u/RyanTechInc

With Microsoft adding more AI options, I feel like the naming alone is starting to get confusing 😅

Microsoft 365 Copilot Chat, paid Microsoft 365 Copilot, and Copilot Cowork represent three distinct capability levels with three distinct value and cost profiles. Copilot Chat is a secure, no-additional-cost AI entry point for eligible users. The paid Copilot license is a full enterprise AI platform with deep organizational context. Cowork is an advanced agentic layer for complex, multi-step work, available only to paid license holders and billed on usage.

Our recommendation: start with a clear-eyed assessment of your current Microsoft 365 environment before choosing a Copilot path. Know your permissions structure, your data governance state, and your user readiness level. That assessment will tell you more about which capability is right for your organization than any feature comparison chart will.

I put together a breakdown of the differences if anyone wants it, Microsoft 365 Copilot Chat vs Paid Copilot vs Cowork but I’m mostly curious how people are actually using these in real environments?

Every company seems to have at least one process that makes everyone ask, “Why are we still doing this manually?”

Maybe it’s copying information between systems, putting together the same reports every week, searching through documents for answers, processing forms, creating project documentation, routing approvals, or following up on the same requests over and over.

This is actually one of the reasons we started building Custom AI Tools at RyanTech. We kept seeing businesses interested in AI, but the biggest opportunities weren’t always solved by buying another off-the-shelf AI product. Sometimes the real opportunity was taking a process employees were spending hours managing and building AI and automation directly around it.

That could be a custom internal app, an AI agent connected to company data, automated document processing, internal search, reporting, or a workflow that automatically moves information to the right person while keeping humans involved when a decision or approval is actually needed.

We wrote more about what we’re building and when we think custom AI makes more sense than another off-the-shelf tool here:

Custom AI Tools: When Off-the-Shelf Isn't Enough

But I’m curious what everyone else is dealing with. What’s the painfully manual workflow at your company that you can’t believe hasn’t been automated yet?

Every company seems to have at least one process that makes everyone ask, “Why are we still doing this manually?”

Maybe it’s copying information between systems, putting together the same reports every week, searching through documents for answers, processing forms, creating project documentation, routing approvals, or following up on the same requests over and over.

This is actually one of the reasons we started building Custom AI Tools at RyanTech. We kept seeing businesses interested in AI, but the biggest opportunities weren’t always solved by buying another off-the-shelf AI product. Sometimes the real opportunity was taking a process employees were spending hours managing and building AI and automation directly around it.

That could be a custom internal app, an AI agent connected to company data, automated document processing, internal search, reporting, or a workflow that automatically moves information to the right person while keeping humans involved when a decision or approval is actually needed.

We wrote more about what we’re building and when we think custom AI makes more sense than another off-the-shelf tool here:

Custom AI Tools: When Off-the-Shelf Isn't Enough

But I’m curious what everyone else is dealing with. What’s the painfully manual workflow at your company that you can’t believe hasn’t been automated yet?

A lot of companies seem to start with the tool first. They buy an AI platform, roll it out, and then try to figure out how their existing processes can fit around what the software is capable of doing.

But shouldn’t it be the opposite?

Every company has different workflows, approval processes, data, systems, and bottlenecks. That’s actually a big reason why we created RyanTech’s Custom AI Tools service. Instead of forcing a business to change the way it operates around an off-the-shelf AI product, we build AI around the way that specific business already works.

That could mean a custom internal app, an AI agent connected to company data, a dashboard, an internal search tool, automated document processing, reporting, or an entire workflow that removes repetitive steps behind the scenes.

The idea isn’t to build custom AI just for the sake of building something custom. Off-the-shelf tools still make sense for plenty of use cases. But when a company has a unique process or wants AI to actually work with its data and operations, sometimes the better answer is building the solution around the business instead of building the business around the solution.

Curious what others think. Would you rather adapt your workflow to the AI product, or have the AI built around your workflow?

One thing I've noticed is that a lot of businesses start their AI journey by asking "Which AI tool should we buy?" I think that's the wrong question. The better question is: What business problem are you actually trying to solve?

A company looking to improve employee productivity may benefit from Microsoft Copilot. Another might see a much bigger return from workflow automation or custom AI applications. Others need governance and security in place before rolling anything out.

That's why there isn't a one-size-fits-all AI strategy.

We recently put together a guide that walks through the different AI implementation paths, what each approach is best suited for, and how to determine which one makes the most sense for your business. I'm curious how everyone here approaches it.

If you were advising a company just getting started with AI, what would your first recommendation be?

