Healthcare and Life Sciences Blog articles

The Fifth Lifecycle: AI Agents Are the New Enterprise Asset Class

Chad Stout — Wed, 30 Sep 2026 13:47:50 GMT

Every so often, IT encounters a new kind of actor inside the enterprise. The pattern is familiar: adoption outpaces governance, inventory lags behind use, ownership remains unclear and access accumulates. Security teams ask what the technology can reach and who is accountable. Eventually, IT builds a lifecycle around it.

We've lived through this before.

In the 1990s, the new actor was identity.

In the 2000s, it was devices.

In the 2010s, it was applications.

In the 2020s, it became data.

Now we're entering the fifth era: AI agents.

And I think that's an important way to think about the governance challenge in front of us. Not as an entirely new problem, but as the fifth enterprise asset class.

This article explains why agents fit a lifecycle pattern IT already understands, what makes them different from earlier asset classes, and why inventory is the practical first step toward responsible governance.

The questions haven't changed

When a new technology enters the enterprise, IT eventually has to answer the same fundamental questions:

What do we have?

Who is accountable for it?

What can it reach?

What is it actually doing?

How do we protect it?

How does it go away?

We asked those questions about employees and their identities. We asked them about laptops and mobile devices. We asked them about applications—especially the ones that appeared outside traditional IT processes. We asked them about data as information spread across sites, mailboxes, endpoints and cloud services. And now we need to ask them about agents. The subject has changed. The control problem hasn't.

Every new asset class starts unmanaged

Think about what happened in each of the previous eras.

Identity management evolved from basic domain accounts into directory services and modern identity platforms. The lifecycle became something everyone in IT understood:

Join. Move. Leave.

Device management followed a similar path:

Enroll. Configure. Prove compliance. Patch. Wipe. Retire.

Applications introduced another set of governance questions:

Discover. Register. Scope consent. Review. Decommission.

Then came data. As information proliferated across Microsoft 365, SharePoint, mailboxes and endpoints, organizations had to develop ways to:

Classify. Protect. Retain. Audit. Dispose.

Different technology. Different tools. Different risks. But underneath them was the same organizational muscle:

Inventory it. Give it an identity. Assign an owner. Define its scope. Observe it. Secure it. Retire it.

We've built this muscle four times. Agents are the fifth.

The difference is that agents act

There is, however, something important about this fifth asset class. An agent isn't just something sitting in the environment waiting for a human to interact with it. It can reason across information, invoke applications, retrieve data, and take actions on someone's behalf. That changes the consequences of poor governance.

An employee with excessive access is a problem. An application with excessive permissions is a problem. An overshared repository is a problem. An agent operating across those systems can compound all three: it can surface exposed information faster, connect it across sources, and act on it at scale. The agent may not create the underlying permission problem, but it can dramatically increase the problem’s reach and operational impact.

That's why agent governance can't be separated from the governance disciplines that came before it.

The uncomfortable truth: your data matters more than your model

This may be one of the most important ideas in agent governance. An agent isn't useful simply because of the model behind it. It's useful because of what it is allowed to see and do.

Consider a simple example:

An employee asks an agent a question. The agent is grounded in information from a SharePoint site or another enterprise knowledge source. If that employee already has access to the underlying information, the agent can work with it in that user's context.

The agent didn't necessarily grant the employee new access. It reflected the access that was already there. That sounds reassuring, until you realize what it means.

If the organization has years of stale documents, poorly governed sites, excessive permissions, unclear ownership and sensitive information that was never properly classified, the agent may expose those weaknesses much faster than a human ever could.

For years, an overshared document could remain effectively hidden because nobody knew it existed. Now someone can simply ask a question. The permission didn't change. The obscurity did. Agents make existing information easier to discover, which means the agent lifecycle depends directly on the data lifecycle.

If your content has no lifecycle, your agent inherits the absence of one.

Governance should enable adoption, not block it

Agent adoption creates a familiar tension: business teams want to build, while IT and security teams need visibility into what agents can access and do. Those objectives do not have to conflict.

We've seen this movie before with devices and applications. Bring-your-own-device programs didn't eliminate the need for device management. Shadow IT didn't eliminate the need for application governance. Instead, organizations eventually developed ways to discover what existed, establish ownership, control access and manage the lifecycle.

Agents will require the same evolution.

The goal shouldn't be to create a governance process so cumbersome that nobody can build an agent. Nor should the goal be to let an uncontrolled population of agents grow indefinitely. The goal is to make responsible agent adoption operationally possible.

That starts with visibility.

Start with inventory, not policy

One of the easiest mistakes an organization can make is beginning the agent conversation with a policy question. A more useful starting point is visibility: before deciding what the policy should be, establish what agents already exist and how they are connected.

Who built them?

Who owns them?

What are they connected to?

What data can they reach?

What identities do they use?

What are they doing?

Which ones are still needed?

Which ones have effectively become digital orphans?

You may discover that the agent population is already larger than anyone in the room expected. That number is more than an inventory statistic: it reveals the scale of unmanaged ownership, access, connections and lifecycle risk. It gives leaders a concrete starting point for prioritizing governance resources and controls—and a business case grounded in what already exists.

The fifth lifecycle

The good news is that we don't have to invent this from scratch. The emerging agent lifecycle is remarkably familiar:

Register. Identify. Sponsor. Scope. Observe. Secure. Retire.

The technology supporting those controls will continue to evolve. Platforms will change. Agent architectures will change. The way agents interact with applications and data will change. But the organizational questions are likely to remain remarkably stable.

Who owns this?

What can it do?

What can it see?

How do we know what it's doing?

How do we know when it should stop?

Those are not AI questions. They're enterprise governance questions. And that's the mindset shift I think organizations need to make.

AI agents may feel unprecedented, but the governance pattern is not. IT has repeatedly moved new classes of technology from unknown to inventoried, owned, controlled, observed and retired. Agents are the latest—and arguably the most consequential—addition to that lifecycle.

The question, then, is not how to invent governance for AI agents. It is how to apply the governance discipline we have spent decades building to a fifth enterprise asset class. The pattern is familiar, and that makes the problem far more manageable.

And it starts with a surprisingly simple first step:

Inventory it.

Because before you can govern the agents you intend to build, you need to understand the agents that are already there.

This is the first article in a series exploring the fifth lifecycle of enterprise IT: governing AI agents alongside identities, devices, applications and data. The next articles will examine the controls organizations need to turn visibility into durable ownership, security, observability and responsible retirement practices.

HLS in Practice: September 2026

michaelgoad — Tue, 22 Sep 2026 16:36:55 GMT

There’s plenty of Microsoft AI news. Figuring out which updates deserve your attention takes more work.

This edition brings together practical reads from the Healthcare and Life Sciences Blog and other Microsoft teams, covering everyday work, clinical workflows, business apps, and governance.

Recent reading from August 24 through September 22, 2026. Availability reflects the sources available on September 22. Readiness items are labeled separately.

In this edition

From the HLS blog: choosing Copilot or Cowork, clinical workflows, and meeting governance.
Beyond the HLS blog: business apps, agent building, and life sciences examples.
Governance watch: meeting-archive retention.
One thing to try: brief a colleague on one update.

From the HLS blog

Choose between Copilot and Copilot Cowork

For: business leaders and operations teams

In a September 10 article, Nick (NicholasAquino) compares everyday work you guide step by step with multistep tasks you delegate toward a defined outcome.

Put it to work: Start with a familiar task. Use Copilot to improve a project update; evaluate Cowork when preparing a recurring briefing requires gathering several approved sources. Confirm access, permissions, review points, and usage-based costs before delegating actions.

Read: Copilot or Copilot Cowork, which tool to use and when
Nick (NicholasAquino) | HLS Blog, September 10, 2026. Practical selection guidance, not a new product launch.

Explore AI assistance where clinicians already work

For: clinical leaders and informatics teams

In a September 9 article, sarahgrey explores Dragon Copilot’s focus capability, which brings assistance into the text field where a clinician is working. The examples include reviewing selected information and drafting or refining text without moving everything into a separate application.

The useful starting point is a workflow discussion. Where does your team repeatedly copy information between applications, reformat documentation, or recreate the same type of document?

Put it to work: Choose one example to review with your clinical informatics team. Confirm the capabilities available in your environment and the required clinical review before using patient information.

Read: AI where and when you need it with Dragon Copilot’s ability to focus
sarahgrey | HLS Blog, September 9, 2026. Availability and deployment requirements should be confirmed for your organization.

Review what your AI-assisted meetings leave behind

For: meeting organizers, privacy teams, and Microsoft 365 administrators

On September 3, Michael Goad published a walkthrough of using Copilot only during a Teams meeting without creating a saved transcript.

The governance question extends beyond the recording button. Copilot prompts and responses may still be retained for compliance purposes, depending on your organization’s configuration. Shared notes and other meeting experiences also need their own review.

Put it to work: Before changing meeting policies, bring your privacy, records-management, and IT teams together around one meeting scenario. Decide what information should persist, who should have access, and which experiences are appropriate.

Read: Using Copilot in Sensitive Healthcare Teams Meetings
Michael Goad | HLS Blog, September 3, 2026. An existing-capability walkthrough, not a new feature announcement.

Current Microsoft guidance on Copilot and meeting transcription

Beyond the HLS blog

Describe a business app, then review what it builds

For: operations teams, makers, and IT administrators

On September 10, Ryan Cunningham announced app building in Copilot Studio, with public preview rolling out. The same announcement introduced an /app skill in Copilot Cowork through the Frontier program.

The experience lets you describe an application, review a working preview, and refine it through conversation. Apps can connect to business systems, which makes access and write permissions part of the discussion from the beginning.

Put it to work: A staff-onboarding tracker using fictional records could be a useful first exploration. Define who would use it, what they should see, and which changes they should be allowed to make. Have the appropriate technical owner review the generated application before connecting real organizational data.

Availability: Copilot Studio public preview; the Cowork route is through Frontier. Confirm access in your environment. Building and running apps follows usage-based billing.

Read: Build apps in Copilot Cowork and Copilot Studio
Ryan Cunningham | Microsoft Copilot Blog, September 10, 2026.

Understand what GitHub Copilot brings to Copilot Studio

For: agent builders and the teams funding their work

Jason Moore’s September 2 update explains the GitHub Copilot harness in Copilot Studio. A harness coordinates how an agent uses a model, context, and tools to complete a task.

This is more than connecting a GitHub repository. It gives builders another foundation for agents that reason through multistep business processes, alongside Copilot Studio’s standard harness.

Put it to work: For HLS operations, consider a nonclinical example: reviewing fictional purchase orders and invoices, identifying mismatches, and routing exceptions to a person for approval.

Availability: The underlying harness reached general availability in August. This is September guidance on how to use it, not a September launch. Related capabilities can have separate preview statuses.

Before building: Review capacity and spending controls. Microsoft documents that building, testing, evaluating, and running these agents can consume Copilot Credits; that usage is not included with a user’s Microsoft 365 Copilot license.

Read: GitHub Copilot harness, agent skills, and richer context
Jason Moore | Microsoft Copilot Blog, September 2, 2026.

Review the current cost-management guidance

A life sciences read: AI across research and operations

For: research, manufacturing, and life sciences teams

Jonathon Dreyer’s September 14 industry article looks at how pharmaceutical organizations apply AI across research, manufacturing, and operations.

Put it to work: Select one example and identify the information sources, scientific expertise, and validation your organization would need to explore it. These customer examples are not a promise that another organization will achieve the same results.

Read: How pharmaceutical leaders are operationalizing AI
Jonathon Dreyer | Microsoft Cloud Blog, September 14, 2026. Industry examples, not a product availability announcement.

Governance watch

Meeting archives need a separate retention conversation

For: Teams administrators, privacy, and records-management teams

Microsoft’s public documentation describes AI-generated meeting archives that can preserve meeting insights for AI experiences. The documentation currently identifies this capability as preview for organizations in the Teams TAP program.

One detail deserves attention: deleting or expiring a transcript does not automatically delete its AI archive. Archive expiration and applicable Microsoft Purview retention policies need their own review.

Before rollout: This is a readiness item, not a recommendation to enable the feature today. Review the documented creation conditions, access, retention, and deletion behavior. Confirm applicable rollout information in your organization’s Message Center before planning changes.

Read: AI Archives for Microsoft Teams Meetings
Supporting Microsoft Learn guidance, last updated July 27, 2026.

Follow Roadmap 558933
Roadmap entry updated September 21, 2026; currently in development. Future rollout timing is subject to change.

One thing to try

Prepare a short briefing on one update

Choose one of this edition’s public announcements. In your organization’s approved Copilot experience, try:

I want to explain this Microsoft update to a healthcare operations manager who doesn’t follow AI news. Help me write a short briefing about what changed, one nonclinical task it might help with, and what we should check before trying it.

Use this public announcement: [paste the announcement link].

Read the response alongside the source. Check whether Copilot preserved the availability status and whether its suggested use case is reasonable. If it couldn’t access the page, provide the relevant public excerpt.

Then ask:

Make this shorter and explain any technical terms.

Use public material for this exercise, and review the briefing before sharing it.

If you’re still finding your starting point, visit Get Started with Microsoft AI in Healthcare and Life Sciences for a beginner exercise, learning paths, and governance resources.

Get Started with Microsoft AI in Healthcare and Life Sciences

michaelgoad — Tue, 22 Sep 2026 18:45:09 GMT

You don’t need to learn every AI tool before finding something useful to do with one. Start with a task you already understand: organizing notes, preparing a presentation, or finding information.

This guide brings together practical examples, governance resources, and learning materials from the HLS blog and other Microsoft teams. If you’re new to AI, begin with the exercise below. If you’re responsible for a rollout, head to the governance section.

