AI & Digital Transformation

Turning technology decisions into industrial performance

We support CIOs and digital leadership functions on their end-to-end transformation journey – from IT strategy and enterprise architecture to data governance and AI deployment. As independent advisors with deep operational expertise, we bridge business strategy and IT execution to deliver measurable results.

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How we shape AI & digital transformation

Digital strategy, enterprise architecture, AI deployment – these decisions lock in technical and organisational choices for years. Made well, they accelerate performance. Made poorly, they create technical debt that compounds with every passing quarter. The difference lies in how the decisions are framed, governed, and connected to business reality. Four priorities shape how we work.

Helping CIOs strategically align IT and business

When IT and business operate in parallel rather than together, investments underdeliver and transformation stalls. The CIO's challenge is to align across every dimension – strategy, organisation, processes, project prioritisation, governance – so that IT becomes a true driver of business performance.

IT master plan aligned with business ambitions

Assessing digital maturity across processes, tools, data, and organisation, then defining a sequenced roadmap that connects IT investments to strategic priorities

Project portfolio governance at scale

Implementing the processes and tools to prioritise, fund, and deliver IT initiatives based on business value and strategic alignment

IT operating model fit for digital

Redesigning the IT organisation around value delivery, developing the right skills, and deploying fit-for-purpose methods: Agile, SAFe, Design Thinking

Business process transformation for digital and AI

Managing the digitalisation of core business processes, from scoping and tool selection to implementation support

Sustainable IT and adaptive governance

Framing a responsible digital roadmap and establishing governance that evolves with the business

Transforming IS architecture to streamline the data product supply chain

When applications accumulate without coherent architecture, data gets trapped, integrations multiply, and every new project takes longer than the last. The shift is from managing an application landscape to building composable architecture that enables data products to flow across the enterprise and into the ecosystem.

Composable IS architecture

Shifting to modular, interoperable architecture that provides greater business agility and adapts to ecosystem-level integration

Strategic API programme

Moving APIs from technical interfaces to strategic assets that unlock ecosystem value and support a marketplace of digital products and services

Data product supply chain

Defining how data products are built, governed, and exchanged across business domains, with clear standards for interoperability and quality

Architecture governance

Establishing the principles, standards, and review processes that ensure every new project strengthens the architecture rather than fragmenting it

Data engineering

Structuring the data pipelines, integration methods, and tooling that make the target architecture operational

Building a pragmatic data strategy to accelerate transformation

Most industrial companies know data is an asset, but struggle to make it visible, reliable, and actionable. The answer is not a data lake – it is a structured approach to governance, quality, and ownership that turns data into a foundation the business can actually build on.

Data visibility and business activation

Making data visible, meaningful, and actionable for the business, not just for IT.

Master data management

Consistently managing master data across ecosystems, applications, and business domains

Data ownership and governance

Federating data ownership and governance across business domains with clear roles, responsibilities, and decision rights

Data quality embedded in operations

Embedding data quality practices into every business domain so that accuracy is maintained at source

Data fundamentals

Deploying the key foundational elements that make a data strategy sustainable over time

Empowering industry through AI

AI creates value in industry when it moves beyond the lab. The challenge is not just to identify the right use cases – it is to build the organisational readiness, develop the solutions, and deploy them at scale in real operational environments. We cover the full journey, from strategic framing to industrial deployment, with our Mews Labs team embedded at every stage.

Impactful and scalable algorithms

Identifying the right use cases, developing business algorithms through machine learning and deep learning, and industrialising deployment from proof-of-concept to production

Generative AI and LLM for industrial use cases

Deploying large language models for engineering knowledge extraction, document analysis, and process automation in operational contexts

Asset modelling and simulation

Modelling complex systems and organisations to test scenarios, simulate change, and support investment decisions before committing resources.

Agentic AI

Designing autonomous AI agents that execute multi-step operational tasks, from planning optimisation to real-time decision support

Business process efficiency through AI

Rethinking how operational processes run by embedding AI natively – automating decisions, reducing cycle times, and improving accuracy at scale

From digital to a 360 transformation

Deploying AI and digital solutions at scale rarely stops at the technology. The adoption challenges, process redesign, and governance changes that come with it often require a broader transformation approach. Transformation 360 supports this transition end-to-end — ensuring that the tools we build actually take root in your organisation.

Explore

Powered by Mews Labs

When the strategy is defined, Mews Labs builds and deploys. Our data science and AI capabilities turn strategic decisions into operational reality.

AI industrialisation Moving from proof-of-concept to production-grade AI: model development, training, validation, and deployment in operational workflows

Digital twins and simulation Modelling complex industrial systems to test scenarios, optimise performance, and support investment decisions before committing resources

Advanced analytics and operational intelligence Building custom analytics layers that answer the specific questions your operations need answered, with decision-ready outputs

Client results

ESA Aerospace & Defence AI & Digital Transformation 01/07/2026

Digital Twin of the organisation

Challenge: Design and implement a digital twin of the agency across all its dimensions, connected to the data lake, to monitor and analyse performance across entities.