This article is contributed. See the original author and article here.
Today we kick off the 14th Microsoft Ability Summit, an annual event to bring together thought leaders to discuss how we accelerate accessibility to help bridge the Disability Divide. There are three key themes to this year’s summit: Build, Imagine, and Include.
This article is contributed. See the original author and article here.
We’re excited to announce the expansion of Microsoft’s data residency capabilities by adding content of interactions with Microsoft Copilot for Microsoft 365 to our portfolio of data residency commitments and offerings. We are expanding our product terms and Microsoft 365 data residency offerings to contractually guarantee that we will store the content of your interactions with Copilot for Microsoft 365 in the same country or region in which you store your existing Microsoft 365 content.
This article is contributed. See the original author and article here.
In today’s fast-paced business landscape, efficient project planning and insightful execution are essential for success. However, the manual processes involved in project management can often lead to inefficiencies, delays, and increased risks. That’s where Copilot for project comes in, revolutionizing the way organizations approach project management.
With the latest update, this trailblazing feature is Generally Available to all Dynamics 365 Project Operations enabled geographies and languages, ensuring that organizations worldwide can leverage its transformative capabilities. Whether you’re a project manager in a professional services organization or leading projects across various industries, Copilot for project is designed to meet your needs.
Copilot for project empowers users to enhance project management efficiency by generating work breakdown structures, assessing risk registers with suggested mitigations, producing comprehensive project status reports, and enabling natural language commands through the sidecar chat feature.
Copilot for project capabilities
Insightful Project Status Reporting
One of the most time-consuming tasks for project managers is the production of project status reports. Gathering data from multiple sources, summarizing project health dimensions, and highlighting risks are all essential but repetitive tasks that can consume valuable time and resources.
Copilot for project changes the game by automating key components of the project status report, allowing project managers to focus on crafting narrative text and refining project-specific insights. Using Copilot for project, the project manager can produce project status reports that integrate concise summaries of scheduling and financial data, as well as generate insightful content that highlights the overall project progress, financial performance, and schedule performance. There are two types of reports to address the reporting needs of both internal and external stakeholders: an internal report that provides a work summary by resource, along with financial data including estimates and actuals, and an external report that excludes the financial data. All reports are saved and can be recalled with all prior edits maintained.
Efficient Task Planning
Streamline project planning with auto-generated work breakdown structures, saving time and effort in creating project delivery plans. Enter the project name and description, and Copilot will provide the suggested task plan for your project. You can tailor further this task plan to suit your project’s needs.
Risk Assessment and Mitigation Planning
Given the disposition of the project’s scope, schedule, and budget, Copilot assesses risk registers, provides mitigation suggestions, and gauges probabilities for each identified risk.
Call to Action
With Copilot for project, project managers can now achieve significant time savings, especially when juggling multiple projects simultaneously. By eliminating mundane tasks like manual data aggregation, maintaining multiple data pivots for collecting insights, and summarization, project managers can allocate their energy towards strategic decision-making and driving project success.
Overall, Copilot for project represents a significant leap forward in project management efficiency and effectiveness. With its advanced AI capabilities, organizations can optimize project delivery times, reduce costs, increase customer satisfaction, and ultimately drive growth and profitability. Embrace the future of project management with Copilot for project and unlock a world of possibilities for your organization.
Learn More
We are making constant enhancements to our features. To learn more about Project for copilot feature, visit Copilot for project.
This article is contributed. See the original author and article here.
A new chapter in business AI innovation
As we begin a new year, large companies and corporations need practical solutions that rapidly drive value. Modern customer relationship management (CRM) and enterprise resource planning (ERP) systems fit perfectly into this category. These solutions build generative AI, automation, and other advanced AI capabilities into the tools that people use every day. Employees can experience new, more effective ways of working and customers can enjoy unprecedented levels of personalized service.
If you’re a business leader who has already embraced—or plans to embrace—AI-powered CRM and ERP systems in 2024, you’ll help your organization drive business transformation, innovation, and efficiency in three key ways:
Streamline operations: Transform CRM and ERP systems from siloed applications into a unified, automated ecosystem, enhancing team collaboration and data sharing.
Empower insightful decisions: Provide all employees with AI-powered natural language analysis, allowing them to quickly generate insights needed to inform decisions and identify new market opportunities.
Elevate customer and employee experiences: Personalize customer engagements using 360-degree customer profiles. Also, boost productivity with AI-powered chatbots and automated workflows that free employees to focus on more strategic, high-value work.
