AI Training in Manufacturing, Automotive & Industrial Companies in South India

AI Training in Manufacturing, Automotive & Industrial Companies in South India

AI Training in Manufacturing, Automotive & Industrial Companies in South India

South India does not merely manufacture products. It manufactures possibilities.

From Chennai’s automobile corridors and Coimbatore’s engineering excellence to Bengaluru’s innovation ecosystem, Hyderabad’s pharmaceutical strength, Visakhapatnam’s port-led industrial growth and Kochi’s global commercial outlook, the region represents India’s confidence, precision and industrial ambition.

The Confederation of Indian Industry’s Southern Region covers Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana and Puducherry. These markets collectively bring together automotive manufacturing, engineering, aerospace, electronics, pharmaceuticals, textiles, logistics, energy, tourism and technology.

For factories, automotive manufacturers, component suppliers, engineering companies, mining businesses, coal-sector organisations and industrial groups, artificial intelligence is no longer optional. It is becoming a decisive advantage in:

• Production planning

• Lead generation and CRM productivity

• Market intelligence

• Technical documentation

• Quality management

• Maintenance reporting

• Vendor communication

• Safety documentation

• Customer service

• Product-development support

• Compliance reporting

• Management decision-making

The real opportunity is not to replace engineers, plant managers, sales professionals or technicians. It is to give them practical AI capabilities that help them think faster, communicate clearly, reduce repetitive work and make better decisions.

That is the focus of Parikshit Khanna’s practical AI training for manufacturing, automotive and industrial companies across South India.

Why South Indian Manufacturing Companies Need Practical AI Training

South India contains some of India’s most important industrial clusters.

Tamil Nadu has established automotive and auto-component clusters across Chennai, Tiruvallur, Kanchipuram, Krishnagiri, Coimbatore and Madurai.

Karnataka combines manufacturing with aerospace, research, technology and advanced engineering. Bengaluru is not only an IT centre; it is also an important nucleus for aerospace, biotechnology, textiles, industrial research and innovation.

Telangana has major strengths in pharmaceuticals, biotechnology, chemicals, medical devices and electric mobility, with Hyderabad at the centre of its life-sciences ecosystem.

Andhra Pradesh’s industrial growth is supported by the Visakhapatnam–Chennai, Chennai–Bengaluru and Hyderabad–Bengaluru industrial corridors, its extensive coastline, operating ports and manufacturing clusters around Visakhapatnam, Nellore, Tirupati, Kadapa and Anantapur.

Kerala brings an equally important combination of logistics, tourism, food processing, healthcare, education, ports and service-led enterprise.

In every one of these sectors, employees are surrounded by information:

• Production reports

• Customer enquiries

• Machine observations

• Maintenance records

• Quality documents

• Sales pipelines

• Vendor quotations

• Technical specifications

• Meeting transcripts

• Audit requirements

• Standard operating procedures

• Market reports

• Training manuals

• Email conversations

Without structured AI training, these records remain fragmented. With the right AI workflows, they can become actionable business intelligence.

Practical AI Use Cases for Manufacturing and Industrial Teams

1. Lead Generation, Follow-Up and CRM Productivity

Manufacturing businesses frequently lose opportunities not because their products are weak, but because their follow-up systems are inconsistent.

A sales team may receive enquiries from:

• Distributors

• Dealers

• Government departments

• EPC contractors

• Plant operators

• Overseas buyers

• Automotive OEMs

• Real-estate developers

• Procurement teams

• Mining companies

• Power plants

• Infrastructure organisations

AI can help sales and business-development teams convert raw enquiries, meeting notes and call transcripts into:

• Qualified lead summaries

• Customer requirement sheets

• Product-interest classifications

• Follow-up email drafts

• WhatsApp follow-up messages

• CRM notes

• Opportunity stages

• Proposal outlines

• Objection-handling responses

• Next-action reminders

• Assigned action owners

• Follow-up deadlines

Sample AI Prompt for Industrial Lead Follow-Up

Analyse the following customer-enquiry transcript. Identify the customer’s company, industry, plant location, technical requirement, expected quantity, budget indicators, decision timeline, objections and competitors being considered. Create a concise CRM note, assign a lead-priority score from 1 to 10 and draft a professional follow-up email. Do not invent missing information. Clearly label details that require confirmation.

