This article is contributed. See the original author and article here.
For more than two decades, customer relationship management (CRM) and enterprise resource planning (ERP) software have been defined by manual entry and high-touch data processes. Since then, businesses have made strides in automating many manual transactions through various means but have largely reached a plateau. Our 2023 survey on business trends found that 9 out of 10 workers hope to use AI to reduce these kinds of repetitive tasks from their jobs.1
Supply chains have been a prime area for the application of AI, due to the vast amounts of critical business data and processes involved. Supply chains have evolved over the years, with emerging technologies and innovations that enable businesses to optimize their operations, reduce costs, and improve customer satisfaction. Yet, while statistical models have been used in processes such as inventory management, forecasting, production planning, and scheduling, there hasn’t been a significant shift in the industry beyond improving algorithms. Learning hasn’t been applied to make supply chain processes intelligent and self-regulating. The next generation of AI will transform the industry by making it more agile, efficient, and responsive to changes.
In March, Microsoft announced Microsoft Dynamics 365 Copilot, introducing the world’s first AI copilot for ERP and CRM applications. With the next generation of AI capabilities in Dynamics 365 Copilot, those high-touch, laborious processes can be transformed with interactive AI-powered assistance.
With Copilot, you can further unlock the potential of ERP by bringing together data and AI to reduce time spent on unfulfilling tasks and accelerate the speed of execution and business outcomes.
We are excited about bringing next-generation AI to virtually every business function, and especially so about the opportunities AI has within the Microsoft Supply Chain Platform. In this post, we look at AI in supply chain management (SCM), both its development and current state, and we share our view of next-generation AI in SCM.
The Fourth Industrial Revolution opens doors that further transform how we work and what we focus on during the workday. We have come a long way from the industrial plant floor automation of the 1960s to the intelligent era of supply chain digital twins of the 2020s. We now have significant technological advances that can transform traditional supply chains into next-generation cognitive supply chains. These cognitive supply chains can proactively predict and self-correct disruptions, trigger replanning, and provide intelligent recommendations. Thus, enabling humans and AI to work together to quickly respond, in real time, to changing environments.
In the age of AI, tools like artificial neural networks and machine learning provide the means to automate personal workflows and processes. Still, low-code applications, natural language processing, and generative AI will not replace human innovation. It will, on the other hand, increase and expand our expertise and ability. This is why at Microsoft, we believe that AI is going to define new ways for humans to amplify their impact, their capability, and their unique potential.
The brief history of AI in supply chain management processes
The first application of AI in SCM is a so-called expert system known as the inventory management assistant (IMA). IMA was designed in 1986 to improve the replenishment of spare parts and reduce safety stock for the US Air Force.2 From there, the 1990s saw a broad resurgence of interest in the decades-old concept of AI. As a result, AI became commercially available in SCM applications on a limited basis during this time.
In the 2000s, computing power continued to increase as hardware costs declined rapidly, making the investment in AI affordable. However, AI’s widespread adoption in SCM really took off in the 2010s with the rise of the Industrial Internet of Things (IIoT) and the associated acceleration in digital transformation. Together, these factors led to an explosion in the amount of data generated by supply chain processes, marking the beginning of big data in the supply chain.
Outside the supply chain, machine learning algorithms matured and refined into efficient and almost standard-like features, such as the Netflix recommendation engine. At the same time, SCM use cases were taking shape and beginning to deliver value. The first applications of machine learning came in the areas of demand forecasting using regression models to achieve high forecast accuracy, short-term demand sensing using pattern recognitions, anomaly detections in assets, and inventory optimization, to name a few.
Today, AI is used in a wide range of applications, including image and speech recognition, natural language processing, and autonomous vehicles. More recent breakthroughs, such as Dall-E2 and ChatGPT from OpenAI, are rapidly opening new doors, as evidenced by our recent launch of Dynamics 365 Copilot. However, most companies are still focused on analytics and promotion use cases, such as forecasting demand or planning production.
As such, organizations have yet to fully explore the potential of AI, which involves self-learning supply chains, more sophisticated supply chain algorithms, and recognizing patterns in big data that are beyond human perception. AI can automate many of the recurring decisions in SCM and interact with supply chain systems in human context, but this requires a platform to connect legacy and modern solutions to unify the vast, growing amounts of supply chain data.3
Microsoft Supply Chain Platform and next-generation AI
As supply chain complexity grows, companies are using next-generation AI to gain a competitive edge and remain profitable. AI is proving to be a game-changer for businesses, whether they’ve already embarked on a digital transformation journey or are considering doing so. Let’s explore some of the cutting-edge AI use cases in supply chain management that can deliver immediate value without undertaking costly transformation initiatives.
AI-powered risk mitigation
By unifying data sources and business applications and combining them with next-generation AI, companies can better predict and act on disruptions across channels, suppliers, and geographies. For example, the AI-powered Microsoft Supply Chain Center news module proactively flags external issues such as weather, financial, and geopolitical news that may impact key supply chain processes. Plus, predictive insights surface affected orders across materials, inventory, carriers, distribution networks, and more.
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With Dynamics 365 Copilot capabilities, users can quickly turn these insights into action with contextual email outreach. With a custom and contextual reply, supply chain users can save time and collaborate with impacted suppliers to quickly identify new ETAs and reroute a purchase order (PO) based on a weather disruption or fulfill a high-priority customer order via an alternate distribution center due to geopolitical tension.
Microsoft Dynamics 365 Intelligent Order Management (IOM) enables organizations to intelligently orchestrate fulfillment and automate it with a rule-based system using real-time omnichannel inventory data, AI, and machine learning. IOM can improve order fulfillment models by using AI to automate the identification and selection of optimized fulfillment decisions. Including the ability to enhance AI models when recommendations are not ideal, using the train, feedback, and improve methodology.