The time has come to think about AI as something much more than a technological tool. It’s a strategic imperative for 2024 and beyond. In this new year, adopting CRM AI for marketing, sales, and service and ERP AI for finance, supply chain, and operations is crucial to competing and getting ahead.
2023: A transformative year for AI in CRM and ERP systems
Looking back, 2023 was a breakthrough year for CRM AI and ERP AI. Microsoft rolled out new AI-powered tools and features in its CRM and ERP applications, and other solution providers soon followed. Among other accomplishments, Microsoft launched—and continues to enhance—Microsoft Copilot for Dynamics 365, the world’s first copilot natively built for CRM and ERP systems.
Evolving AI technologies to this point was years, even decades, in the making. However, as leaders watched AI in business gradually gain momentum, many took steps to prepare. Some applied new, innovative AI tools and features in isolated pilot projects to better understand the business case for AI, including return on investment (ROI) and time to value. Others forged ahead and broadly adopted it. All wrestled with the challenges associated with AI adoption, such as issues around security, privacy, and compliance.
In one example, Avanade, a Microsoft solutions provider with more than 5,000 clients, accelerated sales productivity by empowering its consultants with Microsoft Copilot for Sales. Consultants used to manually update client records in their Microsoft Dynamics 365 CRM system and search across disconnected productivity apps for insights needed to qualify leads and better understand accounts. Now, with AI assistance at their fingertips, they can quickly update Dynamics 365 records, summarize emails and meetings, and prepare sales information for client outreach.
In another example, Domino’s Pizza UK & Ireland Ltd. helped ensure exceptional customer experiences—and optimized inventory and deliveries—with AI-powered predictive analytics in Microsoft Dynamics 365 Supply Chain Management. Previously, planners at Domino’s relied on time-consuming, error-prone spreadsheets to forecast demand at more than 1,300 stores. By using intelligent demand-planning capabilities, they improved their forecasting accuracy by 72%. They can also now quickly generate the insights needed to ensure each store receives the right resources at the right times to fill customer orders.
Trends and insights for CRM AI and ERP AI in 2024
All signs indicate that in the years to come organizations will continue to find new, innovative ways to use CRM AI and ERP AI—and that their employees will embrace the shift.
In recent research that looks at how AI is transforming work, Microsoft surveyed hundreds of early users of generative AI. Key findings showed that 70% of users said generative AI helped them to be more productive, and 68% said it improved the quality of their work. Also, 64% of salespeople surveyed said generative AI helped them to better personalize customer engagements and 67% said it freed them to spend more time with customers.1
Looking forward, the momentum that AI in business built in 2023 is expected to only grow in 2024. In fact, IDC predicts that global spending on AI solutions will reach more than USD500 billion by 2027.2
Some of the specific AI trends to watch for in 2024 include:
Expansion of data-driven strategies and tactics. User-friendly interfaces with copilot capabilities and customizable dashboards with data visualizations will allow employees in every department to access AI-generated insights and put them in context. With the information they need right at their fingertips, employees will make faster, smarter decisions.
Prioritization of personalization and user experiences. Predictive sales and marketing strategies will mature with assistance from AI in forecasting customer behaviors and preferences and mapping customer journeys, helping marketers be more creative and sellers better engage with customers. Also, AI-powered CRM platforms will be increasingly enriched with social media and other data, providing deeper insights into brand perception and customer behavior.
Greater efficiencies using AI and cloud technologies. Combining the capabilities of AI-powered CRM and ERP tools with scalable, flexible cloud platforms that can store huge amounts of data will drive new efficiencies. Organizations will also increasingly identify new use cases for automation, then quickly build and deploy them in a cloud environment. This will further boost workforce productivity and process accuracy.
Increased scrutiny of AI ethics. Responsible innovation requires organizations to adhere to ethical AI principles, which may require adjustments to business operations and growth strategies. To guide ethical AI development and use, Microsoft has defined responsible AI principles. It also helps advance AI policy, research, and engineering.
AI innovations on the horizon for CRM and ERP systems
Keep an eye on technological and other innovations in the works across the larger AI ecosystem. For example, watch for continued advancements in low-code/no-code development platforms. With low-code/no-code tools, nontechnical and technical users alike can create AI-enhanced processes and apps that allow them to work with each other and engage with customers in fresh, new ways.
Innovations in AI will also give rise to new professions, such as AI ethicists, AI integrators, AI trainers, and AI compliance managers. These emerging roles—and ongoing AI skills development—will become increasingly important as you transform your workforce and cultivate AI maturity.
