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  • Dynamics 365 CRM Solutions

    Dynamics 365 CRM Solutions

    A comprehensive guide to Microsoft Dynamics 365 Sales and Customer Service.

    What are the Different CRM Products and How Do They Differ?

    Dynamics 365 Sales

    Designed for sales teams to manage the entire sales lifecycle. It helps teams track leads, manage opportunities, forecast revenue, and close deals. Perfect for real estate agents managing property listings and sales pipelines.

    Best for: Sales teams, lead management, pipeline forecasting, deal tracking

    Dynamics 365 Customer Service

    Focused on customer support and service delivery. It enables teams to manage customer inquiries, track cases, maintain knowledge bases, and provide omni-channel support. Ideal for property management inquiries and after-sales service.

    Best for: Support teams, case management, service delivery, customer inquiries

    Key difference: Sales focuses on acquiring customers and closing deals, while Customer Service focuses on supporting customers throughout their journey and managing inquiries. For a comprehensive CRM, both modules work together seamlessly.

    What are the Specific Features of Each Product License?

    Each product (Sales and Customer Service) is available in two licensing tiers: Professional and Enterprise. The tier you choose determines which features are available to your team.

    Dynamics 365 Sales

    Professional:

    • Lead and opportunity management
    • Account and contact records
    • Product catalog and price lists
    • Quote and order management
    • Basic forecasting
    • Outlook and Teams integration
    • Mobile app access
    • Standard analytics and reports

    Enterprise (All Professional features, plus):

    • Advanced forecasting and revenue analytics
    • Territory management
    • Goal management and tracking
    • Enhanced customization (custom tables and processes)
    • AI-powered insights (Copilot, predictive scoring)
    • Advanced workflow automation
    • Broader Dataverse storage entitlements
    • Advanced reporting and analytics

    Dynamics 365 Customer Service

    Professional:

    • Case and ticket management
    • Customer knowledge base
    • Basic queue management
    • Self-service portal capabilities
    • Email and chat integration
    • Mobile case management
    • Standard analytics for agents
    • Basic SLA tracking

    Enterprise (All Professional features, plus):

    • Intelligent routing and skills-based assignment
    • Advanced SLA management
    • Resource scheduling and optimization
    • Omni-channel support unified across channels
    • Advanced automation (Power Automate integration)
    • Deeper analytics and dashboards
    • AI-powered insights and recommendations
    • Integration with other Dynamics 365 apps

    Integration and Productivity Tools (All Tiers):

    • Outlook Integration – Track emails, appointments, and tasks directly in Dynamics 365
    • Teams Integration – Access records and take actions without leaving Teams
    • Power Automate – Automate workflows, notifications, and task creation
    • Power BI – Create custom analytics and dashboards (Enterprise tiers enhanced)
    • Mobile Apps – Full access to records and functionality on smartphones and tablets
    • Portal Capabilities – Allow customers to self-serve without direct agent contact

    How is Pricing Structured?

    Microsoft Dynamics 365 uses a per-user, per-month subscription model. This means you pay for each named user who needs access, and billing is based on the license tier they use. There are no fixed-price options from Microsoft, though implementation services are billed separately.

    Current Pricing Structure (indicative USD monthly rates for 2025):

    User License Costs (Monthly per User):

    • Sales Professional: $65/user/month
    • Sales Enterprise: $95/user/month
    • Customer Service Professional: $50/user/month
    • Customer Service Enterprise: $95-105/user/month

    Example: 20-25 User Organization

    For a small-to-medium organization with 20-25 total users across Sales and Customer Service, a typical mix might look like:

    • Sales Professional (15 users): $65 × 15 = $975/month
    • Sales Enterprise (3 users): $95 × 3 = $285/month
    • Service Professional (5 users): $50 × 5 = $250/month
    • Service Enterprise (2 users): $100 × 2 = $200/month

    Total Monthly Subscription Cost: $1,710

    Implementation Services (Separate from Licensing)

    In addition to monthly user licenses, implementation and consulting services are billed separately. Stirna Consulting offers implementation, configuration, training, and support at €105/hour for CRM consulting, configuration, implementation, and training.