Elevate forecast accuracy with AI-driven collaborative demand planning
Demand forecasting is an area that has already seen pervasive use of AI. Organizations already use machine learning-powered forecasting algorithms to improve their forecast accuracy. However, trust in the system-generated forecast is still not as high as was hoped. Recent supply chain disruptions have only exacerbated the role of the importance of manual oversight during creation and careful review. As a result, demand planners and other stakeholders continue to spend a significant portion of their time manually analyzing trends and anomalies, and fine-tuning demand plans. The next-generation of foundation models have the potential to disrupt these very use cases. Ability to get answers through AI forecast explainability and natural language querying will help demand planners breeze through their demand plan analysis, reducing the time needed for fine-tuning and adjusting demand plan from days to minutes. Furthermore, AI can help in demand review meetings by using natural language for data-driven decision making, surfacing risks and opportunities, summarizing assumptions behind a plan, providing real-time what-if analysis, and generating transcripts and summaries of the meeting along with action items. The next generation of AI in demand planning promises to make the entire process more efficient, accurate, and collaborative.
Mitigate order delivery risks with data Q&A
Procurement teams often conduct monthly supplier reviews for top vendors by volume and vendors struggling to meet delivery requirements, but which have been painful to stop trading with for some reason. A significant amount of time for two to three team members is usually dedicated to gathering and analyzing monthly performance data in preparation for these reviews. Conversational AI can unlock productivity.
With conversational AI, we can imagine a future scenario where any analyst is prompting Dynamics 365 Copilot to: “Show me all orders which were not delivered on-time and in-full (OTIF) in the last 30 days. Estimate how much of our order backlog is impacted by these late deliveries. Suggest three questions that will help the supplier dig into the root cause of the issue. Write a short recommendation requesting the supplier participate in our monthly supplier review until OTIF is above 97 percent.” This example is only the tip of the iceberg for scenarios where generative AI can be used to democratize access and retrieval of a company’s data through conversation-styled interaction with AI chatbots.
Additionally, AI could significantly accelerate the onboarding of new suppliers by bypassing or speeding up internal legal review. We can envision purchasing managers, supply chain directors, and more benefitting from AI contract review by assisting in tasks like reviewing master supplier agreements.
Autonomous self-regulated supply chains
One of the biggest challenges in managing a complex supply chain is that it is “high touch” with disparate data sources, different cross-functional units, and processes ranging from strategy to execution. Companies struggle to harmonize these disparate data and processes, leaving planners to make intuition-based decisions rather than data-driven ones. AI can address the complexities of mapping a multi-tier network model from several disconnected systems across the value chain, including external business partners. Further, with advances in AI such as reinforcement learning, the networks can be adaptive, and self-regulated with different sub-network agents operating toward a common goal of increasing resilience, profitability, and customer service. Such an adaptive network considers the historical trends, and supply chain internal and external events, along with signals. The system evaluates multiple scenarios and performs business impact analysis to determine the best course of action using techniques such as simulation, optimization, and machine learning. For example, the system may recommend a make-and-buy option, versus solely buying, and provide a balanced scorecard of supply chain metrics and costs, along with a ranking for the recommendation. This type of supply chain offers automation to eliminate manual processes, and intervenes based on exceptions, generating alerts, and providing suggested actions. It also has the ability to self-learn from user actions and automatically execute corrective measures.
Intelligent process automation
As next-generation AI innovation emerges, it will increasingly deliver on the promise of automating many of the recurring decision touchpoints in supply chain management, freeing up valuable human resources to focus on higher-level productive tasks that require creativity, judgment, and complex problem-solving skills. AI bots can carry out tasks like reading email for new procurement requests, logging into multiple systems for data entry, solving supply chain alerts, and triggering workflows. Another example is increasing planners’ productivity by using generative AI to create the artifacts (plans, performance, assumptions, risks, and mitigations) required to run monthly business review (MBR) meetings such as sales and operations planning (S&OP).
Intelligent inventory visibility and optimization
Another example of AI in supply chain management is inventory intelligence where AI can balance inventory more accurately to reduce stockouts, and improve customer satisfaction and loyalty. Consider a scenario where the global inventory position analysis shows a projected inventory depletion in the upcoming quarter, with levels falling below safety stock requirements. With AI, the supply chain analyst can gain insights into the root causelow supply relative to demand for a particular region and time. The scheduled maintenance of a factory in that quarter would lead to no additional production, exacerbating the situation. As a result, the demand must be met from existing inventory. With the help of AI-powered insights, the analyst can now delve deeper into the impacted products and locations and take corrective measures, using AI-powered recommendations to rebalance inventory from other locations or employing a cost-effective contract manufacturer.
Intelligent inventory visibility is also revolutionizing the way businesses search and view their stocks and products, empowering users with unparalleled accessibility and efficiency. The power of AI enables users to swiftly ascertain stock levels and product availability by merely typing their inquiries in natural language, similar to chatting with a friend. Whether it’s a query about products nearing expiration or the availability of limited-edition items across various regions, the AI assistant promptly delivers the desired results. Gone are the days of navigating through cumbersome menus, remembering product IDs or location details; simply use natural language to acquire essential information within seconds. In addition, AI can streamline today’s labor-intensive data-mining and table-joining. AI technology can now streamline the entire process, and even summarize inventory status in dashboard and text reports. Consequently, businesses can liberate their analysts from mundane tasks such as data cleansing and report writing, allowing them to focus on more strategic initiatives that drive success.
Shorten warehouse inventory cycle times
Another area of supply chain management to which AI can be applied is to shorten cycle times in warehouse fulfillment. Today, as demand for different items ebbs and flows, it’s difficult to predict which items should consume forward picking locations in the warehouse. Warehouses typically deal with the situation in two ways: they can pay for more space than they need at the current volume (unlikely) or have workers re-slot bins to bring items from a bulk area to a pick location. The latter is an ongoing, labor-intensive, and time-consuming process that is reactive by default.
