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Talent Acquisition Strategy & Plan Templates: Mastering Audits & Documents

The following is an excerpt from our Talent Acquisition Transformation Guide, a comprehensive playbook to help you audit and improve your recruitment strategy.

Winning more talent means refining your talent acquisition strategy template and getting your team in ship shape. In many organizations, the Talent Acquisition business operates in an isolated camp – no one sees or hears from you unless there’s news about a talent audit or updates on a particular candidate or role vacancy.

Efficiency in recruitment requires absolute alignment. Your people leaders and your executive team must be in alignment with your talent acquisition plan template and new strategy, because they are equally responsible for executing it. Gone are the days when, for example, marketing managers could pass a job description for a copywriter to a Talent Acquisition specialist and wash their hands of the prospecting dirty work. Now, more than ever, the hiring manager and the specialist must form a partnership, sharing the duties of advertising, promoting, vetting, interviewing, and assessing. After all, candidates for said copywriter role will expect it, as shown in many talent acquisition strategy examples.

To get cooperation and buy-in from your people leaders, you need to form a visible, purposeful A-team in line with a solid talent acquisition strategy document.

Step 1: Create your recruitment A-team

Your crack recruitment task force should comprise:

  • Your chiefest people leader (be it a CPO, CHRO, or Head of Talent Acquisition)
  • Talent Acquisition specialists and representatives (obviously)
  • Representatives from each department in the organization
  • Importantly, a representative from the marketing team

Once your team is formed, you need to complete a talent acquisition audit to see where your recruitment pipeline is at. Using a comprehensive talent acquisition audit checklist, identify the roadblocks stopping you from securing the talent you need. It’s essential to audit talent and processes regularly to ensure the best outcomes.

Step 2: Fill out this Talent Requirements Matrix

This step sounds obvious on the face of it, but it actually requires some speculation and problem-solving. Consider this simple matrix, filled in with examples – it’s a good starting point on getting alignment with the A-team on your hiring needs.

Role Critical skills Priority Existing org. strength Applicants/candidate declined Advertised salary Market salary Notes/suggestions
Head of marketing
  • Team leader
  • Responsible for strategy
  • Budget management
Very high Low (no marketing leadership) 40/38 $150k p/a $190k p/a
  • Losing on salary
  • Candidates don’t like lack of existing team strength
Software engineer
  • OOD
  • C/C+
  • Ruby
Low High (replacing a team of 20) 10/10 $120k p/a $130k p/a
  • Losing on salary, perks
  • 50% of applicants said our tech stack was outdated
Office manager
  • Managing customer database
  • Implementing new admin system
High Low (no office manager for ~3 months) 0/0 $100k p/a $100k p/a
  • Suggest reviewing job ad – the JD is a little dry

Once you’ve filled out your Talent Requirements Matrix, the next step is effective triage. Almost everyone in the A-team will already be aware of your highest hiring priorities, but by filling out this matrix, you can focus talent acquisition efforts on coming up with weird and wonderful ideas for attracting the right candidates. Times like these require outside-the-box thinking!

The New Approach to Talent Acquisition: Incorporating a Talent Acquisition Strategy Template

In many companies, the Talent Acquisition division remains an isolated entity, often compared to an isolated camp in the talent acquisition strategy document. They emerge primarily to deliver news of hires, misses, or results from a talent acquisition audit. But for organizations to truly thrive and have an effective talent acquisition plan template, recruitment shouldn’t be a secluded process. It demands the collective effort of the entire organization, much like a collaborative talent acquisition strategy example suggests.

The contemporary approach to talent, reinforced by a robust talent acquisition audit checklist, acknowledges that not only Talent Acquisition specialists are accountable for recruiting. For instance, when hiring a copywriter, it’s not just the Talent Acquisition specialist who carries the weight. The hiring manager, too, has a significant role – from advertising and promoting to vetting, interviewing, and even conducting a thorough talent audit.

This modern method ensures that every step, from the initial audit talent phase to the final decision, is a shared responsibility, ensuring a holistic and efficient recruitment process.

Collaboration: The Key to a Modern Talent Acquisition Strategy

Today’s organizations recognize the importance of a cohesive talent acquisition strategy template that encourages collaboration across departments. This shift, documented in many a talent acquisition strategy document, is no longer just a trend but a necessity. The emphasis is on building teams that are cross-functional, where everyone plays a part in the talent hunt.

