The Sales Qualification Agent (SQA) in Dynamics 365 Sales introduces a new class of autonomous sales AI, one that does far more than assist with drafting or summarization. SQA performs multi-step reasoning, conducts live web research, generates personalized outreach, and engages prospects in multi-turn qualification conversations. These capabilities directly shape pipeline quality, seller productivity, and customer relationships. 

As agentic AI becomes deeply embedded in revenue-critical workflows, trust must be earned through transparent, repeatable, and rigorous evaluation—not anecdotal wins or point demos.

Today, we’re announcing the Microsoft Sales Bench—a collection of evaluation benchmarks designed to assess the performance of AI-powered sales agents across real-world scenarios. Adding to the Sales Research Bench already published as part of this collection to evaluate Sales Research Agent, today we are also publishing the Sales Qualification Bench to evaluate Sales Qualification Agent in Dynamics 365 Sales.

This post presents the detailed evaluation methodology and results for the agent, including a head-to-head comparison against chatGPT using identical data, tasks, and scoring rubrics. These efforts establish the first benchmark purpose-built to measure end-to-end sales agent workflows, from research to outreach to live qualification. 

SQA Architecture  

The Dynamics 365 Sales Qualification Agent (SQA) architecture is designed as an end-to-end, enterprise-grade AI system that autonomously researches leads, synthesizes insights, and generates seller-ready outreach. It combines an intelligence engine powered by large language models with iterative web and enterprise data research, tightly integrated with Dynamics 365 Sales and Microsoft Copilot Studio for orchestration. Built on secure enterprise foundations, the architecture enforces governance, compliance, and data protection while enabling scalable, trustworthy AI-driven sales workflows. 

Evaluation Metrics and Methodology 

To understand how well the Sales Qualification Agent (SQA) performs in real-world sales qualification workflows, we designed the Sales Qualification Bench, a comprehensive evaluation that mirrors how sellers actually research leads, personalize outreach, and engage with prospects. Our goal was straightforward: measure whether SQA can help reps qualify faster, personalize more effectively, and carry higher-quality customer conversations—using the same signals and information they rely on every day. 

To ensure that the evaluations accurately represent real-world conditions, we developed a testbed that closely mirrors the complexity and ambiguity found in contemporary sales environments. This allowed us to evaluate SQA end to end, from autonomous research and reasoning to grounded, actionable research briefs, outreach messages, and multi-turn qualification conversations. 

Evaluation Setup

To ensure real-world fidelity, we constructed a production-like lead evaluation environment that mirrors how SQA operates in Dynamics 365 Sales. 

Lead and Data Corpus 

• Three synthetic but realistic seller companies (C1) across distinct industries, with unique: 
  • Product offerings 
  • Knowledge sources 
  • Ideal customer profiles 

• 300+ lead dataset (C2) expanded into a scenario-rich corpus: 
  • Companies across 6 global regions (North America, Europe, Asia, South America, Australia, Africa) 
  • 33 industries 
  • Mixed clarity (well-known brands and long-tail companies) 
  • Structured attributes (name, role, email) 

• CRM roles represented: 
  • Sales representatives 
  • Digital specialists 
  • Customer success managers 
  • Each linked to relevant accounts, opportunities, and cases 

• Company segment coverage: 
  • Enterprise 
  • Mid-Market 
  • Small Business 
  • Government 
  • Education 

• 500+ email exchanges simulating real sales interactions: 
  • Technical product questions 
  • Meeting requests 
  • Ambiguous or low-intent inquiries 

Simulated Agent Workflows 

All evaluations reflected real SQA behavior: 

• Autonomous web-based research 

• Role-aware outreach generation 

• Multi-turn qualification conversation handling 

Tasks Evaluated and Evaluation Metrics 

1. Company Research 

For each lead, the agent generates a structured research brief including: 

• Business overview, strategy and priorities 

• Financial signals 

• Recent news relevant to the seller 

Metrics	Definition
Recency	Measure of how recent time-sensitive insights are relative to the current date (older insights are not as useful for sellers)
Relevance & Solution Fit	Measure of how well the insights are tied back to sellers’ offerings (relevant insights are more actionable than a regurgitation of facts) and articulate the lead company’s need or interest in then
Completeness	Measure of how well the insights capture all the facts that are useful to a seller
Reliability	Measure of how consistently the agent finds useful insights for the seller (e.g., strategic priorities return current strategic priorities and not generic mission statements, news returns news articles and not generic evergreen statements about a company)
Credibility	Measure of how reputable the sources referenced by the agent are

2. Lead Outreach 

Based on its research, the agent generated a personalized email aligned to: 

• The lead’s role 

• The seller’s value proposition 

• The company’s business context 

• Value-based positioning 
   

Metric	Definition
Clarity	Assesses how clear, precise, and jargon-free the message is, ensuring every sentence adds value.
Personalization	Measures how well the email is tailored to the specific target company, using concrete company-level details rather than generic industry language.
News-anchored opening	Checks whether the email references recent company events or updates, ensuring the outreach feels timely and current.
Relevance and Solution Fit	Measure of how well the insights are tied back to sellers’ offerings/solutions (relevant insights are more actionable than a regurgitation of facts), and articulate the lead company’s need or interest in them
Structure	Evaluates whether the email has a clear logical flow from opening hook to problem, solution, and call to action.