In the future, AI could be applied to analyze incoming orders (or look further upstream in the supply chain) to forecast demand better. Based on this analysis, combined with data like physical product dimensions and the storage capacity of bin locations, recommendations for re-slotting can be offered to warehouse managersallowing plans to be proactively set in motion to ensure that on-hand inventory is available at the time of picking.
Revolutionizing the supply chain industry
The above are just a few examples of how we imagine this next wave of AI innovation can improve supply chain processes and the overall employee experience. And it’s certainly just the starting point. AI has the potential to revolutionize supply chains, offering new possibilities for improved efficiency, cost savings, and customer satisfaction. To fully harness the benefits of AI, businesses must invest in the right technology and infrastructure to unify their supply chain processes and datawhile considering critical aspects like security, accessibility, and company values. Microsoft is uniquely positioned to deliver AI in supply chain, by integrating built-in capabilities across our solutions and delivering a secure, composable, extendable, and interoperable platform. With low-code/no-code automation, collaborative actions, process orchestration, and rich supply chain functional capabilities in a single experience, customers can compose a tailored ecosystem and confidently apply AI to deliver new value.
Next steps for AI in supply chain management
Dynamics 365 Copilot in Microsoft Supply Chain Center is already unlocking a new world of opportunity and redefining what’s possible when teams harness the power of AI. By embracing AI, organizations can gain a competitive advantage and stay ahead of the curve in an ever-changing business landscape. And as we explored through the possible supply chain scenarios covered in the last section, we are just getting started. Sign up for a free trial to get started.
This article is contributed. See the original author and article here.
Many database administrators ask questions like “What rows have changed for a table?” and “How has that row changed in that table?”. Change Tracking is a lightweight solution built right into the SQL Database that gives you the ability to query for data that has changed over time. In this episode of Data Exposed, join Anna Hoffman and Brian Spendolini as we explore this powerful feature of the database. Learn how to enable Change Tracking in your database, what are the best uses cases, and how it can save you massive amounts of time and effort over developing custom, one-off solutions.
This article is contributed. See the original author and article here.
Today, we’re excited to announce Copilot in Microsoft Viva, along with the introduction of Microsoft Viva Glint, to help organizations create a more engaged and productive workforce.
This article is contributed. See the original author and article here.
As mentioned in previous posts, 11/11/2021 and on 11/15/2022, Office 2013 reached the end of the Extended Support lifecycle on April 11, 2023. Continuing to use Office 2013 could increase your organization’s exposure to security risks, impact your ability to meet compliance obligations, and/or affect end user productivity.
Additionally, support for other Microsoft Office products is also coming to an end in the next months. Please review the following list and act before the end of the product’s lifecycle:
Office 2019 for Mac reaches end of support on October 10, 2023. This means Office 2019 for Mac will no longer receive security updates, bug fixes, technical support, or online technical content support.
Connecting Office 2016 and Office 2019 to Microsoft 365 reaches end of support on October 10, 2023. After this end date we won’t block these Office versions from connecting to Microsoft 365 services if they are kept up to date. But after October 10, 2023, improvements to Microsoft 365 services will no longer be tested with these Office versions, so, users could experience performance or reliability issues. Read more about this in our Microsoft Learn article.
If you’re running a version affected by any of the end of support dates, we recommend upgrading to Microsoft 365 E3, which comes with Microsoft 365 Apps – the apps you’re familiar with (e.g., Word, Excel, PowerPoint, Outlook, etc.). It falls under the Modern Lifecycle Policy, so it’s continuously supported.
This article is contributed. See the original author and article here.
In today’s unpredictable market conditions, companies are looking to do more with less, and delivering exceptional customer experiences is necessary to both acquire new customers and retain and increase the lifetime value of existing customers. Faced with a larger set of choices, customers expect the companies they do business with to know them, anticipate their needs, and deliver personalized interactions. To earn their loyalty, it’s critical that every interaction, digital or in person, exceeds their expectations.
To win against their competitors, companies need to deeply understand their customers, personalize every interaction pre-sale and post-sale, and use data and AI to deliver proactive and continuous value to win the hearts and minds of their customers. Marketers and sellers must work closely together as a single unit to deliver experiences where every interaction builds upon the last. Marketers and customer experience (CX) professionals are being tasked with orchestrating these experiences seamlessly across every department, for both new and existing customers, and are being asked to do this with even fewer resources.
At Microsoft, we aspire to empower every company to create amazing experiences for their customers that translate into business success. With Microsoft Dynamics 365 Marketing, companies can orchestrate real-time, end-to-end journeys designed by business users. With Dynamics 365 Customer Insights, companies can use all its data as an enterprise asset and get insights that can be actioned across the customer’s lifecycle. AI assists every step of the way, so employees can be more efficient and have more time to focus on the things they really want to do. Companies can:
Gain deep insights into their customers to deliver relevant interactions.
Use generative AI to orchestrate impactful experiences.
Unify sales and marketing teams to accelerate the pipeline.
Discover how 2023 release wave 1 for Dynamics 365 Marketing and Dynamics 365 Customer Insights will help you delight your customers while increasing your team’s efficiency. Let’s look at some of the features in this wave that I am most excited about.
Gain insights into your customers to deliver relevant interactions
Understanding your customers is key to delivering relevant interactions that drive engagement and loyalty. The use of generative AI empowers companies to understand customers like never before. While analyzing vast amount of data previously required deep knowledge of the data and took time to prepare, thanks to Copilot in Dynamics 365 Customer Insights, companies can now get insights faster and more easily by using natural language. Marketers, sellers, and data analysts can ask questions in simple everyday words to explore, analyze, and instantly understand their customerssegment sizes, preferences, and new insights to uplevel every interaction.
To get value from insights, these must be available where they can be actioned. With the latest customer interactions compiled into a unified timeline and made available directly in Dynamics 365 Sales, Customer Service, and Marketing, each team can be guided by a complete understanding of their customers and their recent activities. Armed with relevant customer information accessible directly within the flow of their work, sellers and marketers can orchestrate next best experiences that exceed their customers’ expectations.
