Artificial intelligence (AI) has increasingly moved to the core of strategic decision making across industries globally and remains a hot topic for the coming year. AI serves as a basis to optimize processes, personalize the customer experience and drive competitive differentiation and we are still learning about the full potential of Generative AI and Agentic workflows. However, the transformative potential of this technology can remain untapped if businesses and AI practitioners alike overlook the important concept of trust in the end-to-end process starting with the data right through to the actions taken.
Many organizations across various industries believe that deploying a highly accurate AI model, built on clean and organized data, is sufficient to create significant value when it is deployed throughout the business. However, that is not always the case and the issue here is not one of data cleanliness, nor risk of bias, lack of innovation or privacy, rather it is a lack of trust in the output from the AI model itself and this is often a barrier that results in lack of return on investment (ROI).
