Smart tech with human touch - The future of HR with AI

Contents

Executive summary 04

01
02

Setting the stage: The future of smart HR tech with a human touch

05

The Changing HR Landscape: Forces Shaping the Future of Work 07

The HR Technology: Current Market Landscape 10

Reimagining HR with AI-Enabled Solutions 13

A governance blueprint for AI enabled HR 15

Implementation Roadmap 16

Change management and Risks: How 17 to make the new ways of working stick

Conclusion and implications 18

10 Acknowledgements

19

Executive summary

In an era defined by disruption, the role of Human Resources is undergoing a profound transformation. This KPMG in India HR Tech White Paper, Smart Tech with Human Touch, outlines a strategic roadmap for HR functions to evolve from fragmented, reactive operations into agile, insight-driven enablers of business value. This transformation is anchored in the convergence of AI-powered technologies, human-centered design, and governed operating models.

HR’s Strategic Evolution

HR is shifting from a compliance-driven cost center to a strategic intelligence hub. The future-ready HR function is embedded in business workflows, orchestrating experiences and outcomes through predictive analytics, dynamic talent marketplaces, and purposedriven systems.

Changing HR Landscape

The modern workplace is shaped by digitalfirst strategies, hybrid work models, and rising employee expectations. HR is now a center of intelligence—driving decisions on talent, performance, and workforce planning. While automation handles routine tasks, human empathy remains central, ensuring technology augments rather than replaces judgment.

The HR Technology: Current Market Landscape

The HR tech market is projected to reach \$76.4B by 20301. Organisations are adopting layered ecosystems comprising HCM platforms, Learning Experience Platforms(LXPs), Employee Experience(EX) tools and GenAI copilots. GenAI is already transforming JD creation, onboarding, and performance management. Enterprises prioritise scalability and analytics, while midmarket firms seek cost-effective, AI-native solutions.

A Governance Blueprint for AI-Enabled HR

Strong governance is key to using AI in HR safely and effectively. KPMG in India recommends setting up an AI-in-HR Center of Excellence (CoE), clear data ownership, and prompt standards. This helps ensure AI tools are fair, explainable, and compliant—building trust while enabling smarter, faster decisions.

Implementation Challenges and Roadmap

A 12-month roadmap guides organisations from pilot to production. Key phases include mobilization, pilot build, scale-out, and industrialization. Success hinges on product ownership, data excellence, and predefined gates for bias, explainability, and adoption.

Change Management

Successful change management in HR transformation means helping people adapt to new ways of working. This involves building the right capabilities—like AI literacy for HR teams—and creating a rhythm for regular check-ins and updates. HRBPs are supported to shift from routine tasks to more strategic roles.

2. Setting the stage: The future of smart HR tech with human touch

High level narrative of HR’s evolution

For most of its history, HR oscillated between compliance engine and cost centre, but the last decade introduced a new challenge: constant disruption. Skills shortages, hybrid work, and digital overload left many HR functions in a state of flux2—fragmented, reactive, and toolheavy. The next phase is flow: an operating state where HR is embedded in the business, experiences are frictionless, and insights guide decisions in real time. KPMG In India frames this hallmark of leading HR organisations—those that integrate technology, analytics, and humancentered design to deliver value continuously, not episodically. Thus, HR’s evolution is no longer about efficiency; it’s about orchestrating experiences and outcomes at scale.

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HR’s Strategic Evolution from KPMG in India’s Lens

KPMG International report2 illustrate what future-ready HR looks like:

Business Strategy connected to skills through dynamic talent marketplaces.
Relational analytics embedded in everyday workflows for faster decisions.
Purpose and well-being operationalised as part of the core system, not side programs.

Technology as a catalyst

Technology is the enabler, not the destination. Cloud HCM platforms, service management, and AI create the capacity and resilience for HR to deliver personalised, predictive services. Generative AI amplifies this by automating content-heavy tasks—job descriptions, onboarding workflows, performance summaries— while copiloting employee and manager interactions. But with ease of use of GenAI, there are risks involved. Hence, value materialises only when work is redesigned, and responsible AI governance is in place.

