Sewer rehabilitation is a costly challenge for cities like Berlin, with annual investments exceeding €100 million, compounded by aging infrastructure and low replacement rates. Traditional CCTV inspections, used since the 1980s, face limitations in data completeness, accuracy and automation. To address this, Kompetenzzentrum Wasser Berlin and Berliner Wasserbetriebe developed SEMAplus, a suite of data-driven tools modernizing sewer asset management. The system uses machine learning to prioritize inspections and forecast long-term network conditions. This abstract highlights advancements in deterioration modeling and innovation pathways for the digitalization of sewer management. As workforce shortages, budget constraints, and sustainability goals intensify, these innovations are crucial for optimizing investments and strengthening sewer system resilience.