India AI Impact Summit 2026

ai-for-industries-resilience-innovation-and-efficiency

7 min read

TRACK:Resilience, Innovation, and Efficiency
SPEAKERS
  • Clas Neuman, SAP
  • Madhav Krishna, Vahan.ai
  • Priyadarshi Mohapatra, Curebay
  • Sunil Mathur, Siemens India
  • Upasna Dash, JBC
  • Upen Barve, GIIC
SESSION OVERVIEW

AI for Industries: Resilience, Innovation, Efficiency explores how artificial intelligence can accelerate India's industrial transformation through global collaboration and proven best practices. As part of the India AI Action Summit, this session brings together international expertise in applied AI, industrial digitisation, and trusted technologies with Indian startups delivering scalable impact in healthcare and livelihoods. The session will provide insights on innovation pathways, responsible deployment, governance, and real-world outcomes through cross-border knowledge exchange that shape the future of industries. ... Read More

VIDEO RECORDING

AI for Industries: Resilience, Innovation and Efficiency

Abstract

The panel explored how artificial intelligence can accelerate India’s industrial transformation by enhancing resilience, fostering innovation, and driving efficiency. Siemens outlined the need for reliable, relevant and responsible AI, while Curebay demonstrated AI‑enabled community health screening in rural India. Vahan.ai presented an AI‑augmented recruitment platform for the vast informal labour market. The discussion then turned to macro‑level “seismic shifts” – data readiness, ecosystem fragmentation, and the three R’s (reliable, relevant, responsible) – before closing with a series of cross‑border initiatives linking India and Europe.

Detailed Summary

The moderator (Upasna Dash) welcomed the panel and highlighted the session’s focus on resilience, innovation and efficiency in Indian industry.

2. Siemens Perspective – Building an AI‑Ready Industrial Ecosystem

Sunil Mathur (Siemens India)

  • From POCs to Production – Emphasised the gap between proof‑of‑concept prototypes and robust, reliable industrial deployments.
  • Mind‑set Shift – Workers now coexist with collaborative robots (cobots) and autonomous agents; this requires a cultural change in how humans interact with machines.
  • Ecosystem Role – Siemens sees the research‑innovation ecosystem as a “many‑to‑many handshake,” involving startups, public‑private partnerships, universities, research institutes, and the GIIC as a catalyst.
  • Three R’s (Inside‑Out View)
    1. Reliability – Achieved when the data layer is mature.
    2. Relevance – Ensured by embedding AI horizontally across business processes.
    3. Responsibility – Ethical design must be baked in at the earliest stage, not patched later.
  • Outside‑In View – Data readiness and ecosystem fragmentation will persist, especially across borders. Critical data classification (e.g., for security‑printing presses) will become a major regulatory focus.
  • Geopolitical Impact – AI‑driven recruitment may be straightforward for manufacturing but complex for sensitive sectors, undersc​oring the need for robust data‑governance.

Key Insight: The next wave for Indian industry will be the maturation of data ecosystems and responsible AI design, rather than merely scaling more models.

3. Healthcare AI – Bringing Trustful Screening to Rural India

Priyadarshi Mohapatra (Curebay)

  • Rural Healthcare Landscape – Primary Health Centres (PHCs) often lack doctors; infrastructure is concentrated in major metros, leaving a billion people underserved.
  • Hybrid Solution – Built a technology platform paired with on‑ground clinics. Community health workers (“Swastya Mitras”) capture images of oral lesions or other signs, feed them to AI, and receive instant risk stratification.
  • Risk‑Stratification, Not Fear‑Induction – AI outputs a risk score and directs patients to the nearest tech‑managed clinic for a doctor’s consultation.
  • Scalable Screening
    • Oral Cancer – Early detection via AI‑driven image analysis of bite‑nut chewers.
    • Breast Cancer – AI‑based pre‑screening replaces costly mammography; high‑risk cases are funneled to doctors, improving early‑stage detection (mortality drops from 50 % to <5 %).
  • Core Pillars of AI Use – Trust, transparency, translation of AI outputs into actionable healthcare pathways.
  • Outcome Focus – AI is a conduit that bridges the digital screening stage with physical clinical care, ensuring that technology does not remain an isolated “fancy product.”

Key Insight: Trust‑centric AI can dramatically extend the reach of diagnostic services in low‑resource settings when coupled with a tangible care‑delivery loop.

4. AI for the Informal Labour Market – Augmenting Blue‑Collar Recruitment

Madhav Krishna (Vahan.ai)

  • Market Reality – 80 % of India’s workforce is “blue‑collar” (low‑skill, fragmented hiring). Hundreds of thousands of local recruitment agencies operate on trust‑based networks (family, community).