Figure 1: KPMG’s Powered Framework3

KPMG’s Powered4 HR framework enables this shift by combining a Target Operating Model with pre-configured processes and digital assets— accelerating transformation while preserving the 20–30% of differentiation that drives competitive advantage.

The human in the loop imperative

“Smart Tech with Human Touch” is not a slogan; it’s a control system. KPMG in India’s GenAI guidance4 is explicit: to realise value and protect trust, HR must institute human-in-the-loop governance—ensuring people remain accountable for judgment, escalation, and context specific decisions. That means documented guardrails for explainability, bias testing, privacy, and model monitoring, plus training that equips HR and line leaders to audit AI outputs, override when necessary, and capture feedback for continuous improvement.

In the next section, we can examine the macro forces pressuring HR to make that leap, and how to translate them into nearterm priorities and measurable outcomes.

GenAI becomes a copilot, not an autopilot; In the context of HR, leaders and professionals should define the purpose and ethical boundaries of AI use (setting intent), critically assess AIgenerated recommendations (validating outputs), and remain accountable for the final decisions and their impact on people and culture (owning outcomes).

Sunit Sinha Partner and Head, Human Capital Advisory, KPMG In India

3. The Changing HR Landscape: Forces Shaping the Future of Work

Human Resource Management has evolved far beyond its traditional administrative roots. Once seen primarily as the department that tracked attendance, processed payroll, and organised cultural events, HR is now a strategic partner5—central to shaping workforce strategy, driving business growth, and enabling organisational agility

From Operational Backbone to Strategic Intelligence Hub

Today’s HR functions are expected to deliver real-time insights that guide cost optimization, skill identification, attrition risk, and workforce sentiment6.

HR now influences key decisions— from performance and succession planning to compensation benchmarking and productivity tracking— shaping Annual Operating Plans.

Yet, despite this strategic role, HR teams still handle routine tasks like policy queries, interview scheduling, and attendance tracking, diverting focus from high-impact work.

Technology as a Force Multiplier

The emergence of HR Tech has been a gamechanger. Automation, analytics, and AI— including Generative AI— are transforming HR operations, enabling a shift from transactional tasks to strategic initiatives while keeping a people-centric approach7.

As per ETHR World8, 69% of organisations have already automated core HR operations.

Consider a simple policy query: instead of waiting 24 hours, employees now get instant, accurate answers via AI-powered chatbots, enhancing employee experience and reducing operational load.

KPMG International survey found productivity increase as the top outcome of technology enablement, followed by work-life balance, as shown below.

Top-ranked technology impacts for the last 12 months
Figure 2: KPMG International Report -The Future of HR: From Flux to Flow9

Top-ranked technology impacts for the next 12 months

The New Mandate: Data-Driven, Experience-Led, AI-Enabled

The future of work demands HR functions that are digitally fluent, emotionally intelligent, and insight-driven. HR must now operate at the intersection of:

Digital-first transformation: Leveraging AI and automation to scale operations.

Human-centric workforce strategy: Designing inclusive, purpose-driven employee journeys.

Data-driven agility: Using predictive analytics to anticipate and respond to workforce dynamics.

Artificial intelligence doesn't replace human intelligence—it enhances it. It is a catalyst for ‘speedy’ creativity and innovation.

AI augments the capabilities of HR professionals cutting through repetitive tasks, surfacing datadriven insights, and enabling faster, more informed decision

Maneesha Garg Partner and Head , Managed Services KPMG In India

A widening capability gap between current HR readiness and future demands is driving urgency. KPMG International’s survey of 300 HR leaders confirms that while strategic transformation is recognised, immediate action is essential to close this gap.

