The Robotics / RPA / AI / ML / Data / IaaS Stack

Unpacking the technology stack from robotics through robotic process automation, AI and Machine learning, data and infrastructure.

The Robotics / RPA / AI / ML / Data / IaaS Stack

We’ll be covering a lot of ground in the upcoming series of posts so I wanted to lay out a bit of a map of where we will be going over time.

Like in the early days of cloud computing, there is much confusion around Robotics, RPA, AI, ML, big data, and even infrastructure. I think that the best way to think about it is hierarchically. Robotics sits on top of AI but it also has other elements such as fleet management and mobility and general dev/ops tooling that is unique. AI is made up of a number of elements, one of which is Machine Learning methods. Deep learning, that magical set of techniques that can find tumors in x-rays and translate hundreds of languages, is a smaller set of tools and techiques inside Machine learning! And all of the above sits on vast amounts of data, stored in a variety of databases and analytics platforms, all of which are sitting on top of the raw infrastructure (whether cloud-based or on-premise.)

Here is a quick overview of the entire stack – Applications & Use Cases, Data & Code, and the infrastructure to support it all.

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I’ll expand on each layer of this below in order of depth.



Robotics requires not only AI but also mobility and overall development and operational tooling. This is a VERY lightweight map – I’ll develop this more fully later. The key is just to understand the overall hierarchy. Deeper levels of each sub-map will be developed over time.

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AI is one of many components within Robotics

Robotic Process Automation

Robotic Process Automation is a terribly named set of practices that does not actually use Robots. Or at least not physical ones. It is really the next generation of what we used to call BPM (Business process management) – essentially the automation of back-office systems, often by tying together legacy systems. It is based on data and automation but can also be augmented with AI, hence I’m adding it to this hierarchy.

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RPA does not necessarily require AI but AI can provide additional leverage

AI Use Cases

Use Cases by Sector

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AI will likely impact every sector and every size of organization

Use Cases by Function

AI will impact almost every major business function. Here are a few examples:

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This is a small sampling of use cases in each functional area


Artificial Intelligence Platforms

Think of AI as yet another development environment, like database development or app development. It’s not useful until you have a function and a sector and a business problem to apply it to. Below you can see a few representative micro-applications built on top of the core services which are in turn built on the underlying frameworks.

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All of these systems require data. The type and volume may vary but I’ve put some representative sizes below that seem to appear in many discussions.

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There is a reason that the phrase “Data is the new oil” exists – it’s key for AI training

Databases and Analytics Systems

Cloud-hosted databases, DB-as-a-Service, and even Data warehousing as a service (DWaaS) have exploded in the last 10 years. There are now a lot of different ways to store, transfer, and access data.

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Beware of data gravity. It’s easy to build data up in one spot and then it costs to get it back out!


Phew! Finally we are at the bottom of the stack. AI sits on big data and big / cheap / fast compute and storage. This is what the Infrastructure stack looks like.

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Now that we have a rough framework, this should make future posts make more sense and help people see the forest for the trees. I’m sure that there are many ways this stack could be drawn, but this is my attempt to bring some hierarchy and order to it for the purpose of this series of posts.

If you think I’ve missed something or am wildly wrong, let me know in the comments! And thanks for following along on this AI series.

And if you'd like to start at the beginning of the series, you can do so here.

Thanks to the many people whose work I used to assemble this. Any errors are most likely mine, not theirs. You can some of the sources here below: