The Challenges of Current AI Architectures

Despite the hype surrounding AI and its potential to transform businesses, only 12% of initiatives achieve superior growth and business transformation according to a report by Accenture. The main limitations present in AI and ML initiatives today include outdated infrastructure and architecture, evolving consumer expectations and business models, an approach focused on predicting individual behavior based on broad demographic data, and the analysis of batch processed historical data rather than real-time inputs. These issues result in increased time, costs, underperforming models, or models that don't work at all. To overcome these limitations, businesses should consider using real-time AI to deliver more intelligent applications with accurate predictions at the right moment for maximum business impact.