Artificial intelligence (AI) is a high-stakes business priority, with companies spending $306 billion on AI applications over the past three years. Companies that implement AI in business can almost triple the return on their investment. But too many companies fail to achieve the expected value. one Effective scaling of AI in the long term will require the professionalization of the industry. Stakeholders – from practitioners to private and public sector leaders – must come together to clearly define the roles and responsibilities of AI practitioners; require an appropriate level of education and training for said practitioners; define processes for developing, deploying and managing AI, and democratize AI literacy in the enterprise. By formalizing AI as a trade with a common set of norms and principles, companies will be ready to benefit more from AI. They will be created to ensure clear accountability, which in turn will help avoid risks such as bias, underdelivery to customers and other unforeseen consequences.

This is why in professional fields such as medicine, construction, and even food service, there is an inherent level of trust between customers and the businesses (or practitioners) that make up the industry. This trust is born from standards that set expectations for all involved. For example, you realize that architects, electricians, and other building professionals know how to build houses. They have received the necessary training and understand their roles and responsibilities, safety standards and protocols to be followed throughout the construction process. It is unlikely that you would entrust the construction of your house to a “citizen architect”, just as you would not visit a “citizen doctor” when you are sick. By formalizing AI as a trade with a common set of norms and principles, companies will be ready to benefit more from AI. However, more and more companies are supporting their core data science teams with “citizen data scientists” (or people who build models using predictive analytics but whose roles are outside the realm of data science) without providing them with the necessary barriers and standards.

to achieve success. . Even among trained and certified data scientists, there are different levels of standards. In addition to the need for formalized and standardized learning, organizations may find that these professionals work in silos and fail to deliver on the promises of AI. Real value can only be realized when trained AI practitioners work hand in hand with business to achieve their organization's goals, and these interdisciplinary teams are guided by standards, rules and processes. Only then will enterprises be able to safely and predictably deliver the final product or service, thereby earning customer trust and raising the quality standards for innovation and applications. .