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What Is An AI ​​Canvas?

Digital transformations in companies cannot be understood without data processing. The following article talks about the AI ​​Canvas tool, proposed in the book Predictive Machines, to facilitate and succeed in AI projects.

The last few years have seen a change in the pace at which society was used to operating. This acceleration has meant a massive increase in the uncertainty that we live in and, its correct management, one of the most demanded so-called soft skills.

To know how to manage this environment, new strategic tools have been developed that streamline and adapt companies and businesses to function more optimally. One of these tools is the Business Model Canvas, which allows you to define your business model from a much more scientific point of view: segmenting the idea into each of its simplest parts and assigning hypotheses to each of them. Beliefs should be iterated as quickly as possible with the market to validate or pivot.

In the book Predictive Machines: The Simple Economy of Artificial Intelligence, the authors propose a new model to analyze any artificial intelligence model or project-based, also, on its division into simpler parts (along with which is called workflow decomposition). In this way, you can understand, build, and evaluate any AI tool much more straightforwardly. What is this canvas made of?

  • Action: What do you intend to do? What is your goal with that project, with that idea?
  • Prediction: What do you need to know to achieve that goal? What do you need to decide for the ACTION to be successful?
  • Judgment: How do you assess each result and error of your models? What costs do they entail in your business? What is defined as success? What is defined as a failure?
  • Result: What are the success parameters of the task you are measuring?
  • Input data: Based on the above strategic points, what data do you need to know or apply in your models to achieve the action?
  • Training: Based on everything defined above… How will the training of the model be?
  • Feedback: How can we use the algorithm’s results to feedback and improve the previous process?

In short, you need to break down tasks to understand where and how these AI models should be used. The canvas allows us to represent the decomposed processes in detail accurately.

Data-Based Decision Making

Daniel Kahneman (2002 Nobel Prize in Economics) speaks in his book Thinking fast, thinking slowly about the two systems of human thought: intuitive, emotional, and more quickly, and rational, slower, and logical. Under this premise develops a series of cognitive biases typical of humans, which limit and condition our decisions. That’s where data-driven decision-making comes in.

The speed at which the world moves and the uncertainty to which society is subjected has opened the way to a new paradigm that allows reducing this variance in decisions, the result of the fallacy of reflection and human bias and that is leading (and will lead even more) to technologies such as artificial intelligence, to be a fundamental value in any company that wants to survive these changes.

To achieve and put these processes on track, we have developed a series of fundamental points in any company to transform and objectify its decision-making:

  • Identify business objectives: At this point, the work of the company’s management is essential, defining the most specific goals and performance metrics and indicators (KPI), always with a view to the business. These indicators must be measurable and objective. From there, you will begin to develop a data-driven strategy.
  • Survey teams to discover key data: Interview critical people to understand short, medium, and long-term objectives. If there are differences between those defined by the management and the key people within each team, we will have to go back to point one.
  • Collect and prepare the data you need: Here begins the technical task. Once the business objectives have been defined, it is time to access the data and prepare it for analysis. This is possibly the point with the highest workload. In general, companies store any information without knowing if they will ever use it. In addition, generally, this information does not meet the quality and governance criteria of the data, both essential to meet business objectives.
  • Explore the information: Through statistical tools, visualizations, and analytical modeling, we can extract objective information that allows us greater control of each of the company’s processes.
  • Develop the information: Contrasting the information resulting from the exploitation of the data is essential to contrast our business hypotheses, which, in many cases, are biased.
  • Take requests from the information and share them: Once the data and hypotheses have been verified, you have to take action. Improve processes, optimize resources and start the cycle again by reformulating new indicators that allow the data wheel to continue turning.

As you can see, a data-based strategy allows your business to make better decisions and optimize resources. In a profound sense, we are experts in the data cycle and applying artificial intelligence models and digital twins to improve your profits.

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