HRTech Interview With Ashutosh Garg, Co-Founder and CEO, Eightfold AI
Ashutosh Garg, Co-Founder and CEO, Eightfold AI chats about the evolving standards in HRTech and what’s causing a transformation across most areas of HR in this HRTech Interview:
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Tell us about yourself and your role at Eighfold.ai?
I’m Ashutosh Garg, CEO and Co-Founder of Eightfold AI. In my role, I lead the company’s vision, strategy, and execution, with a particular focus on how AI and agentic systems are reshaping the future of work and talent decisions at enterprise scale.
A large part of my focus today is around what I describe as the “infinite workforce,” a future where humans and autonomous digital workers collaborate together, enabling organizations to operate with greater speed, adaptability, and intelligence while allowing people to focus more on judgment, creativity, and human connection.
I started Eightfold because I believed, and still believe, that employment is one of the foundational systems of society, and that people deserve not just a job, but the opportunity to find the right career and continue growing over time. Too often, talent systems were designed to filter people out based on narrow signals like titles or keywords instead of understanding skills, potential, and trajectory. What continues to motivate me most is building technology that helps organizations better understand talent while expanding opportunity more fairly and intelligently for people.
We’d love more insights on your most recent product enhancements and the launch of TalentForge.
One of the biggest shifts we’re seeing in HR technology is that companies no longer want to force their workforce strategies into rigid, one size fits all software. Every enterprise operates differently, yet many HR software platforms were historically built around standardized workflows and static architectures.
We recently launched TalentForge in response to a broader shift toward more adaptive enterprise systems that can reflect a company’s industry, operating model, and workforce reality. In enterprise AI, what matters increasingly is not just the ability to build applications, but the intelligence layer those systems are built on.
In HR, that layer provides an understanding of skills, capabilities, and workforce patterns that makes AI useful in real decision making, while also supporting governance, compliance, and explainability as AI moves closer to core talent processes. TalentForge enables enterprises to build custom talent applications and workflows on top of this foundation rather than relying on fixed system designs.
Interviewing is another area being reshaped by AI. Traditional processes were designed around human constraints, including multiple rounds, fragmented assessments, and inconsistent evaluation depending on interviewer availability and experience. With AI Interviewer, we are exploring how interviewing can be made more structured at scale, and 360 Interview, the new capability within it, brings multiple interview types into a single structured conversation. This reduces fragmentation in how candidates are evaluated while keeping hiring teams in control of final decisions.
Additionally, Workforce Readiness addresses a different challenge: many organizations still rely on surveys or lagging indicators to understand AI adoption, which limits real-time visibility. It provides continuous signals on adoption, capability, and where employees may need support, and translates those signals into personalized development and coaching pathways to improve readiness over time.
Across these shifts, HR systems are becoming more adaptive and intelligence-driven as workforce needs continue to evolve.
How should job seekers today optimize for an AI driven hiring process and cycle?
Job seekers today should focus less on optimizing for algorithms and more on presenting clear, structured evidence of their skills and experience in a way that both AI systems and recruiters can interpret. As hiring becomes more structured and evidence driven, clarity and consistency matter more than keywords.
One important shift is toward structured, example-based evaluation, where candidates are assessed on specific experiences, outcomes, and decision-making rather than broad descriptions of past roles. Preparing concise, well-structured examples that show context, actions, and results helps ensure experience can be fairly understood across increasingly standardized evaluation processes.
At the same time, hiring is moving toward earlier and more comprehensive assessment, sometimes combining multiple dimensions of evaluation in a single stage. This means candidates need to be prepared to demonstrate depth across different skill areas earlier in the process, rather than spreading signals across many rounds.
Consistency is also becoming more important. As systems compare signals across resumes, profiles, and interviews, alignment in role history, scope, and outcomes helps reduce friction and creates a clearer picture of experience. In this environment, trust comes from coherence across how a candidate presents their work, not just any single interaction.
Finally, the fundamentals remain unchanged. Candidates who can clearly demonstrate skills, adaptability, and learning agility through credible examples are the ones who stand out, even as the mechanics of hiring continue to evolve.
