# Dr. Linda Xu

Director of Data Analytics & AI, MealSuite.

I lead AI where mistakes are expensive. I own AI end to end, deciding what AI can be trusted with, what stays deterministic, and how it is governed. I work the way I want companies to work, alongside AI agents I built as digital workers. Publishes academically as Wei Xu.

## Questions people ask

### Who is Dr. Linda Xu?

Director of Data Analytics & AI at MealSuite, where she owns AI end to end: AI strategy, AI governance and decision-support systems. PhD in operations research, University of Plymouth.

### Is she the same person as Wei Xu?

Yes. She publishes academically as Wei Xu (ORCID 0000-0002-0271-1944), on supply chains under uncertainty and on technology management.

### What has she delivered?

Over 30 departments under central AI governance, a 38% reduction in AI cost, and 99.9% accuracy in standard reporting.

### How does she work?

Alongside AI agents she built as digital workers. They take the repeat work, so her time goes to strategy, judgement and people.

## Path to AI leadership

1. **PhD, Operations Research.** University of Plymouth. Simulation and genetic algorithms for supply chains under uncertainty.
2. **Research Lead, IoT and Digital Twin.** University of Sheffield, 2017–2019. 32% higher factory productivity from a digital twin for modular housing.
3. **Consultant Director, Digital Supply Chain Strategy.** Syst Dynamics, 2019–2022. More than €16M of digital and AI/ML programmes.
4. **Enterprise Automation and Data Transformation Lead.** Veralto, 2022–2026. 12-person team, $15M budget, 95% fewer support emails.
5. **Director of Data Analytics & AI.** MealSuite, 2026–present. Owns AI end to end: 38% lower AI cost, over 30 departments governed.

## Selected results

| Outcome | Result | What it involved | Role | Evidence |
|---|---|---|---|---|
| AI cost | −38% | Central visibility of AI spend by department, vendor and token use | Current role |  |
| AI governance | 30+ | Departments under central governance; every MCP connection governed; real-time AI cost for every employee | Current role | [Barndoor case study](https://barndoor.ai/customers/mealsuite/) |
| Reporting accuracy | 99.9% | Hybrid deterministic and LLM design; model routing cut LLM spend by up to 30%, part of the 38% | Current role |  |
| Support emails | −95% | AI order assistant across 800,000+ SKUs | Veralto |  |
| Freight audit | $460K | Recovered each year, with 96% less manual work | Veralto |  |
| Supply chain cost | 36–48% | Lower cost from optimised strategies in simulation tests on company data | PhD research | [Operational Research, 2022](/publications/operational-research-2022) |

[All results and roles](/work)

## Featured: Barndoor customer story

"There is no playbook." What I told our CTO on my first day in the role, when staff did not know what to do or if anything should connect to AI. Barndoor's case study describes how my team applied PII and prompt-injection policies to the servers that hold customer information, and brought over 30 departments under central MCP governance. [Inside MealSuite's race to govern AI and take control of their data](https://barndoor.ai/customers/mealsuite/).

## 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, hands-on model work and the people behind each project.

**My digital workers take:**

- The weekly AI spending report
- The weekly AI brief
- Travel booking in Ramp
- Organising my calendar
- Deadline reminders
- Keeping the AI governance onboarding plan up to date

**I keep the judgement:**

- Data architecture under complicated regulations
- How to start AI governance when there is no playbook
- What our strategy should be when new models arrive every few weeks
- How to build the model lifecycle
- What return an agent can really bring

Every MCP connection is governed, my own agents included.

## Research: from mathematical models to machine intelligence

My PhD in operations research, at the University of Plymouth, built mathematical models of multi-stage supply chains under uncertainty. I tested strategies in simulation against what the companies actually did, then tuned them with optimisation algorithms: genetic algorithms, including a multi-objective version, and stochastic approximation. It was machine intelligence before language models: a computer searching for better decisions than people could find by hand.

The tools I use now are different. The discipline is the same: model the decision, define what good means, test against the current way of working, then tune. I publish academically as Wei Xu.

**36–48%** lower cost from optimised strategies against the company's original approach, in simulation tests on its own data ([Xu and Song, *Operational Research*, 2022](/publications/operational-research-2022)).

Methods, from PhD to today:

1. **Mathematical modelling.** Multi-stage supply chains with uncertain lead times and demand.
2. **Simulation.** Strategies tested against what each company actually did.
3. **Optimisation algorithms.** Genetic algorithms and stochastic approximation.
4. **Machine learning.** Decision trees, with rules a person can read and explain.
5. **Language models.** Fine-tuning and reinforcement-learning training on a self-hosted model.

[Research](/research) and [publications](/publications).

## AI strategy notes

- [Give agents the repeat work, keep the judgement](/ai-strategy/give-agents-the-repeat-work) (5 min read)

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Source: https://drlindaxu.com. Dr. Linda Xu (publishes academically as Wei Xu).