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Hiring has always involved a bit of detective work.
But now, with more candidates using AI to write CVs and cover letters, that work has become harder.
Many applications look polished. Many say the right things. Many use the same phrases about being strategic, results driven, collaborative, commercially minded and passionate.
The issue is that polished does not always mean relevant.
A CV can sound strong and still tell you very little about whether the person can actually do the job.
For employers, especially those hiring in edtech, education, healthcare, public sector or other relationship led sectors, this creates a real screening problem. How do you find the right people when applications increasingly look and sound the same?
AI has made it easier for candidates to create tailored CVs quickly. That is not always a bad thing. A well used tool can help someone explain their experience more clearly.
The problem starts when the CV becomes too generic.
Hiring teams are now seeing more applications that use polished but vague language. They mention the job description, but do not give enough proof. They include the right words, but not enough context. They sound confident, but the examples are thin.
This is slowing hiring down. Robert Half reported that 67% of HR leaders say AI generated applications are slowing hiring, while 65% of hiring managers say AI enhanced resumes make skills harder to verify.
So the challenge is not just volume.
It is verification.
The quickest way to improve CV screening is to improve the job description.
If the job description is vague, the applications will be vague too.
Instead of saying you need someone with “strong communication skills” or “commercial experience”, be specific about what that means in the role. For example, do they need to sell into universities? Manage school district relationships? Work with NHS trusts? Build pipeline across EMEA? Renew enterprise accounts? Deliver product training to educators?
The more specific the criteria, the easier it becomes to screen.
This does not guarantee you will get the perfect candidate. But it does give you a stronger filter. It helps you separate people who have the right language from people who have actually done the work.
Skills based hiring is becoming more important for this reason. LinkedIn’s Future of Recruiting report highlights skills based hiring as a major priority, with AI expected to support recruiters in analysing CVs and skills more effectively.
But the human work still matters.
You need to know which skills actually matter for the role.
Keywords matter. They help you screen quickly, especially if you are using LinkedIn Recruiter, an ATS, or Boolean search.
But keywords alone are not enough.
Someone might mention “higher education”, “customer success”, “stakeholder management” or “business development”, but that does not tell you how deeply they understand the space.
You need to look for evidence around the keyword.
For example, if you are hiring an Account Manager for EMEA higher education, look beyond the phrase “account management”. Look at who they worked with, what types of institutions they supported, what size accounts they managed, whether they worked across multiple countries, and whether they were involved in renewals, upsells or implementation.
If you are hiring for sales, look at the sector they sold into, the buyer they sold to, the sales cycle, deal size, territory, and whether they opened new business or managed existing accounts.
The keyword gets you started.
The context tells you whether they fit.
Past employer is not everything, and you should not over rely on it.
But it can be useful context.
If someone has worked for a competitor, similar product, similar market or similar customer base, they may already understand the buyer, the sales cycle, the language, the pain points and the internal pressures of that sector.
That is one reason competitor mapping is so common in recruitment.
For edtech companies, this can be especially useful. A candidate who has sold to universities, supported schools, worked with learning platforms or managed education partnerships may be able to contribute faster because they already understand the end user.
This is not to say that out of sector candidates cannot work. They absolutely can.
Many organisations hire strong sales or customer success people from outside edtech and train them well. But if speed, sector credibility, and network access matter, then prior exposure to edtech, education, learning and development, healthcare, or public sector can make a real difference.
This matters a lot in edtech.
A candidate might be strong commercially, but if they do not understand how schools, universities, trusts, or public sector bodies make decisions, they may need more time to ramp.
Sector understanding can show up in small details. Do they understand academic calendars? Procurement cycles? Budget pressures? Implementation challenges? Teacher workload? Student outcomes? Senior stakeholder complexity?
A good CV will not just say “worked with universities”.
It will show how.
For example, did they manage relationships with academic departments, careers services, procurement teams, senior leadership or IT teams? Did they support adoption? Did they increase usage? Did they reduce churn? Did they help customers move from pilot to paid contract?
These details matter because they show whether the candidate understands the real operating environment.
A very polished CV is not always a strong CV.
Sometimes the most useful candidates have imperfect CVs. They may not have the best wording. They may not know how to sell themselves. They may not include every keyword.
This is especially true for candidates moving from education, public sector or specialist sectors into edtech or tech adjacent roles.
So yes, look for keywords. But also look for signals.
Has this person worked with the right audience? Have they solved similar problems? Have they handled similar complexity? Have they worked in a similar type of environment? Have they shown ownership, not just participation?
AI can make a weak CV sound stronger.
But it can also fail to capture the depth of a genuinely strong candidate.
That is why screening should not only be about the best written CV.
It should be about the strongest fit.
LinkedIn Recruiter can be very helpful when you know what you are looking for.
You can search by skills, past company, current company, job title, sector, location, seniority and keywords. That makes it easier to narrow a large talent pool quickly.
But there is a limitation.
Not everyone lists their skills properly. Not everyone uses the same language. And many strong candidates are not great at optimising their LinkedIn profile.
This means you may miss people if you only search obvious job titles or keywords.
For example, someone who could be strong for an edtech customer success role may have a background in teaching, implementation, training, learning design or account management. If your search is too narrow, they may never appear.
This is where human screening still matters.
You need to understand adjacent experience, not just exact matches.
A strong CV should make it easy to understand what the person did, who they worked with, what outcomes they delivered and how relevant that is to your role.
If you are screening quickly, focus on five things.
First, look at the current or most recent role. Does it show clear ownership or is it just a list of tasks?
Second, look at the customer or stakeholder base. Have they worked with the audience you sell to or support?
Third, look for outcomes. Revenue, retention, adoption, engagement, renewal, implementation, growth, process improvement, team leadership, or customer impact.
Fourth, look for market fit. Have they worked in edtech, education, learning, healthcare, public sector or another relevant space?
Fifth, look for clarity. Can you explain why this person fits the role in one sentence?
If you cannot, the CV may not be strong enough. Or the candidate may need a deeper screen before being ruled out.
This is where many hiring processes go wrong.
Employers often say they want sector knowledge, commercial skills, stakeholder management, resilience, speed, and strong communication.
But then they screen only for exact titles.
That can shrink the talent pool too much.
For example, a former educator with strong technology adoption experience might be a good fit for implementation, customer success or learning design. A higher education partnerships manager might move well into edtech sales. A public sector account manager might understand complex procurement better than someone from a pure SaaS background.
The key is knowing what skills transfer and which ones do not.
That is where recruitment experience makes a difference.
AI can help with screening. It can organise applications, search for keywords, and reduce admin. Some hiring leaders are already seeing efficiency benefits from AI in recruitment, but even reports that are positive about AI still highlight the need for human involvement. Insight Global’s 2025 AI in Hiring report says 99% of surveyed respondents use AI in some part of hiring, while 93% of hiring managers still stress the importance of human involvement.
That makes sense.
AI can help you process.
But humans still need to judge fit.
Especially in roles where success depends on sector knowledge, stakeholder trust, communication, credibility and commercial judgement.