To drive transformation with AI in CRM and ERP systems, you should carefully plan and implement an approach that works best for your organization. The following best practices for AI adoption, which continue to evolve, can help guide you:
Strategic implementation: Formulate a long-term AI implementation strategy to empower employees and optimize business processes, emphasizing data-driven culture, relevant skills development, and scalable, user-friendly AI tools in CRM and ERP systems.
Ethical adoption: Adhere to evolving ethical guidelines, starting with AI-enhanced process automation and progressing toward innovative value creation, while ensuring your organization is hyperconnected.
Data quality and security: Maintain high data integrity and security standards, regularly auditing AI training data to avoid biases and ensure trustworthiness.
Alignment with business goals: Align AI initiatives with strategic objectives, measuring their impact on business outcomes, and proactively managing any potential negative effects on stakeholders.
As you and your organization learn more about AI and discover what you can do with it, don’t lose sight of the importance of human and AI collaboration. Strongly advocate for using AI to augment—rather than replace—human expertise and decision-making across your organization. Remember, although employees will appreciate automated workflows and AI-generated insights and recommendations, AI is not infallible. Successful business still depends on people making intelligent, strategic decisions.
The importance of embracing AI in business
Immense opportunities exist for organizations across industries to use AI-powered CRM and ERP systems to accelerate business transformation, innovation, and efficiency. According to Forrester Research, businesses that invest in enterprise AI initiatives will boost productivity and creative problem solving by 50% in 2024.4 Yet, without leaders who are fully engaged in AI planning and implementation, many organizations will struggle to realize AI’s full potential.
Be a leader who prioritizes and champions AI in your business strategies for 2024. Your leadership must be visionary, calling for changes that span across roles and functions and even your entire industry. It must be practical, grounded in purposeful investments and actions. It must be adaptable, remaining open and flexible to shifting organizational strategies and tactics as AI technologies evolve.
Team up with a leader in AI innovation
Wherever your organization is in its AI adoption journey, take the next step by learning more about how AI works with Microsoft Dynamics 365, a comprehensive and customizable suite of intelligent CRM and ERP applications.
With copilot and other AI-powered capabilities in Dynamics 365, your organization can create unified ecosystems, accelerate growth, and deliver exceptional customer experiences. It can also continually improve operational agility while realizing greater productivity and efficiency. Get started today to make 2024 a transformative year for your organization.
Gartner is a registered trademark and service mark, and Hype Cycle is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
Performance evaluation has been revolutionized by technology, extending its reach to the individual level. Consider health apps on your smartphone. They gather data breadcrumbs from your daily activities, providing analysis of your movement patterns. This isn’t a generic data compilation, but a near-accurate reflection of your physical activity during a specific period.
In the future, it’s conceivable that these apps might be equipped with an AI companion, or Copilot, to guide your next steps based on your past activities. It could suggest rest days or additional exercise to help you achieve your personal health goals.
This concept of performance evaluation based on collected data is the bedrock of process mining and process comparison. Our Copilot functionality adds a layer of assistance, enabling you to make informed decisions about your warehouse operations.
In this context, Copilot can help you optimize warehouse processes. It can identify bottlenecks in certain processes or compare different methods to achieve the same goal, empowering you to choose the most optimal method for your specific case.
In this blog, we’ll explore the essence of this feature, its intended audience, and how and why you should leverage it for your warehousing operations.
Process Mining Insights:
At first glance, using Process Advisor for material movement analysis is easy. The setup process is straightforward:
Go to Warehouse Management > Setup > Process Mining > Warehouse material movement configuration. In the taskbar, select Deploy New Process.
The configuration Wizard will open. Press Next, then enter the name of the process in the field Process Name, choose company, choose number of months to load (12 months = data from latest 12 months) and choose the appropriate Activity. Press Next.
Process is deployed.
The configuration wizard looks like this:
Image: Configuration wizard screenshot.
The easy part is now complete. We have set up a process, named it, and loaded 12 months of data to prepare for our analysis. The difficult part is making sense of our data and using it to make decisions to improve our warehouse output.
Therefore, we will provide you with some real-life examples on how to use the data analysis functionality to understand your processes, and a scenario where we evaluate two different methods and use the Process Advisor to figure out which method would be preferred for our business operations.
Analysis of data
There are multiple ways to analyze your process data to understand and compare your processes.
Start with opening Power Automate and go to the tab Process Mining. The report is accessible on the main page.
Report: When the report is loaded, it can look like this:
Image: Process Mining Case Summary.
3. Select Map
Select the Map tab to display the process map:
Image: Process Mining Map.
