multi-tenant cooperation
Hi everybody, I'm working at a university where we run two tenants, one for staff and one for students. The student tenant was set up for "Microsoft Azure Dev Tools for Teaching" but has not been used for anything else. The staff tenant has been pretty much dormant, because we don't trust the cloud and try to avoid using it. Today everybody is excited about getting Teams up and running for collaboration, so we want teams accessbile by both staff and students. I basically found two options. External access, related to sykpe for business (?), but limited. No group chat, etc. so not really what we are looking for. Guest users (at AAD) level sound way better, but there is a catch: inviting 20.000 students to our staff tenant isn't fun, getting them all to accept those invitations, etc. Invition staff to the students tenant isn't much better (but less staff than students...). All I see is problems while my boss is hoping for solutions. đ Putting all students in the staff tenant and ditching the student tenant might sound like the way to go, but there are compliance requirements that are easier to meet if those tenants stay separated. At least as far as I can tell. Would be nice if I could both invite and accept guest users. Like adding educatinoal staff to the student tenant as guest without the invitation process, since staff hired to teach the students can be told to accept the invitation to the student tenant anyway, so why bother with the process? đ I control both tenants anyway. Basically I'm looking for the least bad option. Teams has some more guest options than AAD, that's why I post here. By the way, we run local AD but it is not connected to the tenants in AAD. No directory synchronisation, no ADFS. Both tenants are managed by our IDM-system and federated with Shibboleth. Any help would be greatly appreciated Best regards PatrickFrom AI pilots to public decisions: what it really takes to close the intelligence gap
Across the public sector, the conversation about AI has shifted. The question is no longer whether AI can generate insightâmost leaders have already seen impressive pilots. The harder question is whether those insights survive the realities of government: public scrutiny, auditability, crossâdepartment delivery, and the need to explain decisions in plain language. That challenge was recently articulated by Sadaf Mozaffarian, writing in Smart Cities World, in the context of cityâscale AI deployments. Governments donât need more experiments. They need decisionâready intelligenceâintelligence that can be acted on safely, governed consistently, and defended when outcomes are questioned. Whatâs emerging now is a more operational lens on AI adoption, one that exposes two issues many pilots quietly avoid. Decision latency is the real enemy In government, decision latency is not about slow analytics, itâs the time lost between having a signal and being able to act on it with confidence. Much of the focus in AI discussions is on accuracy, bias, or model performance. But in cities, the more damaging problem is often this latency. When data is fragmented across departments, policies live in PDFs, and institutional knowledge walks out the door at 5pm, leaders may have insight but still canât decide fast enough. AI pilots often demonstrate answers in isolation, but they donât reduce the friction between insight, approval, and execution. Decisionâready intelligence directly attacks this problem. It brings together: Operational data already trusted by the organization Policy and regulatory context that constrains decisions Human checkpoints that reflect how accountability actually works The result isnât faster answersâitâs faster decisions that stick, because they align with how governments are structured to operate. Institutional memory is infrastructure Cities invest heavily in physical infrastructureâroads, pipes, facilitiesâbut far less deliberately in institutional memory. Yet planning rationales, inspection notes, precedent cases, and prior decisions are often what make or break todayâs choices. Consider a routine enforcement or permitting decision that looks reasonable on current data, but quietly contradicts a prior settlement, a regulatorâs interpretation, or a lesson learned during a past inquiry. AI systems that donât account for this history donât just miss context, they create risk. Decisionâready intelligence treats institutional memory as a firstâclass asset. It ensures that when AI supports a decision, it does so with: Access to relevant historical records and prior outcomes Clear lineage back to source documents and policies Logging that preserves not just what was decided, but why This is what allows governments to move faster without relearning the same lessons under audit pressure. Why this matters now Public sector AI initiatives rarely fail because of a lack of ambition. They stall because trust questionsâgovernance, records, explainabilityâarrive too late. By the time leaders ask, âCan we stand behind this decision?â the system was never designed to answer. Decisionâready intelligence flips that sequence. Governance is not bolted on after the pilot; itâs built into the operating model from the start. Thatâs what allows agencies to scale from a single use case to repeatable patterns across departments. A practical starting point The cities making progress arenât trying to transform everything at once. They start small but visible: Identify one crossâdepartment âmoment of truthâ Define what must be logged, retained, and explainable Connect just enough data, policy, and work context to support that decision From there, they reuse the same patternsâgoverned data products, policy knowledge bases, and humanâinâtheâloop workflowsâto scale responsibly. AI in government will ultimately be judged the same way every public investment is judged: by outcomes, fairness, and public confidence. Closing the intelligence gap isnât about smarter models. Itâs about designing decision systems that reflect how governments actually workâand are held accountable. Learn more by reading Sadaf's full article: Closing the intelligence gap: how cities turn AI experiments into operational impact