    Key Pricing Points:

    • Per-User Billing: You only pay for users who need access
    • Flexible Mix: Combine Professional and Enterprise licenses as needed
    • Scalability: Add or remove users as your team grows or changes
    • Discounts Available: Microsoft often provides discounts for multi-year commitments or bulk licensing
    • No Lock-in: Monthly billing with flexibility to adjust at renewal
    • Implementation Separate: Consulting and setup costs are additional and customized to your scope

    Comparing Models:

    Unlike some CRM systems with fixed-price plans, Dynamics 365’s per-user model means:

    • Transparent: You know exactly what you’re paying for
    • Scalable: Grow your system without major price jumps
    • Flexible: Pay only for the users and features you need
    • Enterprise-Grade: Access to Microsoft’s full support and updates

    Ready to Implement Dynamics 365 for Your Organization?

    Stirna Consulting specializes in designing lean, real estate-focused CRM implementations. We can help you assess fit, plan your phased rollout, and train your team—all at a fixed rate of €105/hour.

    Contact us at contact@stirna.is for a free consultation on Dynamics 365 CRM implementation.

  • Jóla- og nýárskveðja

    Jóla- og nýárskveðja

    Stirna ráðgjöf býður viðskiptavinum sínum gleðilegrar hátíðar. Um leið þá þökkum við samstarfið á árinu sem er að líða.

    Við vonum að þau góðu samskipti sem við höfum átt við viðskiptavini og samstarfsaðila á árinu 2025 muni halda áfram að vaxa og dafna 2026.

    Gleðileg jól og farsælt nýtt ár.

    Kveðja,

    Starfsfólk Stirna ráðgjafar

  • Speaking Icelandic when you are an American AI: Multilingual capabilities of LLMs

    Speaking Icelandic when you are an American AI: Multilingual capabilities of LLMs

    Introduction

    Being an author of a tech blog, one must constantly seek new or noteworthy ideas or material to talk about. Scarcity of ideas is not really a problem. Rather, the ideas are not always good. The idea behind this blog article, that is, to compare AI models in terms of their capacity to speak Icelandic (or any non-English language for that matter), would have been dismissed as ridiculous had I thought of it when I launched this website and blog in 2020. That year, I started a blog and an independent IT practice. I also started my PhD study on AI and e-government, being accepted into the PhD program at Reykjavik University. At the time, generative AI was still recent and apps such as Chat GPT did not exist. Most tech bloggers were not interested in AI’s linguistic potential. Instead, bloggers curious about AI emphasized machine learning and big data capabilities. In brief, AI applications such as machine translations (read Google Translate) have, historically, been terrible at translating small languages, particularly Icelandic.

    Large language models or LLMs, such as GPT series, are enabled by free open big data on the internet and powerful cloud-based infrastructure, have given rise to the development of virtual agents like ChatGPT. Such AI systems are called generative AI (GenAI) applications and can generate text in response to a particular prompt or input from users (Hjaltalin, forthcoming).

    Icelandic is a micro language, only spoken by approximately 300.000 people, most of whom live in Iceland. Icelanders ought not to take for granted that it will be in major LLMs. AI researchers in Iceland have worked hard to create an Icelandic

    language model (not to be confused with LLMs, however). The aim is to ensure that AI models, including popular LLMs (e.g., GPT-4), can speak and understand Icelandic (see Heimisdottir, 2024).

    The method

    Against this (albeit brief) background, let’s delve into the specifics of the analysis. I chose Gemini (Google’s LLM) and Co-Pilot (powered by OpenAI’s LLM, i.e., GPT-4) as my GenAI sample. While OpenAI’s GPT-4 represents the first LLM to learn Icelandic (“Preserving languages for the future”, 2023), Gemini has, more recently, learned it as well (see below). Both LLMs were presented with the following two questions:

    1. Talar þú íslensku? / Do you speak Icelandic?
    2. Hvernig myndir þú lýsa Íslandi? / How would you describe Iceland?

    The evaluation of the results is not based on the content’s size or quality but on grammatical soundness. For instance, I check if they use appropriate words and if they use the correct form (e.g., past tense, case, etc.). Particularly, fallbeyging or declination (cases shaping nouns and pronouns) is a difficult grammar rule that foreigners (and some Icelanders) struggle with when learning the language. If the model accomplishes declination of (pro)nouns, this would indicate superb Icelandic skills.