3. Qualification Conversations (Engage) 

The agent then autonomously engages back and forth with the lead, progressively asking them questions for customer-configured qualification criteria such as budget, need, and timeline and answering the lead’s questions such as: 

• “What does your solution do?” 

• “How are you priced?” 

• “How do you compare to competitors?” 

• “Who else uses this?” 

Metric	Definition
Answer Quality	Assesses whether the agent provides clear, relevant, and complete answers that directly address the customer’s intent.
Agent Comprehension	Evaluates how well the agent understands customer intent, prioritizes requests, and adapts tone and strategy based on the user’s response.
Answer Readability	Checks that responses are natural, professional, easy to read, and fully compliant with formatting and content rules.
Human handoff accuracy	Ensures the agent correctly flags when human intervention is required, such as for unanswered technical questions, legal/billing requests, meeting requests, or explicit requests for a human.
Discovery question coverage	Measures how effectively the agent qualifies leads using indirect, strategic discovery questions across Need, Budget, Authority, and Timeline.

Each metric is scored independently on a 0–10 scale, where higher scores indicate stronger performance. We used an LLM-as-a-judge approach to score outputs against the ground truth and rubric and manually reviewed a sampled subset of evaluations to calibrate the judges and validate scoring consistency. To reduce judge variance and mitigate hallucination risk, each sample was evaluated five times, and the mean across runs was recorded as the final score. 

Benchmarking Strategy with ChatGPT 

To ensure an objective and fair comparison, we replicated a standard seller workflow in ChatGPT UI using GPT-4.1 with Pro license, a more advanced model than the GPT-4.1-mini variant currently used by SQA. 

Standard Prompting 

This setup simulates how a seller naturally interacts with a general-purpose LLM: 

• High-level contextual instructions only 
• Mirrors SQA’s autonomous research-to-outreach flow 

This ensures: 

• Workflows remain representative and unbiased 
• Comparisons reflect real-world usability, not prompt-engineering skill 

Identical Knowledge Sources and Context 

ChatGPT was given the exact same knowledge sources as SQA, including: 

• Full lead information and seller value proposition 
• Seller Q&A documentation via the SharePoint connector 
• Historical conversation context for reply generation 

This isolates differences in agent reasoning and orchestration, not data access. 

Evaluation Results  

Microsoft evaluated the Sales Qualification Agent (SQA) and ChatGPT with over 300 leads, covering research, outreach, and qualification tasks with identical knowledge sources. Evaluations completed on December 4, 2025, showed that SQA consistently outperformed ChatGPT-4.

• Research: SQA was 6% more effective at relevant, thorough company research. 
• Outreach: SQA was 20% better at personalized communication and timely event references. 
• Engagement: SQA scored 16% higher for precise responses and targeted qualifying questions. 

SQA also operates autonomously, reducing overhead and boosting pipeline quality for sales teams. 

Results by Task Category 

1. Company Research 

SQA was 6% better than ChatGPT, winning in its ability to perform more relevant and complete research that highlighted the lead company’s interest in the sellers offerings: 

• SQA provided more relevant results: To ensure sellers spend their time on the most important leads, they need to determine whether a lead is good fit for their offerings. While both SQA and ChatGPT were given the same context (seller company and value proposition of the offerings), SQA consistently did better at tying its research back to this context, helping sellers determine fit. Appendix A shows an example where SQA was able to tie the company’s strategic priorities to its need for a collaboration platform and infer strong purchase ability from its robust operational health and minimal leverage burden.

• SQA synthesized results with higher level of fidelity and completeness: The agent’s value is directly correlated to its ability to eliminate tedious work for the seller. SQA produced more detailed research synthesis (as demonstrated in Appendix A), giving a single, trusted source for the seller to get equipped with any insights they may need.  

These results stem from numerous experiments aimed at optimizing web research for the best outcomes at minimal cost, rather than relying on costly advanced models. Sellers get deeper insights with SQA’s agentic RAG for real-time reasoning with iterative web search results, combined with unique capabilities that increase data coverage, for example, auto-linking CRM records and extraction of company name from lead emails. 

2. Personalized Outreach 

SQA was 20% better than ChatGPT, notably ahead in the level of personalization and mentions of relevant recent events that will resonate with the lead. 