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Similarly, what should hiring leaders do better to ensure the AI features they use to empower hiring cycles do not lead to the loss of quality candidates due to keywords and filtering workflows? This is often cited as a pain point.
Hiring leaders should avoid using AI primarily as a filtering mechanism based on keywords, job titles, or credential proxies, as this can unintentionally exclude strong candidates with transferable or adjacent skills.
Instead, they should design AI-enabled hiring processes around evidence of capability rather than surface-level matching. This means using AI to surface richer signals about skills, experience, and outcomes earlier in the process, and shifting toward more structured and consistent evaluation so candidates are assessed on comparable evidence rather than fragmented impressions or incomplete signals.
Transparency is also critical. Hiring teams need to understand how candidates are surfaced or deprioritized so they can apply judgment and identify when filtering logic may be too rigid for a given role or market.
At the same time, these systems cannot be static. Roles, skills, and labor markets evolve quickly, and hiring approaches need continuous calibration to ensure they remain aligned with what “qualified” actually means over time.
The goal is not faster filtering, but better, fairer visibility into talent so more qualified candidates are consistently evaluated based on evidence of their skills and potential.
How will the future of HR evolve as HRTech and AI reshape the role?
The future of HR will be far more intelligence-driven, skills-centric, and dynamic than the systems we’ve historically relied on. For decades, HR technology was primarily designed around transactions and administration, managing records, workflows, and compliance. AI is now shifting HR from a system of record to a system of intelligence that informs decisions and execution.
A key shift is the move away from static signals such as job titles and degrees toward a deeper understanding of skills, capabilities, and adaptability. Organizations increasingly recognize that talent is more fluid than traditional job architectures suggest, and AI can help surface adjacent skills and nontraditional talent pathways that are often missed in conventional models.
At the same time, HR is moving toward a more continuous understanding of the workforce, where skills, readiness, and capacity are evaluated in real time rather than through periodic cycles. This allows organizations to respond more quickly as roles and business needs evolve.
The role of HR is also becoming more strategic. As AI takes on more administrative and coordination-heavy work, HR teams will spend more time on organizational design, workforce strategy, and enabling transformation across the business. The function becomes more focused on shaping how work gets done, not just managing processes.
Looking ahead, the most successful organizations will be those that treat AI not as a way to automate HR, but as a way to make talent decisions more informed, adaptive, and human-centered. The goal is not to remove judgment, but to enhance it with better signals and greater visibility into how work and skills are evolving.
Five thoughts you’d leave our readers with before we wrap up?
1. HR is shifting from execution to orchestration of a talent system of action.
Organizations are moving toward continuously matching people to work, growth, and opportunity, rather than managing static workflows. The focus is shifting from process administration to connecting talent insight directly to execution at scale.
2. The winners will be skills- and potential-first, not proxy-driven.
The real shift is from titles and keywords to richer, structured evidence of capability. This allows organizations to understand what people can actually do, not just how their experience is labeled.
3. We are entering an agentic era of execution at scale.
AI systems will increasingly handle high-volume coordination tasks like interviewing workflows, scheduling, and screening steps. Humans remain accountable for defining quality and making final decisions.
4. Trust becomes a core system requirement, not a layer on top.
Meaningful human oversight, transparency, and auditability need to be built into the design of systems from the start. This is what allows organizations to scale AI in high-stakes talent decisions with confidence.
5. The HR tech endgame is measurable action, not visibility.
The next evolution is systems that move beyond insights and dashboards to directly drive outcomes such as better hiring decisions, improved mobility, and faster skill development across the organization.
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[To share your insights with us, please write to psen@itechseries.com ]
Eightfold AI is a Silicon Valley-based enterprise software company that provides an AI-powered Talent Intelligence Platform designed to transform human resources (HR) and recruitment.
Ashutosh Garg is the Co-Founder and CEO of Eightfold AI, the pioneer in AI-powered talent intelligence. A recognized leader in machine learning and artificial intelligence, he has authored more than 35 peer-reviewed research publications, holds over 50 patents, and has been cited in more than 6,000 research papers.
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