    Analysis and results

    In this section, I present my evaluation of the performance of the two models. For your information, I am not a linguist or Icelandic teacher, but I have a good understanding of my native tongue. I feel confident that my analysis is robust and valid for Icelandic.

    Results indicate that both models speak (or write) Icelandic. Excellent!

    This covers the first part of my analysis. In the second part, we will explore how well or poorly they perform in their spoken (or written) Icelandic, particularly focusing on grammar rules (see above).

    Gemini’s Icelandic skills are surprisingly good. The text is remarkably elo- quent and creative in terms of vocabulary and descriptions. On the other hand, as presented in Figure 1, there were minor errors in Gemini’s response to my question.

    Figure 1: Gemini’s (G) answer to question 2: How would you describe Iceland?
    Figure 1: Gemini’s (G) answer to question 2: How would you describe Iceland?

    Specifically, three errors were found in terms of pronouns and forming adjectives depending on the gender of the subject at hand.

    Co-pilot’s Icelandic skills were decent, although I hoped for a more creative response. While it is clearly not as eloquent and rich as Gemini’s response, this is not the determining factor in the evaluation. Indeed, Co-pilot performed quite well in terms of grammar and even outperformed Gemini in some areas, such as using pronouns (see Figure 2).

    Figure 2: Co-pilot’s (C) answer to question 2: How would you describe Iceland?
    Figure 2: Co-pilot’s (C) answer to question 2: How would you describe Iceland?

    Gemini is stronger in fallbeyging or declination; I did not detect any errors in its response in terms of declination of nouns. However, Co-pilot uses the incorrect case of Geysir, as illustrated in Figure 2. As such, Gemini’s Icelandic skills are superior to Co-pilot’s.

    Conclusion

    Most (if not all) Icelandic linguists can agree with the claim that declination of nouns is the “holy grail” of Icelandic proficiency. If you have mastered declination (or fallbeyging), you have reached an advanced proficiency in the Icelandic language.

    Overall, I was impressed with the proficiency of both AI models in Icelandic. I am curious to know how well they perform in other languages. Of course, the benchmark used here (i.e., declination) does not apply to other languages, so this should be adapted in future research. Please share your thoughts in the comments section below.

    References

    Heimisdottir, L. (2024, September). The Icelandic Approach: Preserving and Re- vitalizing Linguistic and Cultural Diversity in AI.

    Preserving languages for the future. (2023, March).

  • Generative AI applications in business applications: a comparison between Salesforce and Microsoft’s digital platforms

    Generative AI applications in business applications: a comparison between Salesforce and Microsoft’s digital platforms

    Generative AI and its applications

    Generative AI (GenAI) represents a significant shift from traditional AI models that involves moving from recognizing patterns to generating new content such as text, audio, video, and images. In the context of enterprise software, GenAI has various applications:

    • Automation: Automates repetitive tasks, enhancing productivity and efficiency.
    • Customer Service: Provides personalized responses and support through chatbots and virtual assistants.
    • Data Analysis: Generates insights from large datasets, aiding decision-making and strategic planning.

    Recent Trends and Advancements

    We can note recent advancements in GenAI within the enterprise sector that include the development of so-called large language models (LLMs), such as OpenAI’s GPT series. LLMs can generate human-like text and have an exceptional understanding of natural language. Major tech companies like Salesforce and Microsoft are embedding GenAI into their platforms to innovate. This involves a significant increase in investments in AI technologies and efforts to forge new partnerships with GenAI startups to drive innovation and stay competitive.

    Importance of Efficiency in AI Models

    Efficiency in new AI models like LLMs is crucial to reduce innovation barriers. In fact, very few companies can afford to buy the computational resources needed to effectively run LLMs. In particular, efficiency of LLMs should be improved because of:

    • Computational resources: Efficient models require fewer computational resources, making them more accessible and sustainable.
    • Speed: Faster models can process and generate outputs quickly, improving user experience.
    • Cost-effectiveness: Reducing the computational load lowers operational costs, making AI solutions more affordable (or feasible).

    Evaluating the Efficiency of GenAI Models

    To evaluate the efficiency of GenAI models, we must consider issues such as (Hugging Face, n.d.):

    • Resource utilization: Measuring the computational power and memory required.
    • Processing speed: Assessing the time taken to generate outputs.
    • Cost: Analyzing the operational costs associated with running the models.
    • Performance metrics: Evaluating the generated content’s accuracy, relevance, and quality.

