Release Notes: Sales Qualification Agent (SQA) — Dynamics 365 Sales

In October 2025, we announced the general availability of the Sales Qualification Agent (SQA) in Dynamics 365 Sales —a breakthrough in autonomous lead qualification. Sales Qualification Agent empowers sellers by helping build higher quality opportunity while eliminating tedious, repetitive work. Sales Qualification Agent autonomously researches every lead, initiates personalized outreach, and engages prospects to understand purchase intent, ensuring that sellers spend their time meeting prospects who are ready to take the next step. With modes enabling both seller-driven and fully autonomous qualification, the agent supports a key goal for sales organizations—increasing revenue per seller.

Customers are using Sales Qualification Agent in two ways

• Helping boost revenue beyond current sales capacity
  • Responding to inbound leads within minutes instead of days, increasing response rates and in turn, qualified opportunities.
  • Engaging leads that sellers are unable to follow up on due to capacity constraints, or those deemed economically unviable to pursue.
  • Increasing pipeline quality by focusing the seller’s time on a handful of high intent, engaged leads recommended by the agent.
• Helping reduce sales costs
  • Reducing back-office costs related to lead research and validation, using Sales Qualification Agent in “Research only” mode to hand-off only the leads that meet the ideal customer profile criteria.
  • Automatically disqualifying low-quality leads, saving hours of seller time during the week.

Continuing benchmarking the quality of sales AI agents

Microsoft is building the future of agentic Sales technology with prebuilt AI agents, such as Sales Qualification Agent, the Sales Research Agent, and the Sales Close Agent available in Dynamics 365.

At Microsoft, we’re committed to delivering quality, trust, and transparency with our agents, and that requires rigorous evaluation. As we continue to build new agents and improve existing ones for critical sales workflows, evaluation benchmarks provide a structured and transparent way for our customers to measure quality for the jobs the agent does.

Today, we’re announcing the Microsoft Sales Bench —a new collection of evaluation benchmarks designed to assess the performance of AI-powered sales agents across real-world scenarios. This framework brings together purpose-built metrics, hundreds of sales-specific scenarios, and composite scoring validated by both human and AI judges.

The Sales Bench isn’t starting from scratch. It now formalizes and expands what began with the Sales Research Bench, published on October 21, 2025, which evaluates how AI solutions answer business research questions for sales leaders.

Today, we’re extending the Microsoft Sales Bench with a second benchmark: the Microsoft Sales Qualification Bench, focused on measuring how effectively AI agents qualify leads and generate high-quality pipeline.

Introducing the Sales Qualification Bench for lead qualification

This Microsoft Sales Qualification Bench evolved from rigorous evaluations we conducted since the Sales Qualification Agent’s public preview in April, with the goal of objectively measuring quality as we further developed the agent, partnering with customers from a diverse set of industries. Since the preview, we measured every update against these standards, ensuring improvements are real and repeatable.

We generated a synthetic dataset modeled after companies from three different industries, with 300 leads, with attributes such as name, company, and email ID—representative of what sales teams typically work with before any enrichment or hygiene is performed. In addition to these typical attributes, we also added key knowledge inputs such as value proposition of the products being sold, customer case studies, and documentation for answering customer questions.

In addition to Sales Qualification Agent, we used the evaluation framework to measure ChatGPT by OpenAI on the same dataset. Since we didn’t have access to an autonomous agent from OpenAI, we mimicked how a human seller would use ChatGPT to recreate the three key jobs SQA performs. We provided each system—Sales Qualification Agent and ChatGPT—the exact same lead inputs, knowledge sources, and contextual signals under controlled evaluation configurations. We used a ChatGPT Pro license with GPT-4.1. This model is the closest match (and slightly better) to Sales Qualification Agent’s GPT-4.1 mini, which we intentionally chose to deliver optimal quality at lower cost per lead than newer models. Additionally, Pro license was chosen to optimize for quality: ChatGPT’s pricing page describes Pro as “full access to the best of ChatGPT.”

The framework evaluates outputs from the three jobs across Sales Qualification Agent and ChatGPT:

• Research: Company research for the lead—background, strategic priorities, financial health, and latest news.
• Outreach: A personalized email generated based on research, to make initial contact with the lead.
• Engagement: The agent’s conversation with a lead until it’s qualified or dispositioned.

Our scoring metrics span core quality (accuracy, relevance, completeness), trustworthiness (grounding and citations), and business-specific success criteria (e.g., relevancy of company research to highlight interest in the seller’s offerings, personalization of the initial outreach emails sent to catch the lead’s attention, accuracy of responses to the lead’s questions to drive purchase intent, and the timing of handoff to a seller when the lead is ready to engage).

Outputs were scored independently by both human reviewers and an LLM judge built with GPT-5.1, using a 1–10 scale for each metric. These metric-specific scores were then rolled up using a simple average to produce a composite quality score. The result is a rigorous benchmark presenting a composite score and dimension-specific scores to reveal where agents excel or need improvement. Our methodology, metrics, and their definitions are described in this technical blog.

Results

In evaluations completed on December 4, 2025, using the Sales Qualification Bench, Sales Qualification Agent outperformed ChatGPT on each of the three jobs required for sales qualification:

• Research: The Sales Qualification Agent outperformed ChatGPT with 6% higher aggregate scores, leading on relevancy and completeness in research results that highlighted the lead company’s interest in the seller’s offerings.
• Outreach: Sales Qualification Agent demonstrated 20% better results compared to ChatGPT, generating email drafts with accurate personalization and mentions of relevant recent events that will resonate with the lead.
• Engagement: Sales Qualification Agent’s email responses to engage a lead over a multi-turn conversation scored 16% higher than ChatGPT’s. SQA generated emails that responded to the lead’s questions with accurate answers that develop their purchase interest and with precise discovery questions that qualify the lead before handing off to a seller.

In addition to performing better on these metrics, Sales Qualification Agent has the ability to run autonomously, which can help significantly reduce the time spent generating pipeline while helping sales teams build better quality pipeline.

Sales Qualification Agent scores well on these three jobs as its optimized for sales-specific scenarios and uses the following techniques to get great results:

• It uses agentic Retrieval Augmented Generation (RAG) to relentlessly research each lead, ensuring greater completeness. More on this in the following section.
• With knowledge of what the company sells, it can contextualize every workflow to increase relevancy for both the seller and the lead.
• It can retrieve organizational knowledge from attached documents and internal repositories like SharePoint with greater precision, boosting accuracy of its responses when engaging with the lead.

The technical blog details which metrics SQA excels at relative to ChatGPT, where it falls short, and why.

Translating evals to real-world impact

Running evals led to major Sales Qualification Agent improvements during its six-month preview. Early results prompted us to try agentic AI design patterns, especially agentic RAG, which improved our company research by allowing iterative web searches and real-time reasoning. They also led us to enhance data coverage by auto-linking existing CRM records to each lead and inferring company names from lead emails. These updates provided sellers with deeper insights, revealing strategic opportunities and risks beyond basic facts.