Top Areas with the Largest Gaps
Figure 3: KPMG International Report - Gaps in current capability vs. future importance10

. Building a Talent Marketplace

Organisations need agile platforms that align skills with tasks and roles, enabling internal mobility and rapid redeployment.

Workforce Shaping for the Next three Years

HR must proactively plan for changes in workforce size, structure, and skills using scenario planning and predictive analytics.

Improving Mental Health and Well-being

Well-being is no longer a perk—it’s a strategic imperative tied to retention and productivity.

. Automating HR Service Delivery

Manual processes still dominate, slowing response times and reducing employee experience quality.

. Delivering Predictive Insights from Workforce Analytics

One of the largest gap is turning data into actionable intelligence for business decisions

4. The HR Technology: Current Market Landscape

The HR technology market is evolving at an unprecedented pace, driven by digital-first strategies, employee experience expectations, and the rapid maturity of AI and GenAI solutions. Organisations are no longer asking if they should invest in HR tech—they are asking how fast they can scale.

Adoption Trends: Enterprise vs. Mid-Market

Market Dynamics: Rise of Niche Vendors

Enterprises lead in adoption, prioritizing scalability, integration, and advanced analytics

Mid-market and SMBs are driving volume growth, favouring costeffective, easy-to-implement solutions. Niche Vendors are gaining traction

Nearly 75% of businesses globally use HR tech, and 60% of solutions are cloud-based12

Organisations use an average of four HR tools, with complexity increasing by company size.13

The HR tech market is projected to reach \$42.5B in 2025 and \$76.4B by 2030 (12.8% CAGR)11

Niche vendors are emerging in areas like mental health, DEI analytics, and skills-based planning, challenging incumbents with AInative solutions

Buyer priorities are shifting toward AI capabilities, integration readiness, and responsible AI governance.14

Nearl y66% of HR teams already use GenAI, with JD creation and resume screening as top use cases.15

Digital HR transformation is the strategic reinvention of an organisation—leveraging technology at scale and speed to continuously unlock new sources of value, enhance agility, and drive sustainable growth.

Arun Sharma, Partner, Human Capital Advisory KPMG In India

KPMG in India’s value proposition in a rapidly evolving HR landscape is its ability to deliver AIpowered, human-centric transformation across the employee lifecycle16. By integrating digital solutions into areas like workforce planning, leadership, culture, and experience, KPMG in India helps HR shift from operational support to strategic impact17. From recruitment to wellness and exit, KPMG in India enables smarter, faster, and more HR outcomes—while keeping empathy and trust at the core.

AI Enabled Workforce Transformation

As the HR tech market evolves toward AI-native solutions, organisations must architect systems that embeds data ethics, AI governance, and regulatory adherence into every layer.

Model Layer

Hosts AI/ML models—including LLMs for Gen AI copilots, predictive analytics, and personalization—trained on HR-specific data and continuously refined via feedback loops.

• Knowledge Base: Structured repository of HR policies, FAQs, and documents grounding LLM responses as first-line support.
• Training Data: Includes anonymised HR history, behavioural signals, and synthetic datasets for edge cases20.
• Bias Detection and Explainability: Helps ensure fairness and compliance with Responsible AI standards.
• Confidence Scores: Outputs are tagged with confidence levels to guide decisions and HITL escalation.
• A/B Testing and Sampling: Evaluates model variants to optimise performance across diverse employee profiles.

Data Layer

The data layer serves as the foundational repository for all HR data—structured (e.g., employee records, payroll) and unstructured (e.g., resumes, feedback, interview transcripts). It supports ingestion pipelines from HCM, LXP, and EX platforms, and includes metadata tagging for lineage and traceability. Strong data governance helps ensure quality, access control, and compliance with the DPDP Act and CERT-In mandates18. Encryption is enforced both at rest and in transit. This layer also feeds curated datasets into model training pipelines19.

