Late last year, LinkedIn made its AI-powered Hiring Assistant, an AI agent for recruiters, available in English globally.
This tool is used specifically for recruitment in the form of a proprietary large language model (LLM). Early data has shown that it can personalize and automate pre-screening of job applications, flag skills mismatches and confidence gaps, and test for risks such as bias and bias.
With the growing role of AI in many workflows in enterprises, recruiters are also using it to accelerate hiring, while making informed decisions about candidates.
Earlier this month, LinkedIn announced Verified Skills, which allows professionals to showcase verified abilities on their profiles, in partnership with AI companies Descript, Lovable, Really.app, and Replit.
Speaking to HT, Hari Srinivasan, vice president of product at LinkedIn Talent Solutions, said AI is now fundamentally changing how hiring decisions are being made by giving a more nuanced view of a candidate’s skills beyond traditional markers like degree or school.
He said LinkedIn research shows that 74% of Indian recruiters found it difficult to find qualified talent in the last year. Srinivasan believes India is a key market for LinkedIn because it is a real-world stress-test for hiring systems at scale. Edited excerpts.
Q. How does AI meaningfully transform the hiring process, what signals is AI uncovering today that traditional resume and keyword filters consistently miss? What types of contexts—industry, role complexity, geography, company maturity—are the hardest for hiring models to learn reliably today?
Hari Srinivasan: AI is becoming a force to be reckoned with in recruiting as it tackles the two biggest sources of friction in this market: noise and time. Recruitment is one of the most complex markets in the world where you are considering thousands of potential candidates to find the perfect hire. Where AI helps most is moving beyond surface-level signals like title and keyword matches to deeper evidence of capability – the projects someone has worked on, the skills they’ve demonstrated, and the context behind their experience. The shift from keyword-based hiring to evidence-based hiring allows recruiters to uncover talent they might otherwise miss, while also giving a more nuanced picture beyond traditional proxies like their degree of skills or where they went to school.
What remains hardest for models to learn are the deeper human, situational layers of recruitment such as a hiring manager who is willing to compromise, or the interpersonal dynamics that drive long-term success. That’s why the best systems keep humans in the loop so the AI can produce better evidence, but people still make the decisions. The future isn’t AI versus humans – it’s AI reducing the hard work and uncovering better evidence, so humans can focus on what they do best: decisions, relationships, and long-term thinking.
Q. How do you design AI that broadens opportunity and doesn’t reinforce existing gaps in an organization’s recruiting process? And to that point, how can LinkedIn help individuals understand which skills really matter most?
Hari Srinivasan: AI expands opportunities when it builds on trust, advances skills-based approaches, and allows humans to be in control of hiring decisions. This is the core of how we are building our tools for job seekers and employers in line with LinkedIn’s responsible AI principles. For recruiters, we designed Hiring Assistant to reduce repetitive, time-consuming parts of recruiting so they can spend more time on strategic, people-centric tasks. Early adopters viewed 62% fewer profiles to find a match, achieved a 69% higher InMail acceptance rate, and nearly halved the time it took to review applications. This frees up recruiters to spend more time on the specific human parts of the job, like connecting with candidates, connecting with stakeholders, and making hiring decisions. As AI tools become a larger part of the recruiting process, recruiters will learn new skills like effectively motivating AI, calibrating roles, and interpreting the evidence behind recommendations, but it’s their uniquely human judgment that will continue to define great recruiting.
For job seekers, we aim to make the process more transparent and less cumbersome. It starts with helping people understand the skills and experiences needed for a role, beyond just the job title. That’s why we’re creating experiences that help members more easily find the right opportunities – from AI-powered job search that lets people describe the job they want in their own words, to our Job Matching feature that helps members see how well their skills match a job in seconds.
Q. As hiring becomes more data-driven, what new responsibilities do platforms like LinkedIn take on not only for employers, but also for job seekers? What new tech skills do you think recruiters themselves will need as AI becomes more autonomous?
Hari Srinivasan: As AI transforms work, building and maintaining trust has never been more important. Data can make hiring faster and more efficient, but it only works if people have confidence in who they are interacting with and how decisions are being made. That’s why we’re investing deeply in trust on our platform, from real identities to verified recruiters and now verified skills. More than 100 million members have already verified their identity on LinkedIn, helping to build a network based on authenticity and genuine professional interactions. We’ve also introduced Verified Skills so professionals can highlight their capabilities in a credible, reliable way and demonstrate proficiency in today’s most in-demand skills directly on their LinkedIn profile.
For recruiters working in increasingly crowded and complex applicant pools, verified skills help uncover real potential faster. And for professionals looking to build credibility in a rapidly changing job market, verification gives employers and networks more confidence in their expertise – helping to ensure that opportunities are driven by real skills, not just keywords. On a larger scale, it is about building a hiring ecosystem that works with honesty, transparency and confidence for everyone.
Q. India offers immense scale, linguistic diversity and non-linear careers. What are the key cases in the Indian market that have helped create a global AI-based hiring model, and how do you see it evolving over the next few years?
Hari Srinivasan: India is one of the most important markets for us to learn from as it stress-tests recruitment systems on a real scale. Linguistic diversity, non-linear careers, long notice periods and salary ambiguity are not marginal matters here, they are everyday realities. Our latest LinkedIn research shows that the pressure is clearly on today as 74% of Indian recruiters say it has become harder to find qualified talent in the past year and almost half say they feel additional pressure to explain how AI is being used in the recruitment process.
That is why some of our product innovations have ranked first in India. Features like expected salary and notice periods were built in here because they materially impact hiring speed and talent outcomes in this market – and once they work at the scale and complexity of India, they are easy to implement globally. We are also seeing this reflected in customer outcomes. Wipro India, one of the early adopters of Hiring Assistant, shares that the tool is helping their teams identify specific talent faster, reducing time spent screening profiles and enabling recruiters to focus on onboarding the right candidates earlier in the hiring process.
Over the next few years, I expect AI-led hiring models to increasingly take lessons from markets like India, embedding calibration, transparency, and trust directly into the workflow, so that hiring can become faster, fairer, and more evidence-based without losing the human core.