Recruit inclusively without being overwhelmed by applications

By Atkinson HR

Sponsored content from Atkinson HR

Recruiting inclusively is essential for building diverse, values-driven teams, but for small organisations, it can sometimes feel overwhelming.

One ACEVO member recently shared their experience: when advertising a part-time administrative assistant role, they received 700 applications and had to close the advert early. With a small team, reviewing every application was impossible, so AI was used to help narrow the pool.

Before diving into practical tips, it’s worth reflecting on whether your application process might be too simple. While you don’t want to create unnecessary barriers for candidates, you do want to attract people who are genuinely motivated to work for your organisation. If you only ask for a CV, you risk receiving hundreds of untailored submissions, it’s just too easy to click ‘upload’ and apply.

Instead, consider asking applicants to submit a short supporting statement, for example: “In no more than 1,000 words, please explain how you meet the person specification.”  or answer some standardised application questions. This approach may reduce the number of applications, but 50 well-considered, high-quality submissions are far more manageable and valuable than 300 generic ones.

Finally, if a candidate hasn’t followed your stated process, such as only submitting a CV when you requested an additional statement, you’re under no obligation to take their application forward.

Below, we share practical tips for maintaining inclusivity while longlisting efficiently, including how to use AI responsibly and fairly.

Assess your current recruitment practices to identify barriers to inclusivity

• Are you requiring a degree, or would equivalent experience be sufficient?
• Show the salary – there is no reason to hide it, and people may not always be confident to ask the question, so it can put people off applying. The salary also shows candidates the level of job you’re advertising, which isn’t always clear from the title of a job
• Focus on core skills and qualifications; avoid unnecessary “nice-to-haves” that could include/exclude candidates needlessly: a clear description helps candidates self-assess, reducing applications from unsuitable candidates
• Use gender-neutral and accessible language (e.g., “chairperson” instead of “chairman”)
• Highlight your organisation’s commitment to diversity and equity, with tangible examples in your recruitment pack. Set clear diversity goals, such as increasing representation of underrepresented groups. Ensure values are communicated clearly within the team, from HR to hiring managers

Expand recruitment channels

• Advertise beyond standard platforms, consider diversity-focused job boards, community groups, and sector networks
• Encourage team members to share opportunities with their networks to broaden reach

Develop an inclusive process

• Consider blind recruitment for longlisting to remove unconscious bias (e.g., anonymise names or other demographics)
• Use standardised application questions focused on skills, experience, and achievements rather than personal characteristics
• Include skills-based assessments to measure candidates’ ability to do the role

Use AI responsibly

If you’re unable to reduce large numbers of applications by refining your processes, AI can help narrow large application pools, but it must be applied thoughtfully. AI can help with admin and data, but it can’t replace professional judgment, fairness, or empathy:

Screen for essentials

• Configure AI to focus on non-negotiable criteria such as required qualifications, years of experience, or technical skills
• Avoid using AI to assess subjective qualities like “values driven” or personality traits, which can unintentionally introduce bias
• Set clear parameters and thresholds before running the AI screening. For example, ensure candidates have the minimum qualification or experience level required for the role

Eliminate bias

• AI learns patterns from historical data, which can reflect existing inequalities. To mitigate this, check your data inputs and screening rules for unintended bias (e.g., certain universities, gendered language, or age indicators)
• Use anonymised data wherever possible to prevent demographic factors from influencing the algorithm
• Regularly audit outputs to ensure underrepresented groups are not disproportionately filtered out. Adjust the AI model or screening rules if you notice skewed results

Focus human effort where it matters

• Once AI has narrowed the pool, your team should review candidates’ potential, values alignment, and overall suitability
• Look beyond CV keywords: consider the context of experience, transferable skills, and evidence of collaboration or leadership
• Use structured scoring rubrics for consistency, ensuring human reviewers evaluate all candidates fairly
• Maintain transparency with candidates by letting them know that AI supports but does not make final decisions. This keeps the process accountable and inclusive