AI Training in Coal Mining in India

AI Training in Coal Mining in India: Building Safer, Smarter and More Productive Coal Enterprises

AI Training in Coal Mining in India

From the coalfields of Dhanbad and Jharia to the power corridors of Singrauli and Korba, India’s coal industry represents far more than extraction and transportation. It represents livelihoods, industrial growth, railway movement, power generation and the energy security of millions of Indian families.

The next transformation of this sector will not come from machinery alone. It will come from people who know how to combine operational experience with artificial intelligence, data governance, automation and responsible decision-making.

India possessed approximately 400.715 billion tonnes of geological coal resources as of 1 April 2025. Coal also remains deeply connected with the power, steel, cement, sponge iron, fertilizer, paper and brick industries. Sector research based on Ministry of Coal data reported raw coal production of approximately 1,048 million tonnes in fiscal 2025.

This scale creates a compelling requirement for practical AI capability across:

• Mine operations and maintenance

• Worker safety and occupational health

• Environment and sustainability

• Procurement and tender management

• Finance, HR, administration and legal teams

• Coal marketing and customer service

• MDO, EPC, OEM and contractor coordination

• Lead generation, follow-up and CRM productivity

• Leadership reporting and board communication

AI is no longer optional. It is becoming a decisive capability for competitive advantage, risk management, safety, compliance, operational efficiency, documentation quality and faster decision-making.

Why Coal Companies Need Practical AI Training Now

Coal mining generates enormous volumes of operational information: shift reports, inspection notes, safety observations, equipment logs, maintenance records, tender documents, environmental readings, production summaries, correspondence, meeting transcripts and customer enquiries.

The problem is rarely a complete absence of data. The greater challenge is converting scattered data into timely decisions.

The Ministry of Coal’s technology roadmap has identified digital mine-safety applications capable of processing CCTV feeds and recognising PPE violations, unsafe-zone entry, crowding and abnormal behaviour. Current coal-sector initiatives also cover mine-modelling software, GPS-based vehicle tracking, RFID monitoring, drones, slope-stability systems, ERP and digital transformation in selected opencast mines.

CMPDI’s work covers mine planning, environmental monitoring, GIS, remote sensing, LiDAR, UAVs and digital photogrammetry. Its June 2026 list of ongoing research projects also includes AI-supported detection of mine fires and abnormal conditions in active and closed underground mines.

Technology adoption, however, succeeds only when employees understand how to use it securely, interpret outputs correctly and integrate AI into real workflows. A tool subscription without workforce capability can easily become expensive shelfware—or, worse, an unmanaged data-security risk.

What an AI Training Programme for Coal Mining Companies Should Cover

A coal-industry AI programme must be considerably more specialised than a generic workshop on writing prompts. It must address mine terminology, departmental responsibilities, safety boundaries, confidentiality, regulatory workflows and the realities of remote operational locations.

1. AI for Mine Safety and Risk Communication

AI can assist safety teams with the preparation, analysis and communication of safety information. It should support qualified professionals rather than replace statutory mine-safety responsibilities.

Practical applications include:

• Converting inspection notes into structured observation reports

• Classifying unsafe acts and unsafe conditions

• Drafting toolbox talks in English, Hindi and regional languages

• Creating contractor-induction material

• Summarising near-miss reports

• Producing shift-specific safety briefings

• Identifying repeated safety themes across incident descriptions

• Preparing PPE-awareness campaigns

• Drafting emergency-response checklists

• Converting long SOPs into worker-friendly visual instructions

• Creating quizzes for vocational training centres

• Preparing simulator-training scenarios

A 2025 Standing Committee on Safety record highlighted concerns related to worker safety, dust, HEMM ergonomics, vocational training and the potential introduction of AI-based safety initiatives in coal mines.

Important boundary: An AI-generated safety observation must always be reviewed by the authorised mine manager, safety officer, engineer or competent person before implementation.

2. AI for Production, Dispatch and Shift Reporting

Mine-management teams can use secure AI workflows to transform raw operational records into management-ready summaries.

The training can demonstrate how to:

• Consolidate production data from multiple shifts

• Compare planned and actual overburden removal

• Summarise coal extraction, crushing and dispatch

• Explain production variance in clear management language

• Prepare daily and weekly MIS reports

• Convert handwritten or unstructured notes into standard formats

• Generate action trackers from production-review meetings

• Prepare executive summaries for area and corporate headquarters

• Build role-based Power BI dashboards

• Detect unusual patterns for human investigation

• Draft escalation messages for delayed rakes or equipment downtime

AI should not make final engineering or production decisions independently. Its value lies in organising information, identifying patterns and reducing administrative turnaround time.

