Key takeaways
- Why AI adoption stalls: a European Parliament survey of barriers spans strategy, collaboration, change resistance, time, and budget, alongside a widely cited stat that AI project failure runs above 85 percent
- The three steps behind any AI app: building the predictive model, deploying it into an IT environment, and exposing it through a business facing interface
- How H2O Wave, an open source Python and R SDK, lets data science teams build web apps without touching HTML, CSS, or JavaScript
- A live build of a telco churn app: train a model in H2O AI Hybrid Cloud, deploy it through MLOps, then wire it into a Wave interface with a dropdown, a customer profile card, and a what if price slider
- A second live app for predictive maintenance, monitoring engine sensor data and using model interpretability to flag which signal is driving a likely failure
- Where Wave fits relative to MLOps, and how to run it locally, on premises, or on H2O AI Hybrid Cloud for enterprise security and single sign on
Overview
This session traces AI adoption from its economic potential (PwC forecasts $15.7 trillion in added value over the next decade) down to the reality that more than 85 percent of AI projects still fail to deliver business outcomes. H2O.ai's Asgar Ali walks through the barriers organizations hit, from lack of strategy and cross team collaboration to complexity, time, and budget, then shows how automation and low code tooling close the gap. Using the open source H2O Wave SDK, he builds a customer churn app and a predictive maintenance app live, going from a trained model to a business facing interface in minutes.