Orchestration Layer

This layer integrates AI services with enterprise HR systems via workflow engines and APIs

• Event-driven architecture for realtime HR processes like onboarding, payroll, and grievance redressal.

• Service mesh for routing requests between microservices, ensuring scalability and fault tolerance

4 Control and Governance Layer

This includes Human-in-the-Loop (HITL) checkpoints for sensitive decisions (e.g., hiring, performance reviews, disciplinary actions). It will also have Audit trails, and escalation logic based on confidence scores, as well as override mechanisms for compliance.

of HR Tech Reference Architecture

Security and Compliance Layer

Security is enforced through Role-based access control, identity federation, and continuous monitoring. Compliance alignment with CERT-In, ISO 27001, SOC 2, and sector-specific norms (e.g., RBI for BFSI, HIPAA-equivalent for healthcare). Transparency artifacts like SBOM, CBOM, and AI Model BOM. Compliance is enforced through DPDP Act (India) which requires explicit consent, purpose limitation, and data minimization. Non-compliance may attract penalties up to ₹250 crore. CERT-In Guidelines mandate breach reporting within 6 hours and annual third-party audits. Responsible AI Governance: Frameworks like KPMG Global’s Trusted AI to help ensure ethical AI deployment across the lifecycle21.

5. Reimagining HR with AI-Enabled Solutions

AI is emerging as a key driver of modern HR strategy—streamlining operations, enhancing decisions, and personalizing employee experiences. Based on KPMG’s Powered Framework, this chart highlights real-world AI use cases across HR functions, showcasing how Generative AI, ML, and NLP (Natural Language Processing) enable automation, insights, and strategic workforce planning across the employee lifecycle.

Talent Acquisition

• AI-assisted job description generation • AI-powered interview scheduling • Generative AI for multilingual offer documents

AI Use Cases

Workforce Administration

• AI for dynamic org chart generation • AI for detecting anomalies in personal data updates • ML for root cause analysis of incidents

Onboarding

• AI-driven onboarding checklist generation • Virtual assistant for onboarding queries • AI-driven IT asset allocation

Payroll

• AI-driven payroll rule configurator • Intelligent payroll calendar generator • AI-powered discrepancy detector • Bank File Format Optimiser

Talent

• Personalised career pathing using ML
• AI matching engine for mentor-mentee pairing
• AI-assisted strategic goal alignment metrics

Time Management

• AI-based Skill Gap Analysis • AI-assisted schedule framework optimizer • AI-Based Time Correction Suggestions

Learning

• AI-driven curriculum optimization • Content relevance prediction • Predictive analytics for learning ROI • AI-based Skill Gap Analysis

Workforce Shaping

Total Rewards

• GenAI for strategic workforce planning documents • AI-assisted business scenario modeling • AI-driven workforce transition planning

Workforce Insights

• Predictive analytics for compensation planning
• AI-driven modeling of total rewards scenarios
• Generative AI for personalised reward statements

• NLP-based survey analyser • ML-based pattern discovery • AI-assisted dashboard creation • GenAI for executive summaries

Workforce Experience

• AI-powered brand sentiment analysis • GenAI for employer branding content • GenAI for wellbeing campaign • AI chatbot for employee queries

While AI is transforming HR across the employee lifecycle—from talent acquisition to workforce experience— the human touch remains central.

The use cases shown here demonstrate how AI enhances personalization, efficiency, and insight, but it’s the HR professional who brings empathy, context, and judgment. This synergy between intelligent automation and humancentered leadership defines the future of HR.

In the era of intelligent
transformation, AI is
powering the next great
shift—revolutionizing how we work through
automation, innovation, and reinvention.

Purushothaman K G

Partner, Business Consulting, Head Technology Transformation, KPMG in India

Measuring GenAI Impact in HR

GenAI is redefining how time and value are distributed across HR functions. According to KPMG Global’s March 2025 analysis of HR managers in the tech sector, up to 31% of their time can be freed annually through GenAI augmentation—without compromising quality or compliance22.

