Demand and account-based marketing (ABM) leaders are responsible for driving growth. Across their growth-focused responsibilities, they share a common challenge: delivering relevant, timely experiences that create value for buyers and customers while advancing business outcomes.This quarterly peer discussion brings together these leaders to exchange perspectives, compare approaches, and learn from one another’s experiences. Facilitated by a Forrester expert, each session focuses on a timely topic while creating space for candid conversation, practical problem-solving, and the sharing of lessons learned among peers facing similar challenges. Discussions may explore topics such as buying groups, adaptive programs, revenue process optimization, personalization, customer engagement, content strategy, answer engine optimization (AEO), AI, and evolving go-to-market practices, depending on the priorities most relevant to participants. Upcoming Discussion: How AI Is Reshaping Demand And ABM Strategy in 2027 As organizations develop their 2027 plans, demand and ABM leaders are moving beyond AI experimentation to make strategic decisions about where to invest, how work gets done, and how demand is activated and measured. The conversation is shifting from whether to adopt AI to how it can drive growth by improving the effectiveness, efficiency, and scalability of demand and customer marketing efforts.Join your peers to discuss and compare how AI is influencing 2027 priorities and investments, where leaders are placing their bets, and what changes they're making today to drive future growth.This conversation will be hosted by Renee Irion and Naomi Marr and will include open discussion with participants, so please bring your questions, experiences, and perspectives to share with the group.Starter Questions For Discussion:Where is AI having the greatest impact on your 2027 demand and ABM priorities, investments, and planning?Which demand generation, orchestration, or activation processes are you redesigning because of AI, rather than simply making them more efficient?What changes to your operating model, skills, data strategy, or measurement framework are necessary to translate AI experimentation into measurable business outcomes?