Data pillars 2 – cover example of connecting data pillars and self regulated AI

Hi, we will continue today talking about the pillars, data pillars for the AI.

However, whatever I mentioned last in the last video about those six data pillars for AI, to be honest, there is no easy tool today in the market that can really help the people and executives and CEO for example to make the data ready for AI in the organization.

So, I would recommend every CEO and chairman, first, if AI or IT team is not reporting to you, this is a very wrong step.

So, AI and IT guys should report directly to the CEO and dot lines to the chairman. That's a very important department and very, very important structure to do.

The second: Now, we talk about those pillars and we have to have like a mapping, a mapping of all those data exist, let's say for structured data you have ERP system, you have CRM system, for unstructured data, I put full planning how to do structured or build architecture for unstructured data like the folders for the files, folder for whatever related for each department.

For example, if we're talking about ERP system, we're talking the sales department or the sales modules exist in ERP, you need to have a parallel one in unstructured data, like a folder related to the sales team and the sales module in in ERP.

That's the AI in the future can connect, for example, market research, inside the market ways, whatever the sales team is doing in the sales area, as a sales department.

And then the third area, or the third pillar, which is the media also, you have like videos or images related also to the sales team, you can connect all those three domain-specific data together.

And then you can if you find a dashboard or KPIs metrics related also to the sales team, you can connect those four pillars.

I can just show you how AI can connect those. Now, if you if you go AIoT, maybe you will not find very big relation between the sales team and IoT, or between networking.

However, AI can find for you a relation between, for example, sales department and networking.

However, this is not our subject, but our subject is as a CEO or, you know, the leader, you set with the IT team, the IT IT team, and I recommend if you have data analysis team, data analysis team, upgrade them to AI, give them courses to be in AI expert.

Now, you have to find or to see as a CEO or leader the mapping of all the data exist inside your organization ready, ready for AI.

Means ready is not to make it public, it's just to have it in a proper structure that allow the AI to access it easily and then, you know, to do a processing.

Now, I and I talked about very important subject, which is AI or self-regulated AI.

No one in the future, and I'm assuring you, today networking is not ready for AI, and I talked about this, and also when you have this huge amount of data and you need the AI to handle it, AI need to be monitored and controlled by other AI.

And for example, you have in the future you'll have all the networking devices with coming with with the AI chips like servers, like switches, like Wi-Fi.

Who is monitoring this?

So, that's why when I talk about the architecture design of the AI knowledge base, I also put another layer of AI, or self-regulated AI, to monitor and control the AI around organization.

That's very difficult, it's very complex to do, but you should consider this as one of your strategy, how I can monitor and control whatever AI is running in my organization.

Am I assuring you in future in every single pillar, and even sub-pillars of those, you will have multi-AI.

For example, you have like AI for ERP system, AI for CRM system, AI for e-commerce system, whatever you have it in a system, and then you have AI for the document management, so you have AI everywhere, and you need to monitor and control those AI fragmented across your organization with a single AI.

We call it self-regulated AI, it's it's like monitoring the AI because no single human can monitor all those, you know, huge AI across your organization.

So, you have to have this in your mind. If you don't have it today, or the vendors they don't provide it, I'm sure they will provide it in future.

You have to have the dedicated team to protect your data, to protect your organization from hacking, which we call it self-regulated AI with a human in the loop, meaning the human experience with the AI that you can help your data.

Now, data centralization for AI, decentralization for AI, is different subject, we can talk about this in future videos.

Pero I hope I can now I give you some more information about why I gave the six pillars for building the knowledge base of AI.

And thank you.