This time savings translates into tangible economic value, allowing HR professionals to shift focus from administrative tasks to strategic priorities such as workforce planning, DEI, and employee experience.

To measure GenAI’s impact, HR leaders can track key KPIs such as:

H2RProcess	EconomicPillar(s)	Suggested Key KPls	Illustrative ROl Impact
Talent Acquisition	Productivityand ValueCreation	· Time-to-hire· Cost-per-hire	30-50% faster hiring; 15-37% lower sourcingcosts
Onboarding	Cost EfficiencyandExperience	· Onboarding Completion Rate· No Show rate· Onboarding Issue Resolution Time	20-40% faster onboarding; 4 days shorter cycle, 6% Reduction in no show via Al Nudging,Resolution Time Reduction: From 10 days to 3days
Learning andDevelopment	Value CreationandProductivity	· Learning hours per FTE· Training completion rate· Skills coverage index	Completion rate increase by 20% due topersonalised learning path;accelerated reskillingand upskilling
Performance Management	Value Creationand RiskMitigation	· Goal alignment index· Bias detection in ratings	Improved workforce productivity; Completionsupported by Al nudges;15% increase in promotion equity or 25% reduction in ratinginflation
Compensation andRewards	Cost Efficiencyand Retention	· Comp ratio (actual vs market median)· Pay equity gap	Optimised pay spend; improved retention;reduced pay inequities
EmployeeExperienceand ER	Value Creationand RiskMitigation	· Employee NPS· Grievance resolution cycle time· Automated Resolution Rate(Al chatbots)	20-30% reduction in ER case handling costs; 50- 70% HR queries resolved via Al Resolution,Retention Rate Increased by 3% due to quickgrievance handling
Workforce Insights/ Analytics	ProductivityandCompliance	· Workforce productivity index -· % data-driven decisions· Compliance reporting cycle time	10-15% productivity lift; faster reporting; 30%lower compliance cycle costs
Exit/ Offboarding	Cost EfficiencyandCompliance	· Offboarding Completion rate· Compliance breaches in exit clearance· Alumni Engagement Rate	Al driven checklist automation - 95% timelyclosure; lower compliance risk; Re-HireConversions or more referrals from Alumni

6. A governance blueprint for AI enabled HR

The operating model for HR is moving decisively toward a product oriented, datacentric, and AI governed model. Leading research points to two structural shifts underpinning this change. First, HR is “productizing” services—forming cross functional teams with end-to-end accountability and a strong data and service backbone. Second, AI is automating and augmenting work across roles, forcing clearer decision rights and oversight mechanisms to protect fairness, compliance, and employee trust (and to capture value at speed).

A modern governance design therefore mandates four anchors: an AI-in-HR CoE to set policy, taxonomy ownership to standardise people data for model use, prompt governance to control GenAI outputs, and HRBP enablement to help ensure adoption and impact.

The AI in HR CoE: policy, product, and performance at the core

An effective AI-driven HR CoE acts as the policy owner and capability hub for AI across the HR portfolio23, enforcing ethical principles, managing use cases, and guiding build vs. buy decisions. It is organised around products (e.g., Talent Acquisition, Performance and Growth, Employee Help), with product managers accountable for outcomes, reliability, and human-AI workflow design. As automation reduces routine tasks, the CoE’s role expands to monitoring AI performance (drift, bias, hallucinations) and tracking value24. Research shows HR Ops have the highest efficiency potential, while HRBPs benefit from product-centric teams and unified data/service infrastructure.

Prompt governance turns GenAI from creative chaos into reliable HR tools.

As GenAI enters HR workflows—writing job descriptions, interview questions, feedback— it demands structured oversight. A formal governance model standardises high-impact prompts, applies risk tiers and human intervention for sensitive outputs, and helps ensure review pathways for employees and candidates. The shift isn’t just technical; it’s strategic. Unlocking GenAI’s value depends on workforce redesign and HR-led adoption to deliver impact at scale.

