacy, bias, and governance
    • Dedicated AI leads or councils exist
    • Internal use cases scale (e.g., summarisation, scoring, chat)
    • LLMs integrated with guardrails, safety reviewed
  4. Strategic
    • AI embedded in workflows, not layered on
    • LLM/data infrastructure is regionally compliant
    • AI outcomes measured (accuracy, equity, productivity)
    • Teams restructured around AI capability — not just tech enablement
  5. AI-Native
    • AI informs and transforms core decisions (hiring, pricing, customer service)
    • Enterprise builds proprietary intelligence
    • FAIR™/RAI principles deeply operationalised
    • Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders

AI is a capability.And like any capability, it needs time, structure, investment, and a map.

If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.

Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”

I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.

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Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever

For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.

That era is ending.

“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”

At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.

Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.

Transparency Is the New Talent Advantage

Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.

Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.

“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”

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Metrics That Make Transparency Real (and Actionable)

 

🕒 Time to Hire

 

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What it is: The median time between application and hire.

Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.

Faster time to hire = faster access to revenue-driving talent.

 

💬 Candidate Sentiment, Advocacy & Verbatim Feedback

 

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What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.

Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.

And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.

You don’t just measure experience. You understand it in the candidates’ own words.

 

🔍 Drop-Off Rates, Funnel Visibility & Automation That Works

 

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What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.

Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.

Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.

Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.

 

📈 Hiring Yield (Hired / Applied)

 

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What it is: The percentage of completed applications that result in a hire.

Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.

Hiring yield signals funnel health, recruiter performance, and candidate-process fit.

 

🧠 AI Effectiveness: Score Distribution & Answer Originality

 

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What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.

Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.

Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.

From Metrics to Momentum

To effectively lead, you need more than simply tracking; you need insights enabling action.

When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.

Learn more about Discover Insights here

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