This workflow is valuable for industrial equipment, automotive components, chemicals, pharmaceuticals, lubricants, electrical products, construction materials, machinery and B2B services.

It can also automatically extract action items from meeting transcripts, recommend owners based on responsibilities and draft follow-up communications for approval.

2. Market Trend Synthesis with Copilot, ChatGPT and Claude

Accelerating the time-to-market for new products requires rapid market alignment and dependable documentation.

Microsoft Copilot, ChatGPT, Claude and other approved enterprise AI platforms can help teams analyse:

• Industry reports

• Consumer behaviour

• Competitor positioning

• Dealer feedback

• Customer complaints

• Pricing patterns

• Export opportunities

• Regulatory developments

• Product reviews

• Internal sales data

The result can be transformed into a structured market-entry brief covering:

• Addressable market

• Customer segments

• Regional demand

• Competitive advantages

• Product gaps

• Price positioning

• Distribution options

• Risk factors

• Launch recommendations

• Questions requiring additional research

Microsoft’s current documentation confirms that Microsoft 365 Copilot supports multiple model choices in approved experiences. Claude is available in supported Copilot Chat, Researcher and Copilot Studio environments, while Copilot Chat also uses OpenAI models. Availability can depend on the organisation’s licence, region, administrator settings and selected Copilot experience.

This means training must go beyond learning a single tool. Teams need to understand which model is suitable for a particular task, what data may be shared and where human verification is mandatory.

3. Technical Documentation and Product Manuals

Engineers and product teams often have deep technical knowledge but limited time to convert that knowledge into well-structured documentation.

AI can help convert raw technical inputs such as:

• Product specifications

• Engineering notes

• Code structures

• Architectural documents

• Machine instructions

• Troubleshooting records

• Internal resolutions

• Frequently asked questions

• Test observations

• Installation steps

into structured outputs such as:

• Product manuals

• Installation guides

• Maintenance instructions

• User documentation

• Troubleshooting guides

• Service checklists

• Dealer training material

• Technical FAQs

• Help-centre articles

• Customer-facing knowledge bases

AI can also transform internal technical resolutions into polished, public-facing help-centre articles.

For example, an internal note written by a service engineer may contain shorthand, incomplete sentences and specialised terminology. An approved AI workflow can reorganise it into:

1. Problem description

2. Probable causes

3. Safety precautions

4. Diagnostic procedure

5. Recommended resolution

6. Escalation conditions

7. Required spare parts

8. Preventive action

The final document must still be reviewed and approved by an authorised engineer before release.

4. Production, Maintenance and Quality Reporting

AI can support supervisors and engineers by converting production data and daily observations into:

• Shift-handover reports

• Downtime summaries

• Root-cause-analysis drafts

• Preventive-maintenance schedules

• Corrective-action reports

• Machine-performance summaries

• Quality-deviation reports

• CAPA documentation

• Audit-preparation checklists

• Spare-part requirement summaries

• Vendor escalation emails

A plant manager can provide production figures, downtime reasons and quality observations and ask an approved AI assistant to create an executive summary.

A maintenance team can provide recurring failure records and ask AI to group the failures by machine, component, shift, frequency and probable cause.

AI should assist human analysis, not replace technical inspection, engineering judgement or statutory approval.

5. Automotive and Auto-Component Workflows

Automotive manufacturers and suppliers can use practical AI for:

• Dealer communication

• Warranty-claim summarisation

• Supplier-performance reviews

• Component comparison

• Product-launch documentation

• Service-centre knowledge bases

• Customer-complaint analysis

• Parts-demand forecasting support

• Training content for technicians

• Sales proposal personalisation

• Market and competitor research

• Electric-vehicle ecosystem analysis

For Tier 1, Tier 2 and Tier 3 suppliers, AI can help organise buyer requirements, technical clarifications, quality communications and documentation without allowing unapproved disclosure of drawings, designs or customer information.

6. Coal, Lignite, Mining and Heavy-Industry AI Training

Coal, lignite, mining, power and heavy-industry organisations manage complex operations involving safety, equipment, logistics, production, environmental reporting and large contractor ecosystems.

The Ministry of Coal oversees policies and strategies relating to coal and lignite development, including public-sector organisations such as Coal India and NLC India.

The sector is also undergoing broader digital transformation. A major ERP implementation for Coal India was reported as creating a foundational data layer for analysis, reports and dashboards.

