A shared language to describe, visualize, and evaluate

new models of artificial intelligence.























Why a Canvas?


At this point, it is good that we consider the need to have a model that allows us to share the creation of artificial intelligence within the business.

When we talk about intelligence in a business, we are dealing with a concept, which is naturally collective: The intelligence of a business is formed by the sum of the intelligences of the people that make it up, whether to a greater or lesser degree.

So, when a company decides to create artificial intelligence, it is creating / transforming something that is built collectively.

Therefore in the creation process, your design must have a common language that allows its sharing, this is aibizfy Canvas.

Value Blocks

These are the Blocks where we describe the value of AI

These are the first blocks to focus on.


  • The block of economic values will help us describe the economic values that this AI will generate, both in terms of savings and in terms of economic profitability.


  • The Customer / Social Block allows us to describe the value that our clients and / or society should perceive to a greater or lesser extent.


  • The Organization Values Block helps us describe the internal value of the company, since intelligence is both an external and internal concept for the business.

Data Blocks

They are the Blocks where we describe the input and output data

These are the most important blocks for technical-technological validation.

  • The Data Source Block should describe the sources of the data, not so much the data. Data sources can be: Customers, Factory sensors, Twitter, Cash registers, etc …
  • The Raw Data Block, here we do have to start describing what the type of data we need should be. For example, if they are textual with natural language, or if they are images, or if they are numerical data, etc …
  • The Processed Data Block is part of the input data of the AI, since for example, we could have a Raw Image Data, of which we are only interested in knowing if there are people, therefore we are only interested in a quantitative number of people that another specialized module can give us.
  • The Outcome Data Block, here we describe what data we need to come out of the AI.

Social Blocks

They are the Blocks where we can see the social fit.

Artificial intelligence is a discipline where the focus of ethical and regulatory discussion has currently been placed, since it is expected to decisively affect the evolution of knowledge and consequently humanity.

  • The Regulation Block. Today there is a strong regulation on the right of privacy and therefore on the ability to capture data, so in this block we will have to describe the regulations that could affect it.
  • The Ethics Block. Taking ethics into account is a strategic factor in tackling AI, since ethics is often ahead of regulation. Thus, taking ethics into account can help us prepare for possible regulatory changes or also to make decisions more aligned with social thought.
  • The Social Value Block, we have already described within the Values block, but it is important to take into account its relationship with the two previous blocks.