Leadership reviews, score interpretation and steering sessions are planned around US working windows. Async briefs keep decisions moving between live sessions.
US delivery model
How Tirion works with US teams.
Remote AI and cloud decision work for leadership teams that need security, procurement, timezone and Microsoft ecosystem realities handled before budget moves.
Scope, data handling, owner model, review gates and vendor-onboarding evidence are prepared before procurement asks for them.
European privacy discipline helps US teams frame AI risk, data classes, human review and Microsoft governance before scale.
The work is built around Microsoft 365, Azure, identity, data access and operating ownership instead of tool-neutral AI theatre.
Delivery rhythm
From score to procurement-ready next step.
The process is designed so a US leadership team can move from diagnosis to decision without turning the first engagement into an open-ended consulting program.
The AI & Cloud Leakage Score captures process leakage, cloud waste, governance risk and decision readiness before any review.
Use-case matrix, governance assumptions, procurement notes and open security questions are prepared as decision assets.
A focused review clarifies the strongest lever, quick wins, internal owner and whether Kickstart, Sprint, Advisory or waiting is the right path.
If there is fit, the next offer is framed with scope, deliverables, data boundaries, owner model and operating rhythm.
Security and procurement
Questions answered before they slow the decision down.
For US teams, the delivery model makes security, procurement and data handling part of the decision work, not an afterthought after enthusiasm has already created a pilot.
- Remote and hybrid delivery model
- US timezone overlap for executive reviews
- Security and procurement FAQ
- DPA and vendor-onboarding framing
- EU-based advantage for AI governance
- Score, AI Kickstart and Advisory as clear next steps
Yes. The decision pack can include scope, deliverables, data handling notes, DPA questions, owner model, review gates and security context.
Live sessions are planned around US leadership availability. Reviews, evidence packs and clarifications can move asynchronously between sessions.
The score can start without sensitive data. For deeper work, data classes, owners, systems and human review boundaries are clarified before any implementation scope.
EU privacy discipline can make AI decisions more defensible. It helps teams clarify what data, tools, reviews and ownership should exist before AI moves into production.
US-relevant decision patterns
What you see is the artifact, not a client logo.
Anonymized patterns show how a leadership team can make AI, cloud and governance decisions internally without exposing customer details.
Policy, data classes and approval grid before AI scales
Project artifactAI governance approval grid
Clear gates before the pilot
9 to 3 LLM initiatives, defined approval paths, risk class visible before build.
M365 routines reduced to the first automation paths
Project artifactUse-case matrix
Start, deepen or stop
14 routines to 4 prioritized automation paths with data source and human review boundaries.
Azure owner model before more AI workloads multiply
Project artifactFinOps and governance board
Cloud and AI control in one view
4 platform standards unified, shared FinOps and governance board, clearer platform ownership.
Next step
Start with the score. Move only if the signal is strong enough.
The score routes toward AI Kickstart, Transformation Sprint, Governance Advisory or a resource-only path when there is no strong fit yet.