• More personalized and customer-centricity: A lead is more likely to respond to a cold outreach email that directly explains how the seller’s offering can address their needs. SQA did so effectively by starting with the lead’s situation and recent events, while ChatGPT often focused on the seller and uses heavier technical jargon. A clear, actionable call to action bookends the email and guides the conversation forward. Appendix B shows an example of how SQA was able to tie a recent acquisition the lead’s company made to the value proposition of the seller’s offering. 

These results are based on direct engagement with sellers – every sales team that deploys SQA gives us precious feedback that all other customers benefit from.   

3. Qualification Conversations (Engage) 

SQA was 16% better than ChatGPT. It responded with greater precision to the lead’s questions to develop purchase interest and asked pointed discovery questions to better qualify the lead before handing off to a seller. 

• Answers accurately by correctly understanding the lead’s intent and maintaining conversation context effectively. To drive deeper buyer consideration, SQA independently answered even the most technical questions that leads had about the seller’s offerings while maintaining the context from earlier messages in the simulated conversation, delivering clear, direct, and well-structured responses. Appendix C demonstrates SQA’s ability to pull the most relevant information from provided knowledge sources (in this case, files with technical specifications) during an ongoing conversation with a lead. 

• Handles uncertainty responsibly, handing off to a supervisor/seller when appropriate. Both SQA and ChatGPT were instructed to handoff a lead to a supervising seller when a suitable response cannot be generated or when the lead is considered qualified as per pre-defined criteria. SQA handed off accurately and at the right moment in more tests than ChatGPT.  

• Demonstrates strong discovery coverage. To maximize the value exchange from each follow-up conversation with the lead, SQA and ChatGPT were instructed to include discovery questions in their response to assess pre-configured qualification criteria (covering lead’s need, budget, buying authority and purchase timeline). SQA was able to ask pointed discovery questions to cover more of these criteria than ChatGPT in our simulated conversations. This resulted in SQA identifying and handing off better qualified leads through its engagement.

These gains are attributable to the hard lessons we have learnt through close collaboration with customers to understand the diversity of needs on intent detection and knowledge retrieval across AI agents for Dynamics 365 Sales, Service and Contact Center applications.  

Conclusion 

Sales Qualification Agent (SQA) is not a simple productivity tool—it is a complex multi-step agent directly influencing revenue outcomes. The Sales Qualification Bench represents a foundational step toward enterprise-grade trust, transparency, and continuous quality improvement for agentic AI in sales. 

Through rigorous, scenario-driven benchmarking, SQA demonstrated clear leadership across research, outreach, and qualification—while also surfacing concrete architectural and rule-quality investments for the next phase of growth. 

Upcoming plans for the Sales Qualification Bench include using the benchmark for continuous improvement of the Sales Qualification Agent, running further comparisons against a wider range of competitive offerings, and publishing the eval package so customers can reproduce it on their own data. Evaluation is not a one-time event. It is now embedded as a first-class engineering discipline in the SQA development lifecycle—ensuring the agent evolves safely, measurably, and in lockstep with seller needs. 

Appendix

Sample inputs and responses for each category  

Appendix A. Sample of research synthesis generated by SQA

Appendix B. Sample of an initial outreach email generated by SQA 

Appendix C. Sample of an engagement email generated by SQA 

The post Sales Qualification Agent: How we evaluated and improved AI quality with benchmarks appeared first on Microsoft Dynamics 365 Blog.

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: 

1. 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.
2. 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.”¹

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:

1. 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.
2. 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.
3. 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:

1. It uses agentic Retrieval Augmented Generation (RAG) to relentlessly research each lead, ensuring greater completeness. More on this in the following section.
2. With knowledge of what the company sells, it can contextualize every workflow to increase relevancy for both the seller and the lead.
3. 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.

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¹ChatGPT pricing page, accessed November 24, 2025

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Markets are shifting faster than traditional operating models can adapt. Customer expectations are rising. Capital constraints require every decision to deliver measurable value. In this environment, organizations that thrive are those treating business model innovation as a core capability–not an occasional strategy exercise. They are rethinking how value is created, how operations scale, and how technology supports the enterprise. 

ERP is evolving to meet this moment. As Satish Thomas described in his recent blog, we’re entering the era of agentic business applications: systems moving beyond recording transactions to actively orchestrating processes, anticipating needs, and adapting to change. For leaders, this evolution means ERP is becoming a system of action. It aligns people, data, and workflows around the outcomes the business is driving toward. 

Why rental business models are accelerating 

In asset- and across product-driven industries, the ability to generate value from equipment, tools, and machinery has always been central to business performance. Increasingly, customers want access to what they need, when they need it, without long-term ownership. This asset-as-a-service shift is expanding across industries–from heavy equipment, consumer goods, and automotive to medical devices, technology assets, and renewable energy. 