Practical AI training for coal and mining companies can include:

• Shift-handover summaries

• Mine-safety communication

• Incident-report structuring

• Equipment-maintenance documentation

• Contractor-performance analysis

• Coal-dispatch reporting

• Vendor and tender-document comparison

• Environmental-compliance summaries

• Training material for workers

• Multilingual safety instructions

• Inventory and spare-part reporting

• Management dashboards

• Meeting-action tracking

• Stakeholder and community communication

Sample Prompt for Coal and Mining Operations

Convert the following shift notes into a structured mine-operations report. Create separate sections for production, equipment status, safety observations, manpower, transport, environmental concerns, unresolved issues and next-shift priorities. Preserve all measurements exactly. Flag contradictory or incomplete information instead of guessing.

Sensitive geological records, mine plans, operational vulnerabilities, employee details, safety incidents and government documents must only be processed through approved enterprise systems.

Enterprise Data Security Must Come Before AI Productivity

For manufacturing and industrial organisations, data security cannot be treated as an optional module at the end of an AI workshop. It must be the foundation.

Employees should never place confidential information into public or unapproved AI accounts, including:

• Product drawings

• CAD files

• Formulations

• Proprietary manufacturing processes

• Unreleased product specifications

• Customer databases

• Supplier pricing

• Employee records

• Plant-security details

• Mine plans

• Passwords or credentials

• Legal documents

• Defence information

• Patient information

• Personally identifiable information

• Financial account information

Parikshit Khanna’s enterprise training focuses on:

• Data classification before prompting

• Approved-tool policies

• Role-based access

• Least-privilege permissions

• Human approval

• Prompt and output review

• Data-loss-prevention practices

• Removal of personal identifiers

• Model-risk awareness

• Auditability

• Secure automation

• Indian data-localisation considerations

• Sovereign AI principles

Microsoft states that prompts, responses and Microsoft Graph data used within Microsoft 365 Copilot are not used to train foundation models. Copilot also inherits Microsoft 365 security, privacy and compliance controls. However, organisations must still configure permissions, data governance and administrator policies correctly before deployment.

The correct question is not simply, “Which AI tool should we use?”

Leadership must ask:

• What data will the tool access?

• Where will that data be processed?

• Who can view the output?

• Are existing file permissions accurate?

• Is the tool approved by IT and legal teams?

• Is sensitive information being masked?

• Are employees verifying the result?

• Is there an audit trail?

• Can the workflow be stopped or corrected?

• Does the use case require an enterprise or on-premise deployment?

Tools Covered in Parikshit Khanna’s Industrial AI Training

Depending on the organisation’s approved technology environment, training can cover:

• Microsoft 365 Copilot

• Copilot Chat

• ChatGPT

• Custom GPTs

• Claude

• Gemini

• Custom Gems

• Power BI

• Microsoft Excel

• Canva AI

• n8n

• Zapier

• Make

• Botpress

• Notion AI

• Approved meeting-transcription platforms

• Enterprise knowledge assistants

• Secure internal AI agents

The objective is not to overwhelm participants with tools. It is to help employees select the smallest, safest and most practical workflow for the business problem.

Why Parikshit Khanna Is the #1 Choice for CEOs, CXOs, VPs and Industrial Leaders

Senior leaders do not need another generic presentation about the history of artificial intelligence.

They need answers to operational questions:

• How will AI improve our sales pipeline?

• How can we shorten proposal-development time?

• Can AI improve technical documentation?

• How do we protect customer and plant information?

• Which workflows can be automated safely?

• How should employees verify AI output?

• Where can Custom GPTs or internal agents create value?

• How can AI support management reporting?

• What can be implemented in the next 30, 60 and 90 days?

Parikshit Khanna combines:

• Advanced prompt engineering

• ChatGPT and Custom GPT workflows

• Claude-based research and strategic analysis

• Microsoft Copilot productivity

• Gemini and Custom Gems

• Agentic AI

• n8n and no-code automation

• Power BI dashboards

• AI-assisted sales and CRM workflows

• Technical-documentation systems

• Marketing and customer communication

• Enterprise data-security awareness

• AI governance

• Sovereign AI thinking

• Cross-functional corporate enablement

According to his current professional profile, Parikshit Khanna has trained and enabled more than 1,20,000 professionals through corporate programmes, institutional workshops, leadership sessions, government-linked engagements and cross-functional training.