    Salesforce’s Platform and GenAI Integration

    Salesforce, a leading CRM platform, has integrated GenAI into its various Cloud services, including Sales Cloud, Service Cloud, Marketing Cloud, and Commerce Cloud (see Haki et al., 2025). This integration was found to enhance personalized communication, improve process automation, and data-driven insights.

    Bring Your Own Large Language Model (BYOLLM)

    BYOLLM allows customers to use their LLMs within the Salesforce platform. This approach offers flexibility and ensures customers can leverage models that best fit their specific needs, potentially improving efficiency and performance.

    Examples of Salesforce’s Adoption of Appropriately Sized LLMs

    In addition to the BYOLLM approach, Salesforce’s AI strategy about ‘appropriately’ sized (or not so large) LLMs concerns efficiency and performance enhancement through (1) their own AI platform and (2) existing models:

    1. Proprietary models: Salesforce has developed its models, like xLAM and xGen-Sales, which are optimized for specific tasks and industries.
    2. Fine-tuned models: Salesforce uses fine-tuned versions of existing models like OpenAI’s GPT. These models are customized with Salesforce data to align with customer needs.

    Such strategies (i.e., 1 and 2) ensure that Salesforce can deliver high-performance GenAI capabilities while effectively managing computational resources and costs (Haki et al., 2025).

    Microsoft’s AI Platform and Integration of Generative AI Models

    Microsoft’s AI platform, primarily powered by Azure, integrates generative AI models to enhance enterprise software applications, such as Microsoft Dynamics (CRM). Azure OpenAI Service allows businesses and institutions to leverage LLMs, including GPT-4, for various tasks, e.g., data analysis, customer support, and workflow automation. A key feature of this digital platform is that it includes a framework for integrating AI models with external tools and data sources (“Azure OpenAI Service”, n.d.), linking AI models and external services. The integration framework (or MCP) standardizes communication, which could enhance service integration, accessibility, and AI capabilities (Kaur, 2025).

    Microsoft’s BYOLLM Approach

    Microsoft’s Bring Your Own LLM (BYOLLM) approach allows businesses to use their LLMs on Azure. This approach can enhance efficiency by enabling organizations to customize their AI solutions based on their needs, which can lead to better performance and relevant results. For example, this allows businesses to perform fine-tuning, i.e., a particular machine learning technique (Roberts et al., 2020), which significantly reduces the time and computational resources required.

    Examples of Appropriately Sized LLMs for Efficiency and Performance

    Microsoft’s AI strategy to optimize LLMs involves (Jiang et al., 2023):

    • LLMLingua: A technique that compresses prompts for LLMs, i.e., reduces the length of prompts while maintaining their effectiveness. This technique led to circa 20x reduction in prompt size that improved inference speed and reduced latency.

    • On-Prem LLMs: Developing tools that allow businesses to deploy LLMs on their on-premise IT infrastructure, supporting increased data security and privacy. These tools helped optimize inference performance on existing infrastructure that maintained consistent latency and throughput.

    • Scalable Fine-Tuning: Azure OpenAI Service supports scalable fine-tuning of LLMs, allowing businesses to adapt models to their specific tasks efficiently, as noted above.

    These examples highlight Microsoft’s commitment to providing a resilient and robust digital platform for AI development (i.e., Azure OpenAI Service) tailored to the needs of individual businesses (Jiang et al., 2023).

    Conclusion

    Both Salesforce and Microsoft are actively embedding GenAI into their platforms, focusing on efficiency through strategies like BYOLLM and using appropriately sized or optimized models tailored to specific business needs. Both companies offer flexible and high-performance AI solutions for enterprises.

    Their AI strategies differ slightly, however. Whereas Salesforce emphasizes proprietary and fine-tuned models integrated deeply within its CRM platform, Microsoft leverages its broad Azure infrastructure, offering advanced optimization techniques and flexible deployment options like on-premise solutions.

    Feel free to leave your thoughts in the comments section below.

    References

    Azure OpenAI Service. (n.d.). Retrieved from azure.microsoft.com on April 14th 2025.

    Haki, K., Safaei, D., Magan, A., & Griffiths, M. (2025). Integrating Generative AI Into Enterprise Platforms: Insights From Salesforce. Information Systems Journal. doi: 10.1111/isj.12593

    Hugging Face. (n.d.). LLMs. Retrieved from huggingface.co/docs/ on April 15th 2025.