3. Predictive Maintenance and HEMM Productivity

Draglines, shovels, dumpers, dozers, surface miners, drills, crushers, conveyors and pumps generate critical maintenance information.

A practical programme can help engineering and maintenance teams:

• Summarise breakdown histories

• Categorise recurring fault descriptions

• Create preventive-maintenance checklists

• Draft job cards and maintenance instructions

• Analyse downtime reasons

• Compare equipment performance across shifts

• Prepare spare-parts requirement notes

• Convert OEM manuals into searchable internal knowledge

• Develop troubleshooting assistants using approved documentation

• Create maintenance-review dashboards

• Draft vendor-escalation emails

• Prepare root-cause-analysis templates

AI-generated maintenance recommendations must be checked against OEM manuals, engineering standards, operating conditions and competent technical judgement.

4. Technical Documentation and Faster Project Execution

Accelerating the time-to-market for mining products, engineering solutions and operational projects requires rapid market alignment and reliable technical documentation.

AI can help engineers, project teams and product designers convert:

• Technical specifications

• Architectural notes

• Mine-planning observations

• Equipment configurations

• Code structures

• Field resolutions

• Internal FAQs

• Testing notes

• Commissioning records

into structured documents such as:

• User manuals

• Operating procedures

• Technical proposals

• Product documentation

• Installation guides

• Help-centre articles

• Troubleshooting documents

• Inspection formats

• Commissioning checklists

• Training material

For equipment manufacturers, technology providers, MDOs and EPC companies, this can reduce the time required to convert technical knowledge into customer-ready material.

Market-Trend Synthesis

Microsoft Copilot, ChatGPT, Claude and other approved enterprise tools can assist teams in analysing permitted industry reports, customer feedback, commercial-mining developments, equipment requirements and competitive intelligence.

The output can support:

• Market-entry briefs

• New-product opportunity assessments

• Regional demand summaries

• Mine-technology adoption reports

• Customer-persona development

• Equipment-sector trend analysis

• Commercial proposal preparation

Every statistic, market estimate and external claim must be validated from its original source before commercial use.

5. Environmental Monitoring and Sustainability Communication

Coal companies work with complex information concerning dust, water, land reclamation, mine closure, plantation, emissions, overburden dumps and community development.

AI can support environmental teams with:

• Summarising monitoring reports

• Comparing readings across periods

• Drafting compliance-status summaries

• Creating mine-closure communication

• Preparing community-awareness material

• Structuring ESG and sustainability reports

• Developing dashboards for water, air, noise and plantation data

• Translating technical environmental findings into public-friendly language

• Drafting management responses to observations

• Creating evidence registers for internal review

CMPDI states that remote sensing, GIS, LiDAR, UAVs and digital photogrammetry are already important components of coal-sector planning and environmental monitoring.

AI must never be used to conceal, alter or selectively represent environmental findings.

Lead Generation, Follow-Up and CRM Productivity for the Coal Ecosystem

Coal-sector organisations also need stronger commercial productivity. This applies especially to:

• Mining-equipment manufacturers

• Safety-equipment suppliers

• Technology and surveillance companies

• MDO and EPC organisations

• Coal-washery solution providers

• Environmental consultancies

• Logistics and railway-support companies

• Industrial automation providers

• Maintenance contractors

• Training and manpower organisations

• Laboratory and testing-service providers

How AI Can Improve Coal-Industry Lead Generation

AI can help business-development teams:

1. Segment potential customers by geography, mine type, requirement and organisation category.

2. Create buyer personas for mine heads, project directors, safety heads, procurement teams and plant leaders.

3. Draft account-specific outreach based on publicly available information.

4. Convert tender notices into opportunity summaries.

5. Prepare pre-bid research briefs.

6. Generate discovery questions for technical meetings.

7. Create value propositions for different stakeholders.

8. Draft case-study narratives from approved project data.

9. Build regional campaign calendars.

10. Prepare LinkedIn, email and event-outreach content.

CRM and Follow-Up Automation

With proper approvals, AI-supported CRM workflows can:

• Summarise sales calls

• Extract action items from meeting transcripts

• Assign proposed owners

• Draft follow-up emails

• Update opportunity notes

• Identify overdue follow-ups

• Suggest the next legitimate action

• Categorise leads by readiness

• Create reminders for tender dates

• Produce weekly pipeline summaries

• Draft meeting-preparation briefs

• Track commitments made to customers

The system should not automatically send high-risk commercial, legal or technical communications without human approval.

Sample CRM Prompt

Review the following approved meeting transcript from a mining-equipment discussion. Extract the customer’s operational problem, equipment environment, technical requirement, budget indicators, decision-makers, promised documents, next meeting date and action owners. Draft a concise follow-up email. Do not

invent missing information. Mark every uncertainty as “confirmation required.”

Procurement, Tendering and Contract Productivity

Coal procurement involves substantial documentation, technical qualification and coordination. AI can support—but not independently decide—work relating to:

• Tender summarisation

• Bid/no-bid checklists

• Eligibility-matrix preparation

• Technical-clause extraction

• Commercial-deviation registers

• Vendor-query consolidation

• Pre-bid meeting summaries

• Comparative-statement narratives

• Contract-obligation registers

• Performance-guarantee tracking

• Purchase-order summaries

• Vendor correspondence

• Renewal and expiry alerts

All tender and contract outputs must be reviewed by procurement, finance, legal and technical authorities.

AI for HR, Learning and Workforce Communication

Coal operations involve permanent employees, contractual personnel, operators, engineers, supervisors, safety teams, field staff and administrative personnel.

AI can help HR and learning teams:

• Create role-specific induction programmes

• Prepare multilingual communication

• Draft skill-gap questionnaires

• Build competency matrices

• Generate assessment questions

• Create microlearning modules

• Convert policies into simple employee guides

• Summarise employee feedback

• Design supervisor-development programmes

• Prepare training calendars

• Develop AI-use policies

• Create role-based prompt libraries

• Draft change-management communication

The programme can also train senior personnel to review AI outputs critically rather than accepting fluent text as automatically accurate.

AI for Finance, Legal and Administrative Teams

Finance teams can use controlled AI workflows for:

• MIS commentary

• Budget-versus-actual explanations

• Cost-centre summaries

• Working-capital reporting

• Vendor-payment communication

• Internal audit preparation

• Reconciliation narratives

• Management presentation drafts

• FP&A scenario documentation

Legal and compliance teams can use AI for:

• First-stage contract summarisation

• Obligation extraction

• Clause comparison

• Litigation-document organisation

• Policy simplification

• Regulatory-update briefs

• Internal FAQ preparation

AI must not replace legal opinions, statutory interpretation, accounting judgement or audit evidence.

Data Security Must Come Before Prompting

Coal companies may possess sensitive information relating to mine plans, geological data, employee records, production, contracts, pricing, security arrangements, equipment vulnerabilities, customers and critical infrastructure.

For this reason, enterprise AI training must begin with data classification and approved-use boundaries.

India’s Digital Personal Data Protection Rules, 2025, were notified with an implementation framework and enforcement timeline. Organisations must therefore evaluate AI use in the context of personal-data handling, notices, access controls, retention and organisational accountability.

Secure AI Practices Covered in the Programme

• Never paste confidential mine plans into an unapproved public tool.

• Never upload employee medical or identity information without lawful authority.

• Mask personal and commercially sensitive data before demonstrations.

• Use synthetic datasets for training exercises.

• Apply role-based access.

• Maintain approved-tool registers.

• Review retention and model-training settings.

• Establish human approval for external communication.

• Maintain logs for high-impact workflows.

• Separate experimentation from production deployment.

• Conduct vendor-security and legal reviews.

• Define prohibited AI use cases.

• Prevent unsanctioned “shadow AI.”

• Test outputs for hallucination, bias and unsafe assumptions.

Important Tool-Accuracy Note

Microsoft Copilot, ChatGPT and Claude are separate platforms. They should not be described as automatically bundled inside one another.

Microsoft states that Copilot can coordinate large language models and may use multiple models within its own architecture. ChatGPT and Claude maintain their own enterprise products, controls and data practices. A responsible training programme therefore teaches participants how to select and govern each platform separately.