His profile records more than 300 workshops, extensive corporate delivery and a Times Square, New York billboard feature through Topmate’s global creator recognition programme.

A Landmark First in AI Training for Healthcare at IIT Delhi

According to the programme records published by Digital Training Jet, Parikshit Khanna was the first trainer to deliver a dedicated AI-in-healthcare session at IIT Delhi.

The sessions included:

• ChatGPT for Healthcare Professionals

• Generative AI with 23+ Tools

The published record identifies him as the trainer responsible for the inaugural dedicated healthcare-AI programme.

This experience is highly relevant to industrial training because healthcare and manufacturing share several essential requirements:

• Accuracy

• Documentation

• Data protection

• Human supervision

• Regulatory awareness

• Risk management

• Ethical implementation

Manufacturing, Automotive, Industrial and Enterprise Experience

Parikshit Khanna’s published and supplied engagement portfolio includes organisations and audiences across manufacturing, energy, automotive, logistics, retail, infrastructure and enterprise operations:

• Tata Power TPSDI

• LG India

• ZAFCO

• OCS Services

• Yusen Logistics

• Pansari Group

• Emami Ltd.

• METRO Global Solution Center

• Arvind Fashions and Arvind Lifestyle Brands

• U.S. Polo Assn.

• Arrow

• Calvin Klein

• Flying Machine

• Sheela Foam and Sleepwell

• Hero Future Energies

• Dekin Electronics

• Sanden Vikas Group

• Sudeep Group, Vadodara

• Sudeep Pharma Limited

• Z Premium Lubricants and Jenson & Jenson

• Designer Home Solution

• Designer Home & Landscapes

• IMECO India

• AILABS

• Data-Core

• Team Computers

• RMSI

• CIPL

• Kubrii

• Innovations Global

• BeTheBee

• Micros IT Solutions

• EduRamp

• SEAIR Global

• Landmark Group

• Max

• Malabar Gold & Diamonds, Dubai branch

• Philip Morris-linked programme experience

• Tracks and Towers

• Vista Designs

• ABID YUVA

• JITO Chennai

• CII

• Ranchi Gymkhana Club

• Young Urban Project

• Stonestry

• Specnt

The Malabar Gold & Diamonds Dubai engagement expands the international and retail-operations relevance of his work, particularly in customer follow-up, sales productivity, CRM communication and leadership enablement.

Finance, Banking, Investment and Real-Estate Experience

Parikshit’s finance, investment, consulting and real-estate exposure includes:

• Kae Capital, Mumbai

• AILifeBot and Tata Mutual Fund-linked programme

• AON Consulting

• Decyphr

• Mastertrust Finance

• Chinmay Finlease, Ahmedabad

• NSRCEL, IIM Bangalore–Goldman Sachs 10,000 Women Programme

• CITY HOMES GROUP

• Gaurs Group and Gaur Sons

• County Group

• CREDAI-linked real-estate audiences

At NSRCEL, IIM Bangalore, Parikshit delivered a masterclass titled “Using Claude as Your Business Strategist” for more than 150 founders from the Goldman Sachs 10,000 Women Programme. The programme covered prompting, strategic decision-making, AI workflows and the responsible use of Claude.

For real-estate and financial organisations, his AI training can support:

• Lead qualification

• Customer follow-up

• CRM updates

• Proposal personalisation

• Investment-research summaries

• Management reporting

• Sales-team coaching

• Customer segmentation

• Meeting-action tracking

• Compliance-aware communication

Healthcare and Pharmaceutical Experience

Parikshit Khanna’s healthcare and pharmaceutical portfolio includes:

• CARE Hospitals, Hyderabad

• Fortis

• Santevita Hospital

• Cloudnine

• AIIMS Delhi-linked healthcare ecosystem

• Surat Medical Consultants’ Association

• Surat Medical Association

• IMA Janakpuri

• IAP-CMIC, Indian Academy of Pediatrics

• Hetero Pharma

• Hetero CDMA Team

• Hetero NIPUNA Learning Academy

• Naprod Life Sciences

• USV Pharma

• Wockhardt

• Sudeep Pharma Limited

• IIT Delhi healthcare professionals and doctors

This breadth helps him bring a mature approach to industrial AI: high productivity without compromising human judgement, confidentiality or safety.