Use generative AI to orchestrate impactful experiences
To run successful campaigns, marketers must target the right customers. Often this means finding that one person who understands the underlying data set and can create the right segment to target. Now, using query assist, a Copilot feature in Dynamics 365 Marketing, marketers can build segments in minutes by simply describing audiences’ characteristics in natural language,for example, create a segment from contacts living in Seattle.
Creating compelling emails can be hard and often getting started is the hardest part. Copilot can assist marketers in finding inspiration and generating engaging emails within minutes. With Content ideas, a Copilot feature in Dynamics 365 Marketing, marketers can easily craft engaging emailsit’s like brainstorming with their team. After marketers specify the type of email they want to send and select the tone of voice that is the right fit with their brand, Copilot generates high-quality content that marketers can easily adjust. With the assistance of Copilot in Dynamics 365 Marketing, marketers can significantly reduce the amount of time spent copywriting, and shipping engaging emails is faster, more efficient, and fun.
In order to maximize marketing ROI, it is important to know what activities are having a positive impact. Out-of-the-box AI-powered dashboards help marketers understand how their activities contribute to defined milestones, for example, number of webinar registrations, qualified leads, or opportunities created. Thanks to AI-powered milestones and rules-based attribution models, marketers can easily identify their best performing activities and journeys, drop ineffective tasks, and optimize their marketing spend.
Unify sales and marketing to accelerate your funnel
It is no longer enough for sales and marketing teams to just be “aligned,” they must now be unified so together they can effectively nurture leads, close pipelines, and build a loyal customer base. With the new lead scoring model and qualification model in real time marketing, marketers can define criteria and identify leads to prioritize so every single qualified lead gets attention at the right time from the sales teams.
Furthermore, to make sure leads are actioned immediately, marketers can automatically assign leads to the seller in Dynamics 365 Sales. Leads are identified, prioritized, and while they are still hot, they are seamlessly passed to the relevant or available expert on the sales team to increase the chances of closing the deal.
To ensure the seller is enabled to deliver value in every interaction, sellers can access valuable insights directly within their Dynamics 365 Sales workflow. They can for instance use average transaction amount, total sales, loyalty reward points, and customer lifetime value for each contact, account, or lead and use these insights to hyper-personalize the interaction. This kind of interaction builds relationships, exceeds expectations, and leads to loyal customers.
Start using Dynamics 365 Marketing and Customer Insights wave 1 2023 features
We aim to help you capitalize on your data to better understand your customers. We aim to democratize the use of generative AI so you can deliver experiences that build customer loyalty while improving employee productivity. We aim to facilitate seamless collaboration across departments so your organization can operate as one seamless unit. And we are excited to bring solutions in this wave designed to do just this, while making your work easier and enabling you to transform customer experiences so you can grow your business.
Business Applications Launch Event
Discover new capabilities for Dynamics 365 Marketing and Customer Insights
This article is contributed. See the original author and article here.
Two years ago, we shared that “It’s Time to Hang Up on Phone Transports for Authentication.” Today, we’re adding the public preview of Authenticator Lite to the tools we are offering to help you move from text message (SMS) and voice-based authentication. Our priority is getting every user to sign in with modern strong authentication – passwordless, hardened against phishing, easy to use and adaptable to evolving attacks.
Our top recommendation for modern strong authentication is the Authenticator, which offers the most robust security features, updated the most frequently, for free. Microsoft Authenticator app has over 100 million users worldwide who trust it as a secure and easy way to authenticate, making it the most popular way to sign in with strong authentication in Azure.
Because modern strong authentication is so important, we’re making it even more accessible by embedding it right into the Outlook client! We call this embedded experience Authenticator Lite – and we’re excited to announce it is now in public preview! For users that haven’t yet downloaded Authenticator, they can now complete MFA for their work or school account for free using the Outlook app on their iOS or Android devices. Users can approve authentication requests and receive TOTP codes, bringing the security of Authenticator to a convenient location while simplifying users’ move off phone transports for authentication.
During public preview, admins can choose to enable or disable this capability for a group of users or to leave the feature in a Microsoft managed state. Enabling a group for Authenticator Lite is possible from the Entra portal via the Authenticator configuration page. It’s also possible to enable the feature through MS Graph.
Authenticator Lite, as the name suggests, will extend a subset of the Authenticator’s capabilities into Outlook. Each verification notification will include a number matching prompt and biometric or pin verification if enabled on the device. More information on the Authenticator Lite notification configurations can be found here.
Once enabled for Authenticator Lite, users on the latest version of Outlook without the Authenticator app will be prompted to register Outlook as an MFA method when they launch the app on their device.
Once users are registered, during their next authentication, users will be prompted to authenticate using a push notification in their Outlook app.
Registered users will also have access to a TOTP code found in their Outlook settings under Authenticator.
For more information on enabling this feature for your users, see here. Rollout to support this feature in Outlook is currently underway.
This feature will roll out to tenants in the state ‘Microsoft managed’. For the duration of public preview, leaving the feature set to ‘Microsoft managed’ will have no impact on your users and the feature will remain turned off unless you explicitly change the state to enabled. In late April 2023, we will remove preview tags and enter general availability. On May 26, 2023, if the feature is left set to ‘Microsoft managed,’ your tenant will be enabled for Authenticator Lite by Microsoft. If you do not wish for this feature to be enabled on May 26, set the state to ‘disabled’ or assign users to include and exclude groups prior to May 26.
We hope you and your users enjoy this new feature, and, as always, please let us know of any questions or feedback by leaving comments down below or reaching out to us at aka.ms/AzureADFeedback.
