Current practice
Practical AI use is part of Lokesh's Math2Shine product-development work, with attention to accuracy and human review. Enterprise application, product and solution-engineering experience provides context for evaluating business processes. It does not establish completed industrial AI deployments.
Detailed Math2Shine workflows and outcomes will be added when publishable evidence is documented. Read the product case.
Future opportunity
The following are opportunity areas to assess, rather than claims of completed implementations or measured benefits. Their suitability depends on the process, available data, operating risk and the ability to validate results.
Asset strategy
Explore scenario summaries and investment trade-offs against objectives, risk and lifecycle assumptions. Decisions need traceable inputs and accountable asset owners.
Reliability
Explore anomaly detection and failure-analysis assistance where equipment history and sensor data are sufficiently reliable. Validate recommendations with reliability specialists before changing maintenance policies.
Maintenance execution
Explore work-order triage, knowledge retrieval and technician support. Technical advice needs approved sources, safety boundaries and competent human review.
Master data
Explore duplicate detection, description improvement and classification suggestions. Data stewards must approve changes against the asset hierarchy and maintenance purpose.
Planning
Explore backlog analysis, job-plan assistance and scheduling options. Check skills, materials, access, shutdown constraints and priorities with planners.
MRO inventory
Explore demand patterns, spare-parts forecasts and inventory exceptions. Intermittent demand, criticality and lead-time uncertainty need explicit treatment.
ERP processes
Explore process assistance and exception analysis across connected transactions. Preserve access controls, audit trails and business ownership.
Product work
Explore research synthesis, requirements, documentation and testing assistance. Evaluate output against user needs and acceptance criteria.
Solution engineering
Explore account research, discovery preparation, proposal drafting and demonstration support. Verify customer facts and ensure commitments reflect deliverable capability.
Training
Explore content drafting, assessment support and personalisation. Review subject accuracy, learning outcomes and suitability for the audience.
Make a small evaluation meaningful
Define the current process and a baseline before selecting a tool. Agree the intended improvement, approved data, evaluation criteria and human review responsibilities. Test representative cases, including exceptions, and compare the result with the existing way of working.
Governance belongs in the process
Data quality, security, source traceability and human oversight determine whether an idea can move beyond a demonstration. Separate suggestions from authorised actions; keep accountable people in control of safety, investment and operational decisions.