    Jiang, H., Wu, Q., Lin, C.-Y., Yang, Y., & Qiu, L. (2023). LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023).

    Kaur, S. (2025, March). Unleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration — Microsoft Community Hub.

    Roberts, A., Raffel, C., & Shazeer Google, N. (2020). How Much Knowledge Can You Pack Into the Parameters of a Language Model? Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 5418– 5426.

  • GhatGPT and Microsoft

    GhatGPT and Microsoft

    Earlier this year (Jan 2023), Microsoft announced its partnership with OpenAI concerning artificial intelligence (AI) development in the coming years. This announcement was particularly interesting because, just a few weeks earlier, OpenAI released ChatGPT3, which caught the world by surprise, to say the least. So what does this mean for Microsoft users, in particular those using its business applications?

    To answer this question, I researched one new product, Sales Copilot, and tested it (see below). It is an add-on to Outlook that integrates CRM data with Outlook emails using AI. This seems appropriate as Outlook is the app many people use to talk with colleagues and partners. ChatGPT (the underlying technology) is precisely that, i.e., an interface used to chat with AI to solve problems or find solutions to questions. Imagine an AI agent that is always available in case we have questions or need answers to work-related issues. Wouldn’t that be something?!

    Before we get too excited, GhatGPT, and other systems based on similar technology, are still in their infancy. These systems are not yet mature enough to have a serious transformational impact on our daily work. Nevertheless, the impact has been large in terms of people’s increased interest in AI, which, generally, I think is a good thing. This interest will drive further investments into AI research and development.

    Anyway, turning to the topic at hand. In the announcement mentioned above, Microsoft said: “we will deploy OpenAI’s models across our consumer and enterprise products and introduce new categories of digital experiences built on OpenAI’s technology.” Moreover, it talks about how Microsoft has supplied the company with the IT infrastructure needed to develop its products, including ChatGPT and other solutions. However, Microsoft didn’t reveal much about its intentions to use these solutions until months later when it announced Microsoft Sales Copilot (previously Viva Sales).

    The Sales Copilot is an AI application that enables sales teams to better access relevant client information to help them close more deals.

    Sales Copilot in Outlook

    Sales Copilot can show you highlights from email conversations with clients or prospects, which you can use to perform various tasks, e.g., create a reply, save information to the CRM (Salesforce or Dynamics 365).

    If you would like to test the AI, it’s easy to add it to Teams and/or Outlook. Let me know in the comments what you think.

  • Sending HTML emails through Dynamics 365

    Sending HTML emails through Dynamics 365

    Task: Send HTML email though Dynamics 365
    Difficulty: Advanced
    Time to implement: 1-2 hours

    Check out this video

    Tracking emails is a popular feature in Dynamics 365 Customer Engagement. You can track emails by connecting your mailbox with Microsoft Exchange and either use the Dynamics 365 app for Outlook or Dynamics 365 email capabilities to send out emails. Companies see great value in this feature because it allows them to have a CRM system in place where relevant interactions between a customer and an organization can be observed.

    In this post, I am going to show you how to use the Dynamics 365 emails and discuss some of its benefits and limitations in terms of functionality. Dynamics 365 emails are equipped with a simple email editor for creating emails. The editor covers the basic requirements for what you would normally use when sending an email from your email client such as Gmail or Outlook.

    Email editing experience in Dynamics 365

    Dynamics 365 emails allow you to send emails to multiple recipients, add attachments, insert links and images. However, if you want to create your own HTML email you cannot do that through Dynamics 365 emails. This could be useful in scenarios where the company wants to send out a promotional emails, e.g. because there is a new product release or an event. Dynamics 365 does not support HTML emails.

    On the other hand, if you like to send HTML emails through Dynamics 365 there is a workaround. By leveraging Power Automate and the Dataflex (CDS Current enviornment) connector, you can create a workflow that creates and sends an email with HTML content.

    For example, you have a lead that you would like to send an event invite to. You can achieve this by creating a flow in Power Automate and use a field on the lead to trigger the flow. The salesperson could simply check a box on the lead form to send the invitation. Furthermore, the email would be automatically tracked in Dynamics 365. This gives the salesperson and her manager a better overview of who has received an invitation to an event.