Regards,
Alex Weinert
VP Director of Identity Security, Microsoft
Microsoft Identity Division
Learn more about Microsoft identity:
Get to know Microsoft Entra – a comprehensive identity and access product family
This article is contributed. See the original author and article here.
The role of the seller is evolving. Buyers expect a blend of digital and personalized experiences throughout their journey. To achieve this, sellers must be efficient and effectiveprioritizing who to engage, identifying how and when to connect, and spending more time becoming trusted advisors to their customers. Sellers can’t be overwhelmed trying to make sense of too much data and information; rather, they need the data to work for them by providing value in every customer interaction.
With Microsoft Dynamics 365 Sales, sellers can improve their sales by prioritizing their best bets, collaborating with their sales team in the moment with Microsoft Teams built-in, knowing when they should engage with prospective customers, and then seeing how it went after ending the call. Sellers are given the gift of time, plus intelligence, so they can close more deals faster than before.
For years, customer relationship management (CRM) systems have asked sellers to enter data so sales managers could forecast revenue and assess seller performance. With Dynamics 365 Sales and Microsoft Viva Sales, we have put the applications to work for youusing AI to simplify data capture and recommend in-the-moment interactions whether selling from Dynamics 365 Sales or in Microsoft 365 productivity tools. Starting today, we will begin to roll out new capabilities for Dynamics 365 Sales and Viva Sales that use the power of AI to help sellers:
Prioritize your work to land more deals faster.
Stay productive and collaborate in the flow of work with Viva Sales.
Let’s take a closer look at what’s in store for sellers in the weeks and months ahead.
2023 release wave 1 for Dynamics 365 Sales and Viva Sales
Learn about the new sales capabilities helping sellers with the power of AI.
The sales accelerator in Dynamics 365 Sales helps sellers to sell with intent by building a prioritized worklist and surfacing automated activity recommendations to speed the sales process. Sequences enable sales organizations to automate these processes, tailoring them to their unique sales approach and best practices. Sequences are powered by our common customer journey orchestration engine shared across Dynamics 365 applications. We are enhancing the sequence capabilities to support account-centric selling with multiple sequences to a record, improve effectiveness with actionable AI-powered suggestions, and analyze performance using sequence insights.
Time with customers is precious, so every sales interaction matters. Conversation intelligence helps sellers make the most of their sales calls by transcribing the dialog and using AI to detect sentiment, questions, and actions to ensure no follow-up is missed. In this release, we have enabled text message as an additional channel for sellers to engage with customers and added additional AI capabilities to redact sensitive personally identifiable information (PII) data from phone calls and provide in-the-moment suggestions to guide sales conversations.
Sellers are routinely managing many deals at the same time. As sales engagements progress and sellers learn more about their customers, they need to regularly adjust and review this data while, at the same time, keeping an eye on how they are performing. Sellers can easily maintain the various stakeholders for an account with the new org chart capability, identify and analyze the activity of key decision makers, and ensure they stay on top of their performance and update their pipeline with the new opportunity management experience. The new opportunity experience eliminates many processes that sellers would normally need to do and streamlines everything into a single workspace.
Stay productive and collaborate in the flow of work
Not all sellers spend all their time in a CRM system. Many spend much of their time in productivity tools, emailing, calling, and collaborating with colleagues and customers. In October 2022, we launched Microsoft Viva Sales. We are empowering salespeople with AI-driven insights and data automation right in the flow of workin the productivity and collaboration tools millions are already using every day: Microsoft 365 and Teams.
Selling is a team sport. Enabling sales team members to collaborate with each other effectively with the right tools is key to their success. Collaboration spaces bring together the right users, contextual insights, and productivity apps to boost seller collaboration in Teams. Collaboration spaces makes internal and external collaboration take center stage. Sellers can use sales templates to create a collaboration space. Sales templates speed up structured team/channel creation with predefined channels, pre-pinned apps, and integrated access to CRM data.
For sales teams, the adage “time is money” is more relevant than ever before. Sellers are busy people who find it challenging to balance the time and effort required to respond to customer emails with their other responsibilities. Pain points include:
Pulling data from a CRM system is time-consuming with complicated navigation and menus.
Keeping track of customer opportunities and the history is difficult and increases for sellers managing many accounts.
Responding to a high volume of emails can be overwhelming and might cause the seller to miss important details.
With the help of Copilot in Viva Sales, alongside the context of the email or meeting and CRM data, we will now generate suggested email content for a variety of scenariossuch as replying to an inquiry, creating a proposal, or summarizing the action items from meetings. Viva Sales brings together Microsoft 365 data and CRM data to help sellers quickly generate responses using the power of Microsoft Azure OpenAI Service.
Organizations have taken pride in the customization of their CRM systems, considering it to be critical to their business success. These customized experiences allow sellers to engage and capture customer data effectively within their CRM system. In October, we introduced Viva Sales, which lets sellers use Microsoft 365 and Teams to automatically capture data into any CRM system, eliminating manual data entry and giving more time to focus on selling.
In February, we released the ability for CRM administrators to customize CRM forms, fields, and behavior in Viva Sales for accounts, contacts, and opportunities. With this release, we will add the ability to configure additional out-of-the-box entities as well as custom entities using queries defined in the CRM system. CRM administrators will be able to add or remove relevant custom and out-of-the-box entities to Viva Sales forms and control filtering and sorting behavior of lists using CRM-defined queries. Sellers will be able to see custom and out-of-the-box entities in the Outlook side pane in Viva Sales, share custom entities with colleagues in Teams, search for custom entities in the Teams messaging extension, and connect Outlook email and meeting activities to custom or out-of-the-box entities.
Learn more about 2023 release wave 1 for Dynamics 365 Sales and Viva Sales
These are just a few of the new capabilities that we are rolling out for sellers in 2023 release wave 1. To learn more about these new capabilities in Dynamics 365 Sales and Viva Sales, click on the links below.
If you are not yet a Dynamics 365 Sales customer, check out our Dynamics 365 Sales webpage where you can take a guided tour or get a free 30-day trial.