The workshop can cover:

• Microsoft 365 Copilot and Copilot Chat

• ChatGPT and Custom GPTs

• Claude for analysis and long-document workflows

• Gemini and Gems

• Power BI

• n8n, Zapier and Make

• Secure agentic-AI concepts

• Canva AI for approved visual communication

• Enterprise knowledge assistants

• Retrieval-based internal solutions

AI Training Coverage Across India’s Coal Regions

The programme can be delivered online, at corporate headquarters, at training centres or close to operational areas.

Jharkhand

Dhanbad, Jharia, Bokaro, Ranchi, Ramgarh, Hazaribagh, Giridih, Chatra, Koderma, Deoghar and Godda

Dhanbad and Jharia carry the identity of India’s coking-coal heartland. Ranchi is a major administrative and technical centre, while Bokaro represents the enduring relationship between coal, steel and industrial development. BCCL’s registered headquarters is in Dhanbad, while Jharkhand hosts operations connected with BCCL, CCL and ECL.

West Bengal

Asansol, Raniganj, Durgapur, Bardhaman, Bankura, Purulia and surrounding ECL areas

Raniganj represents one of India’s oldest coal-mining landscapes. Asansol and Durgapur combine coal heritage with railways, steel, engineering and industrial enterprise.

Chhattisgarh

Korba, Bilaspur, Raigarh, Surguja, Ambikapur, Manendragarh, Chirmiri and Gevra–Dipka–Kusmunda region

Korba and the Gevra–Dipka–Kusmunda belt represent the scale and discipline required to keep India’s power system moving.

Madhya Pradesh and Uttar Pradesh

Singrauli, Waidhan, Sidhi, Shahdol, Umaria, Anuppur and Sonbhadra

Singrauli connects the coal operations of Madhya Pradesh and Uttar Pradesh and is one of India’s most recognisable energy hubs.

Odisha

Talcher, Angul, Jharsuguda, Sambalpur, Sundargarh, Rourkela and Ib Valley

Talcher and Angul reflect the powerful intersection of coal, power, aluminium, steel, engineering and logistics.

Maharashtra

Nagpur, Chandrapur, Wani, Ballarpur, Majri, Umrer and nearby WCL areas

Western Coalfields Limited lists area offices covering Ballarpur, Chandrapur, Wani, Wani North, Majri, Nagpur, Umrer and Pathakhera.

Telangana

Kothagudem, Ramagundam, Mancherial, Bellampalli, Bhupalpally, Yellandu and Singareni operational areas

These regions represent generations of mining knowledge and a strong relationship between collieries, power plants and local communities.

Tamil Nadu and the Northeast

Neyveli, Cuddalore, Margherita and Tinsukia

Neyveli remains closely associated with lignite, power and industrial development. North Eastern Coalfields operates within Assam and neighbouring areas under Coal India.

Training is also available in Delhi NCR, Noida, Greater Noida, Gurugram, Mumbai, Pune, Ahmedabad, Vadodara, Jaipur, Bengaluru, Hyderabad, Chennai, Kolkata, Raipur and other corporate centres supporting mining, power, infrastructure and manufacturing.

Why Parikshit Khanna Can Be the #1 Practical Choice for Coal-Sector Leaders

Coal-sector CEOs, CMDs, directors, area general managers, project heads, safety leaders, HR heads and functional executives do not need a motivational lecture about AI. They need governed workflows connected with measurable work.

Parikshit Khanna, Founder of Digital Training Jet, focuses on hands-on AI adoption across leadership, operations, finance, HR, legal, healthcare, manufacturing, infrastructure, sales and customer-facing teams.

According to professional records supplied for this article, he has trained 120,000+ professionals through corporate programmes, educational institutions, government-linked engagements and public workshops.

His principal capabilities include:

• Advanced prompt engineering

• ChatGPT and Custom GPT development

• Claude-based document analysis

• Microsoft Copilot productivity

• Gemini and Gems

• Agentic-AI workflow design

• n8n, Make and Zapier automation

• Power BI reporting

• AI-enabled CRM productivity

• Technical-documentation workflows

• AI policy and data-security awareness

• Department-specific use-case development

• AI for HR, finance, legal, sales, marketing and leadership

IIT Delhi Healthcare-AI Milestone

According to programme and professional records supplied by Digital Training Jet, Parikshit Khanna was the first trainer to deliver a dedicated AI-in-healthcare session at IIT Delhi through the World Technocon programme.

