  The data revolution has generated a huge demand for data-driven solutions.
This demand propels a growing number of easy-to-use tools and training for
aspiring data scientists that enable the rapid building of predictive models.
Today, weapons of math destruction can be easily built and deployed without
detailed planning and validation. This rapidly extends the list of AI failures,
i.e. deployments that lead to financial losses or even violate democratic
values such as equality, freedom and justice. The lack of planning, rules and
standards around the model development leads to the ,,anarchisation of AI".
This problem is reported under different names such as validation debt,
reproducibility crisis, and lack of explainability. Post-mortem analysis of AI
failures often reveals mistakes made in the early phase of model development or
data acquisition. Thus, instead of curing the consequences of deploying harmful
models, we shall prevent them as early as possible by putting more attention to
the initial planning stage.
  In this paper, we propose a quick and simple framework to support planning of
AI solutions. The POCA framework is based on four pillars: Performance,
Opaqueness, Consequences, and Assumptions. It helps to set the expectations and
plan the constraints for the AI solution before any model is built and any data
is collected. With the help of the POCA method, preliminary requirements can be
defined for the model-building process, so that costly model misspecification
errors can be identified as soon as possible or even avoided. AI researchers,
product owners and business analysts can use this framework in the initial
stages of building AI solutions.
