Bringing Artificial Intelligence to Developers and Organizations

AI is leading in the transformation of next-wave innovations that are bound to change the world. AI organizations and other large firms across the globe are empowering other businesses to apply artificial intelligence to engage customers, optimize operations, empower employees, and transform products. To make this possible, we must put these principles to use:

•Boosting productivity among data scientists and developers by empowering them to develop AI solutions faster.
•Enabling the large-scale deployment of AI solutions alongside existing processes and systems.
•Ensuring businesses can develop with the assurance that they own the data and can control it.

Making the Shift to AI

Most companies expect AI technology to give them immediate and high returns. In their quest to get their businesses up and running, they invest a lot of money in data infrastructure, data expertise, and AI software tools. The pilot stage of the product may bring about some returns but in the long term, the big wins that were expected never come to fruition. Despite acquiring the best technology and artificial intelligence developer, it is vital to adjust to a company's structure, culture and ways of operation for the broad adoption of AI. In order to upscale artificial intelligence, it is important to:

•Encourage collaboration among different disciplines within the organization - Bringing people within the business operations department to work with analytics experts ensures broad adherence to organizational priorities, which significantly increases the chances of success. 
•Shifting the focus from leader-driven to data-driven decision-making - The broad adoption of artificial intelligence helps employees up and down the hierarchy to make sound and individual judgments using recommendations by the algorithms. For this approach to work, employees at all levels should have trust in the algorithm's suggestions and at the same time have the capacity to decide individually. Also, the conventional top-down strategy must be abandoned since it limits the application of AI. 
•Transitioning from risky to adaptable methods - AI applications rarely come with all functionalities. However, through testing and learning, AI can fix mistakes hence reducing the fear of failure. Early access to user feedback enables firms to do corrections before they become unmanageable and incorporate the updates to the next version.

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