Resource guide updated September 22, 2026.

Before you start: know your organization’s guardrails

Use the tools and accounts your organization approves. Before bringing work information into an AI experience, confirm:

Permitted data: What can you enter, upload, or connect?
Access: Who can use the tool and see the information involved?
Human review: Who checks the output before someone relies on it?
Ownership: Who handles questions, problems, and changes to a shared workflow?

For learning, start with public or fictional information. Patient information, unpublished research, and other sensitive data require approval for the specific tool and use case. A product setting alone does not establish compliance.

Try your first task: turn rough notes into a follow-up email

For: Anyone getting started.

What you need: Access to your organization’s approved Copilot Chat experience. Microsoft’s Copilot Chat overview explains access and eligibility. Features can differ by license, application, and tenant configuration.

Imagine you’re helping your team put together a welcome guide for new employees. You’ve had a planning meeting and need to send everyone a quick recap. You have a few notes, but haven’t written the email.

This example uses fictional people and information. Open your approved Copilot Chat experience with your work account and copy the whole prompt below, including the notes.

Try this:

I’m helping put together a welcome guide for new employees on our healthcare operations team. Turn these notes into a short, friendly follow-up email to the project team. Make it clear what everyone needs to do next.

Notes: Jordan will draft the welcome guide by Friday. Priya will review it next week. We still need a volunteer to organize an orientation meeting, and we haven’t picked a date for that meeting.

Read the draft alongside the notes. Check that Jordan and Priya have the right tasks and timing. The volunteer and orientation date should still be undecided. Correct anything Copilot adds that the notes don’t support.

Then continue in the same chat with a simple follow-up:

Make it shorter and put the next steps in bullets.

Keep this as a practice draft. The point is to ask for help with a familiar task, check the result, and tell Copilot what you want changed.

For more examples, see Microsoft’s guide to getting better responses with prompting.

Make reviewing the answer a habit

Before sharing or acting on AI-generated content:

Compare names, dates, numbers, and commitments with the source.
Look for missing information and statements the source doesn’t support.
Open citations and confirm they support the claim.
Apply the human-review requirements for your work.

An answer that reads well can still be wrong. When you assess whether AI helped, include the time you spent reviewing and correcting it.

Choose a path that fits your work

Everyday work

For: Business leaders, administrative teams, and operations staff.

Once you’re comfortable with a basic task, choose something you repeat: a project update, a presentation, or a document you rebuild each month.

Start with Copilot or Copilot Cowork: which tool to use, and when to explore different approaches to the work.

For a more advanced exercise, follow How to Set Up Copilot Skills in PowerPoint. Check that your organization has enabled the required capabilities before following the walkthrough.

Your next step: Choose one recurring task and practice with fictional or public content before moving to approved work information.

Clinical workflows

For: Clinicians, clinical leaders, and informatics teams.

Explore Dragon Copilot’s focus capability for examples of assistance within the applications where clinicians work.

Treat this as a workflow discussion first. Reading about a capability doesn’t mean your organization has deployed or approved it.

Your next step: Identify one documentation task and discuss it with your clinical informatics team. Confirm availability, the approved environment, and clinical review requirements before trying it with patient information.

Governance and responsible adoption

For: IT administrators, security and privacy teams, compliance leaders, and people responsible for AI rollout.

Start with our four-part Copilot governance guide, or watch the session recordings.

The series covers:

Admin-center foundations: Access, agent creation, rollout ownership, and billing controls.
Copilot Studio and Power Platform: Environments, connectors, data policies, and agent governance.
Microsoft Purview: Information protection, sensitivity labels, auditing, and risk.
SharePoint Advanced Management: Oversharing, content ownership, access reviews, and lifecycle management.

Recorded in 2025. Use the series to understand the foundations, then consult current product documentation for configuration and licensing. Microsoft’s enterprise data protection guidance is one important reference.

For an applied example, read Using Copilot in Sensitive Healthcare Teams Meetings. Discuss the scenario with your privacy, security, records-management, and IT teams before adopting it.

Your next step: Document one proposed workflow’s owner, permitted data, access requirements, approval checkpoints, and monitoring needs. For agents or usage-based services, include a consumption owner and review process.

Research and life sciences

For: Researchers, life sciences leaders, and data teams.

Explore how pharmaceutical leaders are putting AI into practice across research, manufacturing, and operations.

These examples can help you identify possibilities. They aren’t instructions to reproduce a regulated workflow without validation.

Your next step: Select one relevant example and identify the data, scientific expertise, validation, and approvals your organization would need to explore it.

Learn more from Microsoft

Our HLS blog is one place to learn. These official sources cover developments and guidance we may not address here:

Microsoft Copilot Blog: Product announcements and examples across productivity, business processes, and agents.
Microsoft Cloud healthcare and life sciences perspectives: Industry developments and customer examples across healthcare and life sciences.
Microsoft Security Blog: Security, data protection, and governance perspectives.
Copilot release notes and Copilot Studio updates: Product changes and release information.

When reading an announcement, check its publication date, release status, requirements, and supported platforms. Preview, planned, and generally available features are different stages. Even released features can roll out gradually.

Use the Microsoft 365 Roadmap for planning, rather than assuming a listed feature is available in your environment.

Earlier training and recordings

The Microsoft 365 Copilot Learning Series on-demand recordings remain available as a learning archive. Check current documentation before following older setup instructions.

Choose one resource and one small, approved task. Try it, review the result, and decide what you would change before using it again.

Copilot or Copilot Cowork - Which tool to use - and When

NicholasAquino — Thu, 10 Sep 2026 16:57:13 GMT

Microsoft 365 Copilot vs Copilot Cowork: Choosing the Right AI for Work

As organizations expand their use of AI to improve productivity and automate business processes, understanding which tool to use—and when—is critical. Microsoft offers two complementary capabilities: Microsoft 365 Copilot and Copilot Cowork. While they both leverage AI to support work, they are designed for fundamentally different types of tasks.

Two Different Models of AI Assistance

Microsoft 365 Copilot is designed as a day-to-day productivity assistant embedded within familiar Microsoft 365 applications such as Word, Excel, PowerPoint, Outlook, and Teams. It works alongside users in real time, helping them create, summarize, analyze, and refine content. The user remains in control of each interaction, guiding the AI step by step.

Typical use cases include:

Drafting and improving documents
Summarizing emails and meetings
Preparing presentations
Analyzing information across work content

This model is optimized for frequent, interactive assistance where the user actively directs the workflow.

Copilot Cowork, by contrast, is designed for execution-based workflows. Instead of responding one step at a time, users define a desired outcome, and Cowork independently plans and executes the necessary steps to complete the task. It can operate across multiple tools, gather information, and produce finished outputs such as documents, emails, or Teams posts.

It is especially suited for:

Multi-step workflows
Scheduled or recurring tasks
Event-triggered processes (such as responding to incoming emails or messages)
Longer-running automated business processes

Key Difference: Interaction vs Execution

The primary distinction between the two lies in control and autonomy:

Microsoft 365 Copilot supports guided, interactive work where the user drives each step.

Copilot Cowork executes defined workflows end-to-end with minimal intervention, pausing only for approvals when needed.

In short:

Copilot = assist in doing the work

Cowork = do the work

Pricing and Usage Model

The two systems also differ in how they are consumed:

Microsoft 365 Copilot is licensed per user, providing a predictable cost structure for ongoing everyday productivity assistance.

Copilot Cowork operates on a usage-based model using Copilot credits. These credits are pooled at the tenant level and consumed based on task complexity, frequency, and execution requirements. Organizations can manage usage through budgets, alerts, and governance controls.

Choosing the Right Tool

A simple decision approach helps determine which tool to use:

If the task involves creating, summarizing, analyzing, or understanding information within normal work activities, Microsoft 365 Copilot is the appropriate choice.

If the task requires multi-step execution, automation, scheduling, or event-driven workflows, Copilot Cowork is more suitable.

If Microsoft 365 Copilot can complete the task effectively under user direction, it should remain the default option.

Cowork should be reserved for higher-value, process-oriented automation where autonomous execution delivers clear business value.

Governance and Responsible Use

Because Copilot Cowork consumes metered credits, organizations are encouraged to implement governance controls before scaling usage. This includes:

Spending policies and budgets
Usage alerts and monitoring
Credit limits and cost management strategies

This ensures that automation is aligned with business value and resource consumption is properly managed.

Summary

Microsoft 365 Copilot enhances everyday productivity by assisting users within their workflow, while Copilot Cowork extends AI into autonomous execution of structured business processes. Together, they provide a layered approach to AI in the workplace—balancing human-guided assistance with scalable automation.

Choosing the right tool depends on the nature of the work: interactive support versus end-to-end execution. When used appropriately, both can significantly improve efficiency and operational effectiveness across modern organizations.

AI where and when you need it with Dragon Copilot's ability to focus

sarahgrey — Wed, 09 Sep 2026 16:12:22 GMT

At HIMSS 2026, we showed that the most useful place to apply clinical intelligence is often not in a separate app or side window, but at the cursor, in the field where the clinician is already working. Our last post on this topic described the problem Dragon Copilot's focus capability is designed to solve: clinicians becoming data couriers, moving between windows, copying information from one system into another, and stitching together a day's work across multiple applications. That post looked ahead to what was coming. Now, it's here.

What Dragon Copilot's focus capability does

Dragon Copilot's focus capability brings AI directly into the text field where clinicians work, whether that's an EHR, a browser, Microsoft Word, SharePoint, or another desktop application. There is no separate application to launch and no workflow to rebuild.

Simply place your cursor where you're working, select content if needed, and ask for help using natural language by voice or text. The focus capability works directly within the application you're already using, helping you stay in the flow of work.

With focus, Dragon Copilot can:

Read content from applications such as an EHR, browser, Microsoft Word, SharePoint, and other desktop applications.
Answer questions about selected content and provide responses in context, using information from trusted sources and resources approved by the organization.
Surface relevant information and insights without requiring manual searches, copying, or pasting.
Generate, edit, and refine text directly where you're working.

The experience is conversational and voice-first. Because the focus capability operates directly at the cursor, clinicians can take action without switching applications or rebuilding context.

Questions are answered in context using information from the EHR and trusted sources, including resources approved by the organization.

Focus in action

The following examples illustrate how Dragon Copilot's focus capability helps clinicians read, answer, write, edit and surface information directly within their workflow.

Journal and guideline Q&A

Highlight a passage from a clinical guideline or journal article and ask:

"Summarize the three key takeaways in patient-friendly language for the after-visit summary."

The focus capability analyzes the selected content and generates a draft directly where you're working.

Answer questions in context

Clinicians often have the information they need in front of them but spend valuable time locating or interpreting it.

Highlight relevant content and ask:

"What does this recommendation mean for a patient with diabetes?"

The focus capability reviews the selected information and delivers an answer within the workflow, without requiring clinicians to leave the application. The information is sourced from the EHR, a credible source such as MSD Manual, DailyMed, Medline Plus or from an organization’s internal source.

Edit in place

Select existing text and ask:

"Convert this into a bulleted list." or "Summarize this HPI in two sentences."

The focus capability reshapes the content directly where it already exists, helping clinicians refine documentation without disrupting their workflow.

Surface relevant information

Place your cursor inside a note and ask:

"What's the patient's BMI?"

The focus capability can retrieve relevant information available in context and return the answer inline, eliminating the need to navigate between screens, search for values, or perform calculations manually.

Generate new content

Need a referral letter, patient instruction, or summary?

Simply ask.

The focus capability can generate content directly within the destination field, helping clinicians create documentation where it belongs without copying and pasting between applications.

Why this capability matters

Bringing AI directly to the cursor is more than a convenience. It's what makes AI practical, visible, and actionable within everyday clinical workflows.

Dragon Copilot's focus capability sits in a useful middle ground between ambient AI, which operates largely in the background, and traditional dictation, which requires clinicians to explicitly construct documentation themselves.

Outputs are reviewable and editable before being finalized.
Actions remain visible within the workflow, providing transparency and control.
Clinicians can work without repeatedly navigating between applications or rebuilding context.
Because the focus capability operates directly at the cursor, it creates an extensible foundation for future Dragon Copilot experiences and integrations from Microsoft and partners.

When Dragon Copilot's focus capability brings AI directly to where clinicians are already working, copying, pasting, and window-switching fall away. Clinicians can access information, generate content, and take action without leaving their workflow, helping them spend less time managing technology and more time focused on patient care.

Learn more about Dragon Copilot.

Notes and documents – the way clinicians want them

rhitac — Tue, 08 Sep 2026 15:29:10 GMT

The moment after the visit

There is a moment in every patient encounter when the clinical story needs to become something more than a note. It may become a referral letter, a set of patient instructions, a school or work note, or a summary that helps a patient remember what was discussed.

These documents matter as much as the note itself. A patient needs clear instructions they can understand and follow. A referring physician needs concise context and clinical reasoning. A specialist needs relevant background. Each audience is different; each document has a different purpose. For clinicians, these documents are not administrative leftovers. They are part of care itself. They carry the reasoning, reassurance, precision, and next steps that help patients and care teams move forward with confidence. And yet, creating them often happens under pressure and requires clinicians to rely on memory, whether they are documenting in between patients or after clinic hours.

Introducing custom document templates in Dragon Copilot

Dragon Copilot's custom document templates are designed to solve this problem by allowing the clinician to create these documents directly in the workflow. A custom document template is a reusable, AI-generated structure that turns a clinical encounter into the right finished document — a referral letter, after-visit summary, work note, pre-operative instructions — in the format the clinician defines.

