If you haven’t read through a couple of agent-related posts, you probably should first read this post.

For enterprises, what we can learn from OpenClaw or OpenClaw-like tools.

First off, I think we need to take a look at the AI tools out there:

Foundation Model Platforms

  • Microsoft Copilot + Azure AI Studio — deeply integrated into Microsoft 365, Dynamics, Azure
  • Google Cloud Vertex AI + Gemini — tight Google Workspace integration, strong on multimodal
  • AWS Bedrock — Amazon’s managed LLM platform, Claude/Anthropic available here too
  • IBM watsonx — strong in regulated industries (finance, healthcare, government)

Business/Productivity AI

  • Salesforce Einstein — CRM + AI for sales, service, marketing predictions
  • ServiceNow AI — IT service automation, ticket routing, workflow optimization
  • Workday AI — HR and financial management
  • SAP Business AI — embedded in SAP ERP for supply chain, finance, procurement

Data & Development

  • Databricks — lakehouse + ML, popular for building custom AI pipelines
  • Scale AI — data labeling, RLHF, enterprise data pipelines for AI training
  • Dataiku — collaborative data science, enterprise ML workflows

Vertical/Specialized

  • C3.ai — manufacturing, oil & gas, utilities AI
  • Legal: Harvey AI, Lexis+ AI (law)
  • Healthcare: Abridge, Nabla (clinical documentation)

Open-Source / Self-Hosted

  • Ollama, LM Studio, vLLM — running models locally (like what we’re doing here)
  • Hugging Face Enterprise — model hosting, fine-tuning, private model registries

Now, I haven’t used all of the tools above, my experience with AI includes the following:

  • Microsoft Copilot
  • Google Cloud Vertex AI + Gemini
  • Salesforce Einstein
  • Claude Code
  • OpenClaw
  • A little bit of NemoClaw
  • Ollama
  • Hugging Face - only to use models
  • ChatGPT

I don’t know if it is just me, but after I started using OpenClaw and Claude Code, I really cannot use things like ChatGPT and Copilot. They are just not efficient enough.

There are a couple of processes we need to lay down for AI implementation in a company. The key pillars for AI to run smoothly in a company, I think, need a few things to be sorted out:

  1. Does the company have a good hardware architecture?

    A lot of companies probably don’t want their data to be exposed to risks. Using cloud-hosted models, those model hosting companies will see what data you have. You will probably need to set it all up locally and have your employees be able to access it.

  2. Does the overall software infrastructure in place correctly?

    For different companies, there are different platforms they utilize, it is almost like they Frankensteined different software/platforms together to build their own architecture — it could be Salesforce + ServiceNow + Dynamics 365 + NetSuite + SQL Database or a data lake. This means the AI you are bringing in needs to work with all of those platforms. How should the data flow between each platform? Even, are those platforms talking to each other?

  3. What does the data look like for this company?

    With the number of platforms mentioned above, how does the data look? Sometimes, with different platforms in place, you could have duplicate Accounts or Contacts, etc. Some people say that AI can now work with bad data and sort through it. I don’t think so — I still think clean data is key to the whole AI process; otherwise, you will still end up with garbage in, garbage out.

  4. What business processes do they have?

    Business processes are a really important part of all companies. Let’s also be honest — each company has their own mess, and those messes are just hidden where outsiders won’t see them. A seemingly seamless process from the outside could be pieced together by multiple manual processes, and it could be because your company is using all different kinds of systems that are not linked together, so they are done manually or through some half-automated process. Sorting out a legacy business process is like untangling a messy ball of wool. You need to find the end, and slowly ask more questions based on where you are currently at with the mess to come up with the next set of questions. Sometimes people confuse being good at talking to clients with getting the actual requirements — there is a difference. I honestly also think this is a space where AI can help. If you have a trusted AI already, you could have AI go through your current setup and at least tell you the current platform-related processes. By doing so, you will be able to find the gaps between platforms or within the systems. Those gaps are most likely handled manually, or someone in your company should know how that part works — your employees should be able to fill in the blanks.

  5. Which processes could be reorganized into a more streamlined process?

    Not all business processes should be automated with AI. I don’t think AI is at a point where it can make solid decisions yet, and AI cannot take on responsibilities — humans still can. As humans, we have instincts when it comes to a project or when you need to make a decision. Those instincts come from experience — you went through enough projects or processes to learn from the good and bad, and know what could potentially go wrong and prevent it ahead of time or fix the project when you see a similar trend. For those things, you still cannot replace a human yet. As for what kind of process should be automated with AI, I think we need to take it case by case. If you have a repetitive process and a pattern you can follow, then I would say try it with AI. The logic I follow is: if you can find a pattern, then it can be solved with a program/script/algorithm, which can definitely be handled by AI.

  6. Is your company’s org structure ready for AI?

    Management’s take is: we have AI now, just have AI do everything — in their eyes, AI = Efficiency. That is both true and false. We do see different companies laying off people, but maybe for different reasons. Block laid off 40% of their people because they are moving to a different org structure, one that would work better with AI. I like this read — Jack Dorsey and Roelof Botha walk you through how, two thousand years ago, an army was formed and commands flowed to make it more efficient, and how that process has actually been adopted by us until now. Now with AI, how would everything work more efficiently? Sure, with AI, how information flows is different now. For each company, it is a different case and needs to be dealt with differently. I don’t think, as a company, you should lay people off altogether before you have a clear picture of how you would like the company to run with the remaining people — and what if some of those people have the most insight into your processes? AI doesn’t totally mean efficiency immediately; it is a process.

With all that being said, you can tell that having AI in your company is not as simple as buying a tool and encouraging your employees to use it. I would like to propose a different approach — for example, let’s say you are going to implement OpenClaw or NemoClaw, or even Claude. You should have a centralized place to host those agentic tools and assign them to your employees. Each employee will have their own AI assistant through the hosted agent tool, their own dialog with AI, and their own memories, and each agent can define its own personality, identity, etc. There should be a couple of employees who are superusers, and those superusers can help manage the memories. Depending on the chats/prompts from each user, superusers can help determine what skills the company needs that could potentially benefit everyone. Each worker would have their own workspace and even their own skills as needed. This is just an idea for now, and it is based on my experience using OpenClaw.

It is one thing to encourage your employees to use AI, and another to use AI more efficiently. What I also found is that treating any AI tool as a co-worker — working through a problem with your AI assistant — is where the value of AI actually shines!

How is your company using AI? Have you noticed how your coworkers are using AI? Hopefully, this article helps you think about your own AI strategy!

Happy coding! Happy working with AI!!