Work

Focus

• Enterprise data and AI strategy: priorities, investment cases and executive reporting.
• AI governance and model risk: responsible-AI standards, controls, explainability and oversight in regulated environments.
• Data governance and compliance: quality, privacy, lineage and stewardship under GDPR, CCPA and healthcare rules.
• Hybrid AI architecture: deterministic algorithms and LLMs assigned by task and by the consequences of error.
• Operating models: MLOps, DataOps and adoption programmes that move AI from pilot to scale.
• AI cost visibility: spend tracked by department, vendor and token use, so AI investment is managed like any other.
• AI agents: digital workers for repeat work, connected through the same governed route as everyone else.

Selected results

• Company-wide AI governance: over 30 departments onboarded, every MCP connection governed, and every employee able to see AI cost in real time and in session. Described in a Barndoor case study.
• AI cost: 38% reduction in AI cost overall, tracked through central visibility of spend by department, vendor and token use. Model routing is part of it.
• Hybrid architecture: 99.9% accuracy in standard reporting, and LLM spend reduced by up to 30% through model routing, part of the 38% overall.
• AI order assistant (Veralto): support email volume down 95%, integrating ERP, Snowflake and carrier data across 800,000+ SKUs.
• EU customs automation (Veralto): German customs holds cut from 25% to under 1%.
• Freight audit automation (Veralto): $460K recovered each year and 96% less manual work.
• ERP, transport and IoT integration (Veralto): real-time visibility that avoided $3.5M in potential customs costs.
• Reporting and workflow automation (Veralto): dashboards and automated workflows saving $650K a year.
• Inbound logistics (Veralto): more than 90% shorter inbound supply chain time in the targeted process.
• COVID-19 early warning (NHS): statistical machine-learning model on ten years of mortality data, aimed at a three- to four-week horizon.
• Digital twin (University of Sheffield): 32% higher factory productivity in modular housing production, within a £9M Industry 4.0 programme.
• Optimisation research: a genetic algorithm for inventory management under trade constraints reduced total cost by up to 11.6%.
• Leadership: 12-person team and $15M annual budget, reporting to the CIO and executive leadership.

How I work

I built AI agents that work as digital workers for me. They take the repeat work, so my time goes to strategy, to hands-on model work and to the people behind each project.

• What the agents do: prepare the weekly AI spending report and the weekly AI brief, book travel in Ramp, organise my calendar, remind me of deadlines and keep the AI governance onboarding plan up to date.
• What stays with me: judgement. Data architecture under complicated regulations. How to start AI governance when there is no playbook. What our strategy should be when new models are released every few weeks. How to build the model lifecycle. What return an agent can really bring.
• How I decide: I look for the approach that disrupts users least and gets every department moving in the same direction faster. As a design partner to Barndoor, I give feedback on its product so that governance does not become a barrier to adoption.
• Why visibility comes first: without central governance and visibility you do not even know what each department spends across vendors, or how efficiently tokens are used. Now every MCP connection is governed, my own agents included, and everyone can see AI cost in real time and in session.

I write about this in Give agents the repeat work, keep the judgement.

Roles

• Director, Data Analytics and AI. MealSuite, April 2026 to present. Owns AI end to end: AI and data strategy, AI governance and executive reporting. Designed the hybrid deterministic and LLM architecture for operational and healthcare workflows. Owns model development for a self-hosted large language model served with vLLM on company-managed GPUs: evaluation, A/B testing, adjusting model weights and production-readiness review, working with the platform team, who set up the hardware. Currently working on reinforcement-learning training. Directs data remediation and security decisions for sensitive healthcare data. Brought company-wide AI use under central governance: over 30 departments onboarded, every MCP connection governed, real-time and in-session AI cost visibility for every employee, and a 38% reduction in AI cost tracked. Design partner to Barndoor on its governance product. Her governance work is described in a public Barndoor case study.
• Enterprise Automation and Data Transformation Lead; Global Logistics Manager. Veralto (Trojan Technologies), Canada, August 2022 to April 2026. Led a 12-person team and $15M budget; AI governance committee member; built AI assistants for order handling, KPI reporting and customs and freight automation.
• Consultant Director, Digital Supply Chain Strategy. Syst Dynamics, UK, February 2019 to August 2022. Delivered more than €16M of digital and AI/ML programmes in manufacturing, healthcare and logistics, with data-quality standards and MLOps pipelines for regulated clients.
• Research Lead, IoT and Digital Twin. University of Sheffield, UK, April 2017 to June 2019. Led the manufacturing, supply chain and emerging technology parts of a £9M Industry 4.0 programme.
• International Supply Chain Consultant. Materials System Co., China and Canada, April 2015 to April 2017. Led an eight-person team, advised 40+ OEMs on trade compliance and logistics digitisation.

Personal projects

Built and published on GitHub (DRLinda1 and DrLXu1MS).

• A multi-agent LLM system for search and answer-engine optimisation, deployed on Google Cloud and adopted by local companies.
• Agents that review RFI and RFP documents for a civil-engineering bid process, cutting review from three to four weeks to half a day.
• Mentoring a G7 Challenge team on a knowledge-tree framework for public policy data, using LLM classification to structure legislation and motion drafts.