How it works

A clinician describes what they need in plain language: "Create a patient-friendly follow-up letter after a cardiology visit." Dragon Copilot generates a draft template from that description. The clinician can then refine it — by speaking naturally— to adjust the tone, the structure, or the level of clinical detail until it reflects how they communicate. The finished template is saved and reusable, and ready to populate from the next encounter's notes.

Templates can be built from scratch with a prompt, from a pasted sample, or from an uploaded file, with a preview before the template is used with real patient data.

Built around how each clinician works

Imagine a clinician finishing a consultation. The conversation was nuanced. The patient asked thoughtful questions. The next step requires a referral letter that reflects not only the diagnosis, but also the clinical reasoning behind it.

Traditionally, that letter may be written later, when the clinician is already thinking about the next patient, the next inbox message, or the next task. With custom documents, the clinician can define the structure they need, save it, and generate the right type of output directly in the workflow.

Referral letters are just one example. Clinicians create a wide range of documents every day, each designed for a different audience and purpose.

A pediatrician needs parent-friendly discharge instructions — plain language, no jargon, clear next steps. A surgeon needs structured pre-operative guidance that follows a consistent format every time. A general practitioner needs a concise work note that takes seconds, not minutes. A specialist needs a referral response that reflects both the diagnosis and the clinical reasoning behind it.

Each of these is a different template, built once, refined in conversation, and reused whenever it is needed. The AI adapts to the clinician's specialty, communication style, and intended audience — not the other way around.

Clinical impact in real-world workflows

Custom documents can create value where clinicians feel pressure most: in the moments after the encounter, when the clinical conversation needs to become clear, actionable communication. In practice, this means less time spent on repetitive drafting, manual rework, switching between documentation tasks, reconstructing details after the visit, creating routine letters, instructions, or summaries from scratch.

Custom documents help make documentation more flexible, reusable, and aligned with how care is delivered.

A step toward a more human future

Less time spent drafting, reworking, and reconstructing details after the visit. More consistency across documents. And clinical communication that reflects how care was delivered — not a generic approximation of it. Custom documents help turn the conversation in the exam room into the right document for the right moment, with less friction and more consistency

The goal is a future where clinicians do not have to choose between being present in the room and being precise in the record. Custom document templates in Dragon Copilot are a step toward that future.

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Click here to learn more about Dragon Copilot.

Using Copilot in Sensitive Healthcare Teams Meetings - Without Recording or Transcription

michaelgoad — Thu, 03 Sep 2026 16:02:15 GMT

Healthcare organizations want the productivity benefits of Microsoft 365 Copilot, but those benefits must be balanced with privacy, security, and compliance requirements.

I recently worked with a healthcare customer whose compliance team was concerned about persistent meeting artifacts. They wanted users to benefit from Copilot during Microsoft Teams meetings, but they did not want every conversation recorded or a transcript available afterward—especially for meetings involving sensitive operational, workforce, legal, compliance, or patient-related discussions.

Microsoft Teams provides an option that can help with this scenario: allowing Copilot only during the meeting while disabling recording and transcription.

Important: This article describes a technical configuration and customer scenario. It is not legal or compliance advice. Your privacy, security, compliance, records-management, and legal teams should determine whether this configuration is appropriate for your organization and specific meeting types.

Why This Matters in Healthcare

Healthcare organizations conduct many meetings where the discussion may include sensitive information:

Patient care coordination
Quality and safety reviews
Compliance investigations
Workforce and employee matters
Security incidents
Legal or risk-management discussions
Research and clinical program planning

A recording or transcript can become an additional persistent information asset that must be appropriately protected, retained, governed, and eventually disposed of.

The HIPAA Security Rule requires covered entities and business associates to implement reasonable and appropriate administrative, physical, and technical safeguards for electronic protected health information. This includes evaluating risk, controlling access, implementing audit controls, and periodically reviewing security measures. The U.S. Department of Health and Human Services provides an overview of these requirements here.

HIPAA’s minimum necessary standard also generally calls for organizations to limit unnecessary access to protected health information, although important exceptions apply—including certain disclosures for treatment. HHS provides additional guidance on the minimum necessary requirement.

Turning off recording and transcription does not automatically make a meeting compliant. However, reducing the creation of unnecessary meeting artifacts can be one useful part of a broader, risk-based healthcare data-governance strategy.

Copilot Without a Persistent Transcript

When a Teams meeting is configured for Only during the meeting, Copilot can use temporary speech-to-text data to understand the active conversation. A traditional meeting transcript does not need to be started.

Users can ask Copilot to:

Summarize what has been discussed
Identify decisions
List open questions
Capture action items
Explain areas of disagreement
Help someone catch up after joining late

Microsoft explains that Copilot must be manually started by a participant in this configuration. Copilot can then provide insights based on the conversation taking place while it is active. See Microsoft’s guidance for using Copilot without recording or transcribing a Teams meeting.

There are two important limitations users must understand.

First, Copilot does not automatically start when the meeting begins. If someone activates it five minutes into the meeting, it will not have the same context it would have had if it had been started at the beginning.

Second, users should capture anything they need before leaving the meeting. Without transcription, they will not have the same post-meeting Copilot conversation history or transcript-based recap. Microsoft recommends copying any Copilot content users want to keep before the meeting ends.

Watch the Complete Walkthrough

In the following video, I demonstrate the user experience, the corresponding Teams administrative policies, the Facilitator consideration, and several lessons I learned while implementing this for a healthcare customer.

How to Use Copilot in Teams Without Recording or Transcription: Admin Setup & Gotchas

The Administrative Configuration

For this scenario, the goal was to create a scoped Teams meeting policy that:

Prevented users from recording meetings
Prevented users from starting transcription
Allowed Copilot to operate during the meeting
Avoided enabling a transcript-dependent post-meeting Copilot experience

The settings are managed through Teams meeting policies in the Teams admin center. In the video, I walk through the relevant recording, transcription, and Copilot controls and show the resulting experience from a user’s perspective.

For a healthcare organization, I would normally begin with a limited population instead of immediately applying a new policy globally. A pilot group gives the organization an opportunity to test the technical behavior and evaluate it with stakeholders from:

Privacy and compliance
Information security
Legal and risk management
Records management
Clinical or operational leadership
Microsoft 365 administration
End-user training and adoption

The appropriate configuration may differ between clinical, administrative, research, legal, HR, and general collaboration scenarios. A single organization-wide meeting policy may not be the right answer for every healthcare meeting.

“No Transcript” Does Not Mean “No Compliance Data”

This distinction is critical.

Although participants may not receive a traditional meeting recording or transcript, Copilot prompts and responses can still be subject to Microsoft Purview retention policies. Depending on the organization’s configuration, those interactions may also be discoverable through Microsoft Purview eDiscovery.

Microsoft explicitly notes that Copilot prompts and responses may be retained for compliance purposes even when recording and transcription are disabled. Microsoft’s Purview training covers auditing, retention, eDiscovery, and compliance controls for Copilot interactions.

Healthcare organizations should therefore evaluate more than the visible Teams recap. The governance review should include:

Copilot interaction retention
Microsoft Purview Audit
eDiscovery requirements
Data Loss Prevention policies
Sensitivity labels
Records-management obligations
Access to saved notes or exported Copilot responses
Organizational policies governing the entry of PHI into AI-assisted experiences

The user experience may feel temporary, but the organization’s compliance controls can still operate behind the scenes.

The Lesson We Almost Missed: Facilitator Still Creates Loop Artifacts

This was one of the most important lessons from my recent engagement.

While working to Copilot to operate only during the meeting. Recording was disabled, transcription was disabled, and users did not receive the traditional transcript-based recap we were trying to avoid.

But Loop files were still appearing.

That initially seemed inconsistent with the policy design. After investigating, we discovered that the files were not being created by the personal Copilot experience—they were being generated because Microsoft Facilitator was enabled.

This distinction is easy to miss:

Copilot only during the meeting gives an individual user private, real-time assistance without requiring a persistent transcript.
Facilitator is a shared meeting agent that can generate collaborative notes and other meeting artifacts.

Microsoft documents that Facilitator’s meeting data is stored as a .loop file in the Meetings folder of the OneDrive account belonging to the user who initiated Facilitator. The data is treated as meeting transcript data for governance purposes. Facilitator notes may also be available to meeting participants through Notes and the meeting recap. Microsoft documents Facilitator’s behavior, storage, and administrative controls here.

For a healthcare organization, that Loop file is not simply a convenient set of notes. Depending on what was discussed, it could contain sensitive operational information, workforce information, security details, or protected health information. It becomes another persistent artifact that must be considered in the organization’s access, sharing, retention, eDiscovery, lifecycle-management, and data-protection strategy.

Copilot and Facilitator Require Separate Governance Decisions

Disabling recording and transcription while allowing Copilot during the meeting does not automatically prevent Facilitator from producing shared meeting content.

Healthcare organizations should make separate decisions about:

Whether users should have access to Copilot during meetings.
Whether users should be allowed to initiate Facilitator.
Which meeting types are appropriate for shared AI-generated notes.
Which users or groups should be allowed to use Facilitator.
How the resulting Loop files should be stored, accessed, retained, labeled, and disposed of.

Facilitator can be extremely useful for approved operational meetings where collaborative notes, decisions, and follow-up actions are valuable. However, it may not be appropriate for every sensitive clinical, compliance, legal, HR, or incident-response meeting.

Microsoft allows administrators to control whether Facilitator is available across the organization or only to selected groups of users through Teams app policies and app-centric management. One important administrative nuance is that access to Facilitator can be scoped, while Microsoft’s control for turning off AI-generated meeting notes is currently tenant-wide rather than user-specific.

That makes intentional scoping particularly important. Instead of assuming every Copilot-licensed user should also be able to initiate Facilitator, organizations can identify the populations and meeting scenarios where persistent collaborative notes have a defined business purpose and an approved governance model.

The practical lesson is simple:

If your goal is to use Copilot without creating persistent meeting artifacts, do not stop after validating the recording, transcription, and Copilot policies. Verify whether Facilitator is enabled and inspect the resulting Loop behavior as part of your testing.

The Meeting Option That Can Cause Confusion and Broke Things For Us

One of the most important lessons from this implementation involved the meeting organizer’s Copilot setting.

If an organizer changes the meeting from Only during the meeting to During and after the meeting, Copilot expects transcription to be available. If the organization’s policy prevents transcription, the user may receive an error indicating that Copilot cannot access the meeting.

From the user’s perspective, that can look like a licensing problem or a broken Copilot deployment. In reality, it may simply be a mismatch between:

The organization’s transcription policy
The Copilot meeting option selected by the organizer
The type of Copilot experience the user is attempting to start

Microsoft’s meeting guidance confirms that During and after the meeting requires transcription, while Only during the meeting can operate without it. Review the current Copilot behavior in Microsoft Teams meetings.

This is why user education is just as important as the policy itself.

A Healthcare Deployment Checklist

Before introducing this configuration more broadly, consider the following:

Classify the meeting scenarios. Determine where in-meeting Copilot is appropriate and where AI assistance should be restricted entirely.
Engage compliance early. Validate the design with privacy, security, legal, records-management, and compliance stakeholders.
Use scoped policies. Pilot with specific users or groups before considering a broader rollout.
Review more than recordings and transcripts. Evaluate Purview retention, auditing, eDiscovery, DLP, sensitivity labels, and exported Copilot content.
Evaluate and scope Facilitator separately. Determine which users should be able to initiate Facilitator and which meeting scenarios justify shared AI-generated notes. Test where the resulting Loop files are stored, who can access them, and how retention, sensitivity labels, eDiscovery, sharing, and lifecycle controls apply.
Test for unexpected artifacts. After a pilot meeting, inspect the organizer’s and initiator’s OneDrive, the meeting chat, Notes, Recap, Loop, and relevant Purview experiences. Confirm that the meeting produces only the artifacts your governance team expects.
Train meeting organizers. Explain the difference between Only during the meeting and During and after the meeting.
Prepare users for the temporary experience. Users should capture approved action items or summaries before the meeting ends.
Test the complete workflow. Validate the experience using representative accounts, policies, licenses, meeting types, and organizational controls.
Review the configuration periodically. Microsoft Teams and Copilot capabilities continue to evolve, and healthcare organizations should reassess their controls as features change.

Finding the Right Balance

Healthcare organizations do not necessarily have to choose between disabling Copilot completely and generating a recording and transcript for every meeting.

The Only during the meeting option provides another path: users can receive real-time assistance while the organization limits the creation of traditional post-meeting artifacts.

It is not a substitute for a complete healthcare compliance strategy. It is a technical control that can support a broader strategy built around risk assessment, appropriate access, retention, auditing, training, and clearly defined meeting scenarios.

The Facilitator lesson also demonstrates why organizations must test the entire meeting experience—not just the most visible policy settings. Copilot may be operating as expected while another enabled capability is still creating shared, persistent content.

For the healthcare customer that inspired this video, the technical settings were only part of the solution. The real success came from understanding the user experience, finding the unexpected Loop artifacts, separating Copilot governance from Facilitator governance, and teaching organizers how the different options behave.

That is the lesson I hope this walkthrough helps other healthcare organizations apply.

Extend clinical workflows with Dragon Copilot AI Apps and Agents in Microsoft Marketplace

AlmaChandrasekharan — Fri, 21 Aug 2026 17:04:55 GMT

GA ANNOUNCEMENT

Today, we're excited to announce the General Availability (GA) of Dragon Copilot AI Apps and Agents for physician workflows, a major milestone in building an open and trusted healthcare AI ecosystem.

Healthcare organizations can now discover, build, deploy, and manage trusted AI-powered applications and agents that support specialized clinical and operational capabilities through Dragon Copilot. At the same time, partners can use self-service experiences to build, validate, certify, publish, and manage solutions that integrate directly into clinician workflows.