For instance, when researching leads for a security company, Sales Qualification Agent can link news on recent cyberattacks to increased demand for its software. As highlighted in the technical blog, research synthesized by the agent makes such inferences more consistently than ChatGPT. Enhancing the agent’s research also improved the relevance and personalization of outreach emails, helping agents better engage leads and clarify their ability and intent to purchase before handing them off to sellers.

Sandvik Coromant, a leader in precision cutting tools, partnered with us to pilot Sales Qualification Agent for their Digital Commerce program. After the updates, Pia Cedendahl, Global Sales Manager for Strategic Channels/Partners and Online Sales, noted, “Sales Qualification Agent’s answers became far more on-point to our business—it’s like having a research assistant that already understands what we care about.” Sandvik Coromant saw improved lead conversion and higher engagement from their Digital Account Managers, validating the impact of our evaluation-driven approach. Pia joined Microsoft leaders at the Microsoft Ignite 2025 session, “Accelerate revenue and seller productivity with agentic CRM,” where she shared how the team saved more than 120 hours and $19,000 in just the first three weeks since launching a pilot, and forecasted a 5% increase in revenue with full rollout.

Better insights, more personalization, proven value

Equipped with agentic AI design and backed by data-driven evaluation, customers can confidently use the Sales Qualification Agents so that:

• Sellers receive comprehensive company overviews, timely news highlights, and actionable recommendations that are consistently delivered with high quality—drawing a clear line from insight to action.
• Sales leaders can expand their qualified pipeline cost efficiently, with the agent ensuring high lead quality.
• Prospects benefit from more personalized outreach, enhancing their experience and supporting increased conversion rates.

What’s next

We’ll continue to refine Sales Qualification Agent using agentic design patterns, aiming to make every improvement measurable and meaningful. Stay tuned for the full evaluation results and methodology for the Sales Qualification Bench, which will be published for transparency and reproducibility. We also intend to add more evaluation frameworks and benchmarks to the Microsoft Sales Bench collection including benchmarks that cover future sales agent capabilities.

Get started with the Sales Qualification Agent

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AI-powered Dataverse Search and Copilot Experiences

Imagine being able to ask your CRM system a question like, “Which opportunities are likely to close this week?” or “Who has met with Ernie Kerrigan at Contoso recently?” and getting an instant, accurate answer without writing a single query or navigating through multiple Views in Dynamics 365.

Whether you’re using Copilot in Dynamics 365 Sales, Power Apps customized through Microsoft Copilot Studio, or Microsoft 365 Copilot for Sales, under the hood, these experiences leverage one common engine: AI-powered Dataverse (DV) Search, which seamlessly connects business users to the underlying database schema, translating intent into action without requiring technical expertise. Thousands of enterprise customers already rely on this capability to power their business workflows.

Figure 1: How AI-powered Dataverse Search Connects Copilot Experiences Across Dynamics 365

We’ve reimagined the technology behind Dataverse Search from the ground up. Leveraging recent breakthroughs in agentic AI, the new system delivers answers that are more relevant, contextual, and accurate to your specific business data. Think of it as an intelligent assistant that not only understands your question but figures out the best way to answer it using an adaptive reasoning process.

In this blog, we’ll explore why this agentic approach was necessary, how it works under the hood, and how it scales to enterprise needs supporting complex schemas, massive datasets, and domain specific terminologies while adhering to Microsoft Responsible AI principles. In particular, the agentic approach is model-agnostic, and while different models or fine-tuned models can influence the quality of results, the choice of model is orthogonal to the architecture. For this post, our emphasis remains on the agentic loop and its role in delivering dynamic, context-aware answers. Further, we will demonstrate our success via evaluation results and show you ways to customize it for your business.

Queries to Conversations: Unlocking Your Live Business Data

Every organization’s Dynamics 365 environment is unique, and most customers customize it extensively. Over time, these customizations lead to complex schemas, ambiguous relationships, and massive datasets spanning millions of records and terabytes of data. Our original Dataverse Search system was pioneering, but it relied on a fixed-plan natural language to SQL pipeline. A user’s question was converted to SQL through sequential stages: parsing, schema mapping, data linking, and SQL generation. This design was prone to cascading failure in a sequential pipeline. Each stage operated in isolation without shared context, so a single error could invalidate the entire query. Every question followed the same fixed flow, even when certain steps were unnecessary. This resulted in brittle behavior and suboptimal answers for complex or ambiguous queries that spanned multiple tables.

We recognized the need for a more adaptable, resilient approach to tackle the complexities of enterprise data. This upgrade shifts DV Search beyond simple Search into intelligent, interactive conversations with your business data. For you, this translates into immediate, actionable value by providing:

• Real-Time, Actionable Answers: Ask, “Which of my open opportunities in New York are scheduled to close this month?” and get an instant answer from the live Dataverse data. This isn’t a report from last night’s data refresh; it’s the current state of your business.

• Democratized Data Access: A service manager can ask, “Show me active, high-priority cases that haven’t been updated in 3 days” without needing to understand the underlying table structure of incidents and case/activities.

• Deeper Contextual Conversations: The agent supports multi-turn conversations. After asking about opportunities in New York, you can follow up with, “Of those, which ones are for our ‘Pro’ license?” The agent remembers the context, providing a progressively refined answer.

Under the Hood: Agentic Architecture

To overcome some of the limitations of the earlier system and to meet the complex customer scenarios, the new DV Search architecture introduces an Agentic Orchestrator powered by GPT4.1. It transforms query handling from a static pipeline into a dynamic reasoning loop: plan → execute → refine. Instead of blindly converting text to SQL, the orchestrator treats each question as a goal, intelligently deciding the best steps to reach it.

Figure 2: Agentic Architecture for AI-powered Dataverse Search

Context Awareness and Conversations: When a user submits a new or follow-up question, a dedicated preprocessing component reviews prior conversation history and rewrites the query as a single, self-contained question, enabling coherent multi-turn conversations. For example, if you ask, “Show my top opportunities in Q4” and then follow up with “How about in Europe only?” the component understands the second question is a refinement of the first rather than starting from scratch or losing track of prior context. This conversational capability makes interactions feel natural and efficient. The refined question is then enriched with the business’s domain knowledge (glossary) to fully reflect the user’s intent within the specific business context.

Dynamic Planning and Execution: When the self-contained question comes in, the orchestrator doesn’t simply translate it into SQL. Instead, it breaks the query into logical steps and decides which tools to use and in what order, while also utilizing the domain knowledge encapsulated with the supplied glossaries. These tools include:

• schema_linking_tool: Schema lookup for understanding tables and relationships
• data_linking_tool: Semantic Search for finding relevant data values and resolving data ambiguities
• sql_execution_tool: SQL execution tool for retrieving results
• submit_plan_update_tool: Captures both the original plan and any course corrections made during execution

The orchestrator adapts on the fly if the first attempt fails or returns incomplete results. It analyzes the issue, revises the plan, and retries. This self-correcting loop is a major improvement over older systems that suffered from cascading failures.

Handling Relational Complexity: One of the most powerful aspects of this approach is its ability to handle relational complexity. Operational business application schemas often require multi-hop joins across multiple tables, including custom entities. The orchestrator understands these relationships and can navigate them intelligently, ensuring accurate joins and filters even in highly customized environments. For example, if a question involves linking Accounts to Opportunities and then to a custom Product table, the agent plans the steps and executes them seamlessly.

