Sales Research Agent and Sales Research Bench
arXiv:2602.17017v1 Announce Type: new Abstract: Enterprises increasingly need AI systems that can answer sales-leader questions over live, customized CRM data, but most available models do not expose transparent, repeatable evidence of quality. This paper describes the Sales Research Agent in Microsoft Dynamics 365 Sales, an AI-first application that connects to live CRM and related data, reasons over complex schemas, and produces decision-ready insights through text and chart outputs. To make quality observable, we introduce the Sales Research Bench, a purpose-built benchmark that scores systems on eight customer-weighted dimensions, including text and chart groundedness, relevance, explainability, schema accuracy, and chart quality. In a 200-question run on a customized enterprise schema on October 19, 2025, the Sales Research Agent outperformed Claude Sonnet 4.5 by 13 points and ChatGPT-5 by 24.1 points on the 100-point composite score, giving customers a repeatable way to compare
arXiv:2602.17017v1 Announce Type: new Abstract: Enterprises increasingly need AI systems that can answer sales-leader questions over live, customized CRM data, but most available models do not expose transparent, repeatable evidence of quality. This paper describes the Sales Research Agent in Microsoft Dynamics 365 Sales, an AI-first application that connects to live CRM and related data, reasons over complex schemas, and produces decision-ready insights through text and chart outputs. To make quality observable, we introduce the Sales Research Bench, a purpose-built benchmark that scores systems on eight customer-weighted dimensions, including text and chart groundedness, relevance, explainability, schema accuracy, and chart quality. In a 200-question run on a customized enterprise schema on October 19, 2025, the Sales Research Agent outperformed Claude Sonnet 4.5 by 13 points and ChatGPT-5 by 24.1 points on the 100-point composite score, giving customers a repeatable way to compare AI solutions.
Executive Summary
This article introduces the Sales Research Agent, an AI-first application in Microsoft Dynamics 365 Sales, designed to answer sales-leader questions over live, customized CRM data. The Sales Research Bench, a purpose-built benchmark, scores systems on eight customer-weighted dimensions, including text and chart groundedness, relevance, explainability, schema accuracy, and chart quality. The Sales Research Agent outperformed Claude Sonnet 4.5 and ChatGPT-5 in a 200-question run on a customized enterprise schema, providing customers with a repeatable way to compare AI solutions. This development has significant implications for enterprises seeking AI systems that can provide transparent, repeatable evidence of quality.
Key Points
- ▸ The Sales Research Agent is an AI-first application in Microsoft Dynamics 365 Sales, designed to answer sales-leader questions over live, customized CRM data.
- ▸ The Sales Research Bench is a purpose-built benchmark that scores systems on eight customer-weighted dimensions, including text and chart groundedness, relevance, explainability, schema accuracy, and chart quality.
- ▸ The Sales Research Agent outperformed Claude Sonnet 4.5 and ChatGPT-5 in a 200-question run on a customized enterprise schema.
Merits
Strength in AI-Driven Decision-Making
The Sales Research Agent provides AI-driven decision-ready insights through text and chart outputs, making it an attractive solution for enterprises seeking to improve sales-leader decision-making.
Demerits
Limited Generalizability
The Sales Research Agent's performance may not be generalizable to other CRM systems or data sources, limiting its applicability to a broader range of customers.
Expert Commentary
The introduction of the Sales Research Agent and the Sales Research Bench represents a significant step forward in the development of AI-driven decision-making tools for sales-leader applications. The Sales Research Bench's focus on customer-weighted dimensions, such as text and chart groundedness, relevance, explainability, schema accuracy, and chart quality, addresses a critical need for transparent and repeatable evidence of quality in AI model evaluation. However, the limited generalizability of the Sales Research Agent's performance may require further research to ensure its applicability to a broader range of CRM systems and data sources. Nonetheless, this development has far-reaching implications for enterprises seeking to improve sales-leader decision-making and highlights the need for standardized benchmarks and evaluation frameworks in AI development.
Recommendations
- ✓ Enterprises should consider implementing the Sales Research Agent and the Sales Research Bench to evaluate AI solutions and improve sales-leader decision-making.
- ✓ Researchers should explore the development of standardized benchmarks and evaluation frameworks for AI model performance in specific domains, informed by the Sales Research Bench's customer-weighted dimensions.