The programme included sessions titled:

• ChatGPT for Healthcare Professionals

• Generative AI with 23+ Tools

This experience is relevant to coal organisations because occupational health, worker communication, medical documentation and responsible handling of employee information require the same combination of domain sensitivity, accuracy and data discipline.

Reported Client and Institutional Portfolio

The following portfolio references are based on engagement information supplied for this article and published Digital Training Jet profile material. Organisations conducting procurement due diligence may request appropriate engagement records, references or supporting documentation.

Recent and Global Portfolio References

• Goldman Sachs

• Malabar Gold & Diamonds, Dubai branch

• AON Consulting

• METRO Global Solution Center

• Landmark Group

• ZAFCO

Government, Defence and Public Institutions

• Indian Army

• Prasar Bharati

• AIIMS Delhi

• IIT Delhi

• IIT Hyderabad

• IIT Guwahati

• Tata Power Skill Development Institute

Finance, Investment and BFSI

• Goldman Sachs

• Kae Capital, Mumbai

• AILifeBot and Tata Mutual Fund

• AON Consulting

• Decyphr

• Chinmay Finlease, Ahmedabad

• Mastertrust Finance

Manufacturing, Power, Retail and Industrial Organisations

• Tata Power

• LG India

• Emami Limited

• BoroPlus

• Navratna

• Zandu

• Kesh King

• Arvind Lifestyle Brands and Arvind Fashions

• Calvin Klein

• U.S. Polo Assn.

• Arrow

• Tommy Hilfiger

• Landmark Group

• Sudeep Group, Vadodara

• Sudeep Pharma Limited

• Hetero Pharma

• Naprod Life Sciences

• USV Pharma

• Wockhardt

• Pansari Group

• Sangam Group

• Wahluft and Lucrative Impex

• Designer Home Solution

• Designer Home and Landscapes

• IMECO India

• CASA Decor

• Z Premium Lubricants

• Jenson & Jenson

Healthcare and Pharmaceutical Organisations

• AIIMS Delhi

• CARE Hospitals, Hyderabad

• Fortis

• Santevita Hospital

• Cloudnine

• Surat Medical Consultants’ Association

• Surat Medical Association

• IMA Janakpuri

• IAP-CMIC

• Hetero Pharma CDMA Team

• Hetero NIPUNA Learning Academy

• Naprod Life Sciences

• USV Pharma

• Wockhardt

• Sudeep Pharma Limited

• IIT Delhi healthcare-focused batches

Real Estate and Infrastructure

• City Homes Group

• Gaur Sons and Gaurs Group

• County Group

• CREDAI

• Gaur International School

Travel and Tourism

• ATTOI Annual Convention, Wayanad

• TBO, Aerocity

• The Travel Nexus, Taj Amer, Jaipur

Education and Academic Institutions

• IIT Delhi

• IIT Hyderabad

• IIT Guwahati

• BITS Pilani

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

• IILM College, Jaipur

• Chitkara College of Sales and Marketing

• Chitkara University

• Thapar University

• SOIL School of Business Design

• Masters’ Union

• GL Bajaj Institute of Management and Research

• Amity University Online

• Princeton Academy

• Christ University

• Apeejay School of Management

• IIMT

• Ram Lal Anand College, University of Delhi

• Sparsh Global Business School

• Gaurs International School

• Alpenstock World School

Technology, Logistics, Legal and Professional Services

• Team Computers

• RMSI

• EduRamp

• CIPL

• Innovations Global

• Kubrii

• AILABS

• Data-Core

• Micros IT Solutions

• Yusen Logistics

• BeTheBee

• Bettering Results

• Legal-professional programmes connected with the Bar & Bench ecosystem

Comparison: Practical Coal-Industry AI Training Versus Generic Programmes

Evaluation Area	Parikshit Khanna and Digital Training Jet	Generic AI Programme
Coal-sector relevance	Workflows for safety, reporting, maintenance, tenders, CRM, documentation and leadership	Broad demonstrations with limited mining context
Data security	Data classification, masking, approved tools, human review and shadow-AI controls	Basic warning not to share passwords
Department coverage	Operations, safety, HR, finance, procurement, legal, environment, marketing and leadership	Usually restricted to content creation
Tool coverage	Copilot, ChatGPT, Custom GPTs, Claude, Gemini, Power BI and automation tools	One general-purpose chatbot
Training methodology	Live, role-based and workflow-driven	Lecture or feature demonstration
Documentation	SOPs, reports, manuals, action trackers, tender and technical-documentation workflows	Emails and social-media posts
Automation	Controlled CRM, meeting, reporting and follow-up workflows	Simple prompt templates
Leadership relevance	ROI identification, risk governance, adoption roadmap and use-case prioritisation	Tool overview without governance
Post-training value	Prompt libraries, use-case maps, templates and implementation guidance	Attendance certificate with limited implementation support