Personalization and Learning: Personalization further enhances the experience. Over time, the system learns from usage patterns within your organization. If you frequently work with the Accounts table or use certain custom fields, the agent prioritizes those interpretations in future queries. This learning is based on interaction signals, not external data, and is carefully scoped to respect privacy and organizational boundaries. The result is a system that becomes more aligned with your business logic the more you use it.

Real-World Example

Imagine you run Fourth Coffee Machines, a business selling premium espresso and grinder units to commercial and residential customers. It’s managed through a Power App built on Dataverse. A seller begins with a simple keyword search in top-bar search in Power Apps for “Fourth Coffee” to confirm the account record. Thanks to fuzzy matching and relevance re-ranking, even typos like forth coffee or 4thcoffee surface the right entity instantly.

From there, the seller asks Copilot: “Show me my open opportunity at risk with Fourth Coffee.” The agent rewrites the query, scopes it to the current user, interprets at risk as a cold rating, and joins Account → Opportunity. It executes SQL, returns the results, and summarizes them with citations—no manual filtering, no report building.

Finally, the seller pivots to a KPI question: “What is the HRR for Coffee Grinder 02?” Here, the agent consults the business glossary, which defines HRR as Happy Response Rate (positive sentiment ÷ total reviews in the Product Review table). It computes the metric, explains the formula, and cites the source records. The user now understands exactly how the number was derived.

Under the hood, this seamless experience is powered by an Agentic Orchestrator that plans, executes, and refines dynamically. It chooses the right tools, adapts when errors occur, and injects domain knowledge from glossaries. By combining dynamic planning, iterative refinement, relational understanding, and personalization, it represents a significant leap forward from static query pipelines. It’s not just about generating SQL it’s about orchestrating an intelligent, context-aware process that feels conversational and delivers real business value.

Evaluation Results

To measure how well our agentic system performs in practical enterprise scenarios, we evaluated it against curated datasets of user prompts each representing or assisting with a real job to be done. These prompts reflect the everyday questions and tasks that drive productivity for CRM users— from quick record lookups and aggregation analytics using keyword search or simple filters and joins, to complex multi-join queries requiring domain expertise. By categorizing prompts into levels of complexity, we ensure the evaluations capture the full spectrum of enterprise challenges.

For each complexity level, we report two practical metrics: Relaxed Execution Accuracy (EX Accuracy) and P80 Latency. Relaxed Execution Accuracy measures how often the generated SQL returns the same rows as the reference SQL when both are executed on the same data—extra columns in the predicted query are allowed, but extra or missing rows are not; order is ignored unless ORDER BY is specified. P80 Latency is the 80th percentile end to end response time, from request receipt through retrieval, model inference, and verification to the final SQL result. Together, these metrics give a transparent, action-oriented view of correctness and responsiveness as task complexity increases. It highlights where the Agentic framework delivers reliable, efficient answers that empowers users to get more done with natural language.

† Metrics averaged over multiple runs

In practice, higher accuracy often comes at the cost of increased latency. Conversely, pushing for low latency can reduce end to end quality. This Agentic system is designed to navigate that tradeoff, delivering strong accuracy while keeping latency within practical bounds. This achieves a practical balance for production use.

Tuning for Your Business: Glossaries and Enriched Schema

No AI system knows your business out-of-the-box. We’ve added tuning mechanisms that let makers refine how the Q&A agent understands your data:

• Glossaries: You can define a glossary to teach the agent your company’s unique vocabulary and acronyms. For example, if “QoQ” is common slang on your team for “quarter-over-quarter” or “CTX” refers to a particular set of products, you can add those to the glossary. The next time someone asks “What’s the QoQ growth for CTX?”, the agent will know exactly what that means. This helps align the AI with the lingo of your organization so it interprets queries the same way a knowledgeable employee would.

• Schema Descriptions: Dataverse allows adding custom descriptions to tables and columns. By populating these descriptions with meaningful info, you give the agent extra context. For instance, two fields might both be called “Status” – one on a custom entity and one on a standard entity. If you add descriptions like “(Order Status – custom)” vs “(System Status code)”, the agent can use that to pick the right field during SQL generation. Essentially, you’re able to clarify the semantics of your data model for the AI.

Using the inherent metadata in Dataverse (like relationships and display names) plus these maker-driven additions, the agentic system can be tailored to use the correct terms and relationships in your domain, boosting accuracy even further. And because you control these glossaries and descriptions, you can continuously refine the AI’s understanding as your business evolves.

Conclusion

By reinventing Dataverse Search with an agentic architecture, we’ve moved from a rigid query engine to an adaptive, intelligent assistant for your business. The system understands nuance, handles ambiguity through reasoning, and even lets you inject your domain knowledge. Early adopters are seeing more questions answered correctly and faster than before, turning previously buried data into actionable insights. One leading global financial services company saw an Execution Accuracy surge from 22% to 97% on their marquee set of scenarios. This marks a significant step toward making enterprise data truly conversational. It empowers everyone from business users to power makers to tap into complex data and get the answers they need instantly and accurately, simply by asking.

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Transforming sales with Sales Development Agent

Earlier in 2025, we introduced our vision for how AI agents will transform critical sales processes like building pipeline and qualifying leads. Today marks the next milestone in that journey with the Microsoft Sales Development Agent, available through the Frontier Program in December 2025. Many sales organizations are under pressure to deliver more revenue with limited resources, and the Sales Development Agent helps sales teams scale their impact. This allows sellers to focus on nurturing customer relationships and closing deals.

Features include:

• Revenue and pipeline growth: The agent continuously researches prospects, crafts personalized outreach, and automatically follows up to ensure no lead is left behind.
• Scalability: Fully independent, yet collaborative, the agent acts as a teammate, with the ability to hand off leads to human sellers when needed.
• Security and governance: Built on Microsoft’s trusted security and compliance foundation and when enabled with Agent 365, the agent adheres to robust policies and access controls to ensure user data and workflows are protected.

Sales Development Agent connects with leading CRM systems like Salesforce and Microsoft Dynamics 365, and the Microsoft 365 apps your teams already use like Microsoft Outlook and Microsoft Teams.

The Microsoft sales team is among the first to use Sales Development Agent to reinvent the sales engagement process. With the use of Sales Development Agent, there was a 15.1% increase in the lead-to-opportunity conversation rate.

Sales leaders want to help sellers act on more leads, reach more customers, grow faster, and improve revenue per seller. Microsoft Sales Development Agent can make that possible by creating an infinitely scalable sales organization, so no lead is left behind. Accenture plans to pilot Sales Development Agent across our global inside sales-as-a-service business—which helps clients sell to customers around the world—to boost their reach and revenue while maintaining cost to serve. We’ll use what we learn to help clients leverage Sales Development Agent, scale their teams, and unlock new growth.

—Chris Hergesell, Sales Reinvention Lead, Accenture Song

Learn more about Sales Development Agent with Accenture

From System of Record to System of Action

In October 2025, we shared our vision for agentic business applications—built on agents, Copilot, and unified data. These components are what define Dynamics 365 as a system of action.