Together, these capabilities bring healthcare organizations, clinicians, and partners onto a common platform designed to accelerate innovation while maintaining the trust, transparency, and governance required in healthcare.

Why it matters to clinicians

Clinicians need relevant insights delivered in the flow of care—not in another disconnected tool. Dragon Copilot AI Apps and Agents can surface specialized intelligence within existing workflows to support coding and documentation, medical decision-making, risk adjustment, prior authorization, diagnostic support, behavioral health screening, and preventive care. Dragon Copilot AI Apps and Agents for physicians provide capabilities for use cases such as:

Coding and documentation optimization (Regard, RhythmX AI)
Risk adjustment (RAAPID Inc)
Prior authorization (Humata Health, Genzeon)
Evidence-Based Hyper-Personalized Care Guidance (Atropos Health)
Diagnostic support (Coming soon!)
Cognitive & behavioral health screening (Canary Speech)
Care Gaps & Preventive Health Reminders (Coming soon!)
Clinician coaching (Whetstone Health)

By bringing the right information into the physician’s existing workflow and using patient data from the EHR and the Dragon ambient context (audio, transcript and clinical note) to make insights more relevant, these capabilities can reduce context switching, streamline administrative work, and help physicians stay focused on patient care while maintaining clinical judgment and control workflows.

Why it matters to healthcare organizations

Healthcare organizations need a scalable way to expand the value of Dragon Copilot while maintaining enterprise governance. AI Apps and Agents provide access to a growing portfolio of specialized capabilities through a common platform, helping organizations increase return on their Dragon Copilot investment without adding fragmented solutions. Dragon Copilot AI Apps and Agents

Helps increase ROI by extending Dragon Copilot across more clinical and operational use cases, seamlessly integrated into workflow
Simplify solution discovery and purchase through Microsoft Marketplace
Centralize deployment, policy, and lifecycle governance through the Dragon Admin Center
Maintain clinician oversight and organizational standards for security, privacy, compliance, and responsible AI
Reduce implementation burden with streamlined experiences that require minimal IT lift
Build and deploy custom built apps/agents integrated with Dragon Copilot

This creates a governed, scalable path to adopt new capabilities faster, making it easier to evaluate solutions, deploy them across teams, manage them consistently, and measure value without unnecessary operational complexity.

Why it matters to partners

Healthcare AI companies often have valuable, specialized capabilities but face significant complexity bringing them into clinical workflows and deploying them consistently across healthcare organizations. Dragon Copilot AI Apps and Agents give partners a standardized extensibility model for embedding their innovations where clinicians already work.

Access ambient, patient and encounter context to deliver more relevant, timely experiences
Integrate into the Dragon Copilot experience without building and maintaining bespoke workflow integrations for every customer
Use self-service onboarding, technical validation, and applicable compliance certification to accelerate readiness
Reach healthcare customers through Microsoft Marketplace and scale deployment through a trusted enterprise platform
Build customer confidence with transparent solution information and consistent governance expectations

By reducing the friction of workflow integration, distribution, and enterprise deployment, the platform helps partners focus on differentiated clinical value while expanding their reach at scale.

Creating a trusted healthcare AI ecosystem

Dragon Copilot connects specialized partner capabilities with clinical workflows through a trusted & scalable ecosystem. Streamlined self-service onboarding, package validation, compliance certification, and help partner innovations reach the market faster while giving healthcare organizations confidence to adopt and manage these AI solutions responsibly.

“Healthcare moves at the speed of trust. You look at Microsoft and Dragon Copilot, we absolutely trust the combination of Microsoft and Dragon Copilot for industry leading innovation, highly trusted, responsible AI, and delivery at scale, and also the reach with the number of clinicians that Dragon Copilot has. And if you combine all these elements of innovation, your reach, track record, and the trust, that's a perfect recipe for partnership.”

— Deepthi Bathina, CEO and Founder, GW RhythmX

"I use Dragon Copilot in clinic every day. The extension program let me design, build, and implement the tool I wanted to make me a better clinician. It's never been this easy to be both the user and the builder."

— Aaron Reinke, MD, Family Physician and Founder, Whetstone Health

Explore the ecosystem

For healthcare organizations – discover and deploy

Explore how Dragon Copilot AI Apps and Agents on Microsoft Marketplace can deliver specialized AI solutions tailored to your clinical and operational needs through Dragon Copilot.
Discover new capabilities, purchaseand deploytrusted solutions, and deliver more value to clinicians without disrupting existing workflows in Dragon Copilot, learn more here.

For partners – build and publish

Bring your differentiated expertise directly into Physician workflows by creating and publishing AI Apps and Agents that help healthcare organizations address high-value clinical and operational needs. Learn how to build and publish applications and agents
Review our open-source code samples and start building today with Dragon Copilot’s extensibility framework and reach healthcare customers through Microsoft Marketplace.
Publishyour Dragon offer on Microsoft on Marketplace

Looking ahead

Dragon Copilot is becoming a trusted destination for specialized healthcare AI—bringing partner innovation directly into familiar clinician workflows.

As the ecosystem grows, we will expand support for Radiology and Nursing Apps and Agents, addressing the distinct role-based needs of multiple clinical personas. We will also evolve the end-user experience to be more adaptable, enabling chat-based experiences, richer interactivity, and new ways for clinicians to engage with partner capabilities in the flow of work. Streamlined self-service, transparent governance, and Marketplace distribution will help partners scale secure solutions while giving healthcare organizations confidence to adopt them.

Together, we’re helping clinicians spend less time on administration and access the right intelligence at the right moment in care.

How to Set Up Copilot Skills in PowerPoint (and Build Your Own)

michaelgoad — Thu, 20 Aug 2026 15:47:53 GMT

How to Set Up Copilot Skills in PowerPoint (and Build Your Own)

You have a deck you rebuild every month. The business review, the project update, the readout for leadership. Every time, the same work. Cut the bullets down. Fix the capitalization someone else broke. Take 20 slides down to 7 so an executive will read it.

Copilot in PowerPoint does all of that, and it's good at it. Then next month you open a new deck and type the whole thing out again, because Copilot doesn't remember how you like it done.

That's the problem skills solve.

I help organizations roll out Copilot in front of thousands of users, and skills are the feature I see the fewest people using. Plenty of teams are paying for it and have never turned it on.

What a skill is

A skill is a pattern you wrote down once.

Take the thing you explain to Copilot over and over. Put it in a file with a name, a description, and a set of instructions. Save it. From then on you point Copilot at the skill instead of retyping the prompt, and you get the same result without rebuilding it.

Monthly business review formatting. Your team's deck standards. The way you like speaker notes written. All of it becomes something you call by name.

Two things before you start

A Microsoft 365 Copilot license. Skills live in the Copilot pane inside PowerPoint. No Copilot in your Office apps, no skills.

Choose Your Model. Open the Copilot pane, you can leave the model to "Auto" or find the model picker, and switch to your preferred GPT or Claude model.

**Update** Skills in PowerPoint should now work with both OpenAI and Anthrophic models

Turning skills on

Open Copilot in PowerPoint. Hit the plus button at the bottom of the pane, then Choose skills.

You'll already have a few there from Microsoft. Run one before you build anything of your own.

You can also call a skill by name. Type @ and the skill name in your prompt, the same way you'd mention a person, and Copilot runs that one. Once you know what you have, it beats going through the menu every time.

I opened Background and context on a text-heavy slide and asked it to visualize the slide. It created a new slide and generated an image that carried the same information in a format I could read at a glance. Side by side with the original, it wasn't close.

Skill plus prompt

The image was good, and it was an image, so I couldn't edit any of it.

So I asked, in plain language: I like this, can you make it more native PowerPoint so I can edit it? Keep the structure.

It rebuilt the whole thing with real PowerPoint objects and kept the layout that made it work. I wasn't running any branding at that point and it still came out clean.

Skill first, then prompt. That combination is where most of the value sits. The skill gets you 80 percent of the way with no typing, and the prompt handles the part only you know.

Building your own

Go back to the plus button, Choose skills, then Manage skills. There's a Custom skills section, and it'll be greyed out until you have skills of your own.

Don't hand-write the skill file.

Grab Microsoft's documentation page, the one PowerPoint links to from the Manage skills pane:

https://support.microsoft.com/en-us/powerpoint/copilot/copilot-in-powerpoint-skills

Paste that URL into Copilot and say: using this link, help me build a PowerPoint skill that does X.

Copilot reads the requirements and writes a file that fits the format. You're using AI to build AI, and it saves you more than typing. Skill files have real formatting rules, and Copilot will meet them instead of you finding each one through a failed upload.

The format itself is short. A skill is a folder with a SKILL.md file in it. The file opens with a frontmatter block between three-dash delimiters holding a name and a description, and everything below that is your instructions. Spend your time on the description. That's the part telling Copilot when to reach for the skill, so a vague one means it never fires.

If somebody sent you a skill, paste that in too and let Copilot clean it up.

Where skills live

In Manage skills, if you've never set this up, you'll see an option to create your skills library. Click it and Copilot creates the folder in your OneDrive.

Pay attention to the path, because you'll need it:

OneDrive > Documents > Copilot > Microsoft PowerPoint > skills

Each skill is its own folder with a SKILL.md file inside. Drop new skills in there and they show up in PowerPoint.

One catch: after you add something to OneDrive, refresh PowerPoint. It has to reload the folder. If you added a skill and it isn't showing, that's usually why.

The other one that will bite you is the folder name. It has to match the name inside the SKILL.md file. Copilot skips any folder where those two don't line up, and it skips it quietly. No error, no warning, the skill is missing. If you refreshed and it's still not there, check that next.

Two ways to add a skill, and the limit on one of them

You don't have to touch OneDrive at all.

In Manage skills, Add skill lets you upload an .md file, or type a skill in by hand with a name, description, and instructions. PowerPoint builds the folder structure and the file for you.

I lost time on this one. The box where you type or paste a skill in caps at 1024 characters. I had Copilot write me a few skills, they came out longer than that, and I got errors that didn't explain themselves. I built them with Copilot, so I assumed they were fine.

That cap is on the typed box. Uploading an .md file, or dropping the folder straight into OneDrive, doesn't have it. So if you have a long skill, don't cut it down to fit. Upload it as a file instead.

Three skills on one bad deck

Here's what I have built:

Brand check. Reviews wording, terminology, and capitalization against our team's deck standards. No layout changes.
Handoff notes. Writes speaker notes for someone else to deliver. I write the deck knowing I might not be the one presenting it, and my notes are useless to anybody but me.
Slide diet. Takes a long deck, cuts the fluff, consolidates what's left.
Slide design. The visual pass.

I ran three of them on a plain deck, in order.

Brand check went first. Text only. It consolidated some lines, rewrote a few, and fixed capitalization.

Slide diet went second. It broke walls of text into bullets and cut what wasn't earning its place. Five bullets beat a paragraph block, and it made that call without being told.

Slide design went last. This is the one where you sit back and let it work. Black text on white turned into something branded and visual, with the information in an order that reads.

Three skills, and a deck I didn't want to read became one I'd present.

Brand kits

One more thing in that same plus menu: Brand.

A brand kit holds your colors, fonts, and icons. You can build one for yourself, or your organization can publish one for everybody in the tenant. Pair a brand kit with a design skill and decks come out on-brand without anyone checking hex codes by hand.

What it cost and what it saved

The full run took 30 to 40 minutes, and the design pass was about 10 of that. Copilot needs time to work through a deck. This isn't instant.

Building that by hand, at the quality it came out at, is 4 to 8 hours. A full day.

The documentation

Microsoft's page for all of this is here:

https://support.microsoft.com/en-us/powerpoint/copilot/copilot-in-powerpoint-skills

It has the format requirements, the three ways to add a skill, and how to edit or delete one. Worth a read before you build your second skill.

One thing that page points to is worth knowing: the SKILL.md format is an open spec, not a PowerPoint invention. So the skills you write here aren't stuck in PowerPoint.

Where to start

Pick the pattern you repeat most, not the hardest deck you own.

Write down what you'd tell a new hire about how you do it. That's your first skill. Hand it to Copilot, let it write the file, drop it in the folder, refresh PowerPoint.

Then be curious, and go push some buttons.

Clinical precision starts with more complete documentation

Karen_Couchon — Tue, 18 Aug 2026 20:52:04 GMT

This blog is authored by Josh Waldo, Product Manager

Every patient has a story, and that story deserves to be accurately reflected in the clinical documentation.

Clinicians work hard to assess, diagnose, and care for patients, often while balancing complex cases, competing priorities, and limited time. While the diagnosis itself may be clear, the documentation does not always capture every detail that helps tell the full patient story. Important clinical information about severity, complexity, acuity, or related conditions may be discussed during the encounter but not fully reflected in the medical record.

These details matter. Clinical documentation serves as the foundation for communication across care teams, supports quality reporting and care coordination, and provides an accurate record of the care delivered.

Dragon Copilot helps clinicians create more complete documentation by identifying potential opportunities where additional specificity may be appropriate based on information already present within the encounter. This capability helps ensure that the documentation more fully reflects the clinician's assessment and the patient's clinical story.

Rather than asking clinicians to remember every documentation requirement or revisit records after the encounter, Dragon Copilot can surface contextual suggestions that highlight where additional detail may strengthen the completeness of the note. For example, when documenting certain conditions, clinicians may receive suggestions that help capture additional clinically relevant details that were discussed during the visit and support a more complete description of the patient's condition.

Importantly, these suggestions do not change a diagnosis, influence clinical decision-making, or replace clinician judgment. The clinician remains responsible for every diagnosis and every documentation decision. Dragon Copilot simply helps ensure that the documentation accurately reflects what the clinician already knows and has determined.

Supporting better documentation without disrupting workflow

Documentation quality should not require additional chart reviews, extensive research, or time-consuming follow-up work.

