Ten Months to Build Our Own Enterprise AI Workspace

To move our company forward, we first gave every employee access to AI. Output improved, but four things started to slip out of control: training, visibility into use, repeated setup and knowledge retention. This is why we stopped treating AI as a collection of tools installed on individual computers and built a cloud workspace instead.

From “help me think” to “do the work”: why we gave AI to everyone

We began watching GPT in 2022. At first, the useful tasks were limited: researching a question, checking something uncertain or reviewing code for obvious problems. It was helpful, but it did not yet change how the company worked.

That judgment changed in 2025. An old system had carried a difficult graphics-processing problem for a long time. I tried Cursor on it. The tool entered the codebase and completed the work. The change was no longer from working alone to getting advice. It was from “help me think” to “do it for me.”

Our company is not large, but everyone carries a real workload. In that structure, AI is not a decorative extra; it adds capacity. We began training employees and allocating usage. We saw the gains quickly, but we also found that individual tools did not automatically become an organizational capability.

Company-wide AI adoption exposed four organizational problems

Wall one: employees did not know how to use it

With the same tool, a well-framed request and a weak request can produce results that differ by an order of magnitude. Some employees reached useful outcomes immediately. Others concluded that AI was nothing special.

At first, I thought this was an attitude problem. It was not. People did not know how to use the tools for their own work. Generic tutorials could not solve that problem because every role had different tasks. Giving someone an account without teaching the real workflow did not create capability.

Wall two: the company could not see how AI was being used

Management could see final output, but little else. We did not know who was using AI, where it was used, which methods worked or what the outcome was. A tool could run inside the company for more than six months and we still could not explain whether it was worth the cost.

This is not about monitoring employees. It is about identifying practices worth sharing, finding work that needs training, assigning cost to the right project and having enough context to review a failure.

Wall three: everyone repeated the same installation and setup

Every new tool, skill and configuration had to be installed on every computer. Installation led to configuration; configuration led to tuning. Some tasks also required more computing resources than an employee's machine could provide.

That cost rarely appeared in a procurement spreadsheet, but it occurred every day. The more tools we added, the more fragmented the local environments became, and employees carried the burden of maintenance and switching.

Wall four: the useful work stayed on personal computers

A method that one person discovered, a configuration they tuned and the context they accumulated all remained on that person's machine. When work changed hands, the next employee began again from zero.

Individual productivity had not become a company asset. As long as methods, permissions and records could not be reused or handed over, we had a group of people who were each good at AI—not an organization with an AI capability.

Why ready-made tools did not solve company-wide use

All four walls pointed to the same conclusion. The market was not short of AI tools. What we lacked was an environment where the entire company could use them consistently. Before building anything, we evaluated existing products. Three issues stopped us.

Every product was strong in a different place

A strong coding tool might not be good at document work. A strong document tool might not connect to our business systems. No single product covered the work of the whole company. Adding more tools would add more boundaries for users and managers.

The switching cost always landed on employees

Each provider change meant setting up the environment again, explaining context again and adapting methods that already worked. As the number of tools grew, usage became more fragmented and employees became less willing to switch. We needed one entrance, not one more isolated entrance.

Company data had to remain under company control

Control of data was a hard requirement for us. A company should define the boundary for data, credentials and execution environments before choosing SaaS, private or appliance deployment, rather than leaving each employee to decide where data flows.

What ten months of building produced

We kept releasing, testing and improving the workspace. Over time, it gained network access, model integration and cloud-drive sharing. It was not another chat interface. It put the environment employees needed to use AI and the boundaries the company needed to manage it into the same workspace.

• Private deployment. In a private deployment, data and execution environments can remain inside infrastructure controlled by the company. The exact boundary is confirmed against the company's network and system requirements.
• No lock-in to one model. The workspace manages model access centrally and allows a model to be selected for the task. The best choice today does not have to become the fixed answer next year.
• Centralized management. Usage, cost, permissions and activity sit in one place, so management can at least answer where the capability is being used and whether it is worth maintaining. See the core value of the enterprise AI workspace.
• Multi-device access. Work no longer depends on one fully configured computer. An employee can change devices and return to the same company-managed environment. See the current EachRun product scope.

How AI moves from individual productivity to company capability

This approach is best suited to small and midsize companies with flat management. When the team is compact but every person carries a real workload, the capacity added by AI is easy to see. A short decision chain also makes it easier to standardize training, permissions and working methods.

Whether AI can improve productivity is no longer our main question. The real question is whether it can move from the ability of a few people to the capability of a company. Productivity belongs to the organization only when good methods can be reused, usage can be evaluated, permissions can be managed and work can be handed over.

The wind behind AI is already rising. Our job is to make sure small and midsize companies can raise a sail of their own.

Frequently asked questions

Why does a company need a shared AI workspace?

Once several employees use AI, training, visibility, repeated setup and knowledge retention become organizational problems. A shared workspace lets the company manage workspaces, models, permissions, usage and execution records in one environment.

What becomes hardest to control after company-wide AI adoption?

The EachRun team encountered four problems: employees did not know how to use the tools well, the company could not see how AI was being used, every person repeated installation and configuration, and effective methods and context stayed on individual computers.

Which companies are a good fit for a shared AI environment?

This approach is best suited to small and midsize companies with flat management and clear workloads for each employee. Short decision chains make it easier to standardize training, permissions and working methods.

Does an enterprise AI workspace have to use one model provider?

The working environment does not need to be tied to one provider. The workspace can manage model access centrally and let the company choose models according to the task and its own policy; the available integrations still depend on the deployment environment.