What is the due diligence for verifying nonprofits?
I know that before donating, funding, or partnering with a nonprofit, a few minutes of due diligence can prevent costly mistakes. But what steps can one take to verify that a nonprofit is legitimate, financially transparent, and actually doing what it claims? Are there AI and API tools one can use for this?
Issue with Organization Sharing – Calendar Permission Behavior
Hi team, I have configured organization sharing between two tenants, Tenant A and Tenant B, to allow users to share availability with each other. However, I am facing an issue: When a Tenant A user tries to add a Tenant B user’s calendar, they get a permission error. However, for some Tenant B users, Tenant A users can successfully add their calendars without any issues. The strange part is that even when a Tenant A user cannot add a particular Tenant B user's calendar, they can still see all availability details of that user in the Availability Assistant. Does anyone know why this behavior is occurring? What is the correct method to ensure calendars shared via Organization Sharing are viewable? Also, is there any official Microsoft documentation on this? best regards, Farheen MasterSunderland City Profile: Frontier transformation in practice
Download the SmartCitiesWorld City Profile – Sunderland Cities everywhere are facing the same pressure: modernize infrastructure, grow the economy, and improve quality of life, without widening inequality. Sunderland offers a credible path forward. Once defined by shipbuilding and coal mining, Sunderland has spent the last four decades deliberately reinventing itself. Today, it is positioning itself as the UK’s leading smart city by investing in digital infrastructure, data, and low‑carbon innovation to drive inclusive, long‑term growth. The latest City Profile from SmartCitiesWorld captures how this strategy is being executed and why it matters for city leaders globally. A digital backbone built for outcomes, not optics Sunderland’s progress starts with a clear foundation: connectivity and data designed with purpose. Full‑fibre connectivity across the city Citywide 5G and LoRaWAN coverage A secure, cloud‑based smart city data platform Together, this stack enables real‑time visibility across transport, environment, and public services. More importantly, it shifts the city from reactive decision‑making to proactive, evidence‑led operations. The impact is measurable. Data and analytics now support: Safer, more predictable event planning Smarter traffic and mobility management Earlier environmental interventions More targeted social and health services From digital health hubs that reduce exclusion to intelligent transport pilots that cut emissions and improve safety, Sunderland is applying technology where it delivers the highest public value—not where it looks most impressive on a slide. What comes next: two opportunities to scale impact The City Profile also highlights where cities like Sunderland can go further. Two opportunities stand out. Move from smart services to predictive city operations With real‑time data already in place, the next step is predictive modeling—anticipating demand across social care, transport, energy, and public safety before pressure points emerge. Done right, this enables earlier investment decisions, lower long‑term costs, and better outcomes across services. Turn digital inclusion into a workforce engine Sunderland’s digital health hubs create a foundation for something bigger: linking access and digital skills directly to workforce development. By aligning inclusion efforts with local demand in advanced manufacturing, data, and clean energy, cities can convert access into sustained economic mobility. Why Sunderland’s approach matters Sunderland’s experience reinforces a critical point: smart city transformation is not about technology in isolation. It is about aligning infrastructure, data, governance, and community priorities around a shared vision for inclusive growth. For public‑sector leaders moving from ambition to execution, the full City Profile provides practical insight into the partnerships, operating models, and decisions behind Sunderland’s approach. It’s a useful reference for anyone looking to translate a digital‑first strategy into measurable impact—for people, place, and long‑term resilience.From 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 insight—most leaders have already seen impressive pilots. The harder question is whether those insights survive the realities of government: public scrutiny, auditability, cross‑department 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 city‑scale AI deployments. Governments don’t need more experiments. They need decision‑ready intelligence—intelligence that can be acted on safely, governed consistently, and defended when outcomes are questioned. What’s 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, it’s 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 can’t decide fast enough. AI pilots often demonstrate answers in isolation, but they don’t reduce the friction between insight, approval, and execution. Decision‑ready 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 isn’t faster answers—it’s faster decisions that stick, because they align with how governments are structured to operate. Institutional memory is infrastructure Cities invest heavily in physical infrastructure—roads, pipes, facilities—but far less deliberately in institutional memory. Yet planning rationales, inspection notes, precedent cases, and prior decisions are often what make or break today’s choices. Consider a routine enforcement or permitting decision that looks reasonable on current data, but quietly contradicts a prior settlement, a regulator’s interpretation, or a lesson learned during a past inquiry. AI systems that don’t account for this history don’t just miss context, they create risk. Decision‑ready intelligence treats institutional memory as a first‑class 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 questions—governance, records, explainability—arrive too late. By the time leaders ask, “Can we stand behind this decision?” the system was never designed to answer. Decision‑ready intelligence flips that sequence. Governance is not bolted on after the pilot; it’s built into the operating model from the start. That’s what allows agencies to scale from a single use case to repeatable patterns across departments. A practical starting point The cities making progress aren’t trying to transform everything at once. They start small but visible: Identify one cross‑department “moment of truth” 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 patterns—governed data products, policy knowledge bases, and human‑in‑the‑loop workflows—to 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 isn’t about smarter models. It’s about designing decision systems that reflect how governments actually work—and are held accountable. Learn more by reading Sadaf's full article: Closing the intelligence gap: how cities turn AI experiments into operational impactFrom SOP Overload to Simple Answers: Building Q&A Agents With SharePoint Online + Agent Builder
Government teams run on Standard Operating Procedures, manuals, handbooks, review instructions, HR policies, and proposal workflows. They’re essential—and everywhere. But during every Government Prompt‑a‑thon we've run this year, one theme kept repeating: "Our policies are many and finding the right answer quickly is nearly impossible." Turning SOPs into Simple Q&A Agents with M365 Copilot's Agent Builder or SharePoint Agents is possibly one of the fastest wins for public‑sector teams.