Dragon Copilot is designed to fit naturally into existing clinical workflows, providing documentation support within the course of the encounter. By surfacing opportunities for greater specificity at the point of documentation, clinicians can address potential gaps in the moment rather than correcting them later.

This may help reduce the need for downstream clarification requests from CDI and coding teams, saving time while improving the completeness of the medical record.

Most importantly, clinicians remain in control. Suggestions are simply that suggestions. The clinician decides what is relevant, appropriate, and ultimately included in the note.

Why more complete documentation matters

Clinical documentation is used by many people beyond the author of the note. Physicians, nurses, specialists, care coordinators, coding professionals, quality teams, and healthcare organizations all rely on documentation to understand a patient's condition and the care that was provided.

When documentation more fully reflects the clinician's assessment, it can support clearer communication across teams and a more consistent understanding of the patient's clinical status throughout the care journey.

More complete documentation can also help organizations better reflect patient complexity, support quality initiatives, and reduce the administrative effort associated with documentation clarification and follow-up.

For clinicians, that means less time spent revisiting notes and more confidence that the patient's story has been accurately captured in the record.

AI that supports clinical expertise

Dragon Copilot is built on a simple principle: AI should support clinical expertise, not replace it.

Diagnosis specificity suggestions is built on a foundation of proven clinical AI innovation, informed by more than a decade of real-world healthcare experience. Drawing on years of clinician interactions and documentation insights, the feature is designed to surface meaningful opportunities for greater diagnostic specificity while fitting naturally into existing workflows.

The technology does not diagnose patients, recommend treatments, or override clinical judgment. Instead, it acts as an intelligent assistant that helps clinicians identify opportunities to enhance documentation based on information already available during the encounter.

By reducing some of the cognitive burden associated with documentation, Dragon Copilot allows clinicians to focus their attention where it matters most: caring for patients.

Helping the right patient story reach the medical record

Healthcare organizations continue to look for ways to improve documentation quality while reducing administrative burden. Achieving both goals requires technology that works alongside clinicians, helping them document care more efficiently without creating additional work.

By helping clinicians capture greater detail within their existing workflows, Dragon Copilot supports more complete records, more accurate documentation, and a smoother documentation experience.

Because better documentation is not about changing the diagnosis.

It is about helping ensure that the right patient story is reflected in the medical record with greater clarity, completeness, and confidence.

Learn more about Dragon Copilot.

Amplify Healthcare Intelligence: Data, AI, and Agent-Powered Transformation

CamilleWhicker — Tue, 29 Sep 2026 01:04:53 GMT

Join Microsoft for Amplify Healthcare Intelligence, a webinar and in-person workshop series designed for healthcare and life sciences organizations building the foundation for trusted AI. Every session starts from the same premise: you cannot deliver trusted AI without a trusted data foundation. Across the series you will see how leading organizations unify their data estate, ground AI agents in real business context, and turn that foundation into results they can measure.

What You Will Learn

How to build a unified, AI-ready data foundation across a fragmented healthcare data estate
How to ground AI agents in trusted healthcare data and real business context
How to modernize analytics while reducing complexity and cost
How to accelerate innovation with Microsoft Fabric, Azure AI Foundry, Microsoft IQ, and Copilot technologies
How to deliver measurable impact across clinical, operational, research, and business scenarios

Whether you are defining your AI strategy, modernizing your analytics platform, or scaling AI across your organization, these sessions offer practical guidance, real-world customer examples, and hands-on learning to help you move from AI ambition to business impact.

Webinars & In-Person Workshops

🎥 Webinars

One-hour virtual sessions with actionable guidance, live demonstrations, customer stories, and best practices from Microsoft experts. Each webinar shows how leading healthcare organizations are turning data, AI, and enterprise intelligence into measurable business outcomes. All sessions are from 3-4 ET (12-1 PT) and are free to attend.

Date	Topic	Register
Sep 30 3-4 ET (12-1 PT)	Driving AI-Powered Healthcare: Transforming Clinical, Operational, and Research Outcomes with AI	Register
Oct 7 3-4 ET (12-1 PT)	Building an AI-Ready Healthcare Data Foundation	Register
Oct 14 3-4 ET (12-1 PT)	Building Trusted Healthcare AI: Grounding Agents with Enterprise Data and Context	Register
Oct 21 3-4 ET (12-1 PT)	Finance in the Agent Era: AI-Powered Planning, Forecasting, and Insights	Register
Oct 28 3-4 ET (12-1 PT)	Reduce BI Sprawl, Cut Cost and Build an AI-Ready Analytics Foundation	Register

Cannot join live? Register anyway. We will send you the recording and session materials after the event. Additional sessions will be added through the end of the year, so check back or register for one session to be notified as new dates are announced.

🏢 In-Person Workshops

Our two-day workshops combine executive strategy, healthcare-specific use cases, architecture guidance, and hands-on labs designed to help teams identify and accelerate high-value AI opportunities. Attendance is free. Participants are responsible for their own travel and accommodation, and space at each location is limited. Workshops run 9am to 4pm local time on both days.

Day 1: From Healthcare Data to Healthcare Intelligence

Day 1 focuses on healthcare transformation strategy, customer examples, and the architectural patterns that make trusted AI possible at scale. The day closes with a networking reception and peer exchange.

The Frontier Transformation imperative: from AI ambition to measurable impact
Microsoft IQ: turning data into enterprise intelligence
Building the unified data foundation
Building trusted AI: security, governance, privacy, and compliance
Healthcare transformation in action: clinical, operational, research, and finance scenarios
Activating data with AI data agents and Copilot experiences

Day 2: Hands-On Healthcare AI and Analytics Lab

Day 2 is a guided, end-to-end lab. Participants build a working healthcare intelligence solution from raw data through to a grounded AI agent, using Microsoft Fabric, Azure AI Foundry, Copilot technologies, and modern data architectures.

Build the foundation: data ingestion, lakehouse architecture, and data engineering
Create actionable insights: semantic models and dashboards
Prepare data for AI: AI-ready data assets and data governance
Build and ground AI agents in trusted enterprise data
From insight to intelligent action: planning your organization's next steps

What to bring: a laptop with a current browser. Lab environments and credentials are provided on site, and no prior Fabric or Foundry experience is assumed.

Date	City	Venue	Register
September 29-30, 2026 9am - 4pm	Minneapolis	Microsoft Edina 3601 West 76th Street, Suite 600 Edina, MN 55435	Register
October 13-14, 2026 9am - 4pm	Boston	Microsoft New England One Memorial Drive Cambridge, MA 02142	Register
October 27-28, 2026 9am - 4pm	Silicon Valley	Microsoft Silicon Valley 1045 La Avenida Street Mountain View, CA 94043	Register
November 10-11, 2026 9am - 4pm	Chicago	Microsoft Chicago (AON Center) 200 East Randolph Drive, Suite 200 Chicago, IL 60601	Register
December 8-9, 2026 9am - 4pm	New York	Microsoft Garage 300 Lafayette Street New York, NY 10012	Register

Who Should Attend

This series is built for the people who own the data estate and the people who depend on it. Sessions are technical enough for practitioners and strategic enough for the leaders who fund the work.

Chief data officers and data and analytics leaders
Data platform, data engineering, and business intelligence teams
Data architects, engineers, and data scientists
AI and innovation leaders
Healthcare and life sciences executives
Clinical, operational, research, and finance transformation leaders

No prior Microsoft Fabric experience is required for any session in this series.

Questions

Wondering whether a session is the right fit, or whether to bring a team rather than an individual? Contact Camille Whicker and we will help you choose the right sessions for your organization.

Amplify Healthcare Intelligence In-Person Workshop Series

CamilleWhicker — Thu, 20 Aug 2026 18:29:53 GMT

What You'll Learn

How leading organizations are navigating the Frontier Transformation journey
Strategies for turning healthcare data, workflows, and expertise into organizational intelligence
Best practices for building a unified, AI-ready data foundation
Approaches for implementing trusted AI with strong governance and security
Real-world healthcare scenarios across clinical, operational, financial, and research domains
How AI agents can transform the way people interact with data and make decisions
Practical skills for deploying analytics, AI, and agent-powered solutions

Day 1: From Healthcare Data to Healthcare Intelligence

Explore how leading healthcare organizations are creating a trusted foundation for AI and transforming data into meaningful business outcomes.

The Frontier Transformation Imperative

The future of healthcare in the age of AI and agents
Industry trends, opportunities, and challenges
Moving from AI ambition to measurable impact

Microsoft IQ: Turning Data into Enterprise Intelligence

Connecting data, workflows, and organizational knowledge
Building organizational intelligence for AI
Establishing the foundation for trusted decision-making

Building the Unified Data Foundation

Modernizing the healthcare data estate
Creating a single source of truth for analytics and AI
Leveraging Microsoft Fabric and OneLake as a unified platform

Building Trusted AI

Security, governance, privacy, and compliance
Responsible AI considerations for healthcare and life sciences
Preparing data and processes for enterprise AI adoption

Healthcare Transformation in Action

Clinical transformation scenarios
Operational transformation scenarios
Research and innovation use cases
Finance and performance management opportunities

Activating Data with AI Agents

AI Data Agents and Copilot experiences
From data to decision-making
Real-world examples of agent-powered healthcare innovation

Networking Reception & Peer Exchange

Day 2: Hands-On Healthcare AI & Analytics Lab

Gain practical experience building an end-to-end healthcare intelligence solution using Microsoft technologies.

Build the Foundation

Data ingestion and modernization patterns
Lakehouse architecture and data engineering
Establishing a scalable healthcare data platform

Create Actionable Insights

Semantic models and business intelligence
Power BI dashboards and reporting
Real-time healthcare intelligence

Prepare Data for AI

Building AI-ready data assets
Data governance and trust
Optimizing data for intelligent applications

Build and Ground AI Agents

Creating AI Data Agents
Leveraging business context and trusted enterprise data
Connecting agents to analytics and operational workflows

From Insight to Intelligent Action

End-to-end healthcare AI scenario
Data → Insights → Agents → Outcomes
Planning your organization's next steps

Upcoming Workshop Locations

Date	City	Venue	Register
September 9-10, 2026 9am - 4pm	Dallas	Microsoft Las Colinas 1 7000 N State Hwy 161 Irving, TX 75039	Register
September 29-30, 2026 9am - 4pm	Minneapolis	Microsoft Edina 3601 West 76th Street, Suite 600 Edina, MN 55435	Register
October 13-14, 2026 9am - 4pm	Boston	Microsoft New England One Memorial Drive Cambridge, MA 02142	Register
October 27-28, 2026 9am - 4pm	Silicon Valley	Microsoft Silicon Valley 1045 La Avenida Street Mountain View, CA 94043	Register
November 10-11, 2026 9am - 4pm	Chicago	Microsoft Chicago (AON Center) 200 East Randolph Drive, Suite 200 Chicago, IL 60601	Register
December 8-9, 2026 9am - 4pm	New York	Microsoft Garage 300 Lafayette Street New York, NY 10012	Register

Who Should Attend?

Healthcare and Life Sciences Executives
CIOs, CTOs, and Chief Data Officers
Clinical and Operational Leaders
Analytics and Data Platform Teams
AI and Innovation Leaders
Finance Transformation Teams
Architects, Engineers, and Data Professionals

What You'll Walk Away With

By the end of the workshop, you'll have a clear understanding of how to build a unified data and AI foundation, enable trusted AI and intelligent agents, and create a roadmap for accelerating healthcare transformation within your organization.

Organizational Prompts: Publish Copilot Prompts to Your Whole Org in Minutes

michaelgoad — Wed, 29 Jul 2026 20:33:56 GMT

If you rolled out Microsoft 365 Copilot and then watched most of your people ignore it, this one's for you.

I help organizations deploy Copilot in front of thousands of users, and the pattern is almost always the same. A company buys Copilot, starts the rollout, and then watches it sit there. It's easy to blame the tool. Most of the time we handed everyone a blank box and expected them to figure out what to type.

Your people aren't prompt engineers. They're nurses, knowledge workers, and HR. They open Copilot, see a blank screen, type something like "summarize this email," don't love what comes back, and quietly walk away. Do that across thousands of users and adoption stalls.

There's an admin setting that helps with exactly this. It's called organizational prompts, and if you own Copilot for your org, don't sit on it.

What organizational prompts do

Organizational prompts let you load a library of ready-to-use prompts straight into Copilot for your whole organization. You create and publish them once, and they show up for your users across Copilot Chat, Microsoft Edge, and Teams, suggested automatically as people type. Instead of a blank screen, your users open a menu and pick from prompts you already wrote for them, sorted by department. It gives people a starting point, which is the thing most of them are missing.

One heads-up before you go looking for it. This is rolling out right now, so if you don't see Prompts under Copilot yet, that's likely why, but most are already seeing it in their tenants, so it should be there soon. You'll also need the AI Administrator or Search Editor role to manage them.

How to set it up

Head to the Microsoft 365 admin center. On the left, drop down Copilot and open Prompts. That's where your organizational prompts live.

You've got two ways to add them:

Create them one at a time with the Create prompt button.
Import them in bulk. Download the CSV template, fill in one row per prompt, save it as CSV UTF-8, and import the set. You can import up to 100 prompts at a time (5 MB max per file), and publish up to 1,000 in your tenant. If you already have a prompt catalog in a spreadsheet, this is the fast path.

