Executives & sponsors
Use-case framing, investment logic, and guardrails so AI stays accountable to business outcomes.
Data & AI activation
Put trusted intelligence in the flow of work
We help you activate AI and advanced analytics on a foundation you can explain, so Copilot, forecasting and automation deliver value without bypassing governance.
Explore capabilities What we do
From AI-ready to AI-enabled
Activation starts with trustworthy data and clear use cases, not a generic model rollout. We give Copilot and agents the governed business context they need by extending semantic models your team already trusts, then sequence forecasting, ML and operations automation so each step proves value on Microsoft Fabric.
Capabilities
How we activate data and AI
Select a capability to see how we deliver on Microsoft Fabric, from governed business context to forecasting and operations.
AI understands language, not your business, until your entities, relationships and KPIs are governed. We extend the semantic models your team already trusts into Fabric Ontologies, so Copilot and agents answer from your business vocabulary, with clear policies on data exposure and quality.
What we do
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Readiness assessment
Review workspaces, sensitivity labels, lineage and semantic gaps that would limit how well Copilot and agents perform.
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Fabric Ontologies alignment
Bootstrap and enrich ontologies from certified semantic models, so business concepts and relationships are explicit rather than buried in DAX.
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Agent adoption and guardrails
Define acceptable use, pilot cohorts and feedback loops for Copilot and Fabric data agents, paced to what is production-ready.
Implement forecasting and machine learning on the same governed metrics your reporting already uses, with models your teams can explain, monitor and connect to planning cycles.
What we do
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Use case prioritisation
Identify forecasting and ML bets with business owners (demand, capacity, churn or operations) mapped to available data.
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Model development
Build and validate models using Fabric notebooks or Azure Machine Learning where appropriate.
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Operationalisation
Schedule refreshes, monitor drift, and surface results in Power BI or downstream systems.
Monitor operational signals, automate repeatable data and reporting tasks, and cut manual handoffs, so teams spend less time moving data and more time acting on it.
What we do
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Process and event mapping
Identify high-volume manual steps and operational signals in reporting, reconciliation or data distribution suitable for monitoring or automation.
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Operations monitoring and alerting
Detect conditions and surface alerts on Fabric so teams act on exceptions early, not after month-end.
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Workflow implementation and runbooks
Implement with Fabric Data Factory and pipelines, with logging, failure handling and runbooks your team can maintain after go-live.
Who we partner with
Built for teams activating AI on real data
We tailor depth to the room: executives see risk and ROI; practitioners see architecture and delivery they can run.
Analytics & data science leads
Fabric-native patterns for ontologies, agents and models, with monitoring and integration into the BI your organisation already uses.
Domain & operations owners
Automation and forecasts connected to the metrics they already review, not parallel shadow systems.
How we work
Activation without bypassing governance
We phase AI and advanced analytics from trusted models outward, so every pilot has named owners, clear data boundaries and a route to production.
Always together
We shape use cases with sponsors, data teams and domain owners, so agent scope and automation rules reflect how the business actually works.
Think big, start small
We choose one measurable use case first. Once the pattern works, the same governed context can support more agents, forecasts and workflows.
Build modular
Ontologies, semantic models, notebooks and automations are built as reusable blocks, ready to evolve with Fabric and your operating model.
FAQ
Common questions about data and AI activation
What sponsors and practitioners ask before moving from AI-ready to AI-enabled on Microsoft Fabric.
What does AI-ready mean in practice?
Trusted data, governed access, documented semantics and named owners, with a shared business vocabulary your tools agree on. Copilot, agents, forecasting and automation only scale when stakeholders can verify inputs and explain outcomes.
What is Microsoft Fabric IQ, and do we need it?
Microsoft Fabric IQ is the business context layer Microsoft provides on Fabric: a shared way for agents, Copilot and analytics to reason over your entities, relationships and governed metrics instead of raw tables. It extends your semantic models, it does not replace them. You do not need it to start. We focus first on trusted data and certified models, then phase in ontologies and agents as your foundations mature alongside the Microsoft roadmap.
Can we start small with AI?
Yes. Pick one measurable use case and prove value with your data and your team. One focused use case delivers more value than adding AI everywhere without a clear path to results. When that pattern works, expand the architecture and adoption step by step. Think big, start small applies to AI as much as to analytics.
How does this relate to our Power BI semantic models?
Your semantic models are the foundation. We build and certify those under BI & Analytics, then extend them into Fabric Ontologies so agents inherit the same definitions of revenue, margin and the metrics you already trust. Activation adds agent-ready business context on top of certified models, rather than rebuilding logic in a separate place.
How do you handle AI governance and risk?
We align sensitivity labels, access patterns, documented semantics and acceptable-use policies with your IT and legal stakeholders. Ontology scope and agent access stay tied to named owners and measurable use cases, not open-ended experimentation.
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