The genie is out of the bottle. We’re not early adopters anymore. We’re already behind.
I know that sounds abrupt, but I mean it seriously. AI has moved from conference keynote to daily workflow faster than most of us would have predicted, and the businesses that start learning today will have a meaningful head start on everyone else. The ones who wait are quietly losing ground right now without realizing it.
So let me give you the full picture of where things actually stand, because I think a lot of what’s being written about AI right now is either overclaiming or underselling, and neither is particularly useful.
Claude and Cowork: The Most Capable AI Tool Available to Businesses Today
If you haven’t come across Anthropic’s Claude or the Cowork desktop application, you’re about to. At GCS, we use it every single day for research, client deliverables, documentation, and more. It can read and analyze your files, draft and format documents, research topics in real time, connect to external data sources, and automate multi-step tasks, all within a sandboxed workspace that keeps sensitive work contained. For businesses ready to move quickly, it’s genuinely the most capable AI assistant available right now.
But we’d rather give you the honest picture than oversell it, so here’s what you also need to know.
Cowork requires installation on every machine individually. There’s no central cloud deployment today, which creates a real management burden for IT teams, especially in organizations where device management is already complex. On top of that, IT administrators don’t yet have strong centralized tools to govern usage, manage access controls, or monitor how the tool is being used across the organization. For companies with compliance requirements or strict data policies, that matters quite a bit. Consumption costs can also be difficult to forecast since the usage-based pricing model is flexible but not easy to predict at scale. And as with any AI tool, sensitive data requires careful, deliberate handling before you let it anywhere near your workflows.
None of these are reasons to avoid it. They’re known tradeoffs in an early-stage technology, and knowing them upfront is how you deploy responsibly.
Microsoft Copilot Is Improving, But It’s Not There Yet
Here’s something most people in the Microsoft ecosystem know but don’t say out loud: Copilot is probably the future state for most of us. It already lives inside your M365 security perimeter and it’s centrally manageable by IT. With the recent announcement that Copilot is now leveraging Anthropic’s models (the same AI that powers Claude), the capability gap between the two is starting to close in a meaningful way.
The announcement of Copilot Cowork is genuinely promising. A version of this experience built natively into tools your organization already uses, governed by the same IT infrastructure you already have in place, is a very compelling picture of where this is headed.
It’s just not there yet. Enterprise products built for governance and compliance tend to move more deliberately than point solutions optimized for raw capability, and that’s not a criticism of Microsoft. That’s how thoughtful enterprise software development works. Copilot Cowork is just now being rolled out in preview, and even when it launches publicly it will likely need time to reach the depth and flexibility that Claude Cowork offers today.
So what do you do in the meantime?
Three Steps for SMBs Navigating AI Right Now
The answer is not to wait. The answer is to move with purpose and to do it in a way that sets you up well regardless of which tools ultimately win out.
Start by building your guardrails. Before you deploy any AI tool anywhere in your organization, you need to define what’s in bounds and what isn’t. A clearly written AI Use Policy should cover what employees can and can’t do with AI, and what the consequences are for stepping outside it. You need a clear picture of data governance as well, meaning you know exactly what data AI is allowed to touch and where the hard lines are around client information, financials, personally identifiable data, and anything confidential. Not every employee needs the same level of AI access, so defining role-based controls from the start saves you significant headaches later. And perhaps most importantly, your team needs to understand not just what the policy says, but why it exists.
If you’re not sure where to start on any of this, that’s exactly the kind of work GCS can help you think through.
Have a plan before you deploy anything. AI without a problem to solve is just noise, and the organizations that get the most out of it are the ones that started with intention rather than enthusiasm. Identify two or three specific workflows where AI could realistically save time or reduce errors. Decide which data sources you’ll allow it to access. Define who gets access and to what level. Having clear answers to these questions before you deploy turns a pilot into something that can actually produce useful results.
Deploy in pockets, not company-wide. Pick one department, one use case, and a small group of curious people who are willing to experiment and report back honestly. Let them run with it, collect feedback, and monitor what’s actually happening. Find out what works before you scale it. You don’t need to transform the entire organization at once — you need one solid proof point, and then you build from there.
Find Your Champions
There’s one more piece that most organizations underestimate, and it might be the most important one. The person who will get the most practical value out of AI isn’t the IT director or the executive team — it’s the person doing a specific job every single day who suddenly realizes that AI can cut two hours off their afternoon or eliminate a task they’ve been manually grinding through for years.
Find those people early. They’ll solve problems with AI faster than a top-down initiative will, because they sit in their specific seat and understand the workflow in a way that no one managing from above can fully replicate. Give them room to experiment, ask them to share what they learn, and treat them as partners in figuring this out. Your internal champions will become your most valuable asset in this whole process.
What You Build Today May Not Last, and That’s Okay
I want to be honest about something that doesn’t get said enough in these conversations. The platforms and engineering approaches we’re building on right now will change. Things are moving fast, and the tools we’re using today will likely look quite different in two or three years. Some of what you invest in now may need to be rebuilt, retooled, or replaced as the landscape settles.
That’s okay. In fact, it’s kind of the point.
The businesses that are learning now, working through what actually works and what doesn’t, hitting the roadblocks and figuring out how to push past them, those are the businesses that will lead when things normalize. The problems you couldn’t have predicted until you played in the sandbox long enough are the very problems that will make you better at this than your competitors. Waiting for certainty isn’t a strategy. It’s just a different kind of risk.
Start learning. Start building. Figure out what works, figure out what doesn’t, and do all of it with purpose and security at the center.
We’re on This Journey Too
GCS isn’t watching AI from the sidelines. We’re using it every day in our own operations, and we’ve done the work of building the policies, working through the data governance questions, and finding our own internal champions. We know where the tools shine and where the guardrails need to be tighter.
If you need help building your AI policy, identifying your first use cases, securing your environment, or just figuring out where to even start, that’s exactly what we’re here for.
Talk to GCS about your AI rollout
The businesses learning AI today will lead tomorrow. GCS gets you there without the wasted time and costly mistakes.
FAQ: AI for SMBs
Is AI safe to use if my business handles sensitive client data?
It can be, but only with the right guardrails in place. Before using any AI tool on client work, you need to define exactly what data AI is allowed to touch. Client information, financials, and PII should have hard limits. Using a sandboxed workspace like Cowork helps keep sensitive work contained.
Should my small business use Claude or Microsoft Copilot?
If you want to move fast today, Claude with Cowork is the stronger option — it’s more flexible and capable right now. If you’re already deep in Microsoft 365, Copilot is worth watching as it improves. The two aren’t mutually exclusive, but don’t wait for the perfect tool before starting.
What should an AI use policy cover for a small business?
At minimum: which tools employees are approved to use, what data AI is allowed to access, who has access at what level, and what the consequences are for stepping outside the policy. It doesn’t need to be complex — it needs to exist before deployment.
How do we deploy AI without creating a security liability?
Start with role-based access controls, approved tool lists, and clear data handling rules. Deploy in one department first, monitor how the tool is actually being used, and tighten the guardrails before scaling. Security should be built in from day one — not added after something goes wrong.
What workflows are SMBs using AI for right now?
The most common starting points are document drafting, research and summarization, client deliverable preparation, and internal documentation. These are high-volume, repetitive tasks where AI saves real time without requiring deep technical setup.
How does GCS help if we don't know where to start with AI?
GCS works with SMBs at every stage — from writing your first AI use policy to identifying use cases, setting up governance, and securing your environment. As a managed IT and security company that uses AI in our own operations daily, we bring hands-on experience to every engagement, not just a framework off a shelf.