When you create or edit a prompt, you'll set a few fields:

Title and display prompt. The title is the label on the prompt lab card (35 characters). The display prompt is the short version shown on the pill suggestions and autosuggest (132 characters).
Prompt. The actual text sent to Copilot when someone picks it. This is required and gives you room to write a real prompt (up to 8,000 characters).
Supported apps. Pick where the prompt shows up. This one matters. Not everyone has a premium license, but everyone has Copilot Chat on the web, so you can make a prompt available there too.
Department. Heads up: departments don't map to a security group. They're free-text filters for the end user. Tag prompts with IT, Finance, HR, and so on so people can sort the library by their team.
Task type. These come from Microsoft (Analyze, Create, Edit, and so on). You can't make your own, but tagging them helps users filter.
Language. Set the language for the prompt. There's no auto-translation, so if you need a prompt in multiple languages, create a separate one for each.

Hit publish and it's live. Give it about 3 hours to show up in the prompt lab. You can also pin your most important prompts, up to four, and those pinned ones show up as suggested prompts on the Copilot home screen.

Check the analytics

There's an Analytics tab that's worth your time. Once the data populates, you can see active users and submissions per prompt over the last 7, 14, or 28 days. That tells you which prompts people actually use, which ones to fix, and where adoption is landing. It's a straight read on what's working.

Where your users find them

Setup is half the job. Users need to know where these live. They show up in three spots:

The Suggested button on the Copilot home screen, which opens your pinned prompts.
The prompt lab, where users search by keyword and filter by department or task type. Picking a prompt drops it into the message box so they can tweak it before sending.
Autosuggest right in the Copilot input box as someone starts typing.

So someone on IT can search "IT," grab a prompt that rewrites a help desk reply so it doesn't read like "did you try restarting your computer," and send something that sounds a lot nicer. Pre-made prompts, sorted by team, ready to go.

Do these two things after you turn it on

Tell people it exists. Send a note to your communication channel. You've got a prompt library, it's sorted by department, here's where to find it. A library nobody knows about doesn't move adoption.
Give people a way to submit prompts. Set up a channel where users can send you good prompts to add. When someone finds a prompt that works for Finance or IT, they should have a clear path to say "add this one." That keeps the library growing from the people actually using it.

Organizations have been asking for this because they want to hand users a foundation for getting started with Copilot. Organizational prompts are one of the cleanest ways to do it, and it takes minutes.

If you're an admin, this is a quick win with real impact. Set it up, tell your people, and give them a way to add to it.

For the full field reference and character limits, here's Microsoft's official documentation: Organizational prompts for Microsoft 365 Copilot.

Go push some buttons.

Fabric Data Agents can choose Query Language based on Context

Greg_Beaumont — Thu, 09 Jul 2026 16:12:47 GMT

What if you could combine decades of historical analysis with live, real-time data in one seamless AI chat experience? Traditionally, analyzing large volumes of past data (Analytics / Business Intelligence) has been a separate architecture, or has at least required separate toolsets, from monitoring what’s happening right now (real-time, IoT, etc). With Fabric Data Agents, analytics for large volumes of historical data can be accessible with real-time data via a single AI query endpoint. Analytics can be used to gain understanding from historical data, and findings can be put into action for real-time scenarios, all through a single interface for the end users.

Figure 1.0 – Fabric Data Agent can use different query languages for optimal performance with a lambda-style RTI architecture on Fabric

Many of the demos I’ve seen for Fabric Data Agents will highlight the capability to connect to different types of queries and sources via a single endpoint such as Lakehouses (SQL), Warehouses (SQL), Semantic Models (DAX), Eventhouses (Kusto), and Ontologies. What I have not seen frequently discussed is adding different query engines on the same data for the purpose of optimizing query performance based upon the context of the query and the latency of the data, as per Figure 1.0 above.

To be clear, I am not advocating duplication of data for the purpose of query performance. Rather, this architecture would enable Fabric Data Agents to choose the best query language for the context of the query. Hence the acronym I created for this blog article “NoDAX” which stands for “Not Only DAX.” NoSQL “Not Only SQL” is a real term referring to non-relational database systems designed for flexible schemas, horizontal scaling, and high-performance access to large, distributed datasets. NoDAX exists only within this blog post, and represents the availability of multiple query languages within a single Fabric Data Agent. The best query language can be used based on the context of the question and query. Not only DAX but also SQL, Kusto, and Ontologies can be queried to generate the most efficient contextual query.

Figure 1.1 – Explaining the NoDAX acronym created for this article

Why does this use case matter?

The ability to query both the past and the present in one interface isn’t just a novel technical capability. Many analytic solutions can benefit from a pattern of [analysis + action]. Analytics by itself is great at understanding the past. We can look at years of historical data to figure out patterns, drivers of performance, and what went right or wrong. Historical context is incredibly valuable, but understanding the past doesn’t improve outcomes in the future.

The real value comes when we connect findings from the past to decisions we make right now and in the future. If you discover historical data patterns that lead to an inventory shortage, a fraud event, or a patient risk score increasing, you can apply that insight to the latest operational data and act on ongoing workflows. Analytics and action have to reinforce each other. Analytics without action doesn’t create value. But action without understanding can easily make things worse. I once had a veteran analytics manager say to me “Without carefully considered and vetted KPIs you are flying blind. But be careful, because a KPI without proper context and understanding will quickly become a blunt object that an empty suit uses to whack somebody over the head.”

Figure 1.2 – The purpose of this architecture is to learn from the past to improve the present and future

Different Query Languages without data duplication

A Fabric real-time architecture can be part of a design pattern that is similar to if not a version of a Lambda architecture. With a Lambda architecture, hot path data is available for real-time alerting and analytics while cold path data is stored for deep and complete historical analytics and data science.

Figure 1.3 – NoDAX architecture can query a lambda-style architecture via multiple query endpoints

Per the diagram above, real-time data is available in Fabric ASAP and cycled through an Eventhouse. Historical data can either be batched into a Fabric Lakehouse / Warehouse or copied over from the Eventhouse. A Fabric Data Agent can then generate Kusto queries against the Eventhouse, SQL queries against the Lakehouse / Warehouse, or DAX queries against the Warehouse / Lakehouse via the Direct Lake Semantic Model. DAX is often the best query language for data having deep history with complext analytic logic. SQL can be the best query language for retrieving historical row-level information from a robust relational database. Kusto can be used to query what’s happening right now via a real-time Eventhouse.

Lambda architectures have been around for years, so why is this architecture a new option? Past lambda architectures would have hot path data available in a streaming toolset such as Azure Eventhub, and then store historical cold path data in a tool such as Azure Data Lake. Hot path alerting and reporting was usually disparate from historical cold path analytics. Per the diagram 3.4 below, with Microsoft Fabric, you can now:

implement both the hot and cold path in a single Fabric environment (Eventstream, Eventhouse, Lakehouse / Warehouse). Ontologies are also an option.
query the hot and cold path data via a single agentic endpoint using a Fabric Data agent
Query either the hot or cold path using the optimal query language for the context of the question (DAX, SQL, Kusto, Ontologies)

Figure 1.4 – Fabric not only unifies components of lambda-style architecture, but Data Agent also unites the query endpoints for AI unification

Example use case for Healthcare

Here’s an example of a Healthcare use case:

A user might ask a question “Show me the percentage of patients who had their pain scores checked every hour for gall bladder removals on floor 5 over the last 3 years.” This will ideally filter three years worth of data for patients with specific procedure codes, filtered for specific rooms, and calculate the pain score check compliance for those visits. This query is ideal for the DAX language with a Semantic Model.
The user might then want to see details for a specific time period, and ask “Show me the pain score results for patients who had their gall bladder removed on July 3 2024 on floor 5.” A SQL query might be the best option here against the Fabric Warehouse or Lakehouse, since SQL is better than DAX at retrieving row-level information.
Then the user might want to know what is happening today. “Show me the pain score checks for inpatients right now who had their gall bladder removed on floor 5.” The Kusto language can retrieve the information that streams into a Fabric Eventhouse via an Eventstream. Based on the findings, the user may take an action.

With the example above, a user was able to query deep history with analytic logic, retrieve historical row-level information from a robust relational database, and then view what’s happening right now for those patients. Action can then be taken in the here and now. Here are some additional use cases for Finance, Supply Chain, and Manufacturing in addition to Healthcare:

Figure 1.5 – Industry use cases for Fabric Data Agent with multiple endpoints

Video Summary

Below is my video summary and demo of the Fabric Data Agent NoDAX architecture:

Configure Fabric Data Agent for NoDAX query patterns

When more than one source is added to a Fabric Data Agent, by default the source used for a specific query will be chosen based on interpreting available metadata. The Data Agents have a field called "Agent instructions" which can be used to provide detailed instructions about choosing the right source for the right question. Here’s a screenshot of the Agent instructions:

Figure 1.6 – Agent instructions will guide the Fabric Data Agent to the best query endpoint

I would recommend extensive unit testing and iterative improvements to the Agent instructions based upon your own data and use cases. Here’s a few examples that worked for my initial testing. I would recommend much more robust and carefully designed prompts for a production solution, but this is a baseline of an approach I found to work based on the demo in the video above:

The KQL database named SeattleFireEventHouse is a live stream of 911 calls to the fire department in the city of Seattle. Whenever someone asks for “most recent” or “newest” or “latest” use SeattleFireEventHouse
The lakehouse SeattleFireLakehouse should be queried with a SQL statement when someone asks for a list of incidents before the year 2026. Use SQL to retrieve row level requests for historical data.
The semantic model SeattleFireSemantic Model should be queried when questions ask about historical analytic trends such as call volume averages, Year over year changes, and queries that aggregate data for analytic queries.

Right-Sizing Intelligence: Building a Multimodal Model Portfolio for Healthcare and Life Sciences

MichaelGannotti — Wed, 24 Jun 2026 20:58:16 GMT

For two years, the AI conversation in healthcare fixated on a single question: which large language model is best? That question is now out of date. The frontier didn't just get smarter, it got plural. The most capable healthcare AI systems being built today are not one model but a portfolio: large reasoning models, small efficient models, and specialized models for voice, images, and video, each doing the part of the job it does best.

Microsoft Foundry makes that portfolio practical. Its model catalog spans over 1,900 models from Microsoft, OpenAI, Anthropic, Meta, Mistral, xAI, DeepSeek, Hugging Face, and others, together with the tools to compare, ground, evaluate, and govern them in one place. For healthcare and life sciences (HLS) leaders, the strategic shift is from *pick the model* to *design the portfolio*.

This post walks through one concrete, high-value pattern, turning a clinician's spoken encounter into a verified, structured note, and uses it to show how a model portfolio works end to end, what it costs at scale, and how to keep it safe. Several of the newest capabilities referenced here are in preview; confirm current naming and availability before you build.

Why “model” is now a plural

A modern model strategy spans two axes: capability size and modality. Getting both right is the whole game.

On the size axis, Microsoft Foundry presents a clear menu. GPT-5-class models are the most capable for complex, multi-step reasoning and multimodal scenarios. GPT-4.1 strikes a balance of capability and cost for production workloads, and GPT-4.1 mini is tuned for the lowest-latency, highest-throughput jobs. Alongside them sit Microsoft's Phi small language models, Phi-4, Phi-4-mini, and Phi-4-multimodal, designed for strong reasoning with efficient deployment, including on-device through Foundry Local.

On the modality axis, the catalog reaches well beyond text. Microsoft's MAI (Microsoft AI) family includes MAI-Voice-1, an expressive neural text-to-speech model for natural, long-form English speech, and MAI-Transcribe, a speech-recognition model tuned for both accuracy and efficiency. Phi-4-multimodal accepts text, images, and audio in a single model. And a dedicated set of healthcare AI models, such as MedImageInsight, MedImageParse, and CXRReportGen, brings multimodal reasoning to medical imaging, pathology, and radiology. The point isn't to use them all; it's that the right answer to almost any real HLS workflow is several of them, composed.

A multimodal model portfolio in Microsoft Foundry: diverse inputs, the smallest capable model per task, governed outputs.

The business problem: documentation, education, and unread data

Start with the problem clinicians feel most acutely: documentation. Writing notes, orders, and after-visit summaries consumes hours that clinicians would rather spend with patients, and it is a leading contributor to burnout. Ambient AI scribing, listening to the visit and drafting the note, is one of the clearest near-term wins in healthcare AI.

But documentation is only the first domino. The same portfolio that drafts a note can generate patient-education material in plain language and natural voice, summarize a specialist's imaging findings for a referring physician, and make dense clinical text understandable for caregivers. Much of healthcare's most valuable data, audio, images, and scanned documents, has historically gone unread by software. Multimodal models change that.

The catch is economics. If every one of these tasks calls the largest available model, the combined cost and latency make the program impossible to scale. Right-sizing, matching each task to the smallest model that can do it well, is what turns a compelling demo into a sustainable service.

A technical walkthrough: from voice to verified note

Consider the ambient-documentation workflow in detail. It is a useful template because it touches every part of the portfolio, speech, small models, large models, retrieval, evaluation, and human review, in a single pass.

The inputs

The pipeline starts with two inputs: the ambient audio of the encounter, captured with patient consent, and structured context from the electronic health record (EHR), the patient's problem list, current medications, and recent results. Related images or documents can come along for the ride when the visit calls for them.

Which model does what

Here is where the portfolio earns its keep. No single model touches the whole job:

Speech-to-text. A speech model (Azure AI Speech, or MAI-Transcribe for high accuracy and efficiency) converts the conversation into an accurate, timestamped transcript.
Extraction and structuring. A small Phi model parses the transcript into discrete elements, problems, medications, and follow-ups, at a fraction of the cost and latency of a frontier model, and it can run close to the data.
Grounding (RAG). A retrieval step injects only approved context, the organization's clinical guidelines and the specific patient's record, so the draft reflects real, current, permissioned information rather than the model's memory.
Drafting. A larger GPT-class model composes the narrative note, reconciling the transcript, the structured extraction, and the grounded context into clean clinical prose.
Patient-facing media. For the after-visit summary, MAI-Voice-1 can render a plain-language version as natural speech, and avatar or video generation can produce short education clips for patients or providers.
Imaging insights. When images are involved, healthcare models such as MedImageInsight or CXRReportGen surface findings for a specialist to review, in supported, non-diagnostic configurations.

