AIDirections developed an AI Readiness Model (AI RM) to support organizations in identifying their status of readiness for AI projects, which is a crucial factor to success. This model is based on three contributors, which might be in place for an already advanced organization but might not be in place for everyone.  These contributors to successful AI projects are:

·          Digital Transformation - Digital transformation refers to the changes associated with the application of digital technology in all aspects of an organization; the transformation stage means that digital usages inherently enable new types of innovation and creativity in a particular domain, rather than simply enhance and support traditional methods.

·          Data Governance – Data governance is the data management of all the data which an organization has to ensure that high data quality exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency, data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization.

·          AI Readiness – The AI readiness of an organization is the ability and intend to develop, establish and implement a set of processes for the success use of AI, such as managing the risks and opportunities associated with AI, achieving an AI culture and having an AI strategy.

AIDirections’ model for AI Readiness assesses the maturity of the pre-requisites Digital Transformation and Data Governance, and some basic concepts related to AI Readiness.

Without these cornerstones being in place, implementations of AI are likely to fail or, at least, not being optimally successful. Experience has shown that such cases do happen, therefore it makes sense to measure the aforementioned three aspects to identify how “AI ready” an organization is. Once an organization has achieved maturity in these areas, AI projects have a higher likelihood of succeeding.