As companies scale globally and recruitment volumes surge, artificial intelligence is playing an increasingly critical role in how organisations identify and hire young talent. For fresh graduates, this presents both opportunity and uncertainty. Can AI-powered tools truly see beyond a traditional resume? Are they equipped to evaluate potential, especially in candidates from non-traditional backgrounds?
To explore this question, we gathered insights from senior professionals deeply involved in shaping the future of talent acquisition across leading global organisations. Their perspectives offer a nuanced view of how AI is being deployed in fresher recruitment and where its possibilities intersect with its limitations.
AI and the New Age of Fresher Hiring
Global firms are increasingly relying on AI to streamline the recruitment process for entry-level roles, particularly in campus hiring. The ability to process large applicant pools quickly and objectively has made AI a powerful ally for talent acquisition teams.
“At Onsurity, our approach to fresher recruitment emphasises agility, diversity, and future readiness,” said Dinesh Menon, Founder’s Officer – Strategy, Governance & People. “We prioritise skills and cultural fit over resumes alone. To ensure accuracy and relevance, we leverage AI-powered tools to assess real capabilities, helping us cut through exaggerated claims and identify genuine talent aligned with the role and our values.”
Yadhu Kishore Nandikolla, Senior Human Resources Director at Evernorth Health Services India, described a similar approach, highlighting how AI is already embedded in the hiring workflows of global firms. “AI-powered tools are rapidly gaining popularity in campus recruitment and fresher hiring, especially in high-volume markets like India. Global companies are using AI to streamline tasks like resume screening, skills assessments, and candidate ranking,” he said.
He added that these tools can offer deeper insights into a candidate’s abilities through methods such as gamified assessments or situational judgement tests. However, he also warned of the downsides of excessive automation: “Over-reliance on automation can lead to impersonal candidate experiences, limited interaction with recruiters, and missed opportunities to understand a candidate beyond test scores.”
Assessing Potential vs. Matching Skills
While AI has significantly improved the efficiency of screening, its ability to assess the potential of a candidate, as opposed to simply matching skills, remains a subject of debate.
“New-aged tools help identify better skill fit, not potential,” noted Dinesh Menon. “The output of all AI tools is based on its learning model and data that’s been labelled over time. It also depends on how well the job description has been detailed with clear outcomes.” Nevertheless, he acknowledged the advantage AI offers in creating more equitable and objective assessments, stating that it “fosters a merit-based process that goes beyond traditional resumes.”
Designing Fair and Inclusive AI Systems
The effectiveness of AI in identifying talent is directly tied to how thoughtfully it is designed. When used without intention, it risks reinforcing systemic biases. But when developed responsibly, it can serve as a leveller.
“At Encora, we believe in evaluating potential over pedigree,” said Regina Thomas, Head of Talent Acquisition – India. “That means designing AI systems that are fair by default: trained on diverse datasets, continuously audited for bias, and calibrated to identify qualities like adaptability, curiosity, and creative problem-solving, not just academic credentials.”
She emphasised the importance of blending automation with human insight. “We take a human+AI approach. Our AI tools surface patterns, highlight behavioural signals, and accelerate initial screening. And it’s our recruiters who apply context, empathy, and nuance, especially when evaluating candidates from non-traditional backgrounds, including self-taught coders, career switchers, and talent from Tier 2/3 cities.”
For Gunjandeep Kaur, Director – HR Business Partner at Model N, fair AI begins with responsible data. “It’s critical to ensure that [AI] promotes equitable access to opportunities for all talent, including individuals from non-traditional or underrepresented backgrounds,” she said. “This begins with training AI on diverse, representative datasets to reduce bias and developing algorithms that recognise potential, not just conventional markers like educational background or past employers.”
Balancing Efficiency with Empathy
One of the biggest challenges recruiters face is finding the sweet spot between efficiency and inclusion. AI can certainly speed up shortlisting, but ensuring a fair and transparent experience requires deliberate human intervention.
“At Onsurity, our commitment is clear: every candidate gets transparent feedback, unconscious bias is actively minimised, and our hiring process is as inclusive as it is efficient,” said Menon.
Regina Thomas outlined a structured framework that Encora follows to balance both goals. “Efficiency and inclusivity are not trade-offs; they are twin goals,” she said. “We embed three core practices: structured evaluation through role-specific scorecards, inclusive sourcing by reaching out to underrepresented talent pools, and human oversight in all final decisions.”
She also stressed the importance of feedback in maintaining fairness: “Explainability isn’t just an AI principle, it’s a human one.”
Gunjan reinforced this, highlighting that hiring must remain human-centric even in a tech-heavy world. “Incorporating structured, skills-based assessments and blind evaluations can help identify candidates from non-traditional backgrounds,” she said. “Ultimately, the goal isn’t just to speed up hiring; it’s also about making hiring smarter and more equitable.”
Yadhu Kishore agreed, adding that “fresh graduates from diverse or non-traditional backgrounds may not always fit standard algorithmic profiles, and relying only on AI risks overlooking their true potential.” According to him, companies must ensure that “AI is a valuable tool for initial screening and assessments, but it must be complemented by human insight to ensure an inclusive, balanced, and just hiring journey.”
Humanising AI in Talent Discovery
As these voices make clear, AI is transforming fresher recruitment, but it is not a silver bullet. The quality of its impact depends on how consciously it is applied. With the right balance of data and discernment, automation and empathy, recruitment can become faster, fairer, and more future-ready.
In the words of Dinesh Menon, “Operational excellence means nothing if we overlook real human potential.” The promise of AI lies not in replacing judgement, but in enhancing it, ensuring that potential is never missed just because it does not fit neatly within a digital profile.
This article was originally published on All Things Talent
