Lokesh Tayal · Expertise
Four strengths. One enterprise perspective.
Connect business processes, software products, reliable data and people adoption.
My experience spans consulting, customer transformation, software vendors and product entrepreneurship. EAM Value brings that connected perspective to advisory, solution engineering and training.
01 · Assets and operations
Asset Management and EAM
Scope
The process and asset-management principles apply across EAM platforms. The scope includes maintenance processes, master data, planning and scheduling, and implementation assurance. Deep direct product experience is in Oracle EAM and IFS EAM and MRO; wider career exposure includes SAP Plant Maintenance, IBM Maximo and other EAM environments. ISO 55000 provides an asset-management context, supported by continuing CAMA2 studies.
Evidence
Oracle Asia Pacific EAM, SCM and CMRO work; independent advisory and training through EAM Process; and IFS aerospace and defence solution work inform this perspective.
Representative problems
Unclear maintenance priorities, inconsistent equipment and work-order data, weak planning discipline, and application configurations that do not support the intended process.
AI opportunities
Explore asset-investment scenarios, work-order triage, technician knowledge retrieval, failure-analysis support, master-data checks and schedule alternatives. Engineering judgement, data quality and human review remain essential.
Discuss EAM advisory and assurance →02 · Products and adoption
ERP EAM and SCM Product Management
Scope
Connect industry requirements, enterprise processes, roadmap thinking and adoption. Consider the full journey from a user need to a useful capability, documentation and delivery.
Evidence
Oracle EAM and SCM product-management work, regional solution leadership and partner enablement, alongside Math2Shine software-product entrepreneurship and multilingual learning design.
Representative problems
Requirements disconnected from business decisions, feature priorities with unclear value, gaps between demonstrations and real use, and products that need stronger user understanding.
AI opportunities
Research synthesis, requirements exploration, documentation and test design; ERP exception analysis; spare-parts forecasting and inventory decision support. Validate suggestions against process rules, source evidence and user needs.
SAP and Anaplan are relevant enterprise or target contexts for process discussions. They are not presented here as direct product mastery.
Connect product thinking with enterprise solutions →03 · Practical innovation
AI Data and Industry Innovation
Scope
Start with the decision or learning experience that needs improvement. Bring AI, industrial IoT and data into a process context, with attention to governance, security and the limits of evidence.
Evidence
Ecosystm industry advisory and the talk Monetising IoT Investments connect industrial technology with business value. Practical AI use is part of Math2Shine product development, with attention to accuracy and human review.
Representative problems
Technology pilots without a clear owner or decision, signals that lack operating context, and AI-generated material that needs expert validation before use.
AI opportunities
Anomaly investigation, process-exception analysis, training-content development, assessment and personalisation. These are opportunity areas; completed industrial deployments or performance gains are not asserted.
Explore training in practical innovation →04 · Customers and solutions
Enterprise Solution Engineering
Scope
Market insight and product readiness; lead generation and account entry; discovery, relationships and demonstrations; executive questions and RFI/RFP/RFQ responses; complex deals, partner dynamics, handover and customer success.
Evidence
Oracle regional solution leadership and partner enablement, and IFS EAM/MRO solution work across APAC and the Middle East for aerospace and defence.
Representative problems
Discovery that misses stakeholder priorities, demonstrations that do not answer the buyer's questions, proposals with unclear assumptions, and sales-to-delivery handovers that lose the intended value.
AI opportunities
Account research, discovery preparation, proposal drafts and demonstration storylines. Check customer facts, product capabilities and confidential-data boundaries before sharing or acting.
Explore Enterprise Solution Engineering →How the strengths connect
AI as an enabling capability
AI can assist work across all four areas. A useful result still depends on process intention, software fit, reliable data and accountable people.
- Process knowledge → software products
Turn operational intent and business rules into requirements and priorities. - Software products → data
Design usable workflows around consistent records, definitions and controls. - Data → people adoption
Give planners, technicians and decision-makers information they can understand and trust. - People adoption → process improvement
Use operating feedback to refine the process and the product together.