Hello from New Hampshire đź‘‹
Hi everyone, I’m Lissette. I work in enterprise HR technology and project delivery, with a strong focus on using Microsoft 365 and AI to help teams work more efficiently and scale what’s working. A lot of my day‑to‑day work sits at the intersection of technology, structure, and enablement, translating complex requirements into practical solutions, and using AI and automation to accelerate delivery and reduce friction for teams. I’m here to learn from the community, share practical experiences, and exchange ideas around modern ways of working. Looking forward to learning from you all and contributing where I can. I’m especially interested in real‑world M365 practices that improve collaboration, governance, and adoption at scale.
Unable to @ mention external collaborators in comments within Office online files
Is anyone aware of whether Microsoft will someday enable the ability to @ mention users who do not belong to the M365 tenant within comments in Office online files? I've attached a screenshot of what this looks like in a Word Online file in OneDrive. There are a number of Microsoft feedback posts about this, but it really feels like an unnecessary gap in functionality. Anyone have any insights on roadmap?SharePoint Online Drops One Time Passcodes for External Access
From July 2026. SharePoint Online and OneDrive for Business will use Entra B2B Collaboration (guest accounts) to control external access to shared files. This change has been coming since 2021, but it takes time for organizations to get their heads around changing the way to grant external access. It’s time to embrace guest accounts, and that means doing some work to manage guest accounts on an ongoing basis. https://office365itpros.com/2026/03/06/guest-accounts-spo/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 impact
Optimizing Microsoft 365 Licenses Using Behavior Data (E3/E1/F3)
Hi everyone, We are currently working on a Microsoft 365 license optimization initiative and would appreciate insights from the community and Microsoft experts. Our approach focuses on two main areas: (1) Revoking licenses for inactive users, and (2) Reviewing active users to ensure their assigned license (E3, E1, or F3) aligns with actual usage and collaboration needs. From a data perspective, we are leveraging Microsoft 365 usage signals such as Teams activity, Outlook email interactions, meetings, and SharePoint/OneDrive collaboration. While usage reports provide raw metrics, we are looking for guidance on how these signals should be interpreted and combined in a meaningful and fair way. Specifically, we would like to understand: (1) Which usage metrics best represent user collaboration behavior? (2) Are there any recommended thresholds or patterns that help distinguish light, standard, and heavy collaboration users to map E3, E1, or F3? Any best practices, references, or real-world experiences would be greatly appreciated. I'm sorry if this is the wrong forums to ask for. Thanks in advance for sharing your insights.Microsft Forms Permissions
Hello, We created a form for people to RSVP for an event. We shared the link with our invitees. Some individuals when they follow the link will see other peoples answers displayed in the question fields instead of the prompts we created. Furthermore, one individual opened Forms on their computer and saw that they had editing permissions to our form. How could this have happened and is there a way to remove people from what appears to be an editing permission? Thanks!
