Brock Murray

expert panel

AI observability is quickly becoming one of the most consequential shifts in enterprise AI—not because it adds more dashboards, but because it exposes how AI systems actually behave inside real business workflows. For executives, that visibility is both a breakthrough and a burden. It reveals model performance, data quality, user interaction patterns and system drift in real time, yet it often arrives in a form that is fragmented, technical and difficult to translate into decisions that matter at the board level.Organizations are rapidly scaling generative AI and machine learning systems across core operations, but many are struggling to operationalize oversight in a way that connects technical signals to measurable business outcomes. The result is a widening gap between AI capability and executive clarity—where systems are increasingly powerful, but not always understandable in business terms.Members of the Senior Executive AI Think Tank—a curated group of leaders in machine learning, generative AI and enterprise transformation—argue that the issue is not a lack of data. It is a lack of translation. AI observability, they note, only becomes strategically meaningful when organizations move beyond monitoring and toward decision-making frameworks that connect model behavior, risk signals and user impact directly to business KPIs.In the sections that follow, Think Tank members break down how organizations can close this gap in practice—from building operating models that turn observability into action, to identifying behavioral drift before it becomes business risk, to redefining governance so insights don’t remain trapped in technical teams. They also surface the most persistent obstacles executives face today—including signal overload, fragmented ownership and the absence of shared language between business and technical stakeholders—and offer concrete ways leaders can turn visibility into decisions that drive measurable value.

expert panel

As organizations race to develop generative engine optimization (GEO) strategies, many are approaching AI visibility the same way they approached search engine optimization over the last two decades: Publish more content, optimize keywords and try to improve rankings. Yet the rise of generative AI is changing how information is discovered, evaluated and surfaced.Members of the Senior Executive AI Think Tank—a curated group of executives, technologists, AI practitioners and digital transformation leaders—argue that many organizations are operating under flawed assumptions about how generative systems work. Their collective message is strikingly consistent: AI visibility is less about gaming algorithms and more about establishing trust, authority and credibility across the digital ecosystem.According to a 2024 Gartner forecast on generative AI and search, traditional search traffic is expected to decline significantly as users increasingly rely on AI assistants and conversational interfaces to find information. As AI-generated responses become a primary gateway to information, organizations must rethink how they establish authority online.The experts below explain why many GEO assumptions are misguided and where leaders should focus their efforts instead.