Orchestration, evaluation, and the human checkpoint

Foundry orchestrates these calls, and two stages matter as much as the models themselves. Evaluation runs automated checks on every draft, grounding and faithfulness, safety, and completeness, and routes low-confidence outputs for extra scrutiny. Human review is the non-negotiable final gate: the clinician reads, edits, and signs off before anything reaches the chart. The model drafts; the clinician decides.

From voice to verified note: each stage uses the smallest capable model, and a clinician signs off before anything reaches the chart.

Capture with consent. Record ambient audio and pull the relevant EHR context at the point of care.
Transcribe. Convert audio to text with a speech model sized for accuracy and throughput.
Extract. Use a small Phi model to structure the transcript into problems, medications, and actions.
Ground. Retrieve only approved guidelines and the patient's own record as context (RAG).
Draft. Have a larger model compose the note from transcript, structure, and grounded context.
Evaluate. Score each draft for faithfulness, safety, and completeness; flag the doubtful ones.
Review and sign off. The clinician edits and approves; the edits feed back to improve prompts and routing.

The economics at scale: right-sizing the portfolio

The difference between a pilot and a platform is cost-to-serve. A right-sized portfolio attacks it on several fronts at once.

Match model to task. Most steps above, transcription, extraction, classification, routing, are high-volume but not cognitively hard. They belong on small models like Phi, which deliver strong results at far lower cost and latency. Reserve the large, expensive models for the genuinely difficult step: composing and reconciling the final note. Spending frontier-model dollars on routine extraction is the single most common way AI programs blow their budget.

Cache and batch. Identical or near-identical requests, a common guideline lookup, a standard education snippet, can be cached and reused. Non-urgent work, overnight summarization or cohort processing, can be batched for throughput rather than instant response.

Run small models close to the data. Through Foundry Local, eligible Phi workloads can run on-device or at the edge, cutting per-call cost and keeping sensitive audio and text on local infrastructure, valuable in a hospital where latency and data residency both matter.

Make unit economics predictable. For specialized work, Microsoft's premium healthcare models are offered as pay-as-you-go serverless endpoints with predictable per-image economics, so finance can model cost per study rather than guess at infrastructure. The net effect: cost and latency scale with the difficulty of the work, not with raw volume.

One large model for everything versus a right-sized portfolio: matching model size to task difficulty is what changes the economics.

Governance: safety, responsible AI, and human oversight

In healthcare, governance is not a layer you add at the end; it is the foundation. A right-sized portfolio actually makes governance easier, because each model has a narrower, better-understood job.

Responsible AI by design. Foundry includes built-in evaluation, content safety, and observability, so teams can test models against their own data and monitor them in production.
Data boundaries. The pipeline runs within your tenant, under your existing identity, permissions, and data-protection controls; grounding restricts the model to approved, permissioned sources.
Human in the loop. Microsoft's healthcare models are explicitly designed to support, never replace qualified professionals; many are in preview and intended for research and development, not autonomous clinical decisions. A clinician signs off on every clinically meaningful output.
Provenance and feedback. Capturing edits and approvals creates an audit trail and a continuous-improvement loop for prompts, routing, and model selection.

Responsibility stays with the deploying organization: you remain accountable for verifying outputs, meeting applicable healthcare regulations, and obtaining any clearances required before a model informs clinical decision-making. The portfolio approach supports that accountability by keeping each step inspectable and each consequential decision human.

How to start: a 30-day mini-playbook

Pick one painful, high-volume workflow. Ambient documentation, referral summaries, or patient-education drafting are strong first candidates.
Map the modalities. List the inputs (audio, text, images) and outputs (note, summary, voice) so you know which model types you need.
Draft the portfolio, smallest-first. Assign each step to the smallest capable model and reserve large models for the hardest step only.
Ground it. Connect approved guidelines and the relevant record through retrieval before you optimize anything else.
Stand up evaluation early. Define faithfulness, safety, and completeness checks, and a human-review step, from day one, not after launch.
Measure cost and latency per task, then tune routing, caching, and batching against real usage.
Expand by reuse. Once the portfolio works for one workflow, most of it, speech, extraction, grounding, governance, carries straight over to the next.

The organizations that win with healthcare AI over the next year won't be the ones who picked the single “best” model. They'll be the ones who designed the best portfolio, matching capability and modality to each task, grounding every output, and keeping clinicians firmly in the loop. Right-sizing isn't a cost-cutting afterthought; it is the architecture that makes AI affordable, fast, and safe enough to use everywhere.

Subscribe to the Microsoft Healthcare & Life Sciences blog for weekly, practical deep dives on building AI that scales. And one question to leave you with: if you mapped your top clinical workflow today, how many of its steps actually need a frontier model, and how many are quietly overpaying for one?

Sources

The Agentic Workday: A Technical Deep Dive into Microsoft Scout for Healthcare and Life Sciences

MichaelGannotti — Mon, 22 Jun 2026 16:22:22 GMT

The Agentic Workday: A Technical Deep Dive into Microsoft Scout for Healthcare and Life Sciences

Healthcare and life sciences teams do not have a motivation problem. They have a time problem. The people closest to patients, studies, and customers spend a striking share of their week assembling information — pulling records, reconciling notes, cross-checking criteria, and stitching together the context a single decision requires. The work is essential, but most of it is assembly, not judgment. And assembly is exactly what a well-governed agent can take off their plate.

That is the promise of agentic AI, and it is why Microsoft Scout — an agentic AI desktop assistant — belongs at the center of the modern HLS workday. This is a technical deep dive, not a teaser. We will walk two concrete workflows end to end: how the work happens today, how Scout would actually do it step by step, where a human stays in control, and how the whole thing stays inside your existing security and compliance boundaries. The product claims are kept functional and defensible, and the guardrails are made explicit — because in this industry the guardrails are the point.

From assistant to agent: why the shift matters in HLS

First-generation generative AI was reactive. You asked a question; it produced text. Helpful, but the human still did all the connecting — opening the file, navigating the portal, copying the answer into the next system. Agentic AI changes the unit of work. Instead of a single response, an agent can plan a sequence of steps, operate the tools already on your machine, draw on the sources you permit, and pause for your approval before anything consequential happens.

In most industries that is a convenience. In healthcare and life sciences it is the difference between a demo and a deployable workflow, because the steps between "knowing" and "doing" are wrapped in regulated systems, sensitivity labels, and review obligations. An agent that respects those boundaries does not just save time; it makes the time savings auditable.

How Microsoft Scout is grounded in your work

Microsoft Scout is designed to act, not just chat. It can read and organize files, run routine multi-step tasks across your everyday applications, browse the public web for current information, and reach into your Microsoft 365 work — Outlook email and calendar, Teams messages, and documents in OneDrive and SharePoint — to assemble the context a task genuinely needs. Where clinical or proprietary systems are involved, the practical and defensible pattern is to ground Scout in permitted exports and connectors and your existing permissions, rather than assuming a built-in line into any system of record.

Microsoft Scout draws on permitted sources and returns review-ready drafts — within your existing identity, permissions, and sensitivity labels.

The experience HLS leaders notice is momentum. Rather than narrating every click, you describe an outcome — "prepare the prior-authorization packet for today's queue" — and Scout drafts the path, does the assembly, and brings the result back for review with its work shown. Two design choices make that adoptable in a regulated setting: a human stays in the loop on anything consequential, and every step is visible, so reviewers can trust what they sign.

Deep dive 1: Prior-authorization preparation

The business problem, and what it costs today

Prior authorization is one of the most friction-heavy workflows in provider operations. Before a procedure or therapy can proceed, someone has to demonstrate it meets the payer's medical-necessity criteria — and that proof lives in fragments scattered across clinical notes, prior results, correspondence, and policy documents. The cost is rarely a single dramatic number; it is the steady drag of skilled coordinators and clinicians spending hours on document hunting and formatting, delays that push back care, and the rework that follows when a submission comes back incomplete. Every hour spent assembling is an hour not spent on patients or on the genuinely hard calls.

The manual process today

Walk the current path and the pattern is familiar: a coordinator identifies the cases in the queue, then opens system after system to locate the supporting documentation. They copy relevant notes into a working document, compare what they have against the payer's criteria for that specific service, and — often late — discover a missing result or an unanswered question. They chase it down, assemble the submission, give it a final review, and key it into the payer portal. The judgment at the end is real and valuable. Almost everything before it is assembly.

The agentic how-to: how Microsoft Scout would do it

Scout automates gathering, drafting, and gap-flagging; the coordinator reviews, approves, and submits.

Trigger — a scheduled run. Scout starts on a schedule (for example, early each morning) against the day's work queue, so a first-pass packet is waiting before the team sits down. It can also be launched on demand for a single case.
Gather from permitted sources. Operating under the coordinator's existing permissions, Scout pulls the relevant context: documentation provided through permitted exports or connectors, related Outlook email threads, and supporting files in OneDrive or SharePoint. It only ever sees what that user is already allowed to see.
Draft the packet against criteria. Scout assembles a structured summary, organized to mirror the payer's medical-necessity criteria for the specific service, and lines up the supporting evidence next to each requirement.
Flag gaps and questions. Crucially, it surfaces what is missing up front — an absent result, an unsigned note, an unanswered clinical question. The expensive "discovered late" moment moves to the very beginning, where it is cheap to fix.
Human review and edit (checkpoint). The coordinator or clinician opens a ready draft rather than a blank page. They verify every linked source, correct anything off, and resolve the flagged gaps. This is the human-in-the-loop checkpoint, and it is non-negotiable.
Approve and submit. The person — not the agent — makes the final call and submits in the payer portal. Scout prepared; the human decided.

The same standards are met, but the assembly that used to consume the morning is done before review begins.

The shape of the win is visible in the comparison: the standards do not move, but the order changes. Gaps are caught first, the human starts from a review-ready draft, and the rote assembly happens off the critical path.

Governance and compliance

None of this is adoptable unless it is safe, so the controls are the feature. Scout operates within your existing identity and permissions — it acts as the signed-in user and inherits exactly their access, no more. Microsoft Purview sensitivity labels travel with content, so classified material keeps its protections as it moves through the workflow and is not written out to unprotected destinations. Consequential actions — submitting, sending, finalizing — wait for explicit human approval. And because Scout shows its steps and the sources it touched, you get an audit-friendly trail of what was assembled, from where, and who approved it. In a setting where "show your work" is a compliance requirement, that visibility is as valuable as the speed.

How to start this week

You do not need a transformation program to begin. Pick one payer and one common service line with well-understood criteria. Confirm which sources are already permitted and which exports or connectors are available. Have Scout assemble draft packets for a handful of cases, and ask your coordinators to do what they always do — review and decide — while noting where the draft saved time and where it needed correction. Keep the human checkpoint firmly in place, and let the evidence from one narrow workflow make the case for the next.

Deep dive 2: Field medical pre-engagement briefs

The business problem, and what it costs today

In medical affairs, the quality of a field medical engagement often comes down to preparation. Before a meeting with a healthcare professional, a medical science liaison needs a clear, accurate picture: relevant background, the latest approved internal materials, prior interactions, and the open scientific questions worth exploring. Pulling that together is time-consuming, and when calendars are full it is the part that gets compressed — which means well-qualified experts sometimes walk in less prepared than they would like. The cost is a softer one: engagements that are good when they could be excellent, and institutional knowledge that lives in individual inboxes rather than in a repeatable process.

The manual process today

Today an MSL typically prepares by hand: scanning email and notes from previous interactions, hunting for the most current approved materials, checking the calendar for context, and drafting their own talking points. Done well it is excellent; done under time pressure it is uneven. And because it is manual, the standard varies from person to person and week to week.

The agentic how-to: how Microsoft Scout would do it

From a scheduled trigger through source-checked drafting to a required field-medical review before the brief is final.

Trigger ahead of the engagement. Scout runs on a schedule tied to upcoming engagements — for instance, the day before each scheduled meeting — so a draft brief is ready in advance.
Gather context from permitted sources. Under the MSL's own permissions, it draws on approved internal materials, prior interaction notes, related Outlook threads, and calendar context.
Summarize into a usable brief. Scout drafts a structured read-ahead — concise background, suggested talking points, and a short list of open scientific questions — shaped for the person to refine, not to send as-is.
Check sources. It grounds the brief in approved, permitted content and shows where each element came from, so nothing rests on an unverifiable claim.
Field medical review (checkpoint). The MSL edits and confirms accuracy. In medical affairs this review is essential — the human owns scientific accuracy and compliance, every time.
Brief ready. The reviewed read-ahead is in hand before the meeting, and the same high standard applies to every engagement, not just the ones with time to spare.

The benefit is consistency as much as speed: the floor rises, because every brief starts from a thorough, source-checked draft, and the expert's time goes to sharpening the science rather than gathering the inputs.

Governance and compliance

The same guardrails apply. Scout works within the MSL's identity and permissions; sensitivity labels stay attached to the materials it touches; the field medical review is a hard checkpoint before anything is finalized; and the trail of sources keeps the brief defensible. For regulated medical affairs work, an agent that drafts transparently and then steps back for human sign-off is precisely the right division of labor.

How to start this week

Choose one engagement type and one well-curated set of approved materials. Have Scout produce draft briefs for the next few meetings, and ask your MSLs to review and refine as they normally would. Compare the agent-drafted starting point with a blank page, and watch what happens to both preparation time and consistency across the team.

