Every major cloud vendor is embedding AI assistants into enterprise suites with wildly different cost structures and contractual dependencies. This paper provides the head-to-head procurement analysis — focused on total cost, contract terms, data governance, and vendor lock-in — designed to create negotiation leverage with your primary vendor.
Per-seat cost modelling across three deployment scenarios (1,000 / 5,000 / 20,000 seats) including prerequisite platform costs, implementation, and 3-year projections. List price comparisons understate the real differential by 40–60%.
Side-by-side analysis of commitment structures, auto-renewal provisions, mid-term seat reduction rights, exit costs, and data portability terms across all three vendors. Identifies where each vendor's default terms are weakest.
Deep dive into data processing agreements, model training exclusions, data residency guarantees, retention policies, and subprocessor transparency. Highlights the governance gaps that most procurement teams miss.
How each vendor ties AI assistant licensing to broader platform commitments. Dependency architecture diagrams showing how Copilot deepens M365 lock-in, Gemini bridges Workspace to GCP, and Q connects to AWS EDPs.
A step-by-step multi-vendor evaluation framework — including RFI templates, PoC design, and term sheet collection — that generates 18–40% pricing improvement from your primary vendor without requiring a platform switch.
Actionable recommendations for procurement leaders: separate AI from platform renewals, negotiate DPAs before signing, deploy to subsets before committing, and run structured competitive evaluations to maximize leverage.
You don't need to be willing to switch to win. You need to be credibly able to switch. That credibility is built through process — RFIs, PoCs, and alternative term sheets — not through verbal threats.