This article is contributed. See the original author and article here.
In our previous blog about hunting for network signatures in Microsoft 365 Defender, we described how we used device discovery capabilities to capture some network event information in deeper detail and expose them in advanced hunting with the NetworkSignatureInspected action type. Since then we have made several developments, the most significant being the integration with Zeek. This release has expanded what is possible for generating network detections across Microsoft Defender for Endpoint. That announcement, shared examples of detections created for PrintNightmare and NTLM password spraying attempts.
Today, we would like to share a variety of Zeek-based events in advanced hunting that will help you expand your investigation, hunting, and detection capabilities for identifying and addressing network-layer anomalies across HTTP, SSH and ICMP protocols. Using the new Zeek events, we will demonstrate how to perform network threat hunting while also covering some of the MITRE ATT&CK Matrix.
Note: As the integration with Zeek continues to mature, more action types will gradually be released over time. With the Zeek integration only supported on Windows devices, these action types will surface for connections to and from Windows device.
To identify these action types in your tenant, look for the value ConnectionInspected in the ActionType field of the DeviceNetworkEvents table of advanced hunting. The extra information is stored in the AdditionalFields column as a JSON data structure and has the commonly known Zeek fields per event, which can be parsed. These field names are identical to those that Zeek uses, which are documented on Zeek’s site. You can also check the Schema Reference flyout page on the advanced hunting pages to check for any new action types that were recently released.
The result of this query looks something like this:
Figure 1 – Sample result upon checking for ConnectionInspected in the ActionType table
The format of the action type will follow the [Protocol_Name]ConnectionInspected standard.
Inspecting HTTP connections
The HttpConnectionInspected action type contains extra information about HTTP connections, inbound or outbound. In cases where you click on an event of the HttpConnectionInspected action type, the page flyout will parse the additional fields and present them in a format like the example below:
Figure 2 – Sample result of an HttpConnectionInspected action type
Below, you will find a complete list of fields that this action type can expose and the respective descriptions:
Field Name
Description
direction
The direction of the conversation relevant to the Microsoft Defender for Endpoint-onboarded device, where the values are either ‘In’ or ‘Out’
host
The host header content
method
The HTTP method requested
request_body_len
Length of the HTTP message body in bytes
response_body_len
Length of the HTTP response body in bytes
status_code
The HTTP response code
status_msg
The full text message of the response
tags
A set of indicators of various attributes discovered and related to a particular request/response pair.
trans_depth
Represents the pipelined depth into the connection of the request/response transaction
uri
The complete URI that was requested
user_agent
The user_agent header of the request
version
The HTTP version used
Let’s look at a few examples of using the HttpConnectionInspected action type. In the first example, you want to look for rare user agents in the environment to identify potentially suspicious outbound web requests and cover the “T1071.001: (Application Layer Protocol) Web Protocols” technique.
// Identify rare User Agent strings used in http conversations
DeviceNetworkEvents
| where ActionType == ‘HttpConnectionInspected’
| extend json = todynamic(AdditionalFields)
| extend direction = tostring(json.direction), user_agent = tostring(json.user_agent)
| where direction == ‘Out’
| summarize Devices = dcount(DeviceId) by user_agent
| sort by Devices asc
Suppose you have identified a suspicious-looking user-agent named “TrickXYZ 1.0” and need to determine which user/process/commandline combination had initiated that connection. Currently, the HttpConnectionInspected events, as with all Zeek-related action types, do not contain that information, so you must execute a follow-up query by joining with events from ConnectionEstablished action type. Here’s an example of a follow-up query:
In another example, let’s look for file downloads from HTTP, particularly files of executable and compressed file extensions to cover the “T1105: Ingress tool transfer” technique:
| where uri matches regex @”.(?:dll|exe|zip|7z|ps1|ps|bat|sh)$”
The new HTTP action type will unlock a variety of possibilities for detection on this protocol. We look forward to seeing the queries you come up with by sharing your contributions with the community.
Looking at SSH connections
The SshConnectionInspected action type will display information on SSH connections. While decrypting the entire SSH traffic is not possible, the cleartext part of the SSH session initiation can provide valuable insights. Let’s look at the data found in the AdditionalFields section.
Figure 3 – Screenshot of additional fields that SshConnectionInspected generates.
The fields depend on the activity that was observed. Some of these fields might not appear depending on the connection. For example, if the client disconnected before completing the authentication, you will not have an auth_success field populated for that event..
Below, you will find a complete list of fields that this action type can expose and the respective descriptions:
Field Name
Description
direction
The direction of the conversation relevant to the Defender for Endpoint-onboarded device, where the values are either ‘In’ or ‘Out’
auth_attempts
The number of authentication attempts until the success or failure of the attempted session.
auth_success
The success or failure in authentication, where ‘true’ means successful user authentication and ‘false’ means the user-provided credentials are incorrect.
client
The version and type of client used to authenticate to the SSH session.
host_key
Host public key value
server
SSH server information
version
SSH protocol major version used
uid
The unique ID of the SSH session attempt
Let’s look at a few advanced hunting examples using this action type. In the first example, you want to look for potentially infected devices trying to perform “T1110: Brute-Force” against remote servers using SSH as an initial step to “T1021.004: Lateral Movement – Remote Services: SSH”.
The query below will give you a list of Local/Remote IP combinations with at least 12 failed attempts (three failed authentications on four sessions) of SSH connections in the last hour. Feel free to use this example and adapt it to your needs.
In the next example, let’s suppose you are looking to identify potentially vulnerable SSH versions and detect potentially unauthorized client software being used to initiate SSH connections and operating systems that are hosting SSH server services in your environment:
// Identify Server/Client pairs being used for SSH connections
DeviceNetworkEvents
| where ActionType == “SshConnectionInspected”
| extend json = todynamic(AdditionalFields)
| project Server = tostring(json.server),Client = tostring(json.client)
| distinct Server ,Client
Figure 4 – An example result with a short description of the different components
The results above describe breaking down the SSH banners to identify the different components. A short analysis of the banners shows that the server is Ubuntu 22.04, running OpenSSH version 8.9, and the client software is WinSCP version 5.21.3. Now, you can search these versions online to verify if they are vulnerable.
