How Hunar.AI Is Redesigning Frontline Hiring

At the India AI Impact Summit 2026, conversations around AI impact extended beyond enterprise infrastructure and into a less discussed layer: frontline hiring.

The interaction with Krishna Khandelwal, Founder & Co-founder, Hunar.AI highlighted a shift in how AI is being positioned in workforce transformation: not as automation, but as access infrastructure.

The discussion centred on attrition, informal hiring, bias, and the role conversational AI could play in formalising India’s blue- and grey-collar workforce.

From Talent Scarcity To Access Gaps

A key theme emerging from the interaction is that fragmentation in frontline hiring is less about lack of talent and more about broken discovery.

Traditional hiring processes, resumes, job portals and intermediaries were built for digitally fluent candidates. That model excludes large sections of India’s voice-first workforce.

Conversational AI introduces a different entry point: language-led engagement, continuous interaction and structured evaluation without requiring formal resumes.

The shift reframes AI impact. Instead of replacing recruiters, AI becomes a discovery layer connecting employers with previously invisible talent pools.

Bias Debate Is Moving From Humans To Systems

The conversation also addressed a sensitive question: whether AI risks amplifying hiring bias. The argument presented was structural. Most hiring bias today is human, often embedded in subjective signals such as “fit” or communication style.

AI changes the mechanics of evaluation when designed with guardrails enabling structured scoring, monitoring and explainability. This positions AI not as a neutral technology by default, but as a system whose fairness depends on design choices, observability and governance.

For high-volume sectors where speed matters, the balance shifts toward standardised evaluation combined with human escalation rather than fully automated decisions.

Why Hiring Cannot Be Treated Like Logistics

Drawing from experience scaling logistics technology, the discussion highlighted why workforce optimisation differs from supply-chain optimisation.

Supply chains operate on structured inputs and predictable outputs. Hiring does not. Human potential is probabilistic, contextual and dynamic.

This exposes the limits of document-based hiring models.

Conversational interaction rather than static resumes becomes the scalable way to assess motivation, intent and adaptability. The implication is that AI impact in hiring is less about automation and more about signal discovery.

Infrastructure Exists; Adoption Is The Real Constraint

Another important signal from the interaction challenges a common narrative: that AI workforce platforms are waiting for infrastructure readiness.

Smartphones, digital identity and cloud capabilities already exist at scale in India.

The bottleneck is enterprise behaviour. Organisations must redesign hiring for accessibility, voice-first interaction and continuous engagement. Without that shift, AI risks being layered onto legacy workflows rather than transforming them. This reframes AI impact as a redesign problem, not a technology problem.

At the summit, frontline hiring emerged as one of the clearest examples of AI moving from experimentation to structural change.

Three signals stand out:

• AI is shifting hiring from screening to discovery

• Fairness is becoming a system design question

• Workforce formalisation is emerging as a major AI impact category

The broader implication is that AI impact in India may be defined as much by labour market access as by enterprise productivity.

If enterprise AI is about control, frontline AI is about reach.

Interview with Krishna Khandelwal, Founder & Co-Founder, Hunar.AI, Excerpts 

India’s frontline workforce remains largely informal and fragmented. Can AI genuinely formalise hiring and workforce management at scale, or does it risk digitising inefficiencies without addressing structural labour issues?

Let’s be clear: AI cannot solve the speed of policy implementation. Legal formalisation is a governance issue. But digital formalisation? That is absolutely solvable, and AI is the fastest path to it.

India’s frontline workforce is not fragmented because talent is scarce. It is fragmented because access is broken. Millions of capable workers are locked out of opportunity not due to lack of jobs, but due to lack of reach. Traditional hiring systems were built for desktop users with resumes and email IDs. That model simply does not map to India’s voice-first workforce.

Conversational AI rewrites that equation. When a worker can engage in their own language, through voice, without navigating intimidating forms or intermediaries, access expands instantly. Bias embedded in informal networks weakens. Employers can evaluate potential at scale. Engagement becomes continuous, not episodic.

This is not digitising inefficiency. It is bypassing it. If India wants to digitally formalise its workforce, conversational AI is not a feature; it is infrastructure.

As AI platforms increasingly influence hiring decisions, how should companies balance efficiency and automation with bias mitigation, transparency, and worker rights, especially in high-volume, low-margin sectors?

The irony in the AI bias debate is striking. Most hiring bias today is human. It hides behind phrases like “culturalfit",” “communicationpolish",” or"background".” It is subtle, unintentional and extremely costly.

When built responsibly, AI can actually reduce this arbitrariness. Machines can be audited. Humans cannot. AI systems can be instrumented with guardrails, structured evaluation logic, bias monitoring layers, and explainable scoring. They can be designed not to drift. They can be observed in real time.

The answer is not less AI. It is better AI.

In high-volume sectors, efficiency is survival. But fairness cannot be optional. The real opportunity is to eliminate inconsistency, not eliminate humans. AI should standardise evaluation, document decision logic, and escalate edge cases for review. That creates more transparency than most manual hiring processes ever had.

Used carelessly, AI can amplify bias. Used correctly, it can neutralise it. The responsibility lies with how we build, not whether we build.

Having seen logistics tech scale globally, what parallels do you see between optimisation in supply chains and optimisation of human capital—and where does that analogy break down?

Very few, and that distinction matters.

Supply chainoptimisationn is deterministic. You input structured variables and generate a defined output. It’s math-heavy, but it’s codifiable. Human capital is not. People are not inventory units.

Hiring is probabilistic. Inputs are incomplete. Signals are contextual. Outcomes are uncertain. Two identical resumes can lead to completely different performance outcomes. That alone exposes the limits of document-based hiring.

For decades, we tried to reduce human potential to keywords and checkboxes. It was always flawed. Resumes are static proxies for dynamic capability.

The only scalable way to understand potential is through conversation. Conversation reveals intent, adaptability, clarity of thought, and motivation dimensions no document can capture. Conversational AI makes that evaluation scalable without turning humans into spreadsheets.

Supply chainsoptimiseethe movementt of goods. Conversational AIoptimisess discovery of potential. That’s a fundamentally more complex and more powerful problem to solve.

With India pushing AI adoption under national initiatives and digital public infrastructure, what foundational gaps—data quality, digital identity integration, or enterprise readiness—still limit AI-driven workforce platforms from becoming mainstream infrastructure?

The truth? The infrastructure already exists.

Smartphone penetration is deep. Digital identity is established. Cloud infrastructure is mature. India has already proven through digital public infrastructure that adoption can move at breathtaking speed.

The constraint is not technology. It is intent.

We overstate readiness gaps and understate inertia. Every major digital shift in India was met with scepticism, until it wasn’t. Once adoption crosses a threshold, scale becomes self-reinforcing.

AI-driven workforce platforms are not waiting for infrastructure. They are waiting for conviction from enterprises willing to redesign hiring for accessibility, voice-first engagement, and inclusion.

India does not need better rails. It needs the will to use the ones it has.