According to Statista, businesses spent USD 1.31 trillion globally on digital transformation technologies and services in 2020.
FREMONT, CA: Organizations worldwide are pursuing digital transformation to boost productivity, improve corporate operations, boost marketing efforts, and acquire expert talent, among other things. Statista claims that businesses spent USD 1.31 trillion on digital transformation technologies and services in 2020. In 2025, they are expected to spend over USD 2.8 trillion on digital transformation. Although digital transformation can help an organization achieve excellent outcomes, business executives must plan for successful change management. An organization trying digital transformation or any other substantial change has a high likelihood of failing if it does not have a robust change management strategy in place. For change management, firms can take advantage of corporate AI technologies.
Enterprise AI can assist in developing effective change management techniques that enable staff to adjust to impending changes. Change managers can use AI to collect critical data about employees and develop analytics to design the best plan. Organizations can train machine learning models that can predict change learning initiatives using the data acquired from employees. As a result, with the help of corporate AI solutions, firms can better manage the human aspect of change.
Many firms manually collect information about earlier adjustments, as well as their success and failure. This strategy, however, can prove unproductive because manually recorded data may contain biases and inaccuracies. In addition, positive or bad episodes that occurred after the change may not be related to the change itself, but the data collected may indicate otherwise. Machine learning models can correctly assess the impact of change in a company. Enterprise AI systems can collect meta-data that reveals the link between evolution and specific problems in a company. The system calculates a similarity score to see if a change had an unintended consequence.
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