The impact of AI on recruitment processes is already making waves. According to the 2024 Global Workforce Report by Remote, 3 out of 4 companies are encountering AI-generated CVs that include false information about skills and/or experience. (View Highlight)
Many candidates struggle to pass the initial screening in recruitment processes. Stand out with a CV that captures attention. This often doesn’t happen because candidates use templates that are not ATS-friendly, make mistakes in their writing, or fail to tailor their CVs to specific job offers. The reasons vary. (View Highlight)
However, thanks to AI, more and more candidates are breaking through this barrier. (View Highlight)
AI enables the creation of optimized CVs designed to pass ATS filters. By simply providing a job description, it can generate a tailored CV that successfully navigates ATS screening. (View Highlight)
In a time when the volume of applications far exceeds the capacity to manage them, recruiters face an increasing burden of tasks that add little value to the company. Distinguishing genuine CVs from fabricated or “enhanced” ones becomes a growing challenge—hunting the proverbial catfish. (View Highlight)
When overwhelmed by CVs, what’s the best way to filter? Keywords in ATS searches are proving ineffective, as CVs are now crafted to exploit these systems.
Some companies are falling into the trap of filtering by education, prestigious universities, or well-known employers. However, this approach doesn’t guarantee better candidates—it’s merely a way to reduce the pile, often at the cost of missing out on talent. (View Highlight)
This is where the battle begins—AI vs AI. ATS systems using AI to filter CVs, which are also generated by AI. Who will come out on top? (View Highlight)
Without a doubt, the real loser here is the company, as it will spend significantly more time filtering CVs and verifying the authenticity of candidates’ experience. What happens when, instead of 20 candidates passing the filter, there are 200? (View Highlight)
ATS systems have been using keyword filtering algorithms for years, and now, with AI, have been “supercharged”. However, the real challenge with AI lies in the data samples we provide to train these algorithms. Effective AI filtering requires “megatons” of data—data that many companies don’t even track or have access to, in order to personalize the process. In the end, these algorithms are only as good as the quality and volume of their data. (View Highlight)
In Europe, within 18 months,AI models used in sensitive or “high-risk” processes—such as filtering job applications—will require registration. This measure aims to prevent biases in candidate screening, an issue that can easily arise when AI is trained on biased datasets. (View Highlight)
Interviewing.io—an online platform for technical testing and interview preparation—conducted an experiment this year with candidates and companies to see how many interviewers would notice if candidates were using LLMs during the interview process. (View Highlight)
SPOILER: None of the interviewers realized that candidates were “cheating” by using LLMs. Moreover, 72% of the interviewers stated they would hire the candidates who passed the tests. (View Highlight)
**But is using AI really “cheating”?**It depends on the rules set by each company for their technical tests. However, there’s no denying that AI has become a tool anyone can use in their daily work. (View Highlight)
So, what’s the solution? Should we prohibit or encourage the use of AI in technical testing?
Common sense suggests that banning a tool that will likely be used daily in the job itself—and is readily available to everyone—shouldn’t be a detriment to the hiring process. Instead, it could be seen as an aid. (View Highlight)
That said, this creates new challenges for interviewers. It becomes increasingly difficult to assess a developer’s true experience and skills in areas like programming fundamentals, system design, or coding proficiency. The lines between different levels of seniority—such as an L4 and an L5—are becoming blurred in interviews. (View Highlight)
Nobody likes being deceived, and feeling like you’re not competing on a level playing field is inherently frustrating. As an interviewer, realizing that the answers you’re getting might not be from the candidate but from an AI creates a sense of unfairness. (View Highlight)
As much as it may sound like classic McKinsey bullshit, the answer is simple: adapt to change. There’s no alternative. At present, there are no tools that can reliably differentiate between candidates using AI and those who aren’t. (View Highlight)
Option 1: Redefine CV ScreeningWhen we delegate CV reviews to an ATS because we don’t have time to go through the volume of applications, we risk missing out on candidates who haven’t optimized their CVs for that specific ATS. (View Highlight)
If we enrich the ATS AI with our job description requirements and data from previous processes or successful candidates, we must be mindful of the bias in the data we feed it. This is a complex issue, especially when buried under a pile of CVs. (View Highlight)
Our solution? We do it manually. We review, prioritize, and respond to each candidate, step by step, one by one. (View Highlight)
Option 2: Redefine the Job ListingAs my colleague Anxo Pérez once said, “English is taught poorly. Period.” The same could be said about job offers. In most cases, job descriptions are poorly written. A quick search for “Software Engineer” on LinkedIn Jobs will reveal countless listings that are nothing but endless lists of requirements—and in recent times, an even longer list of perks or benefits. (View Highlight)
Letting hiring managers write the job offers is like asking children to write the letter to Santa Claus. They’ll end up searching for the perfect candidate—the unicorn, the one who ticks every box. But the reality is, that person doesn’t exist. This leads to either few applications or overly long hiring processes for a role. (View Highlight)
Many of the technical questions used in interviews are taken from the Internet. And the LLM’s have been trained with Internet data. Therefore, they also have the answers. So, if your candidates have access to the Internet, they have the answers to your questions. To yours and to those of thousands and thousands of companies around the world. (View Highlight)
Asynchronous technical tests are starting to face this issue. By the time candidates present their code for review, they may not be able to fully explain their solution. Or, on the other hand, you might find that every candidate you interview “passes” the technical test, making it hard to gauge their actual abilities. (View Highlight)
In a conversation on X/Twitter, Javi and Jorge discuss the use of LLMs in asynchronous tests. They both suggest a practical approach: if a candidate uses an LLM, they should provide a .txt file detailing the prompts they used, along with an additional section explaining how they would integrate an LLM into a production environment for a similar task. (View Highlight)
Some companies are starting to include tests focused on a candidate’s ability to craft effective prompts for LLMs. This trend is gradually gaining traction and is likely to become a standard part of many recruitment processes. (View Highlight)
As LLMs become a common tool in daily workflows, the ability to maximize their potential—through well-crafted instructions—will increasingly be seen as an essential skill. Writing effective prompts, which requires a mix of clarity, precision, and creativity, will soon be a requirement in many job descriptions. (View Highlight)
As AI-powered agents like Copilot, Cursor, and others continue to evolve, they will enable developers to contribute to teams much more quickly, regardless of their familiarity with specific languages, stacks, or tools. This means that technical skills, once central to the hiring process, will become less relevant over time. (View Highlight)
Instead, soft skills such as written communication, problem-solving, and the ability to learn and adapt will take precedence. These are qualities that AI cannot replicate or replace. (View Highlight)
In the job offers of the near future, soft skills will be prioritized over technical expertise, as they remain essential for thriving in a dynamic, AI-driven environment. (View Highlight)
In a time when technical skills increasingly depend on AI, soft skills are becoming even more critical. (View Highlight)
AI will enhance the abilities of developers at all levels, from juniors to seniors, and the distinctions in mastering languages and technologies will blur more and more. (View Highlight)
As a result, we’re likely to see job offers placing greater emphasis on cultural fit—focusing on alignment between the candidate’s current situation and the company’s stage of growth, experience in similar organizations, adaptability to comparable work models, and integration into new environments. Soft skills will become the deciding factor in who moves forward in the hiring process. (View Highlight)
Key soft skills include:
• Teamwork
• Problem-solving
• Adaptability to change
• Learning ability
• Autonomy
• Written and verbal communication (View Highlight)
Candidates who may have slightly less technical expertise but excel in soft skills will often outshine technically superior candidates with weaker interpersonal abilities. AI will help level the playing field in terms of technical skills, making soft skills the true differentiator. (View Highlight)
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