---
id: "concept-explainable-ai-in-negotiation"
type: "concept"
source_timestamps: ["§ Build trust through explainability."]
tags: ["explainability", "trust", "ai-adoption"]
related: ["entity-dell", "entity-walmart", "action-deploy-explainable-models", "quote-trust-decisions-understand"]
definition: "AI systems that transparently articulate the reasoning behind their negotiation decisions, which is critical for building trust and driving enterprise adoption."
sources: ["tail2"]
sourceVaultSlug: "hbr-seg-tail2"
originDay: 2
articleStem: "hbr-tail-129-ai-supplier-negotiations"
sourceUrl: "https://hbr.org/2025/07/how-ai-is-reshaping-supplier-negotiations"
sourceTitle: "How AI Is Reshaping Supplier Negotiations"
---
# Explainable AI in Negotiation

Explainable AI in supplier negotiations refers to **AI models that can transparently articulate the reasoning behind their recommendations, contract markups, or pricing decisions**. The authors argue that **'black-box' systems are detrimental to high-stakes procurement** because human operators and suppliers struggle to trust decisions they do not understand — captured in the quote [[quote-trust-decisions-understand]] ("It is difficult to trust decisions you don't understand").

Transparency is treated as a **prerequisite for scaling AI adoption** inside an enterprise. Both [[entity-dell]] and [[entity-walmart-d2]] experienced **significantly stronger internal adoption** of AI negotiation tools once those systems were upgraded to show *how* specific decisions were made, rather than only presenting the final output.

The operational move is [[action-deploy-explainable-models]]: select explainable models over black-box ones for high-stakes procurement.

**Enrichment / external validation:** Explainability as a prerequisite for trust and adoption in high-stakes AI is strongly supported by XAI research and by the [[entity-eu-ai-act-d2]]. The specific Dell/Walmart *causal* adoption-lift narrative comes primarily from the article and is not independently verifiable in that exact form. **Counter-perspective:** more interpretable models can be less performant, and post-hoc explanations may oversimplify — potentially masking model bias and producing *misplaced* trust.

**Related:** [[entity-dell]] · [[entity-walmart-d2]] · [[action-deploy-explainable-models]] · [[quote-trust-decisions-understand]]


## Related across articles
- [[action-demand-ai-transparency]]