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Learn how to implement AI in business-critical systems through a detailed case study of the Financial Times' successful machine learning initiatives that more than doubled projected lifetime revenue per customer. Explore the real-world architecture, trade-offs, and unexpected challenges of integrating AI into subscriber acquisition and retention systems at a major media organization. Discover the decision-making process behind choosing different AI providers and model architectures for acquisition versus retention, including partnerships with Sub(X) for paywall personalization and VectorLabs for churn reduction. Examine the technical implementation details of integrating predictive models into user journeys on FT.com, including the infrastructure considerations for running AI models both externally and internally. Understand how to navigate unintended consequences such as risks to advertising revenue and learn from mistakes in initial handling approaches. Gain insights into the unique operational challenges of maintaining AI systems in production, including strategies for adapting to disruptions like price changes and monitoring requirements during model training. Master practical techniques for evaluating continuously evolving AI models and discover how to adapt testing approaches to handle the explosion of possible user journeys that AI introduces. Acquire strategies for minimizing risk and building stakeholder confidence when deploying machine learning in critical business contexts, along with lessons learned about stakeholder dynamics and the surprising complexity of real-world AI applications.
