When most organisations are layering AI onto legacy recruitment systems, Atul Sahgal, SVP and Global Head of Talent Acquisition at Cognizant, is thinking far more structurally. For him, the opportunity is not about adding tools — it is about redesigning the entire architecture of hiring. What emerges from his vision is an AI-native, intelligence-first talent acquisition ecosystem — one that combines automation, skills architecture, natural language intelligence and human oversight into a cohesive operating model.
Building an AI-Native Talent Acquisition Engine
“While most organisations layer new AI tools onto traditional recruitment workflows, Cognizant is building an AI native, intelligence-first talent acquisition ecosystem,” he explains. “It is about rewiring the full recruitment lifecycle around AI as the design principle rather than an add-on.”
A key differentiator is the development of an AI-native applicant tracking system that embeds intelligent capabilities such as contextual matching, automated parsing and conversational screening into the core architecture of the platform itself. The ambition is clear: create a system where intelligence is built in, not bolted on.
Equally central is what Atul calls a human-centred AI strategy. “We view agentic AI as a force multiplier for recruiters, not a replacement. This ensures better decisions, stronger candidate experiences and far greater trust in the hiring process.”
In his words, Cognizant is not simply ‘using AI’ in recruitment; it is redefining what modern talent acquisition looks like when engineered from the ground up for an AI-first world.
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When Data Effort Collapses to Zero
One of the most transformative shifts lies in how recruiters interact with data.
Natural language business intelligence (BI) fundamentally changes the experience. Recruiters can now ask questions in plain English and receive instant, analysis-ready insights. A query such as, ‘Show me candidates stuck in screening for more than five days,’ or ‘Which high-priority roles have no candidates in the interview stage?’ prompts the system to generate the necessary joins, filters and visualisations; work that previously required hours of manual effort.
Questions like ‘Which channel delivered the highest-quality candidates last month?’ or ‘How are we converting applicants to hires by sourcing channel?’ now return near real-time answers without specialised analytics expertise.
The impact extends into operational forecasting. Recruiters can request time-to-hire trends by skill family, identify potential SLA breaches for the week, or ask for predictions of offer-drop risks across open requisitions. Insights surface in seconds, enabling far more agile and proactive decision-making.
As Atul puts it, natural language BI has “essentially collapsed data effort to zero”, freeing recruiters to focus on strategy, candidate experience and forward-looking workforce decisions.
Architecting a Skills-First Recruitment Ecosystem
Skill-based matching is gaining global traction, but scale and fairness remain challenges. Cognizant’s response is to architect what Atul describes as a skills-first recruitment ecosystem that integrates AI, enterprise-wide skill ontologies and continuous talent development into one unified system.
At the foundation sits an AI-native matching engine using semantic search and deep language models to understand profiles and job requirements contextually. This removes reliance on keyword-based matching and significantly improves both precision and fairness.
Enterprise skill frameworks — such as Role Skill Clusters (RSC) and Cognizant Careers Architecture (CCA) — ensure that every job, skill and capability is defined consistently across the organisation. Standardisation at this level is critical for global scale, allowing the matching engine to operate with a unified skill vocabulary.
The system also extends to candidate-side intelligence, enabling individuals to receive role recommendations aligned not merely with their current title, but with their broader career potential.
Internally, skill-ready talent pools and redeployment platforms create structured pathways for internal mobility, reducing dependency on external hiring. For early talent, bootcamps and structured academy models generate verified skill pipelines at scale, strengthened by assessment partnerships that reinforce the validation loop.
Together, these elements form a complete skills-based hiring ecosystem — improving accuracy and fairness while promoting internal growth and long-term capability building.
The ‘Super Recruiter’ and the Human-Supervised Future
Looking ahead, Atul envisions what he calls a “Super Recruiter” agent — capable of orchestrating a suite of specialised AI agents across sourcing, screening, scheduling, insights and interview enablement.
This multi-agent system would dramatically compress the hiring process, enhance speed and precision, and allow recruiters to concentrate on relationship-building and high-quality decision-making.
Yet, fully autonomous hiring is not the aspiration.
“We believe that AI reaches its highest value when paired with human judgement,” Atul emphasises. Ethical governance, fairness, context-sensitive decision-making and candidate trust still require human oversight.
The future, therefore, is not machine-led autonomy, but human-supervised autonomy — where AI performs the operational heavy lifting and pattern recognition, while recruiters provide the judgement, empathy and governance required to ensure responsible talent decisions at scale.
For Cognizant, the endgame is not automation for its own sake. It is the deliberate engineering of a recruitment function that is intelligent by design, scalable by architecture, and human at its core.
Atul Sahgal is a talent acquisition expert with over 25 years of experience in developing high-impact hiring strategies. He leads a team of creative, goal-oriented recruiters, focused on meeting large-scale organisational hiring needs without compromising on quality. A firm believer in collaboration with business leaders, Atul ensures that recruitment outcomes closely align with business expectations. His core strengths lie in designing scalable hiring strategies and driving continuous improvements in recruitment processes, making him a trusted advisor in navigating the evolving talent landscape.
