Claude CoWork and the Rise of AI Platforms: From Experiment to Enterprise Reality

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I recently spent some time experimenting with Claude co-work, and the experience was quite different from the usual “prompt and response” flow most of us are used to.

Instead of asking questions and getting answers, I tried to make Claude part of how I actually work through tasks.

I started small. One of the first things I built was a simple meeting brief skill. The idea was to give Claude a structured way to prepare me before a meeting. I defined what inputs it should consider, how it should organise context, and what kind of output would actually be useful. Not just a summary, but something I could act on.

For example, instead of just summarising documents, the skill would:

• Pull together relevant background

• Highlight key discussion points

• Identify risks or open questions

• Suggest how I should approach the conversation

The difference was subtle but important. I wasn’t asking Claude to “summarise.” I was asking it to think with me before a meeting.

That shift changed how I interacted with it.

From tasks to workflows

Then I tried something slightly more operational.

I gave Claude access to a folder that had a bunch of screenshots. These were not neatly organised. Just raw captures from different contexts. Instead of manually sorting them, I created a simple workflow where Claude could go through them, interpret what they represented, and organise them meaningfully.

Some screenshots were product flows. Some were error states. Some were just references I wanted to keep. The task itself was simple, but repetitive.

This is where things started feeling less like AI usage and more like delegation.

I was no longer giving one-off instructions. I was setting up a repeatable way of handling a type of task.

And importantly, I was still in the loop.

I would review what it did, make small corrections, refine instructions, and run it again. Over time, the workflow improved. Not because the model changed, but because the way I worked with it became clearer.

Human in the loop, but with leverage

This is where the idea of co-work really clicked for me.

It is not about fully automating everything. And it is definitely not about removing human involvement.

In fact, the human-in-the-loop aspect becomes even more important.

You define the intent. You shape the workflow. You review the output. You refine the system.

Claude handles the repetitive thinking in between.

Things like:

• Structuring information

• Organising inputs

• Following a defined process

• Executing repeatable tasks

Over time, this changes your role.

You move from doing the work… to designing how the work gets done.

And that is a very different kind of productivity.

Where this starts breaking in teams

While this worked well at an individual level, it also made one gap very visible.

All of this was happening in my own setup.

My meeting brief skill. My screenshot organisation workflow. My way of structuring these interactions.

Now imagine a team.

Different people build their own ways of working with Claude. Some effective, some not. Some documented, most not. Some reusable, many forgotten.

This is where co-work hits a natural limit.

Because unless these workflows are shared, structured, and connected to organisational knowledge, they remain personal productivity hacks.

Not organisational capability.

The need to go beyond individual co-work

For co-work to really scale, a few things need to happen:

• Workflows should not live with individuals

• Knowledge used in these workflows should be consistent and accessible

• Teams should be able to reuse and build on what already exists

• There should be a way to manage and evolve these workflows

At that point, you are no longer just “using AI”.

You are building a system around it.

This is where a new layer starts becoming important. Not just access to models, but environments where these workflows can live, evolve, and connect with real organisational context.

As workflows grow, context becomes everything

One thing I realised quickly is that the quality of these workflows depends heavily on context.

The more relevant information Claude has access to, the more useful the output becomes.

This is where emerging ideas like Model Context Protocol (MCP) start becoming very interesting.

MCP, in simple terms, is about standardising how models connect with external tools, data sources, and systems.

As more connectors become available, you can start linking workflows to:

• Internal documents

• Knowledge bases

• File systems

• Tools like email, calendars, CRMs

• Even operational systems

Now imagine the earlier examples again.

The meeting brief skill is no longer limited to static inputs. It can pull live context from emails, past meetings, documents, and notes.

The screenshot workflow can go beyond folders and integrate with product tools, bug trackers, or design systems.

Workflows become richer. More contextual. More aligned to real work.

Towards an AI fabric inside organisations

When you combine this level of connectivity with structured workflows, something bigger starts to emerge.

Not just isolated use cases, but an interconnected layer of AI-driven work.

You can think of this as an AI fabric inside the organisation.

• Workflows are defined and reused

• Context flows across systems

• Knowledge is embedded into how tasks are executed

• Humans stay in control, but with much higher leverage

And importantly, this is not about one tool.

It is about how different capabilities come together.

Models like Claude provide the intelligence. Concepts like co-work define how we interact. Protocols like Model Context Protocol enable connectivity. Platforms bring it all together into something usable at scale.

Where things still feel early

At the same time, it’s important to acknowledge that this way of working is still evolving.

There are practical limitations.

For example, setups like this often depend on specific environments. Something like Claude Co Work requires the desktop setup to be running for things to work smoothly. It is not yet something that sits seamlessly across all tools and systems in the background.

There is also effort involved in designing workflows.

Defining a good skill takes clarity. Iterating on it takes time. And until it stabilises, you are actively shaping how the system behaves.

But this is expected.

We are still early in figuring out what good AI-native workflows look like.

A founder’s perspective on what’s changing

As someone building in this space, this shift is very visible.

Over the past year, most conversations around AI have been about capabilities. What models can do. How accurate they are. How fast they are improving.

Now the conversation is slowly moving.

From what AI can do… to how organisations actually use it.

And that’s a much harder problem.

Because it involves workflows, systems, people, and context.

What experiments like Claude Co Work show is that the direction is becoming clearer. Structured interaction, human-in-the-loop workflows, and connected context are not edge ideas anymore. They are becoming central to how AI will be adopted in organisations.

Closing thoughts

What stood out to me from these experiments is that the real shift is not about better prompts.

It is about better ways of working with AI.

Claude Co Work is a strong step in that direction. It pushes us to move from one-off interactions to structured collaboration. From isolated tasks to repeatable workflows.

But for this to scale, it needs to sit on top of something more.

As connectors improve, as standards like Model Context Protocol evolve, and as generative AI platforms mature, we will start seeing these workflows become part of everyday organisational systems.

That is when AI stops being an experiment and starts becoming part of how work actually happens.