Global forecasts underscore this momentum. Industry forecasts from the American Rental Association and independent market analysts indicate the North American equipment and tool rental market will exceed $80 billion. Global rental and leasing revenues are already well above $500 billion annually. Similar trends are emerging in adjacent verticals, all signaling that the opportunity extends far beyond traditional equipment categories. 

Today, rental processes often run across fragmented systems for quoting, dispatching, billing, and financials. The result: avoidable idle time, slow handoffs, margin erosion, and most critically, subpar customer experiences. 

This is where Dynamics 365 ERP can help you with transforming your rental operations business processes. 

A strong connected foundation  

Operational excellence in rental management demands more than isolated workflows. It requires seamless integration and orchestration across the entire lifecycle. From rental rate management, quoting, and asset reservation to contract management, inspections, maintenance, and billing, every step must work in harmony to keep revenue moving and customers delighted. 

Building these capabilities into Dynamics 365 connects the full lifecycle of work within a single agentic ERP. 

Today, we are announcing that we are making investments to accelerate adding new capabilities for rental operations. These capabilities are now in development, planned for release in Q4 of 2026, including new ERP capabilities designed for: 

• Quoting and reservations to confirm availability and seamlessly convert opportunities into contracts. 
• Contract and pricing management for short- and long-term rentals, rent-to-own programs, or seasonal pricing with flexible terms and rate structures. 
• Inspections orchestration to coordinate inspections upon deliveries, transfers, and returns. 
• Billing and invoicing tied directly to rental activity to improve accuracy and reduce reconciliation effort. 

Rental operations succeed when every handoff–from quoting to return–is coordinated and timely. The forthcoming capabilities bring structure and clarity to these moments. They are designed to help organizations accelerate deal cycles, improve asset utilization, enhance customer satisfaction, and reduce reliance on custom or manual processes. 

By unifying these processes inside Dynamics 365, leveraging the composability of Dynamics 365 Finance & Supply Chain Management, Project Operations, and Field Service, organizations will be able to run rental as a natural extension of their operations rather than as a separate system or afterthought. 

Driving utilization, uptime, and margin 

The levers that shape rental performance–utilization, uptime, margin, and cash flow–are all influenced by how well operational data connect across the lifecycle. When organizations have a single view of reservation status, asset availability, and maintenance needs, they can plan more effectively and limit avoidable idle time. Consistent pricing and billing structures then help ensure every transaction reflects the same rules and logic, reducing confusion and rework. When maintenance activities are linked to actual rental usage, teams can schedule work proactively, support asset longevity, and reduce the risk of unplanned downtime. 

With these elements working together, rental operations can run with greater predictability – improving financial clarity while delivering more reliable, trusted customer experiences. 

Turning operational telemetry into financial clarity 

Operational data is only as valuable as the financial clarity it enables. Information such as rental item status, reservations, and maintenance history can become a strategic asset when used to drive accurate forecasting, informed capital allocation, depreciation planning, and profitability analysis. By connecting operational metrics with financial outcomes, organizations can optimize resource utilization, reduce risk, and uncover opportunities for growth. 

Enabling a strong ecosystem  

The rental management capabilities that we are developing in Dynamics 365 will form a robust foundation for rental businesses. Rental operations vary significantly within and across industry verticals. To address this, we continue to build on our proven model of success. We are empowering the extensive ecosystem of Microsoft Dynamics 365 partners and ISVs to deliver specialized, deeply vertical solutions that meet unique business needs. 

Because these foundational capabilities will run natively in Microsoft Dynamics 365 on the Microsoft Cloud, customers, ISVs, and partners can extend them with AI agents using MCP and Microsoft Copilot Studio to support vertical-specific requirements from front-office process optimization and automation to compliance, pricing strategies, and equipment lifecycle planning. The flexibility of the Microsoft Cloud, combined with advanced AI, is designed to help organizations accelerate innovation, optimize operations, and deliver differentiated customer experiences that drive growth and profitability. Microsoft remains committed to enabling innovation across the ecosystem.  

Microsoft Ignite 2025: Powering Frontier Firms with agentic business applications

Looking forward: the future of rental, built into agentic ERP 

Flexible, service-based operations are transforming how organizations create value from their assets. Our investment in rental management capabilities is designed to help customers meet this moment. It will simplify processes, improve visibility, and deliver measurable business outcomes. 

If your organization operates or supports rental models today, now is an ideal time to explore what’s possible with Dynamics 365. If you’re attending Convergence 2025, you’ll see firsthand how these investments align with our broader vision for adaptive, agentic ERP systems–solutions that work alongside your teams to drive operational excellence and unlock new opportunities for growth. 

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