    In the remainder of this post I will show how you can create and send a HTML email through Dynamics 365 Sales with the help of Power Automate.

    1. Create the flow

    1. You start by navigating to make.powerapps.com and create a solution for your Flow. For information on how to create a solution in Power Apps please refer to this article.
    2. Click on the ‘+New’ button and select Flow
    Create a Flow from within a Power App solution
    Create a Flow from within a Power App solution
    1. Search for and select the Dataflex ‘When a record is created, updated or deleted’ trigger (Common Data Service current).
    2. Give your Flow a name.

    2. Configure the flow

    Once you have created the flow you can define the entity name, the type of trigger condition as well as the filtering attribute for the newly created trigger. In this case we have created a custom field which will serve as the trigger for this flow or the filtering attribute.

    1. In trigger condition choose Update.
    2. In the entity name choose Leads.
    3. For the Scope select Organization.
    4. In the filtering attribute write the name of your custom field.
    Dataflex 'When a record is created, updated or deleted' trigger (Common Data Service current)
    The When a record is created, updated or deleted trigger (CDS current)

    Next we need a variable to store our HTML. To do this we:

    1. Click on the ‘New Step’ button.
    2. Select Initialize variable.
    3. Give our variable a name and choose type string. Leave the value field empty.
    Power Automate Initialize variable
    Initialize variable

    Before we set the variable you first want to check if our field value meets the condition.

    1. Click on the ‘New Step’ button.
    2. Select control.
    3. Choose control condition.
    4. For the condition choose the custom field and that it should be equal to ‘true’. The field is a two option field which can be either true (1) or false (0).
    The condition should be that send invitation value is true.

    When the condition is true you want the email to be sent. First you need to set the variable we created in our previous step and then you will create the email. Lastly you want to send the email message using a bound action.

    1. In the ‘If yes’ box click ‘Add an action’.
    2. Choose Set variable.
    3. In the name select the variable you initialized in steps 2.6-2.7.
    4. In the value field paste in your HTML code.
    Set variable and have it store the HTML code

    Now you have created the condition and set the variable. This leads to the creation of the email message. To create the message you:

    1. Below the ‘Set email content’ action click ‘Add an action’.
    2. Choose Create a new record – Dataflex (Common Data Service current).
    3. For the email message choose the sender and receiver of the message. You must refer to the entity’s path as shown in the following screenshot. For more info about how this connector’s attributes are referenced see this article.
    How lookup fields are referenced in the create a new record current action
    How lookup fields are referenced in the create a new record current action
    1. Click ‘Show advanced options’ and add the email body to the ‘Description’ field, owner in the ‘Owner (Owners)’ field, the lead (‘/leads/{the lead GUID value}’) in the ‘Regarding (Leads)’ field, and the subject in the ‘Subject’ field.

    The final step is to send the email. This requires a special kind of action called a bound action. To see the difference between bound and unbound actions please refer to this article.

    1. Below the ‘Create a new record’ action click ‘Add an action’.
    2. Choose Perform a bound action – Dataflex (Common Data Service current).
    3. In the Entity name choose Email Messages
    4. For the Action Name choose SendEmail.
    5. In the Item ID insert the Email Message ID. You find the ID by using the dynamics content and choosing the unique ID of the email message you created in steps 2.16-2.19 above.
    6. In the IssueSend select Yes.
    Perform a bound action - Dataflex (CDS current) trigger
    Perform a bound action – Dataflex (CDS current) trigger

    3. Test the flow

    You can now test your flow to see if everything works as it should. Do a test run on a lead in your Dynamics 365 Sales to verify that the flow runs when the custom field is changed on the lead and if the email is sent when the flow condition is true. For more information about performing the test, please refer to the blog post’s video above.

  • Integrate your website with Dynamics 365 Sales

    Integrate your website with Dynamics 365 Sales

    Task: Integrate your website with Dynamics 365 Sales using Parserr parsing software
    Difficulty: Advanced
    Time to implement: 1-2 hours

    I am quite excited about sharing with you a great discovery I made when surfing the web looking for new ways to handle automatic record creation in Dynamics 365 from emails messages. In my research, I came across a parsing tool that enables you to create any record in Dynamics 365 based on form submissions and email messages.

    (more…)