Today, we’re taking that vision further with updates to Model Context Protocol (MCP) servers across Dynamics 365 and Microsoft Power Platform, strengthening the foundation for agentic capabilities across your entire business. MCP servers are configurable bridges between the business data within your line-of-business (LOB) apps, and the agents you build using tools like Microsoft Copilot Studio. It serves as a universal intermediary, unlocking a unified platform agnostic access to app data, modernizing how AI agents are interoperable with your apps.

For customers of Dynamics 365 Sales and Customer Service, we’ve used MCP to simplify integration between agents in Dynamics 365 and the platforms used by sellers and service reps to execute complementary workflows, like lead research, engagement, and qualification, as well as case management and case resolution, available in public preview on November 21, 2025.

Discover how the Dynamics 365 ERP MCP server unlocks real-time functions and AI-driven insights for faster, smarter decisions

For customers of Dynamics 365 ERP, we are announcing the public preview of the MCP server that unlocks hundreds of thousands of ERP functions for real-time use. We are also introducing a new analytics MCP server in public preview starting in December 2025. These two servers provide a secure, standardized foundation to connect ERP data with AI-powered analytics, helping customers make faster, more accurate decisions and innovate without sacrificing governance.

We are also announcing the Power Apps MCP server in public preview that enables agents to seamlessly trigger app capabilities such as approvals, form submissions, and data retrieval. This makes every Power App a composable, reusable building block in your organization’s AI ecosystem empowering both citizen and professional developers to expose app functionality to agents with confidence and control.

Lastly, the Dataverse MCP server, now generally available, allows people to benefit from natural language interactions, receiving real-time answers grounded in Dataverse data, while makers and admins gain powerful, built-in tools for data operations, search, and prompt execution.

We see tremendous excitement from customers and partners for agentic Dynamics 365 applications. Take Ramp, a financial operations platform designed to save companies time and money. Ramp built an agentic solution, currently in preview, using Microsoft Foundry that integrates with Dynamics 365 Business Central and Teams to streamline employee expense management.

Join the movement to the Frontier with Copilot, agents, and agentic business applications

We know that moving to the Frontier isn’t just about technology. That’s why we’re partnering with Harvard Business School to collaborate on research and executive education to help you put this into practice at your own company. We’re also sharing new resources for leaders on their journey to the Frontier Firm with Frontier Function Guides for Sales, HR, and IT and a look inside our learnings at Microsoft–including three ways to turn insight into action. We’re committed to helping you transform—we’ll see you at the Frontier!

Microsoft Ignite 2025: Copilot and agents built to power the Frontier Firm

If you’re interested in learning more:

• Explore the Microsoft Ignite 2025 sessions to see agents and agentic business applications in action.
• Register for our year-end webinar to learn about more agents in Dynamics 365 (December 2, 2025)
• Join us at the Convergence ERP Conference (December 9-11, 2025).

Internal Microsoft sales team data based on time period January 1 to November 7, 2025. Total customers outreach by the agent: 61,734. Lead-to-opportunity ratio (sales qualification): 15.1%.

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The New Frontier: AI Research Agents in Sales

In today’s hyper-competitive business landscape, sales leaders face a relentless challenge: how to drive growth, outpace competitors, and make smarter decisions faster in a resource constrained environment. Thankfully, the promise of AI in sales is no longer theoretical. With the advent of agentic solutions embedded in Microsoft Dynamics 365 Sales, including the Sales Research Agent, organizations are witnessing a transformation in how business decisions are made, and teams are empowered. But how do you know if these breakthrough technologies have reached a level of quality where you can trust them to support business-critical decisions?

Today, I’m excited to share an update on the Sales Research Agent, in public preview as of October 1, as well as a new evaluation benchmark, the Microsoft Sales Research Bench, created to assess how AI solutions respond to the strategic, multi-faceted questions that sales leaders have about their business and operational performance. We intend to publish the full evaluation package behind the Sales Research Bench in the coming months so that others can run these evals on different AI solutions themselves.

Sales Research Agent in Dynamics 365 Sales empowers business leaders to explore complex business questions through natural language conversations with their data. It leverages a multi-modal, multi-model, and multi-agent architecture to reason over intricate, customized schemas with deep sales domain expertise. The agent delivers novel, decision-ready insights through narrative explanations and rich visualizations tailored to the specific business context.

For sales leaders, this means the ability to self-serve on real-time trustworthy analysis, spanning CRM and other domains, which previously took many people days or weeks to compile, with access to deeper insights enabled by the power of AI on pipeline, revenue attainment, and other critical topics.

Image: Screenshot of the Sales Research Agent in Dynamics 365 Sales

Image: Screenshot of Sales Research Bench

“As a product manager in the sales domain, balancing deep data analysis with timely insights is a constant challenge. The pace of changing market dynamics demands a new way to think about go-to-market tactics. With the Sales Research Agent, we’re excited to bridge the gap between traditional and time-intensive reporting and real-time, AI-assisted analysis — complementing our existing tools and setting a new standard for understanding sales data.”

Kris Kuty, EY LLP
Clients & Industries — Digital Engagement, Account, and Sales Excellence Lead

What makes the Sales Research Agent so unique?

• Its turnkey experience goes beyond the standard AI chat interface to provide a complete user experience with text narratives and data visualizations tailored for business research and compatible with a sales leader’s natural business language.
• As part of Dynamics 365 Sales, it automatically connects to your CRM data and applies schema intelligence to your customizations, with the deep understanding of your business logic and the sales domain that you’d expect a business application to have.
• Its multi-agent, multi-model architecture enables the Sales Research Agent to build out a dedicated research plan and to delegate each task to specialized agents, using the model best suited for the task at hand.
• Before the agent shares its business assessment and analysis, it critiques its work for quality. If the output does not meet the agent’s own quality bar, it will revise its work.
• The agent explains how it arrived at its answers using simple language for business users and showing SQL queries for technical users, enabling customers to quickly verify its accuracy.

Why Verifiable Quality Matters

Seemingly every day a new AI tool shows up. The market is crowded with offers that may or may not deliver acceptable levels of quality to support business decisions. How do you know what’s truly enterprise ready? To help make sure business leaders do not have to rely on anecdotal evidence or “gut feel”, any vendor providing AI solutions needs to earn trust through clear, repeatable metrics that demonstrate quality, showing where the AI excels, where it needs improvement, and how it stacks up against alternatives.

While there is a wide range of pioneering work on AI evaluation, enterprises deserve benchmarks that are purpose-built for their needs. Existing benchmarks don’t reflect 1) the strategic, multi-faceted questions of sales leaders using their natural business language; 2) the importance of schema accuracy; or 3) the value of quality across text and visualizations. That is why we are introducing the Sales Research Bench.

Introducing Sales Research Bench: The Benchmark for AI-powered Sales Research

Inspired by groundbreaking work in AI Benchmarks such as TBFact and RadFact, Microsoft developed the Sales Research Bench to assess how AI solutions respond to the business research questions that sales leaders have about their business data.

Read this blog post for a detailed explanation of the Sales Research Bench methodology as well as the Sales Research Agent’s architecture.

