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The AI Ground Report 2026
The AI advantage belongs to those who built the right stack
Primary data from 500+ HR leaders across India, at every stage of AI adoption.
74%
name AI as #1 priority for HR stack
The figure holds across industries, across organisation sizes, and across every stage of the adoption journey.
84%
have begun some form of AI activity
Organisations that have not yet started are a shrinking minority. Just 16% of the survey base.
3.51/5
avg satisfaction among implementers
41% are neutral. Not failed, not delivered. Simply uncertain whether what they are doing will eventually work.
6x
more likely live if stack is extensible
Extensibility is the most decisive single variable. It determines whether an org can act on AI in weeks or requires extensive IT engagement.
About the Study
Demographics
Based on responses from 500+ HR leaders across 16 industries and organisations of varying employee strengths, across India.
Industry distribution
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Organisation size
10,000+ employees
500–1,000
5,001–10,000
2,501–5,000
1,001–2,500
Under 500
60% of respondents come from organisations with 1,000+ employees, where HR technology decisions carry the most weight.
Chapter 01
The consensus is clear. The outcomes are not.
India's HR leaders are bullish about AI. But the most active adopters report the lowest satisfaction.
AI adoption journey
16%
Not started
These organisations have the clearest opportunity. The consolidation decision is still available before complexity sets in.
35%
Exploring
The gap between what they want AI to do and what their architecture can support is the defining challenge at this stage.
34%
Piloting
Dealing with the data silo problem in real time. The 21-point satisfaction gap is not theoretical. It is the difference between a pilot that delivers and one that stalls.
15%
Already Live
These organisations treat their stack as a strategic asset. Use case priorities shift toward onboarding (60%), which requires full integration.
69% are concentrated at Exploring and Piloting, working through the difficult middle of a technology transition.
The satisfaction paradox
Very dissatisfied2%
Dissatisfied8%
Neutral41%
The 'neutral majority'. Not failing, not delivering. Simply uncertain of their progress.
Satisfied35%
Very satisfied14%
3.51
/5 avg satisfaction
41% are neutral, uncertain whether their efforts will pay off. The average hides strong tails at both ends.
74%
High Ambition
AI named #1 priority across all industries
<50%
Satisfaction Gap
Fewer than half describe outcomes as satisfying
6x
Extensibility Multiplier
Easy-to-extend stacks are 6x more likely live with AI
+21pts
Data Silo Gap
Satisfaction gap caused by data silos at piloting
69%
Mid-Journey
Stuck between exploring and piloting
Want the complete picture?
Download the full AI Ground Report 2026. Adoption playbooks, maturity benchmarks, and what's actually working.
Chapter 02
The barriers that surface once you start moving
Cost and skills dominate early conversations. The real blockers, data silos and integration, intensify as organisations progress.
AI extensibility challenges
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Most frustrating parts of the HR stack
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+21pts
satisfaction gap
Data silos are the single strongest predictor of pilot failure
At piloting, data silos create a 21-point satisfaction gap. Integration is universal. Everybody hits it. Only those with coherent data get through.
The iceberg model
The concerns visible before implementation are not the ones that determine outcomes.
VISIBLE BEFORE
YOU START
YOU START
High cost
No AI skills
declines as orgs progress
WHAT DETERMINES
OUTCOMES
OUTCOMES
Data silos
+21pts
satisfaction gap at piloting
Integration
59%
never fully resolves, even live
Cost and skills surface in budget discussions. Data silos and integration determine outcomes. These require architectural decisions, not more budget.
The consolidation imperative grows with experience
When asked what they would do differently if redesigning their HR stack from scratch, organisations at every stage give the same answer with increasing conviction: consolidate first.
7%
Not started
Only 7% name consolidation as top priority. The problem hasn't surfaced yet.
22%
Exploring
Priority triples when use cases are mapped onto a fragmented architecture.
32%
Piloting
#1 redesign priority. "Fewer, better-integrated systems" beats out "more AI features" at this stage.
31%
Already Live
#1 redesign priority. Top wish: "I would consolidate first if starting again." Experience makes the argument louder.
Fragmentation is a silent tax on AI performance. Every manually patched integration is a failure mode waiting to happen. The consolidation imperative grows steadily with experience.
Are these barriers holding you back too?
Find out with your personalised benchmark report.
Chapter 03
Where India's industries sit
Four distinct strategic positions emerge from plotting AI adoption maturity against stack satisfaction. Select your industry to see where it sits.
Find your industry
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Maturity
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Avg Sat
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% Live
↑ Higher stack satisfaction
PLATEAU
Low adoption + high satisfaction. Foundation ready, adoption lagging.
Manufacturing
Logistics
HIGH GROUND
Sat: 3.46
High adoption + high satisfaction. Active adoption on a solid foundation.
Pharma
BFSI
IT/Tech
Media
Hospitality
Highest satisfaction of all quadrants. Virtuous cycle: high adoption + high satisfaction = self-reinforcing AI momentum.