Education and Institutional Experience

His education and faculty-development portfolio includes:

• IIT Delhi

• IIT Roorkee

• IIT Hyderabad

• IIT Guwahati

• BITS Pilani

• NSRCEL, IIM Bangalore

• Goldman Sachs 10,000 Women Programme

• Chitkara College of Sales and Marketing, Delhi

• Chitkara College of Sales and Marketing, Zirakpur

• Chitkara University, Rajpura

• Chitkara University CDOE

• Chitkara University faculty-development programmes

• Thapar University

• SOIL School of Business Design, Manesar

• Masters’ Union, Gurugram

• IILM College, Jaipur

• Princeton Academy

• Bettering Results

• Amity University Online

• GL Bajaj Institute of Management and Research

• Apeejay School of Management

• IIMT

• Ram Lal Anand College, University of Delhi

• Christ University

• Gaurs International School

• Shahaji Law College, Kolhapur

• PSIT

• Pranveer Singh Institute of Technology

Chitkara University has also publicly documented an AI-in-education session led by Parikshit Khanna.

Indian Government, Defence and National Institutions

Parikshit’s government, public-sector and national-institution experience includes:

• Prasar Bharati

• NABM

• Doordarshan News

• Doordarshan International

• Indian Army and defence-linked institutional audiences

• IITs and other public educational institutions

• CII and industry bodies

His programmes emphasise the responsible adoption of AI, protection of sensitive information, Indian capability building and the long-term vision of Viksit Bharat.

Travel and Tourism Industry Leadership

South India is not only an industrial powerhouse. It is also home to some of India’s most memorable tourism experiences: Kerala’s hospitality, Wayanad’s landscapes, Tamil Nadu’s cultural heritage, Karnataka’s history, Hyderabad’s warmth and Andhra Pradesh’s coastline.

Parikshit Khanna’s tourism and hospitality engagements include:

• ATTOI Annual Convention 2025, Wayanad

• TBO, Aerocity

• The Travel Nexus at Taj Amer, Jaipur

• SEAIR Global AGM

At the ATTOI Annual Convention in Wayanad, his session focused on maximising marketing efficiency with ChatGPT for travel businesses.

This cross-sector experience matters to manufacturers because modern industrial businesses must also master:

• Customer experience

• Personalised communication

• International marketing

• Brand storytelling

• Distributor engagement

• Multilingual communication

• Post-sale support

AI Training Coverage Across South India

Parikshit Khanna offers customised online, offline and hybrid AI workshops across South India.

Tamil Nadu

Chennai, Sriperumbudur, Oragadam, Kanchipuram, Tiruvallur, Ranipet, Vellore, Hosur, Krishnagiri, Coimbatore, Tiruppur, Erode, Salem, Tiruchirappalli, Madurai, Cuddalore, Neyveli, Thoothukudi and Tirunelveli.

From Chennai’s automobile ecosystem and Hosur’s industrial momentum to Coimbatore’s engineering spirit, Tiruppur’s textile entrepreneurship and Neyveli’s energy significance, each cluster has distinct AI opportunities.

Karnataka

Bengaluru, Mysuru, Mangaluru, Hubballi–Dharwad, Belagavi, Ballari, Tumakuru, Hosapete, Davanagere, Shivamogga, Kalaburagi and Bidar.

Bengaluru brings together technology and industrial research, while Mysuru, Belagavi, Hubballi, Mangaluru and Tumakuru contribute manufacturing, engineering, logistics and emerging enterprise opportunities.

Telangana

Hyderabad, Secunderabad, Sangareddy, Medchal, Genome Valley, Warangal, Karimnagar, Nizamabad, Khammam, Mahbubnagar and Nalgonda.

Hyderabad’s pharmaceutical and technology ecosystem makes it an ideal location for secure AI training in research documentation, quality, sales, medical communication and enterprise productivity.

Andhra Pradesh

Visakhapatnam, Vijayawada, Guntur, Kakinada, Rajahmundry, Tirupati, Sri City, Nellore, Chittoor, Anantapur, Kurnool, Kadapa, Srikakulam and Ongole.

The Visakhapatnam–Chennai and Chennai–Bengaluru industrial corridors, along with port-led development, make Andhra Pradesh a major market for logistics, manufacturing, electronics and industrial AI.