Note: The query above can be used to surface potential “T1046: Network Service Discovery” attempts, as attackers may try to search for unpatched or vulnerable SSH services to compromise.
Reviewing ICMP connections
The IcmpConnectionInspected action type will provide details about ICMP-related activity. The breadth of fields generated creates opportunities for some interesting detections. Here’s an example of the human-readable view of the event as shown on the event flyout page
Below, you will find a complete list of fields that this action type can expose and the respective descriptions:
Field Name
Description
direction
The direction of the conversation relevant to the Defender for Endpoint-onboarded device, where the values are either ‘In’ or ‘Out’
conn_state
The state of the connection. In the screenshot example OTH means that no SYN packet was seen. Read the Zeek documentation for more information on conn_state.
duration
The length of the connection, measured in seconds
missed_bytes
Indicates the number of bytes missed in content gaps, representing packet loss.
orig_bytes
The number of payload bytes the originator sent. For example, in ICMP this designates the payload size of the ICMP packet.
orig_ip_bytes
The number of IP level bytes that the originator sent as seen on the wire and taken from the IP total_length header field.
orig_pkts
The number of packets that the originator sent.
resp_bytes
The number of payload bytes the responder sent.
resp_ip_bytes
The number of IP level bytes that the responder sent as seen on the wire.
resp_pkts
The number of packets that the responder sent.
Uid
Unique Zeek ID of the transaction.
Let’s explore a few examples of hunting queries that you can use to leverage the ICMP connection information collected by Defender for Endpoint.
In the first example, you wish to look for potential data leakage via ICMP to cover the “T1048: Exfiltration Over Alternative Protocol” or “T1041: Exfiltration Over C2 Channel” techniques. The idea is to look for outbound connections and check the payload bytes a device sends in a given timeframe. We will parse the direction, orig_bytes, and duration fields and look for conversations over 100 seconds where more than 500,000 were sent. The numbers are used as an example and do not necessarily indicate malicious activity. Usually, you will see the download and upload are almost equal for ICMP traffic because most devices generate “ICMP reply” with the same payload that was observed on the “ICMP echo” request.
Below is an example result after exfiltrating a large file over ICMP to another device on the network:
In the last example, you wish to create another hunting query that helps you detect potential Ping sweep activities in your environment to cover the “T1018: Remote System Discovery” and “T1595: Active Scanning” techniques. The query will look for outbound ICMP traffic to internal IP addresses, create an array of the targeted IPs reached from the same source IP, and display them if the same source IP has pinged more than 5 IP Addresses within a 10-minute time window.
| where Direction == “Out” and ipv4_is_private(RemoteIP)
| summarize IpsList = make_set(RemoteIP) by DeviceId, bin(Timestamp, 10m)
| where array_length(IpsList) > 5
Identifying the origin process of ICMP traffic can be challenging as ICMP is an IP-Layer protocol. Still, we can use some OS-level indications to narrow down our search. We can use the following query to identify which process-loaded network, or even ICMP-specific, binaries:
This article is contributed. See the original author and article here.
By the time of writing this article, the Logic App Standard SQL Connector does not have the functionality to monitor the row version of SQL Views so it can’t be triggered by a change in the View’s data, which would have allowed us to configure a trigger on a View in SQL. Until it gets rolled out, we are exploring a way in this article to imitate this functionality.
The SQL built-in trigger (SQL Server – Connectors | Microsoft Learn) is based upon tracking update on SQL table and tracking cannot be enabled for SQL views. Azure SQL trigger uses SQL change tracking functionality to monitor a SQL table for changes and trigger a function when a row is created, updated, or deleted.
Assuming that we have a SQL Server, with three tables, and a View that joins the three tables. If any of the tables has an update, it will reflect on this View. This is what we tested in this POC, you can change this based on your requirements and based on how your View gets updated, if it gets updated only by two tables and the third is just static data then you will only need two Parent workflows to trigger the child one. The idea here is to pass the triggered value and use it as a where condition in the child workflow. The child workflow will execute a Get rows action on the SQL View using the “where condition”, it will then do the select on the View instead of a table as we use the View name instead of a Table name.
SQL side:
To begin, you might need to whitelist your client IP if you are connecting to your SQL Server from your machine.
If that doesn’t work, you can whitelist your IP from the Networking section under the SQL Server (browse to the SQL Server from the Database Overview page then go to Networking).
We create the tables in SQL Server.
We create the SQL View.
We enable Change Tracking on the Database and on the Tables. (right click, properties), you can also do this using code as well.
Create the Logic App and Workflows:
We create a Logic App Standard.
We create four workflows. (for the triggering workflows, you can have one only or more, based on your requirements)
tst_Workflow_SQL_Trigger_Tbl1
tst_Workflow_SQL_Trigger_Tbl2
tst_Workflow_SQL_Trigger_Tbl3
tst_Workflow_SQL_Get_View_Updated
Design for Child workflow that will get the updated rows of the SQL View:
Add a Request trigger.
Add the below schema to the Request Body so we can easily pass the values when calling this workflow from the Parent workflows.
{
“properties”: {
“Id”: {
“type”: “string”
},
“Value”: {
“type”: “integer”
}
},
“type”: “object”
}
Add an action to Get Rows for a table. Select the built-in SQL Connector and select the Get Rows Action.
In the Table Name click on Custom value and enter the name of the SQL View manually.
In the Parameters, add Where condition.
Select the Outputs of the Request Trigger to populate the where condition (will translate to: id=value)
Add a Response Action so the Child workflow can respond back to the Parent workflow.
