Enterprise AI deals require navigating a complex web of stakeholders. Master the art of identifying champions, blockers, and the right meeting sequence across industries.
Controls IT budget and infrastructure decisions. Cares about IT alignment, security posture, and enterprise architecture fit. Often the final gatekeeper for technology purchases.
Evaluates technical feasibility, integration complexity, and architecture compatibility. Wants to understand APIs, data pipelines, and deployment requirements.
Focused on operational efficiency and workflow impact. Measures success in time saved, error reduction, and throughput improvement. Often the strongest champion when pain is operational.
Demands cost justification and clear ROI. Wants payback period, TCO analysis, and risk-adjusted returns. Can veto deals that lack financial rigor.
Owns data governance, quality, and architecture. Concerned about data lineage, model training data, and compliance with data regulations.
Drives AI strategy and vendor evaluation. Evaluates model performance, explainability, and alignment with AI roadmap. Often the internal champion for AI initiatives.
Reviews liability, compliance requirements, and audit trails. Concerned about AI-specific regulations, data privacy (HIPAA, GDPR), and contractual liability for AI decisions.
Manages contract terms, vendor comparison, and procurement process. Focuses on pricing structure, SLAs, and competitive benchmarking.
Selling AI agents exclusively to technical buyers (CTO/CIO). The best AI deals are championed by operators (COO, Head of Operations) who feel the pain daily, with technical stakeholders validating feasibility rather than driving the purchase.
For each prospect company, select the right buyer roles to engage, identify the champion, and spot the blocker.