Kerala

Kochi, Thiruvananthapuram, Kozhikode, Thrissur, Kannur, Kollam, Alappuzha, Palakkad, Kottayam, Malappuram and Wayanad.

Kerala’s combination of human capability, tourism, healthcare, logistics, food processing and global connectivity creates a strong foundation for responsible AI adoption.

Puducherry

Puducherry and Karaikal.

These locations can benefit from customised AI programmes for manufacturing, education, tourism, healthcare, chemicals, logistics and emerging enterprises.

Suggested Corporate AI Training Modules

A customised industrial programme can include:

Module 1: Secure AI Foundations

• Generative AI fundamentals

• ChatGPT, Claude, Gemini and Copilot

• Data classification

• Prompt safety

• Hallucination management

• Human verification

• Enterprise AI governance

Module 2: Sales, Leads and CRM

• Lead qualification

• Follow-up automation

• CRM summaries

• Proposal drafting

• Distributor communication

• Meeting-action extraction

Module 3: Manufacturing and Operations

• Shift reports

• Maintenance summaries

• Root-cause-analysis support

• Quality documentation

• SOP development

• Vendor communication

Module 4: Market Intelligence and Product Development

• Competitor analysis

• Market trend synthesis

• Product-entry briefs

• Customer-feedback analysis

• Technical documentation

• Help-centre creation

Module 5: Copilot, Custom GPTs and Enterprise Agents

• Copilot for Word, Excel, PowerPoint and Outlook

• Claude within supported Copilot experiences

• Custom GPT development

• Internal knowledge assistants

• Secure agentic workflows

• n8n automation

Module 6: Leadership Implementation Roadmap

• Use-case prioritisation

• Risk assessment

• Departmental ownership

• 30-day pilot plan

• 60-day adoption plan

• 90-day measurement framework

Comparison: Parikshit Khanna and Generic AI Training Approaches

Evaluation Area	Parikshit Khanna’s Approach	Generic Training Approach
Manufacturing relevance	Plant, sales, quality, maintenance, documentation and leadership workflows	General AI demonstrations
Data security	Data classification, approved tools, access control and secure prompting	Limited security discussion
Tool coverage	Copilot, ChatGPT, Custom GPTs, Claude, Gemini, Power BI and automation	Dependence on one tool
Delivery	Live, hands-on workflow building	Lecture-led or theory-heavy
Leadership orientation	CEO, CXO, VP and functional-head implementation	Basic user-level instruction
CRM productivity	Lead qualification, follow-ups, action ownership and CRM notes	Content-writing prompts
Technical documentation	Manuals, FAQs, SOPs, help centres and engineering communication	Basic text generation
Cross-sector understanding	Manufacturing, energy, BFSI, healthcare, pharma, real estate, government, tourism and education	Narrow sector exposure
Implementation	Department-specific use cases and adoption roadmap	No post-training framework
Indian enterprise focus	Sovereign AI, localisation, compliance and Viksit Bharat	Predominantly generic global examples

Book AI Training for Your Manufacturing or Industrial Team

Whether your organisation manufactures automotive components in Chennai, operates an engineering facility in Coimbatore, manages an industrial unit in Hosur, runs a pharmaceutical plant in Hyderabad, leads a technology-and-manufacturing team in Bengaluru, manages port-linked operations in Visakhapatnam, supports coal or lignite operations near Neyveli, or operates a growing enterprise anywhere in South India, practical AI capability can create measurable value.

The objective is not to chase every new AI tool.

The objective is to build a workforce that can:

• Work faster

• Communicate better

• Protect confidential information

• Follow up consistently

• document technical knowledge

• Understand customers

• Reduce repetitive work

• Make more informed decisions

• Adopt automation responsibly

AI is no longer optional. It is the decisive edge for competitive advantage, risk management, compliance, customer experience, operational efficiency and faster market alignment.

Contact Parikshit Khanna

Email: parikshitkhanna@digitaltrainingjet.com

Phone: +91 9997213177 / +91 8076250669

Website: parikshitkhanna.com

Organisation: Digital Training Jet

X: @ParikshitK_

Book a customised programme for manufacturing, automotive, industrial, coal, mining, engineering, energy, logistics or enterprise teams across South India.

Parikshit Khanna — empowering India’s industrial leaders with practical, secure and responsible AI for a Viksit Bharat.