AI learns what we teach it—so joint data governance and taxonomy is critical.

A cross-functional council (HR Analytics, CoEs, Enterprise Data) owns the skills and roles taxonomy, job architecture, and lifecycle definitions to help ensure consistent, bias-free AI interpretation across platforms. This shift from process excellence to data excellence is key to next-gen HR models and a strong foundation for scalable, AI-enabled services.

How will HRBPs enable this change?

As AI takes over high-volume tasks, HR Business Partners (HRBPs) must evolve into strategic advisors focused on talent strategy, skills planning, and organisation design. This shift requires three key enablers: AI literacy to interpret dashboards and guide responsible use; embedded analytics from product squads25 that deliver actionable insights with change narratives; and role redesign to free HRBPs from escalations, allowing greater focus on workforce planning, culture, and leadership. Studies show that when AI is paired with redefined roles and product-centric structures, HRBPs see a significant boost in productivity and impact.

7. Implementation Roadmap

The goal is to reach from governed pilots to productised, AI enabled HR services with clear accountabilities, service levels, and benefits tracking. The roadmap below follows the operating model patterns that leading HR functions are adopting—product ownership, a strong data/ service backbone, and agile ways of working—while incorporating AI governance and model risk controls from day one so we scale safely and credibly.

Phase	Timeframe	Objective
Mobilise and Baseline	Weeks 0-8	Confirm 3-5 Al use cases with clear value hypotheses; map data/privacy constraints; capture KPl baselines
Pilot Build and Safeguard	Weeks 8-16	Build two-three governed pilots and institute human-in-the-loop review
Scale-Out and Productise	Months 4-9	Expand pilots to 3-5 business units; move from project to product teams
Industrialise and Assure	Months 9-12	Normalise the operating rhythm(governance board, model-risk council)

What to pilot first?

Pilot A: Talent Acquisition Fast Lane

Uses GenAI for job descriptions, bias-safe screening, and interview scheduling—while keeping hiring decisions human-led. Targets include faster hiring (↓20–30%), improved shortlist diversity, higher manager satisfaction, and full explainability for automated steps26.

Pilot B: Employee Help Copilot

Deploys a GenAI assistant over approved policies to answer employee queries and deflect cases, with human escalation for edge scenarios. Aims for almost 30–50% Tier 0 deflection, faster response times, improved employee experience, and minimal hallucinations.27

Pilot C: Skills Intelligence for L&D and Workforce Planning

Builds a governed skills ontology and uses AI to infer skills, recommend learning paths, and support mobility decisions. Focuses on better skills coverage, faster content curation, increased internal mobility, and bias-aware recommendations.

8. Change management and Risks: How to make the new ways of working stick?

To help ensure smooth Change Management, lead with products, teach with practice.
We will change both operating rhythm and capabilities, so adoption keeps pace with delivery.

Operating rhythm and decision rights

Establish a quarterly AI Governance Board (policy, prioritization) and a monthly Model Risk and Data Council (taxonomy changes, release approvals). Bi-weekly Product review adoption and value. This blends agile delivery with the assurance disciplines HR needs for trust at scale.28

Capability and Culture

Run AI literacy sprints for HRBPs/recruiters (prompt safety, bias detection, confidence interpretation); create communities of practice to share prompt patterns and analytics stories;

use “show don’t tell” demos anchored on business outcomes. Skills gaps and training deficits routinely stall AI scaling; plan for reskilling + selective hiring

Communication and stakeholder engagement

Executive narratives emphasise augmentation over replacement and “time given back” to leaders and HR; change agents in each BU localise messages and collect feedback. Managing AI in HR requires anticipating risks and embedding controls early. This highlights key risk areas, their leading indicators, and mitigation measures29.