  • Augmentation Model – Vahan.ai aggregates ~3,000 human recruiters across 900+ cities. The AI recruiter acts as a tool, not a replacement:

    1. Recruiter enters candidate details → AI initiates a multilingual voice call.
    2. AI gathers basic data (name, location, skillset, education) and answers FAQ (e.g., salary).
    3. AI performs initial job matching and drives the onboarding workflow.
    4. When a candidate shows hesitation or a process stalls, the system re‑introduces the human recruiter (human‑in‑the‑loop).

  • Productivity Gains – Early pilots show a 5× increase in hires per recruiter (1 → 5 per day); the goal is to reach 10× as the technology matures.

  • Trust as a Core Lever – Human‑AI collaboration preserves the essential trust element that is culturally vital in India’s labour market.

  • Future Outlook – Anticipates scaling to 50 hires per recruiter within 2–5 years, dramatically improving employment outcomes for informal workers.

Key Insight: In trust‑centric environments, AI works best as an augmentation layer that empowers, rather than replaces, human actors.

5. Panel Synthesis – “One Seismic Shift”

5.1 Macro‑Economic Outlook (Sunil Mathur)

  • Data & Ecosystem Readiness – The decisive factor for AI adoption is a clean, readily usable data foundation.
  • Three R’s Recap – Reliability, relevance, responsibility must be embedded from design to deployment.

5.2 Workforce & Gender Perspective (Swati – moderator)

  • Skill Gap – Global industrial sectors face a shortage of skilled knowledge workers; AI can help reskill and upskill existing labour.
  • Human‑in‑the‑Loop – Emphasised AI as an empowering tool for factory workers, not a replacement.
  • Future Workforce – Anticipates a shift from “AI‑native” to “AI‑empowered” industrial personnel, with deeper integration of foundation models that understand engineering drawings, IoT time‑series, and 3‑D simulations.

5.3 Healthcare Ecosystem Shift (Priyadarshi Mohapatra)

  • Foundational Data Governance – Consent‑driven, privacy‑preserving pipelines are mandatory before medical data can be leveraged.
  • Ecosystem Convergence – Integration of med‑tech, wearable, EHR, and physical care pathways will enable a move from reactive to preventive health, with AI handling early screening and risk‑stratification.

5.4 Informal Economy Catalysts (Madhav Krishna)

  • Language & Data Footprint – India’s linguistic diversity (~20 major languages) limits AI reach; building digital footprints for regional languages is essential.
  • Demographic Dividend – A young, growing population can be upskilled through AI‑driven tools, converting a potential exclusion risk into a productivity engine.

6. Closing Address & Strategic Announcements

Sheen Masin (Guest Speaker)

AnnouncementDetails
JIIC Agenda 2026Focus areas: manufacturing, mobility, aerospace, defence, health – new programs to be launched throughout the year.
German‑Indian Innovation Summit – Edition 2Scheduled for 28‑29 September, Berlin; platform for cross‑border collaboration.
German‑Indian Open Innovation InitiativeFull‑stack collaboration backed by 10 corporates and VCs on both sides; includes monetisation models and infrastructure.
India Desk at Neuland (Berlin)Dedicated hub for Indian startups to explore Europe; located in Europe’s largest innovation space.
Democratic AI Alliance FrameworkJoint EU‑India effort to align on AI governance (EU AI Act, India’s DP/AI regulations); kickoff planned later in the week.

Call to Action: The session’s overarching message was that AI must move from adoption to deep integration to deliver resilience, innovation and efficiency across industries. Stakeholders – founders, investors, corporate leaders, and policymakers – were urged to cooperate across geographies to realize this vision.

Key Takeaways

  • Data readiness and ecosystem cohesion are the primary bottlenecks for scaling AI in Indian industry; without clean, reliable data, AI reliability and relevance suffer.
  • Three R’s (Reliability, Relevance, Responsibility) must be baked into AI design from the outset; retro‑fitting ethics is far less effective.
  • Trust‑centric AI augmentation (human‑in‑the‑loop) delivers far greater productivity in fragmented markets such as blue‑collar recruitment.
  • Rural healthcare can be transformed through AI‑driven risk‑stratification that feeds directly into physical clinics, provided the solution is built around trust and transparency.
  • Language diversity is a strategic AI challenge; creating digital footprints for regional Indian languages will unlock wider adoption.
  • Cross‑border collaboration (India–Europe) is essential: India contributes speed, scale, and talent; Europe offers regulatory depth and long‑term capital.
  • Upcoming initiatives – JIIC 2026 agenda, Berlin summit, Open Innovation Initiative, India Desk at Neuland, and Democratic AI Alliance – aim to institutionalise the India‑Europe AI partnership.
  • Future workforce: AI will enable reskilling and upskilling, moving the industrial labor pool from low‑skill tasks to complex, value‑adding activities.
  • Strategic shift required: Move from isolated AI pilots to deep, production‑grade AI integration across manufacturing, healthcare, energy, logistics, and defence.

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