This benchmark is based on our customers’ real-life experiences and priorities. From engagements with customer sales teams across industries and around the world, Microsoft created 200 real-world business questions in the language sales leaders use and identified 8 critical dimensions of quality spanning accuracy, relevance, clarity, and explainability. The data schema on which the evaluations take place is customized to reflect the complexities of our customers’ enterprise environments, with their layered business logic and nuanced operational realities.

To illustrate, here are 3 of our 200 evaluation questions informed by real sales leader questions:

1. Looking at closed opportunities, which sellers have the largest gap between Total Actual Sales and Est Value First Year in the ‘Corporate Offices’ Business Segment?
2. Are our sales efforts concentrated on specific industries or spread evenly across industries?
3. Compared to my headcount on paper (30), how many people are actually in seat and generating pipeline?

Judging is handled by LLM evaluators that rate an AI solution’s responses (text and data visualizations) against each quality dimension on a 100-point scale based on specific guidelines (e.g., give score of 100 for chart clarity if the chart is crisp and well labeled, score of 20 if the chart is unreadable, misleading). These dimension-specific scores are then weighted to produce a composite quality score, with the weights defined based on qualitative input from customers, what we have heard customers say they value most. The result is a rigorous benchmark presenting a composite score and dimension-specific scores to reveal where agents excel or need improvement.

Sales Research Bench uses Azure Foundry’s out-of-box LLM evaluators for the dimensions of Text Groundedness and Text Relevance. The other 6 dimensions each have a custom LLM evaluator that leverages Open AI’s GPT 4.1 model. 100-pt scale has 100 as the highest score with 20 as the lowest. More details on the benchmark methodology are provided here.

Running Sales Research Bench on AI solutions

Here’s how we applied the Sales Research Bench to run evaluations on the Sales Research Agent, ChatGPT by OpenAI, and Claude by Anthropic.

• License: Microsoft evaluated ChatGPT by OpenAI using a Pro license with GPT-5 in Auto mode and Claude Sonnet 4.5 by Anthropic using a Max license. The licenses were chosen to optimize for quality: ChatGPT’s pricing page describes Pro as “full access to the best of ChatGPT,” while Claude’s pricing page recommends Max to “get the most out of Claude.” Similarly, ChatGPT’s evaluation was run using Auto mode, a setting that allows ChatGPT’s system to determine the best-suited model variant for each prompt.
• Questions: All agents were given the same 200 business questions.
• Instructions: ChatGPT and Claude were given explicit instructions to create charts and to explain how they got to their answers. (Equivalent instructions are included in the Sales Research Agent out of box.)
• Data: ChatGPT and Claude accessed the sample dataset in an Azure SQL instance exposed through the MCP SQL connector. The Sales Research Agent connects to the sample dataset in Dynamics 365 Sales out of box.

Results are in: Sales Research Agent vs. alternative offerings

In head-to-head evaluations completed on October 19, 2025 using the Sales Research Bench framework, the Sales Research Agent outperformed Claude Sonnet 4.5 by 13 points and ChatGPT-5 by 24.1 points on a 100-point scale.

Image: Sales Research Agent – Evaluation Results

Results: Results reflect testing completed on October 19, 2025, applying the Sales Research Bench methodology to evaluate Microsoft’s Sales Research Agent (part of Dynamics 365 Sales), ChatGPT by OpenAI using a ChatGPT Pro license with GPT-5 in Auto mode, and Claude Sonnet 4.5 by Anthropic using a Claude Max license.

Methodology and Evaluation dimensions: Sales Research Bench includes 200 business research questions relevant to sales leaders that were run on a sample customized data schema. Each AI solution was given access to the sample dataset using different access mechanisms that aligned with their architecture. Each AI solution was judged by LLM judges for the responses the solution generated to each business question, including text and data visualizations.

We evaluated quality based on 8 dimensions, weighting each according to qualitative input from customers, what we have heard customers say they value most in AI tools for sales research: Text Groundedness (25%), Chart Groundedness (25%), Text Relevance (13%), Explainability (12%), Schema Accuracy (10%), Chart Relevance (5%), Chart Fit (5%), and Chart Clarity (5%). Each of these dimensions received a score from an LLM judge from 20 as the worst rating to 100 as the best. For example, the LLM judge would give a score of 100 for chart clarity if the chart is crisp and well labeled, score of 20 if the chart is unreadable or misleading. Text Groundedness and Text Relevance used Azure Foundry’s out-of-box LLM evaluators, while judging for the other six dimensions leveraged Open AI’s GPT 4.1 model with specific guidance. A total composite score was calculated as a weighted average from the 8 dimension-specific scores. More details on the methodology can be found in this blog.

The Sales Research Agent outperformed these solutions on each of the 8 quality dimensions.

Image: Evaluation Scores for Each of the Eight Dimensions

The Road Ahead: Investing in Benchmarks

The Road Ahead: Investing in Benchmarks

Upcoming plans for the Sales Research Bench include using the benchmark for continuous improvement of the Sales Research Agent, running comparisons against a wider range of competitive offerings, and publishing the full evaluation package including all 200 questions and the sample dataset in the coming months, so that others can run it themselves to verify the published results and benchmark the agents they use. Evaluation is not a one-time event. Scores can be tracked across releases, domains, and datasets, driving targeted quality improvements and ensuring the AI evolves with your business.

Sales Research Bench is just the beginning. Microsoft plans to develop eval frameworks and benchmarks for more business functions and agentic solutions—in customer service, finance, and beyond. The goal is to set a new standard for trust and transparency in enterprise AI.

Why This Matters for Sales Leaders

For business decision makers, the implications are profound:

• Accelerated Decision-Making: AI-driven insights you can trust, when delivered in real time, enable faster, more confident decisions
• Continuous Improvement: Thanks to evals, developers can quickly identify areas for highest measurable impact and focus improvement efforts there
• Trust and Transparency: Rigorous evaluation means you can rely on the outputs, knowing they’ve been tested against the scenarios that matter most to your business.

The future of sales is agentic, data-driven, and relentlessly focused on quality. With Microsoft’s Sales Research Agent and the Sales Research Bench evaluation framework, sales leaders can move beyond hype and make decisions grounded in demonstration of quality. It’s not just about having the smartest AI—it’s about having a trustworthy partner for your business transformation.

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We’re excited to announce a major upgrade to how sellers interact with Copilot summaries in Dynamics 365 Sales. Beginning with the 2025 Release Wave 2, Copilot summaries for Opportunities, Leads, and Accounts will appear directly at the top of the record in a sleek insights bar. This update brings AI-powered insights directly into the seller’s workflow, thereby reducing clicks, improving focus, and enabling faster decisions.

Why This Matters

The sidecar experience has been useful, but many sellers find it disconnected from the main form and difficult to use due to limited space. Scrolling through long summaries in a narrow pane often slows down productivity and disrupts focus.

The new insights bar presents key insights directly on the form, at the top of each record. This eliminates the need to open a separate pane. With this streamlined experience, sellers can ramp up faster, stay in context, and respond more effectively to customer needs.

This change is not just a visual update. It is a step toward making selling more intelligent and contextual. Whether you’re new to the sales team or an experienced seller, this improvement ensures that essential insights are always visible when you need them.