Risk	Leading indicators	Primary controls (pre-/post-release)
Bias/discrimination in outputs	Disparate impact in screens;skewed recommendations	Pre-deployment fairness tests; risk-tiered prompts; human-in-the-loop review
Privacy and data leakage	Unauthorised access; policydocument exposure	Retrieval-augmented generation with entitlements; encryption and logging;
Model drift and hallucination	Accuracy drop; confidenceanomalies; user distrust	Release criteria/SLOs; red-teaming; hallucination tracking; rollback procedure;
Adoption stall/change fatigue	Low active use; bypassbehaviours	Pilot only "moments that matter";"show-don't-tell" demos; measure time-to-value;
Vendorlock-in/technical debt	Single-vendor dependence;costly migrations	APl-first integrations; portable prompt and retrieval logic; internal prompt/model registry owned by CoE; multi-year exit clauses
Regulatory non-compliance	New local rules (e.g., auditable Alin hiring)	Governance policy cards; minimum-viable governance checklists; compliance mapping

Conclusion and Implications

The future of HR lies in its ability to blend smart technology with human empathy. This white paper outlines a clear path for HR leaders to move from fragmented, reactive operations to a state of strategic flow—where experiences are seamless, decisions are data-driven, and outcomes are continuously optimised.

Key takeaways

AI is not a replacement, but a partner: Generative AI and automation can free up to 31% of HR time30, allowing professionals to focus on strategic priorities like workforce planning, DEI, and employee experience.

Governance is non-negotiable: Responsible AI use demands clear guardrails—bias checks, explainability, and human-in-theloop oversight—to help ensure trust and compliance.31

HR must evolve its operating model: Moving beyond traditional structures, HR should adopt productised, data-centric models with embedded analytics and agile delivery32

Change is a capability, not just a plan: Successful transformation requires AI literacy, stakeholder engagement, and a culture of continuous learning.

Implications for HR:

Strategic Alignment: HR must be tightly integrated with business strategy, using AI to connect skills with organisational goals.

Technology Investment: Leaders should prioritise scalable, AI-native platforms that support personalization, predictive insights, and experience-led design.

Talent and Culture: Upskilling HR teams and fostering a mindset of augmentation over automation will be critical to adoption and impact.

Measurement and Value Realization: A balanced scorecard approach—tracking productivity, experience, financial impact, and safety—will help quantify ROI and guide reinvestment.

Acknowledgements

Author:

• Arun Sharma, Partner, Human Capital Advisory, KPMG in India Gaurav Gulati, Associate Partner, Human Capital Advisory, KPMG in India

Research Team:

• Swarup Banerjee, Manager, Human Capital Advisory, KPMG in India Leya Mohan, Manager, Human Capital Advisory, KPMG in India Divya Gupta, Assistant Manager, Human Capital Advisory, KPMG in India • Sukanya Chatterjee, Consultant, Human Capital Advisory, KPMG in India

Design Team:

• Arun Choudhary, Assistant Manager, Markets, KPMG in India

CII Kerala Team:

Dominic Jose, Convenor, CII Kerala People Culture & Rejuvenation Panel Rajanarayanan N, Co-Convenor, CII Kerala People Culture & Rejuvenation Panel • Martin Jacob, Co-Convenor, CII Kerala People Culture & Rejuvenation Panel

KPMG in India contact:

Akhilesh Tuteja Head – Clients & Markets E: atuteja@kpmg.com

Maneesha Garg Partner and Head, Managed Services E: maneesha@kpmg.com

CII Kerala contact:

Sunit Sinha
Partner and Head, Human Capital Advisory
E: sunitsinha@kpmg.com

Purushothaman K G Partner and Head, Technology Transformation E: purushothaman@kpmg.com

B Jeikrishnan Director & Head E: b.jeikrishnan@cii.in

Gracy Skaria Deputy Director E: gracy.skaria@cii.in

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The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.

The views and opinions expressed herein are those of the quoted third parties and do not necessarily represent the views and opinions of KPMG in India.

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