Where Cowork and Microsoft 365 Copilot fit

Scout is the star at the individual desktop, but it is part of a broader fabric. Microsoft Cowork extends agentic collaboration into the flow of teamwork, so momentum is shared rather than personal. Microsoft 365 Copilot keeps AI close to the documents, meetings, and messages where so much HLS work already lives. The practical sequence for most organizations is to start where the friction is sharpest — a recurring prep workflow like the two above — prove the model with the human firmly in control, and then extend across the team.

The throughline: do more with less, without doing less

Across both deep dives the pattern is identical. The agent does the assembling; the human does the deciding. Speed to market improves because the busywork shrinks, not because the standards do. Compliance gets easier because the agent operates inside your existing identity, permissions, and labels, and because every consequential action waits for a person. That is what makes agentic AI a fit for healthcare and life sciences specifically: it is fast and it is accountable, and in this industry you are not allowed to choose only one.

Subscribe to the Microsoft Healthcare and Life Sciences blog for weekly, practical deep dives on putting Microsoft AI to work — safely — across care, research, and commercial teams. If you mapped your own highest-friction prep workflow onto the six steps above, which step would you let an agent own first — and what evidence would you need before you trusted it with the rest?

Why AI Can't Find Your Files (And the Simple Fix)

michaelgoad — Mon, 22 Jun 2026 16:07:44 GMT

I was watching someone the other day and learned something about AI that I believe is going to be important in the future. It wasn't a flashy new model or some prompt trick. It was something way more practical: how the way we name and build our files is either going to help AI or quietly work against it.

And the more I sat with it, the more I realized this is going to matter a lot as we build for an AI-first world.

We organize files for humans, not for AI

Think about how most of us organize things today. We nest everything. A folder for 2026, then the customer, then the opportunity, and inside it a file called "Notes." Or maybe a policy and SOP folder tree that goes five levels deep with broad, generic titles.

That structure makes sense to us (humans). We navigate by memory and location. We know the file is in the customer folder, under this opportunity. That's how humans think.

But AI doesn't think like that.

Now picture that same sales folder with three or four different "Notes" files spread across different subfolders under the same customer. When you ask AI to find something in there, it's going to guess. A lot. Especially when everything is named "Notes." And the messier the structure, the worse the guessing gets.

It comes down to metadata

So here's what I learned that actually helps. It comes down to metadata.

When AI looks for a file, it isn't always reading every word of every file. It works in two levels, and once that clicked for me, the whole thing made sense.

Level one: the title

The first thing AI looks at is the title. And this is where most of us are leaving value on the table.

A file called "Customer, Opportunity, Subject" gives AI so much more to work with than a file called "Notes." Same thing on the policy side. A file named "Benefits, Parental Leave, 2026" tells AI exactly what it's looking at before it even opens it.

Compare these two:

notes.docx
acme-corp-renewal-q3-discovery-notes-2026

One of those is invisible to AI. The other one is basically answering the question before it's asked.

Level two: the top of the file

Once AI decides a file might be relevant, it starts reading it. And some of the most valuable real estate in the entire file is the very top.

If you put a short structured block at the beginning that spells out what the file is about and the topics inside it, AI immediately understands what it's looking at. Something like this:

Title: Parental Leave Policy (US)
Type: HR policy Topics: parental leave, benefits, time off
Effective date: January 1, 2026
Last reviewed: May 14, 2026
Version: 3.0 Status: active

That little block does a lot of work. It tells AI what this is, who it's for, and just as important, whether it's current.

So when I say metadata, that's really all I mean. It's the title of the file, plus a little bit of context right at the top, so that when AI scans it, it knows fast whether this is the file it actually needs.

You don't need this on every file

Now, I'm not saying do this for every file. That's overkill, and honestly it would be exhausting.

But think about the static stuff. The files that don't change much. HR policies. Benefits. SOPs. The things people search for in a ServiceNow or SharePoint knowledge base using AI.

For those files, a little structure does two things.

First, it helps AI find the right answer for the person asking. When someone types "how much parental leave do I get," the right file basically raises its hand.

Second, it cuts down the latency. If AI is sitting there scanning through a pile of vaguely named files, the user is just waiting around for it to think, and that gets annoying fast. When AI has the right context up front, it gets to the answer quicker.

Pro Tip: You can use AI to create the metadata for you, just ask it!!

Where this is headed

I keep coming back to this because it's such a small change with a real payoff.

We've spent years building file structures that make sense to people. Folders inside folders, organized by how we remember things. That's not wrong. But as more of how we find information runs through AI, the files that win are the ones that are easy for a machine to read, not just easy for a human to browse.

I don't think every file needs this. But as we get more serious about how we build file structures for an AI-first world, I think this is one of the concepts that's going to really matter.

Curious if anyone else is already thinking about this in how they build their knowledge bases. If you are, I'd love to hear how you're approaching it in the comments!

How Microsoft Scout Brings Agentic AI to Everyday Healthcare and Life Sciences Work

MichaelGannotti — Mon, 22 Jun 2026 15:36:14 GMT

Healthcare and life sciences teams are asked to do the impossible every day: deliver better outcomes, move faster, and stretch every dollar — all while navigating some of the most regulated, documentation-heavy workflows in any industry. The promise of AI has never been about replacing the experts who do this work. It’s about giving them their time back.

That promise is entering a new phase. We’re moving from AI that answers to AI that acts — agentic AI that can carry out multi-step work across your applications, your documents, and the web, with you in control. Nowhere is the opportunity more concrete than in the daily operational grind of healthcare and life sciences.

From answering to doing

Most knowledge work in HLS isn’t blocked by a lack of information — it’s blocked by the effort of pulling that information together and turning it into action. Gathering the right documents. Summarizing a thread. Drafting the first version. Updating five systems with the same three facts.

Microsoft Scout is designed for exactly this layer of work. Think of it as an agentic AI teammate on your desktop — one that can read and organize files, search across your email, calendar, and Teams, browse the web, and complete genuinely multi-step tasks on your behalf. Crucially, it can run on a schedule, so routine work happens before you sit down, and it keeps a human in the loop for anything that matters.

Real-world ways HLS teams can do more with less

Provider operations: Assemble the documentation needed for a referral or prior-authorization request, summarize the relevant history, and draft the submission — turning a 30-minute scramble into a two-minute review.
Clinical research coordination: Pull together study start-up documents, track outstanding site communications, and draft consistent follow-ups, so coordinators spend their time on sites and patients rather than inboxes.
Medical affairs and field medical: Prepare for an engagement by gathering the latest publications and prior interactions into a single brief, then capture a structured summary afterward — every meeting, consistently.
Commercial and market access: Stand up an account briefing or a competitive news roundup on a recurring schedule, so the team starts every week informed instead of researching from scratch.

The thread running through all of these is the same: reclaim capacity, increase consistency, and accelerate speed to market — doing more with the people and budget you already have.

Built for the trust HLS demands

In this industry, “helpful” is not enough; it has to be trustworthy. Agentic AI for healthcare and life sciences has to respect enterprise security and data boundaries, keep sensitive information under your control, and keep a person in command of consequential decisions. The goal is to automate the busywork around expert judgment — never to automate the judgment itself.

A family of AI that works the way you do

Scout is part of a broader shift in how Microsoft is bringing AI to work. Where Microsoft 365 Copilot brings AI into the flow of the apps you already use, and Microsoft Cowork reimagines how teams collaborate with AI, Scout focuses on agentic action at the desktop — automating end-to-end tasks and recurring workflows. Together they point to the same future: more of your day spent on the work only you can do.

Start small, compound the gains

You don’t need a transformation program to begin. Pick one repetitive, high-friction workflow — the weekly roundup, the recurring briefing, the documentation prep that nobody enjoys — and let agentic AI take the first pass. The time you reclaim funds the next idea.

We’ll be sharing practical, healthcare- and life-sciences-specific playbooks here every week. Subscribe to the blog, and tell us in the comments: what’s the one recurring task you’d hand to an AI teammate first?

Learn more about Microsoft Scout:

Build a Financial Dashboard in Copilot Cowork (Ideas Coach to Researcher to Build)

michaelgoad — Thu, 18 Jun 2026 16:51:58 GMT

I sat down with Nick Aquino, a Principal Solution Engineer at Microsoft, because he built something that stuck with me. A real, interactive financial dashboard. No code. All of it in Copilot and Cowork.

I didn't want slides. I didn't want theory. I asked him to show me exactly how he did it, step by step, so you can do the same thing.

A few takeaways below.

The problem most of us recognize

Nick gets a financial export every month. Same format, same system, same columns every time. For a while he turned that file into Excel charts and pivot tables by hand.

Then people noticed he was good at it. The CFO asked for his own view. Then the COO. Then sales. Then marketing. Pretty soon one report became five, and a monthly task turned into two full days of work. Every single month.

If you've ever become "the Excel person" on your team, you know the trap. Lots of visibility, lots of extra work.

That repetitive, well-defined task is exactly what AI is good at.

The part that changed my mind: plan in Copilot first

This is where Nick's approach got interesting. He didn't jump straight into Cowork and start building.

He thinks about it like one gallon of gas and 15 packages to deliver. What's the most efficient route? Cowork can run for 10, 30, even 60 minutes on a single prompt, and there's a cost to that. So he does the thinking in Copilot first, using the built-in agents, then hands a tight, finished plan to Cowork.

His workflow looks like this:

Ideas Coach. He uploads the financial file and asks for dashboard ideas that would matter to a finance org. A CFO, a financial analyst, that kind of audience. Nick's a solution engineer, not a finance person, so he lets the agent fill the domain gap. Context matters, so he gives it the actual file instead of just describing it.
Prompt Coach. He takes those ideas into Prompt Coach and asks it to build a strong prompt he can actually run.
Researcher. This is the step that does the heavy lifting. He runs that prompt in Researcher, uploads the file again, and gets back an application requirements document. A real spec built around his ideas and his data.
Cowork. Now he feeds Cowork the requirements and the file. It reads the spec, reads the data, and builds the dashboard as a single-page HTML file.

The prompt Researcher produced followed a clean structure: goal, context, source, expectations. Single-page HTML. Client-side libraries from a CDN. No external calls. A file picker and a loaded-file summary in the interface. That's a prompt Nick says he'd never write from scratch. The agents wrote it for him.

The bonus that made me laugh

After the dashboard was done, Nick asked Cowork for one more thing. A click-through walkthrough to teach someone how to use the tool, because he'd be presenting it to the C-suite.

Cowork built the walkthrough. With real screenshots of the process. Choose your file, load it, here's your dashboard.

The funny part: the screenshots came back styled like a Mac. Small detail, but it tells you the model grabbed real screen captures to document the steps.

Why "Copilot first" actually matters

This is the line organizations need to hear. Cowork is powerful, and that power has a cost, especially once teams start building with it at scale.

Doing the ideation, prompting, and planning inside Copilot uses the agents that come with the license you already have. You save the expensive build time in Cowork for the build itself. Nick compares it to hiring an architect before you call the contractors. Get the plan right up front so you're not paying to fix mistakes later.

Same idea applies to scope. Crawl, walk, run. If your dashboard will eventually connect to a financial system, don't start with the integration. Start with the export you already get. Build the dashboard on that, prove it's useful, then take it to your IT team and talk about a real connection. You learn fast and you don't burn a build on the wrong approach.

Sharing it

A few people asked about getting an HTML file into something easier to send. Nick's take: ask Copilot first.

Upload the HTML to the PowerPoint agent and ask it to convert. It gets close to the original. If it's a flat, scroll-through page, print it to PDF. Pick the output that fits how the thing was built.

The bigger shift

Here's what I keep coming back to. Developers are still important. None of this replaces the back end or real engineering.

But a business leader with an idea can now build the front end. The UI, the experience, the demo. That's maybe 60% of the way there. Then you hand it to IT and developers and say, "this is the idea, here's the working interface, help me make it real."

That changes who gets to start. More people can turn an idea into something you can actually see and react to. And that moves the business forward faster.

Thanks to Nick for showing his work. If you build something with this, I want to see it.

Copilot Cowork is GA - Here's how to track your credit usage as an end user

michaelgoad — Wed, 17 Jun 2026 15:42:28 GMT

Now that Copilot Cowork is generally available, a lot of us in Healthcare & Life Sciences are putting it to work — drafting summaries, running research prompts, moving through tasks faster than ever. But with GA comes something worth paying attention to: Cowork now runs on billing and credit usage. And if you're not keeping an eye on that, it's easy to burn through credits without realizing it.

Here's the good news: you don't need to dig through settings or a billing portal to check (as an end user). There's a command built right into Cowork.

The trick: /cost

I actually picked this up from a peer this morning, and it's about as simple as it gets:

Open your Copilot Cowork session — one you've already been working in.
Go down to the prompt box at the very bottom.
Type /cost and hit enter.

That's it. Cowork instantly shows you the credit usage for that task, right inline. No extra clicks, no leaving your workflow.

Why monitoring can matter

In healthcare, we're mindful of how we use every resource — and AI consumption is no different. As teams lean on Cowork for more of their day-to-day, a quick credit check helps you:

Stay aware of what a given task actually costs.
Budget intentionally across sessions and projects.
Have the data when someone asks, "How are we using this?"

A 10-second habit now saves you from surprises later.

One honest heads-up

A heads up: a few of my peers haven't gotten /cost to work just yet, so your mileage may vary. In the cases I've used it, though, it's been reliable. If it doesn't show for you right away, you're not doing anything wrong — the feature is still rolling out.

Try it

Next time you're in Copilot Cowork, drop a /cost at the bottom of your session and see your credit usage for yourself. It's the fastest way to stay on top of consumption across your different sessions.

Give it a shot — and let me know if it works on your end.

Go push some buttons. 🚀