What’s Changing

Summary displayed at the top of the record form

The new insights bar provides a one-line synopsis for quick scanning. It remains visible in a collapsed state and can be expanded to view more details. An example synopsis for a lead is:

“Acme is exploring Airpot coffee machines for their new manufacturing site with a $24.9K budget expected to close by May 25th, 2025”

You can expand the insights bar to read more details:

You can click on options like “See full summary” to open the lead’s research page:

For organizations using the Sales Qualification Agent, the insights bar displays agent-generated summary. Sellers can navigate to the agent’s research page to access deeper insights about the lead’s company, view tailored recommendations, and identify clear next steps.

What’s coming next

We are working to bring the same agent-powered experience to Opportunity and Account records. Until then, these records will continue to display summaries in a popup.

Once the agentic experience is available, sellers will get access to a full research page for these records as well.

What’s not changing

• Earlier summary customizations will be honored in the new experience.
• Ability to copy the generated summary.
• Providing thumbs-up or thumbs-down feedback on summary quality.
• Using copilot sidecar chat to generate summary for any specific record.

Launch plan and next steps

The new summary experience is part of the 2025 Release Wave 2 updates for Dynamics 365 Sales:

• Early access of the new experience is available from July 2025. Opt in to early access updates.
• General availability starts in September 2025, with that, the new experience will be default enabled for users.

We recommend enabling early access in your environments and preparing your sellers for the new summary experience. Share any feedback or challenges with us so we can continue to make selling easier and more effective.

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Today at Microsoft Build 2025, we’re excited to announce the new Model Context Protocol (MCP) servers for Microsoft Dynamics 365 ERP and CRM business applications. These MCP servers will help remove the tedious work of connecting systems together to build agents and accelerate the ability for our customers and partners to build AI-powered agents to drive business processes quicker, accelerating their journey to the Frontier Firm in the era of the autonomous enterprise.

To provide some context, generative AI is fundamentally reshaping the way organizations work, introducing a new way of interacting with technology—using natural language to simplify and accelerate tasks. This innovation is driving unprecedented productivity gains, streamlining complex processes that once required manual effort and specialized tools. As this technology matures, we’re entering the next phase: the autonomous enterprise, where organizations and people use technology, particularly AI and automation, to operate and adapt in an age of rapid transformation and innovation. Where there once was “an app for that,” there will now be “an agent for that”.

This transformation isn’t just about automation—it’s about people. By putting intelligent agents in the hands of every employee, organizations are empowering individuals to focus on higher-value work, make decisions faster, and drive innovation. Sales teams can deepen customer relationships without being bogged down by administrative tasks. Finance professionals can move from manual reconciliation to strategic forecasting. Marketers can go from idea to execution, and product managers can orchestrate complex workflows with clarity and speed.

The autonomous enterprise is the future of business. Business applications will work with agents built by Microsoft and our partners. In this new era, organizations aren’t just streamlining operations, they’re amplifying human potential and accelerating their journey to the autonomous enterprise.

This is why we’re so excited about the Dynamics 365 ERP and CRM MCP servers. These servers help eliminate data and application silos, allowing agents to work seamlessly across processes and help enable new autonomous scenarios for improved business functionality and productivity.

Dynamics 365: Agent-ready business applications

Agentic AI is an AI system that can take actions generated by the system, with very limited or even no direct human intervention. Autonomous actions built into agents operating across various business processes, industries, and segments, can make businesses more efficient and responsive. Designed not just to support tasks, but to operate autonomously, AI agents can intelligently orchestrate workflows and make context-aware selections. But how do you create a context-aware agent when data, information, and processes are ever-changing?

MCP standardizes how applications provide context to language models, helping enable seamless integration with different data sources and tools. This open standard connects AI assistants and agents to various systems where data resides, such as content repositories, business tools, and development environments. An MCP-compliant agent uses rich contextual information to act efficiently, unlike a non-MCP-compliant agent, which lacks necessary context.

Using the MCP server, makers can easily connect agents to existing knowledge sources and APIs, helping enable them to interface directly with Dynamics 365 applications. Actions and knowledge synchronize automatically, facilitating real-time updates and the evolution of functionality. This model significantly simplifies agent development and minimizes ongoing maintenance efforts.

Central to this innovation is Microsoft Copilot Studio, which provides a standardized protocol for agents to seamlessly interact with Dynamics 365 applications, helping to ensure consistency, reliability, and scalability. Security and governance are also prioritized from the start as Dynamics 365 MCP servers require authentication and enforce authorization. Agents that access Dynamics 365 through the MCP server must authenticate as a valid Dynamics 365 user, helping to ensure the benefits of Entra ID identity protection. This also prevents escalation of privileges, meaning the agent will only be able to perform the MCP actions that they’re authorized to do. The MCP servers are also made available to Microsoft Copilot Studio using connector infrastructure. This means they can employ enterprise security and governance controls such as data loss prevention controls and multiple authentication methods.

For partners and customers, MCP standardization dramatically reduces complexity, accelerates development, and increases time to value.

MCP-compliant agentic AI

At Microsoft, we bring a deep understanding of critical business processes for small and medium business (SMB) as well as large enterprise organizations through our market-leading Dynamics 365 ERP and CRM business solutions—combined with our industry-specific expertise delivered through our Microsoft Cloud for Industry solutions. This combination of experience and expertise uniquely positions us to deliver on the needs of customers across size, business process, industry, or region.

Our newly introduced set of MCP servers help enable multiple scenarios across business processes. Below are a few examples of what’s possible with Dynamics 365, Microsoft Cloud for Industry, and our broad ecosystem of partners.

Sales and service

• Custom agents and AI assistants can now be connected to Microsoft Dynamics 365 Sales, Microsoft Dynamics 365 Customer Service, and Microsoft Dynamics 365 Business Central applications through MCP servers. Agents can retrieve and update CRM data, create quotes, and complete orders. They can also get order/case summaries and email drafts. These MCP servers open endless possibilities in automating tedious jobs in sales and service functions, irrespective of company size or industry.

• For example, telesales representatives can use intelligent assistants, such as Claude, connected to Dynamics 365 MCP servers to prioritize leads, qualify them, generate quotes, and send personalized emails—without needing to switch contexts or rely on complex integrations. And when customers encounter an order issue, service representatives can resolve it quickly by using Dynamics 365 Customer Service data to retrieve/update case information and create replacement orders in real time.

Supply chain and finance

• The AI procurement agent illustrated below efficiently validates purchase requisitions against company policies, existing inventory, and delivery records to identify a suitable supplier that meets the criteria for cost, speed, sustainability, and reliability. It further consolidates multiple items from the same supplier into one purchase order and sends it for purchase. The agent can significantly enhance efficiency in procurement processes, where timely and budget-conscious supply delivery is critical.

Business Central

• For small and medium size businesses, for example, looking to optimize sourcing information and vendor compliance, the custom agent demonstrated here can quickly identify shipments containing materials that require compliance checks. The agent provides guidance on recycling requirements and updated sourcing standards, reads supplier contracts, and suggests next steps like confirming vendor certifications and updating shipment checklists. A solution like this could streamline the compliance process, which can help customers gain a competitive advantage.

