Key Takeaways
- AI has flooded hiring with high-volume, automated applications: candidates now use LLMs and auto-apply tools to send hundreds of tailored resumes and cover letters at once, overwhelming HR teams.
- The strain is especially acute for nonprofits, which have limited HR resources and cannot easily match the personalized, high-cost assessment processes that larger organizations are adopting.
- AI now appears on both sides of the table: employers use it to screen and summarize applications, while some candidates use real-time AI assistants and even AI video tools during interviews.
- To adapt, nonprofits can start with an “AI audit” of their hiring funnel, identifying which steps (like resumes and cover letters) are easiest to automate or game.
- Practical, low-cost responses include network-based and referral hiring, more in-person or personalized interviews, evidence-based assessment (portfolios, references, and verifiable metrics), and de-emphasizing text-based materials.
- There is no foolproof filter, but nonprofits can lean on their volunteer and professional networks to identify genuine, qualified candidates rather than the AI tools they may be using.
Ever since ChatGPT was released in 2022, people have been using LLMs to automate significant parts of their personal and professional lives. There has been a steady drumbeat of articles documenting the “death” of many longstanding business practices, from graphic design to cover letters. HR managers and teams have decried the flood of AI-generated job applications. The scale is striking: by mid-2025, LinkedIn was receiving roughly 11,000 job applications per minute, about 45% more than a year earlier. Each applicant seems perfect for the role, but many of these ideal candidates end up being less experienced or skilled than their applications suggest.
Recruiting has always been challenging for nonprofits, and this new environment has the potential to stretch their already limited HR resources to the breaking point. That a single applicant can send out hundreds of applications automatically not only challenges hiring managers, but it also makes it harder for other applicants to stand out from the crowd. Large public and private sector organizations have adjusted to this new normal by investing in more personalized assessment or by bringing candidates in for in-person interviews; companies like Google, Cisco, and McKinsey have reinstated in-person rounds specifically to counter AI-assisted fraud. However, nonprofits and mission-driven organizations that don’t have the resources to overhaul their processes will need to find low-cost ways to account for this new paradigm.
Here are some of the ways AI has already changed the hiring process:
How LLMs are being used in the hiring process
These are some of the most common and impactful ways candidates and hiring managers are using AI tools:
Text generation and analysis
Candidates are using LLMs to write personalized cover letters, resumes, and applications replete with flattering language or signals designed to appeal to hiring managers, and, increasingly, to automated recruitment tools. On the hiring side, HR teams are using AI tools to parse and summarize applications or generate lists of ideal candidates. It’s common for candidates to reach the interview stage without ever having their application viewed by a human.
AI video tools
Companies have started to rely on AI interviewing tools to conduct recruiter interviews, which use voice generation and analysis tools to assess candidates without any human input. Some companies also offer tools that help candidates answer live interview questions in real time, generating an ideal response that the candidate then recites. Some tools will even assist candidates during remote video assessments, helping them reach answers faster and test their approach in order to appear more qualified.
How nonprofits can identify quality candidates in the age of AI
The exact approach you use will depend on your organizational resources, the type of work you do, and the hiring process you’ve historically relied on.
Do an AI audit of your hiring process
You can start by discussing the impact of AI with your hiring managers and asking whether there were any instances of obvious AI usage. Did a seemingly ideal candidate fall flat in their interviews, or has there been an increase in application volume? You can also analyze how most of your current staff entered the hiring funnel to determine which sources have historically performed the best.
The next step is to break down each step that applicants take and determine how easily it can be automated or “gamed” by a candidate using an LLM. Many hiring experts consider text-heavy steps like resumes and cover letters to be “dead,” rendered obsolete by the fact that they can be generated and personalized without the candidate’s direct input or effort. Other tasks, such as video interviews and work assignments, can also be gamed, but hiring managers may have an easier time detecting AI usage.
Another thing to keep in mind is that candidates who use AI aren’t necessarily good or bad because they use these tools, and you can think about what kinds of AI assistance you consider inappropriate. This audit can be a starting point for a deeper conversation about what kinds of candidates seem to perform best and what signals your team uses to determine applicant fit.
Experiment with alternative approaches
- If you are struggling to deal with a high volume of applications to your job posts, you can look into more network-based hiring, asking your team and managers to recommend people they have worked with in the past and incentivizing referrals.
- Some organizations are leaning into more personalized interview techniques, particularly interviews and in-person assessments over video and remote assessments. If you have a fully remote team, you can train your hiring managers to watch for and detect evidence of AI usage. There are tools and techniques for this, such as latency between questions and answers, different answer delivery when someone is reciting rather than thinking, and so on.
- Some organizations have been leaning heavily into evidence-based applicant assessment, asking candidates for records of their past performance like work portfolios, references, and verifiable metrics that can be cross-referenced.
- De-emphasize text-based assessment, particularly cover letters and even resumes. As part of this process, you can think about what purpose cover letters and resumes have served for your hiring managers in the past and discuss other ways you might assess these signals. Asking for more organic, creative submissions not only raises the applicant bar, but can also prevent applicants from using a “spray and pray” approach when applying to your organization.
The bottom line
Even large organizations with significant financial resources are struggling to contend with the issue of misleading AI applications, which means there may be no foolproof method for sorting through the noise. It can be more costly and time-consuming to rely on personalized, human-centered assessment, but this might still be less costly than hiring the wrong candidate. Thankfully, nonprofits often have extensive volunteer and professional networks to draw from, and can play to their strengths to identify and recruit quality contributors.
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The information contained in this article is not a substitute for legal advice or counsel and has been pulled from multiple sources.