If anyone's interested, here's the guide:
https://www.ryantechinc.com/blog/ai-implementation-path-right-for-business

A manufacturer automating production workflows has fundamentally different needs than a financial services firm rolling out Copilot to 500 knowledge workers. Treating them the same is where most implementations go wrong. That's why I think the biggest mistake organizations make is treating AI implementation like a software installation instead of a business transformation.

I'm curious what's the biggest challenge you've seen with AI implementations? Is it choosing the right tools, governance, user adoption, or something else?

Maybe it's just me, but I feel like this is one of the biggest misconceptions around Microsoft Copilot. I'll hear people say "Copilot isn't that impressive" or "I tried it and it wasn't very useful." Then I find out they're only using the free Copilot Chat experience.

To me, these are very different things:

Copilot Chat (free): Great for asking questions, brainstorming, summarizing, and general AI chat.

Microsoft 365 Copilot: A paid license that works inside Outlook, Teams, Word, Excel, PowerPoint, SharePoint, OneDrive, etc., using your organization's Microsoft 365 data and permissions.

Copilot Studio / Copilot actions / usage-based AI: Where you can build agents and automations that actually perform work across business systems, with usage-based billing for those AI actions.

They're completely different experiences, but I feel like they're often lumped together under the name "Copilot." Has anyone else run into this? Do you find people judging the entire Copilot platform based only on the free chat experience?

I'm curious how everyone explains the differences without making it even more confusing.

Everyone seems to be asking which AI tool they should use, but we think a better question is whether their business is actually ready for AI in the first place.

Before implementing Microsoft Copilot or any AI solution, ask yourself:

Are your business problems clearly defined?
Is your data organized and accessible?
Do you have the right security and governance in place?
Are your employees prepared to adopt AI?

Our latest blog breaks down the five signs your business is truly AI-ready and how to identify gaps before deployment. Read it here:
https://www.ryantechinc.com/blog/is-your-business-ready-for-ai

Figured I'd ask here before posting everywhere else. My team at RyanTech is growing, and we're looking for an experienced Cloud Solutions Engineer to join us. It's a remote, full-time role working with Microsoft Azure, Microsoft 365, Azure Virtual Desktop, networking, automation, and AI/Copilot solutions.
Resumes can be sent to Jobs@RyanTechInc.com

We recently wrote a deeper breakdown of how we approach responsible AI implementation and keeping humans in control. Curious how others here are handling this as Copilot and AI agents become more capable?

One misconception we keep seeing is that because Microsoft Copilot runs inside Microsoft 365, it's automatically secure.

In reality, Copilot only respects the permissions that already exist in your environment.

That means if users have access to files, SharePoint sites, Teams, or sensitive documents they probably shouldn't, AI can surface that information too.

Before enabling AI, we recommend reviewing things like:

• SharePoint and Teams permissions
• Conditional Access
• MFA
• Microsoft Purview
• Data Loss Prevention (DLP)
• Human approval workflows for sensitive actions

Governance isn't something you add after deployment. It's what allows AI to be useful without creating unnecessary security or compliance risks.

For those of you who have deployed Copilot or another enterprise AI tool, did you review your permissions and governance first, or did those conversations happen after rollout?

We recently published a deeper explanation here if anyone wants more detail:

One misconception we keep seeing is that because Microsoft Copilot runs inside Microsoft 365, it's automatically secure.

In reality, Copilot only respects the permissions that already exist in your environment.

That means if users have access to files, SharePoint sites, Teams, or sensitive documents they probably shouldn't, AI can surface that information too.

Before enabling AI, we recommend reviewing things like:

• SharePoint and Teams permissions
• Conditional Access
• MFA
• Microsoft Purview
• Data Loss Prevention (DLP)
• Human approval workflows for sensitive actions

Governance isn't something you add after deployment. It's what allows AI to be useful without creating unnecessary security or compliance risks.

For those of you who have deployed Copilot or another enterprise AI tool, did you review your permissions and governance first, or did those conversations happen after rollout?

One thing we've noticed is that many organizations are excited to roll out Microsoft Copilot but skip reviewing their Microsoft 365 permissions first. Since Copilot works within the permissions users already have, we've found it's a good opportunity to clean up access before enabling AI.

Things like:

• SharePoint permissions
• Teams access
• Sensitive HR or finance files
• DLP and Purview policies
• Conditional Access and MFA

We've seen that governance often has a bigger impact on a successful rollout than the AI itself.

Curious how everyone else approached it. Did you review permissions before enabling Copilot, or did governance come afterward? Any lessons learned?