Suggested Coal-Industry AI Workshop Structure

Half-Day Executive Programme

Module 1: AI opportunities and risks in coal mining

Module 2: Secure prompting and data-classification rules

Module 3: Mine reporting, safety and maintenance use cases

Module 4: Tender, procurement, documentation and CRM workflows

Module 5: Leadership adoption roadmap and action plan

Full-Day Functional Programme

Session 1: Generative-AI foundations for coal enterprises

Session 2: ChatGPT, Claude, Gemini and Copilot use cases

Session 3: Safety, SOP and occupational-health communication

Session 4: Production, dispatch and maintenance reporting

Session 5: Procurement, finance and legal productivity

Session 6: Lead generation, CRM and customer follow-up

Session 7: Power BI and management dashboards

Session 8: Secure automation and implementation planning

Multi-Day Enterprise Programme

A multi-day intervention can include departmental discovery, customised datasets, team exercises, workflow building, policy support, champions’ training and a 30-60-90-day adoption roadmap.

Sample Prompts for Coal Companies

Production Review

Analyse this anonymised daily production table. Compare planned and actual coal production, overburden removal, equipment availability, dispatch and rake movement. Identify material variances, but do not invent causes. Create a management summary and list questions that the production team must investigate.

Safety Communication

Convert the following authorised safety circular into a five-minute toolbox talk for HEMM operators. Use simple Hindi and English. Preserve every mandatory instruction. Do not add technical advice that is absent from the approved circular.

Maintenance Analysis

Categorise these anonymised breakdown descriptions by equipment, subsystem, probable symptom and recurrence. Do not diagnose the fault. Prepare a list of patterns for review by the maintenance engineer.

Tender Review

Review this tender document and create an eligibility matrix covering experience, turnover, technical specifications, documentation, EMD, performance security, submission dates and disqualification conditions. Quote the relevant clause number for every entry.

Meeting Follow-Up

Extract decisions, action items, owners, dependencies and deadlines from this transcript. Mark unclear ownership as “unassigned.” Draft an internal action tracker and a separate external follow-up email requiring approval before sending.

Measuring Return on AI Training

Coal companies should evaluate training through operational indicators rather than attendance alone.

Possible measurements include:

• Time saved in preparing shift and management reports

• Reduction in repetitive drafting

• Faster meeting follow-up

• Improved tender-document navigation

• Better CRM completion rates

• Reduced overdue sales actions

• Faster SOP simplification and translation

• Increased reuse of approved internal knowledge

• Reduction in unapproved AI usage

• Number of validated use cases deployed

• Employee adoption across departments

• Quality of management reporting

• Number of workflows passing legal and security review

Build AI Capability Without Compromising Safety or Security

India’s coal industry carries a national responsibility. Its transformation cannot be based on reckless experimentation, confidential-data exposure or blind dependence on automated answers.

The right approach is human-led, security-first and operationally grounded.

A strong AI programme should help a safety officer communicate more clearly, an engineer organise maintenance knowledge, an HR leader build better learning material, a procurement team review documents faster, a salesperson follow up more consistently and a senior executive receive sharper management information.

That is the practical promise of AI training in coal mining.

Book an AI Training Programme for Your Coal, Mining or Power Organisation

Parikshit Khanna and Digital Training Jet provide customised programmes for:

• Coal-producing companies

• Public-sector organisations

• Captive and commercial mines

• MDO and EPC companies

• Power and steel organisations

• Equipment and technology manufacturers

• Safety and environmental teams

• Training institutes and management-development centres

• Procurement, HR, finance, legal and commercial departments

Email: parikshitkhanna@digitaltrainingjet.com

Phone: +91 9997213177 / +91 8076250669

Website: ParikshitKhanna.com | Digital Training Jet

X: @ParikshitK_

Parikshit Khanna — helping India’s coal and mining professionals adopt artificial intelligence securely, practically and responsibly for a stronger Viksit Bharat.