Partners using the Dynamics 365 MCP server

Our partners play a crucial role in driving innovation and delivering value to customers. We’re dedicated to making Dynamics 365 MCP servers accessible, helping enable our customers and partners to develop diverse agent scenarios across industries and business processes, regardless of their business application vendor.

With MCP server becoming the standard of the future for agents, partners can use it to more quickly and efficiently orchestrate headless business services in ERP and external systems. It turns simple intent into action, automating procurement for faster, efficient, and resilient supply chain operations. Our ecosystem of partners has started using MCP server for Dynamics 365 to create a host of industry-specific agents.

• Avanade, an early adopter of Microsoft 365 Copilot for Sales and a leading Microsoft partner, is excited to use MCP servers for Dynamics 365 to enrich their AI-powered request for proposal (RFP) Insights agent. This agent helps sellers summarize, evaluate, and respond to RFPs using historical Dynamics 365 data, further streamlining proposal generation. While initially for internal use, Avanade is exploring deployment for clients in engineering, construction, and professional services.

• Emission AI agent by Fellowmind will use AI and MCP servers for Dynamics 365 to automatically classify and organize purchase transactions to prepare it for greenhouse gas (GHG) emission accounting purposes by categorizing spend-types (such as office supplies, raw materials, and travel expenses) through data extraction, classification, algorithms, taxonomy mapping, and real-time feedback and learning. The agent provides support to procurement and environmental, social, and governance (ESG) professionals, helping them streamline their processes and achieve more accurate results.

• HSO’s PayFlow Agent improves invoice payment efficiency in accounts payable. Streamlining timely payments and reducing inquiries that require manual intervention leads to faster resolutions and enhanced supplier relationships. Using MCP server for Dynamics ERP MCP, PayFlow processes seller payment inquiries, identifies invoice statuses, matches them against buyer receipts, and retrieves tracking information to notify responsible parties to either remit payment promptly or set an expectation of when payment can be received.

• JourneyTeam is enriching its Strategic Account Manager agent that accesses MCP servers for Dynamics 365 to optimize lead engagement. The agent summarizes historical services and projects, compares lead summaries and interests, compiles recommendations, then, after manual reviews, will initiate next steps by utilizing MCP servers, Microsoft Azure AI Search, and Document Intelligence.

• MCA Connect is building a smart sourcing agent that accesses MCP servers for Dynamics 365 to automate requisition processing, supplier assignment, and workflow submission. The MCP servers give the agent access to actions like getting open requisitions, approving vendors, and assigning suppliers based on supplier performance metrics without the need to create new APIs and integrate with Dynamics 365.

• Publicis Sapient Hummingbird is building an agent to improve lead management using MCP servers for Dynamics 365 to access data that will streamline the process of managing business-to-business leads. This agent automates lead qualification, scoring, and personalized engagement, accelerating hot leads to quotes faster and nurturing warm leads through a series of targeted emails. This innovative approach enhances efficiency, improves customer experience, and drives higher conversion rates and revenue growth.

• RSM is building intelligent, secure, and context-aware agents that accelerate workflows, improve decisions, and expand capabilities by embedding them directly into real-world business processes. These agents, developed using Microsoft Copilot Studio, will access MCP servers for Dynamics 365 to support humanitarian logistics by coordinating critical supply chains, helping to ensure timely delivery of life-saving equipment, and automating procurement tasks.

• TTEC Digital is building a post-service upselling agent that accesses MCP servers for Dynamics 365 to prospect for warranty plans after a purchase, turning each sale into an upsell opportunity. The agent will help drive personalized sales and service conversations at scale by using the knowledge, tools, and actions from the MCP server.

As we look ahead, the convergence of intelligent agents, standardized platforms, and deep domain expertise will define the next frontier of business transformation. The ability to harness autonomous capabilities will define tomorrow’s market leaders. Businesses that act now will gain a decisive competitive edge and chart a course toward sustained success. The autonomous enterprise is no longer a vision of the future—it’s here, built with Microsoft and its partner ecosystem.

Join us at Microsoft Build 2025 to explore how MCP servers are transforming Dynamics 365 and the broader Microsoft Cloud–MCP server focused sessions at Microsoft Build 2025.

Let’s shape what’s next, together.

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Microsoft Copilot for Sales is reimagining sales. Integrated seamlessly into your daily tools across Microsoft 365 and Teams, Copilot for Sales harnesses the power of generative AI and customer data to keep sellers in their flow of work so they can spend more time with customers.
We’re excited to announce improved extensibility for 3rd party insights in email summaries in Outlook, allowing partners to surface richer sales insights. We’re also delivering the ability to Save AI notes to CRM (both Microsoft Dynamics 365 and Salesforce) directly from the meeting recap, allowing sellers to be able to save meeting notes directly to CRM.
Capabilities highlighted below are being released this month. It may take time for specific capabilities to reach every tenant in each market. We look forward to bringing you even more features in the coming months!

In Outlook

Improved email summary extensibility for richer third-party insights

Email summaries can now show richer third-party insights. As of this month, the extensibility API now passes more input parameters such as resource data and related entities. Resource data includes the full email content and metadata, message ID, conversation ID, and CRM related entities. Third parties can leverage the additional data to improve the quality of their insights.
These insights appear in:

1. The email summary on the Outlook canvas (that Premium users see)
2. The Key email info card in the Outlook side pane (that Standard users see).

In Teams

Easily save meeting notes to CRM following Teams meetings

Today, sellers receive AI generated meeting notes following a meeting as part of Teams’ recap capability, but it is difficult to manually save the notes to CRM. Starting this month, sellers can save AI generated meeting summaries to CRM directly in Teams, eliminating manual logging and context switching. Sellers can also edit the meeting summaries before saving to CRM. This not only saves time but also ensures that your organization’s business data remains up to date and accurate.
As a seller, you can update your CRM directly from the post-meeting Microsoft Teams recap page. Select the Sales button to see sales-related insights.
Select Save to CRM to update your CRM by performing the following actions:

• Review and edit the AI-generated meeting summary prior to saving.
• Link the meeting entity to a record of your choice.
  By default, meeting summaries are saved to the description field of the appointment in Dynamics or event in Salesforce for the sales team to access. For custom entities, admins will choose the save field.

Get Started

Join us and other top-performing sales organizations worldwide. Reach out to your Microsoft sales team or visit our product web page.
Install Copilot for Sales. Have a look at our deployment guide for Dynamics 365 Sales users or our deployment guide for Salesforce users.

Learn more

All the details. Check out the Copilot for Sales product documentation.
The latest tips…and more. Copilot for Sales Tip Time can serve as a foundation for your training of Copilot for Sales users, customers, or partners! This content includes use cases and demonstrates how each feature will benefit sellers, administrators, and sales managers.
The latest adoption resources. Visit the Copilot for Sales Adoption Center and find the latest information about how to go from inspiration to adoption.

Stay connected

Learn about the latest improvements before everyone else at https://aka.ms/copilotforsalesupdates. Join our community in the community discussion forum and we always welcome your feedback and ideas in our product feedback portal.