Here you can precede this Action with a condition to check the output of the Get Rows Action and respond accordingly.
You can respond with the Output of the Get rows Action, but to steer away from repeating the work in the Parent workflows it is better to do all the work in the Child workflow. So you can act upon the result of the triggered SQL View in the Child workflow.
Design for the triggering workflow for Table 1 (Parent workflow):
Add a trigger. Select from the built-in tab the SQL Connector, select the Trigger When a row is modified (note the difference between this trigger and the When a row is updated, select the one that matches your requirements, even When a row is inserted)
After creating the connection, select the Table that you want to trigger this workflow. Table 1 in our scenario.
Add a Parse JSON Action. Use a sample of the table single row data to create the schema.
Sample:
{
“Id”: 20004,
“Auhthor_id”: 7346,
“Price”: 57,
“Edition”: 6,
“RowVer”: “AAAAAAAAtA8=”
}
Finally for this workflow, add an Action to call another workflow, the child workflow.
As we have created the child workflow earlier, the parameters for the workflow should be accessible.
For the Id, use the name that is used in the View, so you can easily select that exact column.
For the value, pass the value from the parsed JSON for that column. In our case it is called Id.
Create the other two workflows in the same manner. Point the trigger for each workflow to the correct Table.
In the Parse JSON use the schema for the relevant table.
In the Invoke Action, use the correct name of the column, and select the correct value from the Parse JSON output parameters.
Testing:
Add or update a row for one of the tables in SQL, you will notice that the corresponding Parent workflow was triggered, and called the Child workflow.
The Child workflow would get the updated row in the SQL View based on the passed where condition.
You can alter the where condition and the passed parameter based on your requirements.
This article is contributed. See the original author and article here.
Disclaimer
This document is not meant to replace any official documentation, including those found at docs.microsoft.com. Those documents are continually updated and maintained by Microsoft Corporation. If there is a discrepancy between this document and what you find in the Compliance User Interface (UI) or inside of a reference in docs.microsoft.com, you should always defer to that official documentation and contact your Microsoft Account team as needed. Links to the docs.microsoft.com data will be referenced both in the document steps as well as in the appendix.
All the following steps should be done with test data, and where possible, testing should be performed in a test environment. Testing should never be performed against production data.
Target Audience
Microsoft customers who want to better understand Microsoft Purview.
Document Scope
The purpose of this document (and series) is to provide insights into various user cases, announcements, customer driven questions, etc.
Topics for this blog entry
Here are the topics covered in this issue of the blog:
Applying Retention Policies to a Teams Channels
Out-of-Scope
This blog series and entry is only meant to provide information, but for your specific use cases or needs, it is recommended that you contact your Microsoft Account Team to find other possible solutions to your needs.
Applying a Retention Label Policy Teams Channels
Overview
By default, you can set up Retention Policies for Teams Channels which is applied at the Team level for ALL channels under a single team, NOT a single channel under a team.
The Note below is from the following Microsoft documentation:
Below are some excerpts that I find to be of value in understanding out this retention operates.
Information Point #2
“You can use a retention policy to retain data from chats and channel messages in Teams, and delete these chats and messages. Behind the scenes, Exchange mailboxes are used to store data copied from these messages. Data from Teams chats is stored in a hidden folder in the mailbox of each user included in the chat, and a similar hidden folder in a group mailbox is used for Teams channel messages. These hidden folders aren’t designed to be directly accessible to users or administrators, but instead, store data that compliance administrators can search with eDiscovery tools.
These mailboxes are, listed by their RecipientTypeDetails attribute:
UserMailbox: These mailboxes store message data for Teams private channels and cloud-based Teams users.
MailUser: These mailboxes store message data for on-premises Teams users.
GroupMailbox: These mailboxes store message data for Teams standard channels.
SubstrateGroup: These mailboxes store message data for Teams shared channels.”
Information Point #3
“Although this data from Teams chats and channel messages are stored in mailboxes, you must configure a retention policy for the Teams channel messages and Teams chats locations. Teams chats and channel messages aren’t included in retention policies that are configured for Exchange user or group mailboxes. Similarly, retention policies for Teams don’t affect other email items stored in mailboxes.”
Information Point #4
“After a retention policy is configured for chat and channel messages, a timer job from the Exchange service periodically evaluates items in the hidden mailbox folder where these Teams messages are stored. The timer job typically takes 1-7 days to run. When these items have expired their retention period, they are moved to the SubstrateHolds folder—another hidden folder that’s in every user or group mailbox to store “soft-deleted” items before they’re permanently deleted.
Messages remain in the SubstrateHolds folder for at least 1 day, and then if they’re eligible for deletion, the timer job permanently deletes them the next time it runs.”
Question #1 – What if I have an existing Team (or Teams) and for each Channel under that Team, I want to apply a DIFFERENT retention Ppolicy? Or in other words, I do not want to reconfigure my Team(s) to have 1 Channel mapped to 1 Team and therefore be able to map 1 Retention policy to that Channel.
Answer #1 – At the writing of this blog entry, because of the underlying architecture of how Teams Channel message are stored (See Information Points #1 and #2 above) there is currently NO method to apply a Retention Policy to an individual Channel under a Team.
Question #2 – Follow-up, I cannot even do this with Adaptive Scopes?
Answer #2 – The answer is still currently NO. Adaptive scopes do not have attributes that apply to Teams Channels specifically. Here is a summary of attributes and properties used in Adaptive scopes.
Question #3 – Do I have any other way to delete data from Teams Channels?
Answer #3 – Yes and No. Through the Adaptive Scopes mentioned above, you can apply retention policies to users’ mailboxes and thus the data held within those mailboxes. However, this approach would limit those retention policies to the users specified AND to all their email data, not just one specific Teams Channel data held in the Substrate. Refer to Information Point #2 above to see how Teams Channel date is organized and stored in M365 tenants.
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