Recently, a philanthropic organisation shared with me that they received over 1,000 applications for a single fellowship position. How many were genuinely qualified? A fraction. The rest? Many were AI-generated applications from candidates who had never worked in philanthropy, applying to dozens of roles per day with a few clicks. Welcome to recruiting in 2026.
Through PurposePhil Career, I support organisations and networks with mid-level to senior candidate sourcing, and foundations and strategy/MEL consultancies regularly access my talent pool when they need experts in education, EdTech, MEL, or philanthropy. I also mentor dozens of professionals navigating this market. I see both sides daily: employers drowning in noise, and highly qualified candidates getting lost in it.
The recruiting landscape has fundamentally shifted. And our sector, with its emphasis on nuance, context, and human-centered values, needs to respond thoughtfully.
The Perfect Storm
Three forces have collided to make hiring in education, philanthropy, and MEL more challenging than ever.
First, AI has made it trivially easy to apply for jobs. A candidate can now generate hundreds of tailored-looking applications per week using ChatGPT or Claude. The barrier to applying has dropped to nearly zero, which means the signal-to-noise ratio has collapsed.
Second, the contraction of USAID and the broader international development field has flooded the market with experienced professionals seeking their next opportunity. Many are highly qualified. But distinguishing them from opportunistic applicants has become exponentially harder.
Third, the roles themselves have changed. Senior positions in our sector increasingly require combinations of skills that don't fit any classic specialization: systems change facilitation, multi-stakeholder partnership coordination, self-regulated leadership, MEL expertise. These competencies come from exposure and experience, not credentials, making them nearly impossible to screen for through traditional CV review.
What Candidates Experience
From my mentoring work, I hear the same frustrations repeatedly.
The silence. Applications disappear into a void. No confirmation of receipt, no timeline, no rejection, just ghosting. Organizations that champion human dignity somehow forget to extend it to applicants.
The unpaid labor. Assessment exercises that take two or three days to complete. Presentations prepared for panels. Strategic recommendations developed. All uncompensated. Sometimes these submissions even inform organizational strategy, without acknowledgment or payment.
The opacity. "Hiring on a rolling basis" with no indication of timeline. Salary ranges hidden until late in the process. Panel interviews scheduled with two weeks' notice, then rescheduled, then rescheduled again.
I understand the pressures hiring managers face. But in a sector that talks about equity, dignity and systems change, we can do better.
What Actually Works
Based on my experience supporting both employers and candidates, here's what separates effective, humane recruitment from the frustrating norm.
Transparent communication from day one. State the salary range in the job description. Publish the timeline. Send confirmation of receipt. Notify unsuccessful candidates, all of them. This should be business as usual, but unfortunately it isn't.
Compensate assessment exercises. If you're asking candidates to spend more than two hours on an exercise, pay them. A few hundred euros signals respect for their expertise. It also ensures you get their best work rather than rushed submissions squeezed between paying commitments.
Design exercises that reveal what CVs cannot. The best assessment exercises simulate actual work. They show how a candidate thinks through complexity, not just what they've done before. But keep them focused, three hours maximum, and be realistic about what context candidates lack and what that means for their time committment.
Use AI carefully, if at all. Generic AI screening tools cannot fully distinguish between someone who has genuinely led a foundation's learning agenda and someone who asked ChatGPT to describe the experience convincingly. The nuance and context understanding required for mid-to-senior roles in our sector demands human judgment.
But there's a deeper concern: AI is trained on biased datasets. This means bias can creep into every stage of your recruitment process, from AI-generated job descriptions that inadvertently discourage certain applicants, to interview questions that favour particular communication styles, to screening algorithms that systematically disadvantage women and candidates from marginalized communities or affected populations. The research on gender bias in AI recruitment tools is well-documented, but the impact on candidates from the Global South or other underrepresented contexts is equally significant and less discussed.
If you use AI anywhere in your process, such as writing job descriptions, screening CVs, generating interview questions, have a human review the outputs through an equity lens before proceeding. Ask: who might this language exclude? What assumptions are embedded here? In a sector that talks about serving affected communities, our hiring practices should reflect those values.
Partner with sector-specific recruiters for specialized roles. For positions combining philanthropy expertise with technical skills, such as systems change, MEL leadership, foundation finance, a curated talent pool will surface candidates a generalist platform never could. These professionals often don't have conventional CVs. Their value lies in combinations of experience that require human judgment to recognize.
Practical Steps for Hiring Organizations
If you're about to launch a recruitment process, consider this checklist:
- State salary range and benefits in the job description
- Set a clear application deadline (not "rolling basis")
- Plan your timeline before posting and share it with candidates
- Limit assessment exercises to three hours; compensate anything longer
- Send confirmation of receipt to all applicants
- Notify all unsuccessful candidates, even with a brief automated message
- Review any AI-generated content (job descriptions, interview questions, screening criteria) for hidden bias before using
- If using AI screening tools, always have humans review results through an equity lens and never share personally identifiable data
- Consider whether a sector-specific recruiter could save time while improving candidate quality
How I Can Help
If your organization is hiring in education, philanthropy, or MEL, I offer two ways to cut through the noise:
Job posting visibility and access to my curated talent pool. Your role reaches 7,200+ sector professionals across 100+ countries through my network and you can access the talent pool with over 630 professional experts, people who have opted in because they're specifically interested in mission-driven work in education, EdTech, philanthropy, and MEL and bring the expertise. Foundations and research/strategy consultancies access this pool regularly when they need specialized expertise.
Longlist sourcing. I work with you to understand the role, then provide a shortlist of qualified candidates from my network, handling job posting on the platform, initial outreach and screening of applicants.
Both approaches are designed to save you time while treating candidates with the respect they deserve.
See services and pricing here: https://www.purposephilcareer.com/pricing
And if you're a candidate navigating this market, my mentoring services can help you stand out for the right reasons, articulating the breadth and unique combination of your experience in ways that resonate with hiring committees who may not recognize unconventional career paths.
Learn about mentoring: https://www.purposephilcareer.com/services
What approaches have worked for your organization? What frustrations have you experienced on either side of the hiring process? I'd genuinely like to hear, comment below or connect with me on LinkedIn.
Nora