Industry advisory — Microsoft

I turn an industry point of view into a working demo, then into an architecture.

My work sits early in the conversation with manufacturing and automotive organisations across ASEAN, Australia and New Zealand. I bring domain experience from across the sector, position a point of view on where AI actually changes the economics, and build a vision demo to make it concrete — which is where the honest discussion about data, constraints and opportunity usually starts.

How the engagement runs

  1. 01

    Frame the point of view

    Start early with manufacturing and automotive organisations across ASEAN, Australia and New Zealand — shaping an industry point of view from external research and internal field knowledge before the first conversation.

  2. 02

    Build the vision demo

    Turn that point of view into a working vision demo — cloud data platform, AI services and GitHub Copilot — so the discussion starts from something tangible rather than a slide.

  3. 03

    Engage on challenges

    Use the demo to surface the real constraints and opportunities: where data sits, what the plant can act on, and which decisions are actually worth automating.

  4. 04

    Scale with platform & partners

    Move into richer architecture discussions across core platform capabilities and the ISV and partner ecosystem, mapping AI to the organisation's broader transformation.

The architecture underneath

Every demo is built against the same shape: plant and engineering sources landing in a governed lakehouse, intelligence layered on top, and a decision that someone on the floor can actually act on.

Sources
  • MES / SCADA
  • Historian & IoT
  • PLM / CAD
  • ERP & Quality
01
Lakehouse
  • Bronze ingest
  • Silver conformed
  • Gold metrics
  • Governance & lineage
02
Intelligence
  • Feature & ML models
  • Vector + RAG
  • Agents & copilots
  • Simulation loops
03
Decisions
  • Warranty signals
  • Plant & OEE
  • Schedule recovery
  • Energy & throughput
04

Vision demos by sub-vertical

Each one targets a specific manufacturing sub-vertical and a specific decision the AI has to get right. Patterns only — no customer detail.

Automotive & Mobility / OEM Quality & Warranty

Warranty & Quality Analytics

Unifies claims, telematics and parts data in a lakehouse, with copilot-driven root-cause insight on emerging failure modes.

  • Lakehouse unification
  • Root-cause reasoning
  • Predictive warranty
Automotive & Mobility / Digital Engineering

Connected Engineering Platform

Accelerates vehicle software and design workflows with generative AI across a connected digital-engineering stack.

  • Generative design support
  • Engineering workflow
  • Software-defined vehicle
Pharma & Life Sciences / Bioprocess & API Manufacturing

GMP Manufacturing Intelligence

Batch, deviation and process-parameter intelligence for the plant floor — GMP process monitoring grounded in operational data.

  • GMP monitoring
  • Deviation analysis
  • OT data grounding
Manufacturing & Industrial / Discrete & Packaging

Factory Copilot Command Center

OEE, quality and shift intelligence surfaced through a plant copilot, so supervisors act on the line rather than in a report.

  • OEE analytics
  • Shift intelligence
  • Plant copilot
Cross-industry / Enterprise AI Operating Model

AI Factory

An operating model that industrialises AI use cases from ideation to production, with an enterprise assistant grounded on a medallion lakehouse.

  • Medallion lakehouse
  • Use-case pipeline
  • Grounded assistant
Energy, Chemicals & Resources / LPG & Process Facilities

Plant GPT

Read-only industrial Q&A over a process facility — equipment health, process knowledge and maintenance procedures for rotating equipment.

  • Read-only Q&A
  • Equipment health
  • Maintenance procedures
Metals & Mining / Steelmaking

Furnace Operations Dashboards

Real-time melt, energy and reduction-process intelligence for electric-arc and multi-hearth furnaces, with an embedded AI assistant.

  • Real-time process data
  • Energy intelligence
  • Embedded AI assistant
Logistics & Supply Chain / Shipping & Multimodal

Voyage & Multimodal Control Tower

Fleet, port and cargo visibility with disruption-aware recommendations, and agents coordinating flow across transport modes.

  • Real-time visibility
  • Disruption recovery
  • Agent orchestration
Shipbuilding / Shipyard Operations

Shipyard Control Tower & Agentic Recovery

Unifies vessel schedule, dry-dock and hull-block progress, with a multi-agent scenario that detects schedule risk and orchestrates recovery.

  • Schedule risk detection
  • Multi-agent recovery
  • Yard progress tracking
Construction & Real Estate / Infrastructure Delivery

Unified Project Intelligence

Project, field, asset and safety data unified across rail, road, tunnel, water and defence programmes with real-time intelligence.

  • Cross-project data
  • Safety signals
  • Real-time intelligence
Agriculture & Food / Protein, Crop Science & Food Service

Agri-Food Operations Control

Feed-formulation optimisation under heat stress, crop-science control-tower views, and central-kitchen cold-chain and allergen intelligence.

  • Optimisation models
  • Control tower
  • Cold-chain & allergen
Manufacturing & Industrial / Equipment Servitization

Equipment-as-a-Service Portal

Asset registry, predictive service events and monthly performance summaries for an installed base sold as a service.

  • Asset registry
  • Predictive maintenance
  • Service reporting
Built for Databricks

Factory Playbook

A lakehouse-native take on the same idea: plant data unified, agents reasoning over it, and factory decisions surfaced where operations can use them.

Open the Factory Playbook →