Ensuring Successful Machine Learning and AI Adoption in Enterprise


Application of decisions driven by artificial intelligence (AI) spans across various departments in an enterprise / Photo by: ArtisticOperations via Pixabay


Application of decisions driven by artificial intelligence (AI) spans across various departments in an enterprise, which seeks the guidance of a chief information officer (CIO) in its adoption and execution throughout the organization in myriad use cases in order to drive better results across the business landscape.

Collecting information, analyzing noise, and making rear-view mirror predictions of succeeding procedures are not enough, according to tech news site RT Insights. It added that the CIO should help the enterprise in putting data to better use, as well as continually improve decision-making as per machine learning from previous outcomes.

One of the ways to ensure successful AI adoption is to make sure that more business stakeholders—particularly analytic gurus—in the organization have access to it. This way, the tech news site said it will not leave out the knowledge of domain experts, which is crucial to the success of any initiative. Programs that put AI tools directly at the hands of stakeholders were found to bear great success.

Another way is to prioritize and focus on the incorporation of AI in the organization's digital transformation journey. Integrating the technology into enterprise doesn't happen overnight, especially since the main objective is to identify a faster, smarter way to get from data to action.

RT Insights noted that the CIO can offer a lot of experience with iterative processes—the base of agile development methodology. They can also support the organization in prioritizing AI implementation, concentrate on producing success in such areas, and provide consistency throughout various departments.

The success of AI adoption also anchors on the business' understanding of the technology and its system. It's crucial to comprehend why machines act on certain tasks or make certain decisions, as well as on the basis that led up to that decision.

The tech news site stated that explainable AI should generate more transparent models and retain a significant level of predictive accuracy, all while allowing users to understand, have trust, and be able to manage the system. It added that this is specifically important when AI-driven decisions will affect consumers.