AI-Supported IP Portfolio Strategy and Long-Term Management

IP portfolios that deliver real value rarely arise by accident. They reflect deliberate choices about which inventions to protect, which to keep as trade secrets, and which to let lapse as priorities shift. AI-supported analytics are increasingly part of those choices, but the strategic judgment remains firmly human. This course explores how the two work together in long-term IP strategy. You will work through written scenarios that look at portfolio decisions across multiple years, including new technology areas, competitive shifts, and budget constraints. The course also addresses the governance and communication work that turns analytics into board-ready insights. What you'll learn: - Use AI-supported analytics to map portfolio coverage against current and emerging business priorities - Identify white space, competitive concentration, and strategic gaps with structured analyses - Plan portfolio building strategies including filing decisions, family expansion, and trade secret tradeoffs - Run pruning decisions on aging or off-strategy assets with clear cost and risk frameworks - Communicate portfolio insights to executives, boards, and product leaders using accessible visuals and narratives - Govern AI-supported IP analytics including data sources, model assumptions, and ongoing validation The course begins with portfolio mapping, moves through building and pruning strategies, and closes with governance and executive communication. A capstone written exercise asks you to draft a three-year IP strategy supported by AI analytics for a specific organization scenario. This course is designed for IP leaders, chief legal officers, and technology integrators bringing AI tools into established IP teams. No prior AI experience is required. The course is informational, respects the role of qualified attorneys in any consequential decision, and does not provide legal advice for specific situations.