BASE CAMP
Low adoption + low satisfaction. Stack improvement is the priority.
BPO
Energy
Healthcare
Construction
ASCENT
Sat: 3.14
High adoption + low satisfaction. Speed outpacing infrastructure.
Telecom
Consulting
Retail
Urgency signal: high adoption despite low satisfaction. Stack gap must close or churn risk rises.
← Lower AI adoptionHigher AI adoption →
The 4 dimensions that determine AI outcomes
They operate as a system. Weakness in any one constrains the others.
AI Outcomes
Successful transformation rests on all four
Extensibility
6x
more likely live if stack is easy to extend
Strategy Clarity
53%
of live orgs fully confident vs 18% not started
Stack Quality
3.57
live orgs avg vs 3.03 not started
Consolidation
Fewer
deeper platforms outperform multi-tool stacks
Your HR Technology Foundation
Which quadrant are you in?
The full report includes detailed industry profiles, quadrant analysis, and the complete methodology.
Chapter 04
Where HR leaders want AI to work
The use cases prioritised reflect the problems they most want to solve.
Top AI agent priorities
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How priorities shift with maturity
Not started
Candidate screening
Entry-level
Exploring
Candidate screening
Focus holds
Piloting
L&D personalisation
Lifecycle shift
Already live
Onboarding (60%)
Full integration
Ready to see your AI readiness score?
9 questions. 2 minutes. Your personalised benchmark with specific next steps.
Deep Dive
Watch the webinar
Hear from the research team as they unpack the findings, share what surprised them, and answer live questions from HR leaders.
Full session recording
Watch at your own pace
Live Q&A included
Real questions from HR leaders
Data walkthrough
Charts & findings explained
Personalised Benchmark
Your AI readiness assessment
Answer 9 questions to generate your personalised benchmark report.
1 · Your industry
2 · AI adoption stage
3 · HR tools in your stack
4 · Stack extensibility
5 · AI initiative satisfaction (1–5)
1 = not satisfied · 5 = very satisfied
6 · HR tech satisfaction by area
Skip areas you don't use
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7 · Can you explain your HR tech strategy to your CEO?
8 · Most frustrating part of your HR stack
Select all that apply
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Your personalised AI benchmark
AI readiness report
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Section 01
Your AI readiness score
AI Readiness
{{ rScore }}
out of 100
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Section 02
Where you stand
Stack satisfaction vs. AI adoption maturity.
← Lower satisfactionHigher satisfaction →
You
{{ bIndustry }} peers
You're in {{ quadName }}
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Section 03
Your AI transformation pillars
Where you stand, how you compare, and what to do next.
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/100
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What to do next
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Section 04
Shared pain points
The frustrations you flagged show up across the 500+ organisations surveyed.
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Ready to strengthen your HR foundation?
Talk to us about getting your HR stack AI-ready.
Schedule a DemoFrom Research to Action
Get your AI Advantage with PeopleStrong
PeopleStrong runs on a unified data layer with native AI built in. Every module, from talent acquisition through core HR, payroll, and workforce intelligence, runs on the same architecture.
Extensible Architecture
Designed to extend. Move from AI pilot to AI scale in weeks, not quarters.
Unified Data Layer
Every module shares the same employee record, org structure, and policy set. No manual handoffs.
End-to-End Scope
Full context across the entire hire-to-exit lifecycle. No context lost between systems.
AI Embedded
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MAAX: Multi-Agent AI Architecture for HR
Specialist agents run by one super-agent, all operating on the same organisational data.
AI Co-Recruiter
End-to-end hiring: JD creation, screening, scheduling, offer.
10x hiring velocity
ER Agent
Policy-accurate HR answers for every employee, in their language, instantly.
70% fewer HR tickets
Analytics Agent
Plain-language queries across all people data. No SQL, no configuration.
Instant workforce intelligence
Jinie
Super Agent. Orchestrates all specialist agents
Shorter cycles, hours not weeks
Fewer escalations, resolves at source
Real-time visibility on hiring, engagement, payroll
Zero friction for employees
ADAPT: From readiness to live deployment in 13 weeks
A structured programme that moves organisations from honest assessment to live deployment.
A
Assess
WEEK 1–2
Audit readiness across people, data, tech. Map extensibility and data quality.
D
Define
WEEK 3–4
Rank AI use cases by feasibility and impact. Prioritise strong foundations.
A
Assure
WEEK 5–7
Establish governance, compliance architecture, and data frameworks.
P
Pilot
WEEK 8–10
Deploy first AI use case with strict KPIs. Measure impact rigorously.
T
Transform
WEEK 11–13
Embed via SOPs. Scale from pilot to enterprise-wide with governance intact.
See what native AI does inside your HR stack
Built to extend. Built for AI. PeopleStrong solves the exact problems this research found.
The AI Ground Report 2026 · Based on a survey of 500+ HR leaders across India
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