Once this has been established and agreed , they must ensure data quality and accessibility . This involves implementing data governance practices , validating data sources , and establishing robust data integration processes to ensure access to relevant , accurate , and up-to-date data .
Promoting a collaborative approach is important too . If organisations can foster collaboration between the teams involved , they can ensure that AI models and analytics align with the business need and objectives rather than being unaligned . This interdisciplinary collaboration will help in developing insights that are reasonable , meaningful , and actionable . This can be achieved via small , focused AI projects and iterating based on feedback and insights too .
After doing this , organisations can gradually scale up by incorporating additional data sources and expanding the scope of analytics applications . By following these practices , organisations can effectively integrate AI into their decision-making processes , leading to data-driven insights and informed strategic decisions that will drive business success and competitive edge .
Royles ends by saying : “ As a final cautionary note , if an AI service is too slow , inaccurate , or responds in ways that don ’ t add value , users will quickly lose trust , and it will be very hard to recover .” aimagazine . com 97