Navigate intellectual property ownership in AI agreements with practical clause language and negotiation strategies covering training data, outputs, and model customizations.
Learn how to calculate the true cost of AI investments including hidden costs in integration, training, change management, and exit—not just the sticker price.
Identify warning signs early during AI vendor evaluation. Covers security evasiveness, unrealistic claims, and financial instability indicators.
Practical negotiation tactics for AI contracts covering pricing, data rights, liability, and exit provisions with decision framework.
Navigate AI-specific liability issues including errors, bias, and data breaches with risk allocation framework and contract provision examples.
DPA requirements for AI vendors including AI-specific provisions for model training, data retention, and PDPA compliance with review checklist.
Guide to AI-specific contract provisions covering data rights, model training, performance, and liability with example clause language.
Arm your evaluation team with specific questions to ask during AI vendor demos. Covers technical, security, and commercial topics with red flag indicators.
Design and execute AI POCs that actually inform decisions. Covers success criteria, data preparation, and evaluation with decision framework.
Comprehensive AI-specific RFP template covering technical requirements, security questions, and AI-specific provisions for vendor evaluation.
Practical methodology for comparing AI vendors using weighted scoring matrices. Move from long list to confident selection with objective criteria.
Structured methodology for evaluating AI vendors covering technical capability, security, viability, and commercial terms with risk register template.
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