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What is Sales Order Agent in Dynamics 365 Business Central?

The Sales Order Agent is an autonomous tool for addressing, clarifying, and fulfilling customer requests received via email.

Here’s what the Sales Order Agent does in very basic terms:

• Customer request: A customer sends an email requesting a sales quote.
• Agent action: The agent processes the email, reviews the request, and finds the customer and requested items.
• Quote creation: It verifies item availability, creates the sales quote, and prepares an email response.
• User review: The agent engages with an end user to approve the quote before sending it to the customer.
• Customer confirmation: The customer reviews the quote and requests an order.
• Order conversion: The agent converts the quote to an order and sends a confirmation email to the customer.

Want to see what this looks like in action? Watch this video:

Pre-AI, this would be a reasonably straightforward workflow, but adding each sales line individually can be a laborious and error-prone task, and the workflow could bread down as soon as there were any bumps in the road, for example: What if a customer’s message is vaguely-worded? What if customers don’t approve the quotes? What if the customer isn’t recognized in the system?

The good news is that with the power of Copilot, Sales Order Agent has been meticulously engineered to proactively address these issues. Read more about scenarios and limitations.

Passing the power to the user

Sales Order Agent is engineered to take away the manual processes of simple order-taking from the end user, allowing them to spend more time on more strategic elements of managing a growing business, offering a faster turnaround for customers, and quicker realization of value. But the end user ultimately has control over Sales Order Agent operations:

• Users are, by default, notified to approve any outbound correspondence from the agent for accuracy and to ensure that appropriate actions have been taken.
• Users can at any time see a full picture of the task’s context and history (“timeline” view), including the key steps displayed in the Copilot pane.
• Users will see a detailed review of each entity the agent created (such as sales quotes or sales orders), review all changes and suggestions the agent makes for a specific task, and adjust appropriately.
• Users have the power to discard the steps performed by the agent, adjust the quote or order as needed, and ask the agent to proceed with the flow.

All these actions (and more) provide users with the ultimate oversight, and over time, will improve the Sales Order Agent’s accuracy and usability. Learn more about how this works in practice.

The business value of autonomous sales orders

So, in a world where you’ve taken away the mundane tasks of cross-referencing customer discounts and terms, double and triple-checking inventory, and fumbling through procurement systems to generate purchase orders, there are naturally repercussions throughout your business.

Outside of the binary gains that have been mentioned already, let’s talk about what this next step in autonomous ERP processes can help enable:

• Scale your business without the traditional friction that accompanies growth: Traditionally, scaling up a business was a resource balancing act: How do we ensure that we’re able to meet the product, relationship, and personnel needs to increase operations? While the Sales Order Agent certainly doesn’t erase the full scope of challenges inherent with a growing business, it can help eliminate some of the friction by automating the systems that work within your current install base.
• Sales spends more time doing what sales does best: Rather than getting sidelined with lengthy order evaluations and wasting time on guaranteed revenue, sales reps can focus on building client relationships, generating net-new business, and evaluating ways to expand existing customer relationships with the knowledge that transactional interactions are managed automatically, with their oversight, when needed.
• Enjoy greater data accuracy across all systems of record: Business Central has always allowed finance, supply chain, and CRM systems to work in harmony with one another, and by further eliminating the prospect of human error, this is extended even further. This positive feedback loop will be further enhanced by prebuilt agents in the future, along with the custom agents that partners and end users build to help minimize clicks to complete and realize the goal of end-to-end ERP automation.

Sales Order Agent

Now available for customers in paid public preview

Learn more

Agents: The next frontier of AI

Our Dynamics 365 Business Central team is pleased and proud to bring this first agentic experience to market, but this is—of course—only the first. With our Payables agent launching later this year, along with the ability for partners and customers to design their own agentic workflows into Business Central, now is the time to begin this journey toward end-to-end business process automation.

If you’re a current Business Central customer, be sure to talk to your partner about how you can easily activate Sales Order Agent and accelerate your time to value in every inbound sales interaction today.

New to Business Central? See how partnering with Microsoft can accelerate your growth and unlock a new level of operational oversight and efficiency.

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AI Accelerator for Sales

We’re announcing AI Accelerator for Sales, an exclusive program designed to help more customers experience a new way of working with Copilot and agents, help transform your sales organization, and migrate off legacy CRM system vendors. This program includes:

• Microsoft 365 Copilot to empower every salesperson with an AI assistant.
• Prebuilt agents to accelerate time to value for common sales processes, including the Sales Research Agent, new to Microsoft Dynamics 365 Sales, that uses deep research to help drive strategic decisions.
• Custom agents with Microsoft Copilot Studio to automate bespoke sales processes.
• Model fine-tuning by Microsoft AI experts, further tailoring AI models and agents to your unique business needs.
• Dynamics 365 Sales to manage customer accounts and drive sales from lead to close.
• White-glove engagement, working hand in hand with Microsoft’s AI experts.

This program will be available for eligible customers through December 31, 2025. Contact your Microsoft representative to learn more.

Watch: Introducing the Sales Research Agent in Dynamics 365

Customers are making the change

Customers are already realizing the transformative impact of Microsoft’s platform within their sales organization. Lenovo, a global powerhouse in technology solutions, has experienced a streamlined and shortened sales cycle and improved customer service and responsiveness, boosting productivity—and profitability.

“We’re seeing the benefit of having one standardized system and a global view to all geographies’ activities. This is the foundation for Lenovo’s sales digital transformation—enabling better connections and an increase in sales productivity and actionable insights.”
—Wei Bi, Business Strategy Senior Manager, Lenovo

Lexmark, a global leader in innovative, cloud-enabled printing, imaging, and Internet of Things (IoT) products, software, solutions and services set out for a sweeping transformation, looking to replace their legacy Salesforce system. With Lexmark’s migration to Microsoft’s platform, the company now has a unified, omnichannel platform that centered the customer experience, standardized service delivery systems, offered user-friendly interfaces, streamlined sales and account management and reporting, and increased efficiency.

“There is magic in a single source of truth with standardized data. Data analytics, coupled with operational excellence, provides the level of service that only Lexmark can offer to customers, allowing real-time access to actionable data and insights to increase revenue generation and customer satisfaction.”
—Billy G. Spears, Senior Vice President and Chief Product Delivery Officer, Lexmark

New sales agents to help close more deals, faster

We’re also announcing two new agents that can free sellers’ time to focus on higher-value activities and help close deals.

Sales Agent can research and prioritize leads, set up meetings, and reach out to customers. Sales reps can focus time on closing the biggest deals while the agent ensures that no lead is left behind.

Sales Chat provides actionable takeaways from CRM system data, pitch decks, meetings, emails, and the web—so sales reps can spend less time digging and more time selling.

Both of these agents, which will be in public preview in May 2025, work with first-party and select third-party CRM systems and integrate with Microsoft 365 Copilot Chat. Learn more about the capabilities of these agents on the Microsoft 365 blog.

In addition, Customer Intent, Customer Knowledge Management, Case Management, Scheduling Operations, and Sales Order agents for Microsoft Dynamics 365 will be available in March 2025. Please note that use of these agents during public preview will require Copilot Studio capacity packs.

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