AI and Copilot
Information architecture is the work that decides whether Copilot succeeds
Generative AI answers are only as good as the content behind them. The preparation that determines quality happens before any licence is assigned.
New guide: assessing organisational readiness for Microsoft 365 Copilot. Read the guide
Move from AI experiments to governed, measurable Copilot and agent capability across Microsoft 365, Dynamics 365 and Power Platform.
Sales, service, field operations and customer data on a platform your teams will use, with the data quality that makes it dependable.
Apps, automation, portals and analytics delivered quickly, inside a platform model that keeps ownership and data boundaries under control.
Cloud platform, migration, integration, Microsoft Fabric analytics and the Microsoft security stack, designed as an operating model rather than a diagram.
Collaboration, content, intranet and employee experience with the information architecture and governance that keep them working.
Independent assessment, roadmapping and business-case support that turn ambition into a sequenced, fundable plan.
Solution, data, integration and security architecture with the decisions written down so the solution can be owned later.
Agile delivery, configuration, low-code development, migration and integration, released through a controlled path.
Functional, integration, acceptance, security and performance testing that produces artefacts you can review.
Stakeholder engagement, communication, training and adoption programmes measured by what people actually do.
Application support, platform administration, monitoring, incident management and a funded enhancement backlog.
Public and financial
Commercial and industrial
Who we are, how we work and how to join us.
Legal and standards
Report ยท Digital Transformation
An observational summary of recurring patterns in Microsoft modernisation programmes, based on Avanteria delivery experience.
Where integration and infrastructure once determined feasibility, the limiting factor in current programmes is more often the state of the content and data estate: ownership, currency, classification and permission accuracy. This became visible when generative AI made the condition of that estate immediately obvious to end users.
Adoption activity commonly begins at user acceptance testing, by which point the design decisions that determine whether people will use the solution have been made. Programmes that involve change practitioners during design consistently encounter less resistance later.
Platform governance is usually introduced after an incident rather than before enablement. The resulting controls tend to be stricter and less well targeted than they would have been if designed alongside adoption, and they are harder to relax once in place.
Effort estimates are frequently transmitted as single numbers, detached from the assumptions and exclusions that produced them. Change control then becomes a negotiation without a baseline. Preserving assumptions as structured data alongside the estimate removes a recurring source of dispute.
Business cases routinely fund implementation and treat ongoing enhancement as an operational residual. Platforms that continue to deliver value are those with a funded backlog, a named product owner and a regular release cadence after the programme closes.
Written by
Marta covers Azure platform design, migration, Microsoft Fabric and the analytics layer above it. She is particularly interested in landing zones as operating models rather than diagrams, and in semantic models that survive a reorganisation.
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