One such initiative is the talent acquisition audit. Periodic audits offer a clear perspective on what’s working and what’s not in the recruitment process. It’s a proactive approach, ensuring that the company doesn’t fall behind in the competitive talent market. Through a detailed talent acquisition audit checklist, organizations can pinpoint areas of improvement, be it in outreach, interview processes, or onboarding.

Moreover, the role of a hiring manager has expanded. Beyond just defining the job requirements, they now actively participate in the talent audit process, ensuring that the candidates not only fit the job description but also align with the company’s culture and values. This comprehensive approach to auditing talent guarantees a more holistic view of potential employees, reducing the chances of a mismatch.

Furthermore, the notion that talent acquisition is solely the responsibility of HR is outdated. Every department, from marketing to finance, has a stake in bringing in the right people. As showcased in various talent acquisition strategy examples, when each department takes ownership of the recruitment process, the results are transformative. It fosters a sense of community, where everyone is invested in the company’s growth and success.

In conclusion, the modern approach to talent acquisition is all about synergy. It’s about breaking down silos and understanding that the quest for top talent is a collective endeavor. As the landscape of work continues to evolve, organizations that embrace this collaborative spirit will undoubtedly lead the way.


 

 


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Neuroinclusion by design. Not by exception.

Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.

In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.

This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.

We’ve come a long way, but we’re not there yet.

Shifting from retrofits to inclusive-by-design

Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:

  • Sharing interview questions in advance

  • Replacing group exercises with structured simulations

  • Offering a variety of assessment formats

  • Co-designing assessments with neurodiverse candidates

But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.

Hiring needs to look different.

Insight 1: The next frontier of hiring equity is universal design

Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.

That’s why Sapia.ai was built with universal design principles from day one.

Here’s what that looks like in practice:

  • No time limits — Candidates answer at their own pace
  • No pressure to perform — It’s a conversation, not a spotlight
  • No video, no group tasks — Just structured, 1:1 chat-based interviews
  • Built-in coaching — Everyone gets personalised feedback

It’s not a workaround. It’s a rework.

Insight 2: Not all “friendly” methods are inclusive

We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.

When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:

“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”

“It was less anxiety-inducing than video interviews.”

“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”

Insight 3: Prediction ≠ Inclusion

Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.

Inclusion means seeing people for who they are, not who they resemble in your data set.

At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.

Where to from here?

Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.

That’s why we built a platform that:

  • Doesn’t rely on disclosure

  • Removes ambiguity and pressure

  • Creates space for everyone to shine

  • Measures what matters, fairly

Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat. 

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Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?

There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.

Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.

But we need to separate the two ideas:

  • Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance. 
  • Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data. 

The risk lies in conflating the two.

The Problem Isn’t Inference of Skills. It’s the Data Feeding It

CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad. 

And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.

Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.

Sophisticated ≠ Objective

The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.

But sophistication doesn’t equal accuracy.

These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.

Transparency (or Lack Thereof)

In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.

Presence ≠ Proficiency

Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill. 

Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.

By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale. 

But here’s the thing: we don’t have to choose one over the other.

A Smarter Way Forward: The Hybrid Model

The real opportunity lies in combining the rigour of measurement with the scalability of inference.

Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure. 

Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.

Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.

This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.

The Bottom Line

Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.

References

Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.

 

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Making Healthcare Hiring Human with Ethical AI

Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high. 

Hiring for care is exactly where leveraging ethical AI can make the biggest impact.

Hiring for the traits that matter

The best carers don’t always have the best CVs.

That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:

Empathy, Self-awareness, Accountability, Teamwork, and Energy. 

The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.

Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.

Inclusion, built in

Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry. 

When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.

And it works:

  • 91.8% of carer candidates complete their interviews
  • Carer candidates report 9/10 Candidate Satisfaction with their interview experience 
  • 80% of candidates would recommend others to apply 
  • Every candidate receives personalised feedback, regardless of the outcome

Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete. 

Real outcomes in care hiring

Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love: 

Anglicare – a leading provider of aged care services
  • Time-to-offer dropped from 40+ days to just 14
  • Candidate pool grew by 30%
  • Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
  • 1,213 recruiter hours saved  in the first month (67 hours per individual hiring team member) 
  • $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
  • Time-to-hire down from 22 to 7 days
  • 95.4% of candidates completed their chat interviews

A smarter way to hire

The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart. 

Download it here:

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