The Build Versus Buy Question: Making GTM AI Agents Drive Revenue

Revenue and go-to-market leaders face mounting pressure to convert AI experiments into predictable, revenue-generating workflows while keeping cost and engineering overhead under control. Teams report pilots that show promise but never scale, AI agents that hallucinate because account and contact records disagree, and rich signals such as call transcripts and meeting notes that never reach a usable form for automated reasoning. Given these operational realities, what is the clearest path from a promising experiment to repeatable production value?

Organizations are responding by creating a stronger data and context foundation that sits between raw AI models and front-line workflows. Some are consolidating entity resolution and hierarchy logic so agents reason from a single trusted profile, others are operationalizing unstructured signals into indexed, queryable context for agents and reps, and a growing number are weighing managed GTM workflows against in-house builds to reduce ongoing engineering maintenance. These approaches are framed as data governance, context orchestration, and pragmatic build-buy economics rather than a single technical fix.

Join your executive peers to explore practical steps for resolving fragmented entities, capturing unstructured buying signals, and matching strategy to available engineering capacity so pilots become repeatable workflows. Bring examples of the pilot barriers you face and be prepared to discuss the trade-offs you will accept around speed, control, and cost to get agents working for pipeline. The sponsor will be present to listen and share operational lessons where relevant.

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