FY26 at MDW: Our Scaling with Microsoft

What FY26 tells us

MDW (My Data Works) is publishing the key performance indicators of its Microsoft practice for fiscal year FY26. Beyond the headline figures, these numbers illustrate a broader shift in enterprise Data & AI: the challenge is increasingly moving from proving individual use cases to building the platforms, governance and operating models required to run them reliably in production.

$46M of Azure Data & AI budget entrusted to MDW by its clients in Europe

In FY26, MDW’s clients across Europe entrusted it with the governance of Azure Data & AI environments representing $46M in budget.

This figure doesn’t reflect a sales volume: it reflects a budget whose governance our European clients have entrusted to us: production environments running their Data, AI and Business Applications solutions, generating real, recurring Azure consumption.

This includes the infrastructure and services required to operate unified data platforms, analytics environments, machine learning and generative AI workloads, and business applications at enterprise scale.

Clients only entrust the governance of this volume of critical Data & AI workloads to a partner when they have confidence in its architecture, its governance, and the team responsible for it. The more central these platforms become to an organisation’s operating model, the more consequential the architectural, governance and cost decisions surrounding them become.

16 Data & AI platforms deployed from initial design to production

In FY26, MDW designed and deployed 16 new Data & AI platforms, taking them from initial architecture through to production.

These platforms support use cases including data foundations for AI readiness, document and process automation with agentic workflows, customer intelligence and next-best-action, predictive maintenance in industrial environments, and governed foundations for Copilot and agentic AI at scale.

Across these 16 deployments, the recurring challenge was not the availability of individual technologies, but the integration of data, governance and AI capabilities into an operational platform.

These platforms combine governed data foundations, analytics, machine learning and AI capabilities in production. Depending on the use case, this can involve technologies across the Microsoft ecosystem such as Microsoft Fabric, Azure Data Factory, Azure Synapse, Azure Machine Learning, Microsoft Foundry and Power BI.

At enterprise scale, however, technology alone does not solve the problem.

A production platform also requires clear ownership of data and indicators, consistent semantic definitions, controlled access, traceability, data quality and governance across the full data lifecycle. When AI agents are added to the environment, these requirements become even more important: agents need access to reliable, governed information and clearly defined boundaries in the same way human users do.

This is where the technical architecture connects directly to the operating model.

12 industries, one recurring challenge

MDW’s FY26 mandates within the Microsoft ecosystem spanned 12 industries, including consumer goods and retail, pharma, financial services, industrials and energy, technology and telecommunications.

The contexts differ significantly. Regulatory requirements, data structures, operating models and business processes vary from one industry to another.

Yet across these environments, the same underlying challenge repeatedly appears: organisations need to make increasingly sophisticated use of their data without creating additional complexity that they cannot govern.

This becomes particularly important in large organisations, where data platforms support multiple business units, countries and decision-making processes. Questions of data ownership, semantic consistency, access rights, auditability and cross-border data access are not secondary considerations. They directly shape what can be deployed, how quickly it can be deployed, and whether it can be trusted once in production.

The relevance of the platform therefore extends beyond the technology itself. It becomes part of the organisation’s decision infrastructure.

From deployment to production

For MDW, deploying a Data & AI platform is not the end of the engagement. Once a platform is in production, architectural decisions become operational decisions: performance, cost, security, governance, adoption and continuous improvement all become recurring concerns.

This is why long-term engagement matters. Working with the same client beyond initial deployment preserves knowledge of its data landscape, architectural choices, business priorities and regulatory constraints. It also allows the platform to evolve without repeatedly rebuilding the context required to make good decisions.

The objective is not simply to deploy a platform that works. It is to establish a production environment that can continue to support new use cases, new data, new users and increasingly autonomous forms of decision-making without losing control.

A Microsoft partnership built over time

MDW has been a Microsoft partner since its foundation. The partnership has been recognised across several stages of Microsoft’s evolution, with Partner of the Year awards in Analytics & AI (2021), Power Platform (2022), Data & AI (2023), Global Partner of the Year Switzerland (2023), Azure (2024) and AI Excellence (2025), alongside a Global Finalist recognition for Power BI in 2022.

The categories have evolved, but the underlying focus has remained consistent: helping organisations turn data and AI capabilities into operational systems.

For clients, the value of this relationship is therefore not limited to a technology badge. It reflects a partnership that has developed alongside Microsoft’s own Data, AI and cloud ecosystem, while MDW has remained focused on the same underlying problem: structuring complexity so that technology can support better decisions at scale.