This is a screenshot of the process map from our example. On the Map, there are separate locations on which actions(tasks) have taken place, as well as the time spent on this location and between locations. You can change the time unit to, let’s say mean duration, to see how long each activity in a particular location takes per average.
4. Use the Co-Pilot to get started.
We provide you with suggestions for frequent prompts, but you can of course choose to enter whatever you want. In this case, we will use the suggested “provide the top insights” prompt.
Image: Process Mining map with Copilot.
5. Copilot Generates
The Copilot generates a response based on the data in your process map. In the example, we can see that the Copilot has found the “BULK” as the longest running activity, and provided us with a list of the activities with the greatest number of repetitions:
Image: Process Mining map and Copilot generated answer.
6. Copilot Follow Up
We can also ask the Co-pilot follow-up questions. In this case, we will follow-up with the suggested “How to identify my bottleneck?” and “Find my Bottleneck” prompts. The Co-pilot generates a message explaining what the bottleneck is and its mean duration. In this instance, since we have selected the metric Mean duration, we will generate an answer reflecting this metric.
Image: Process Mining map with Copilot generated answer.
The message we receive tells us that the Variant with the highest duration is “Variant 2” with a mean duration of 2 minutes and 47 seconds. It also tells us that the activity with the highest mean duration is “BULK” with a mean duration of 15 minutes.
From this, we can draw the conclusion that “Variant 2” is the variant that takes the longest time to complete, and that the most amount of time is spent in the “BULK” location.
By using the process advisor for warehouse movement material analysis, we can streamline warehouse operations and ensure we don’t spend more time than we need on a particular task or operation. Another example where the Process Advisor can be utilized to enhance operational fluidity in your warehouse is by comparing different methods of achieving a similar goal, to understand which method is more effective to reach your desired goal. We will try to explain how to conduct such a comparison to with a test-case.
In our test-case, we will compare two different methods of picking goods in the Warehouse to figure out which picking method takes less time, so we can increase the Warehouse output.
Test Case : “Single order picking” vs “Cluster picking”
In this test-case, the user wants to know which method of picking is faster, “Single order picking” vs “Cluster picking”. To compare the two, the user goes through the following steps. First, the user creates a Hypothesis for the purpose of this test-case. In this case, the user wants to determine which picking method is faster.
Secondly, the user decides the scope of the test. For both methods, the user will have 5 sales orders with one to five different items per order, in different quantities. Both methods will use identical sales orders for test purposes. In the Work Details screen, we can see the work details for the work that has been created. The Variants are the different Variants of work, so in this instance, for work ID USMF-002327 with Order number 002375 (displayed in the picture) the worker will “Pick” 1 piece of item LB0001 in 5 different variations (in this case colors), then “Put” these 5 items away in packing area (location “PACK”).
Image: Work details screenshot.Image: Work details label(s).
With the “Single order picking” method, the worker picks one order at a time and puts it in the packing location. To clarify, the warehouse worker will go to each location where the item is located, pick and scan that item, repeat the process for each item in that order, take the order to pack location and then repeat with next order.
Worker goes to 5 different locations to pick items, then proceeds to “PACK” location to put items away for packing. Then, the worker repeats the process for the other orders.
Image: Picking locations
After we have constructed our hypothesis and determined the scope, we can go ahead and prepare for the analysis.
First, we will have to deploy our process comparison. We head into Warehouse Management > Setup > Process Mining > Warehouse material process configuration, and in the taskbar, we select Deploy New Process. We select a fitting description as the Process Name, select company and number of months to load. In this test case, we will only be loading one month of data since we don’t need more for this test’s purposes.
Usually, you would want as much correct data(not corrupted/faulty data since this will affect the analysis) and necessary (scope needs to determine how much and what is necessary) data as possible to get a high-quality analysis. When our process has been deployed, we can move on to the analysis and evaluate this process.
We load our process map into Power Automate, and in the beginning, it will look something like this:
Image: Process Map Starting view.
We can press the Play Animation button to get a representation of the process.
Image: Process Map Starting view.
In the Statistics tab, we can see basic information of the process.
Image: Process mining statistics tab overview.
In the Variants tab, we can view the different work-Variants. By selecting one, we can get an in-depth view of, in this case, “Variant 3”. We can see that in this variant, 6 cases occurred, the total duration was 8 minutes and 15 seconds, and the total active time was 8 minutes and 14 seconds. In this case, the attribute selected is Zone. If we look closely at the Variants, we can see that “Variant 2” has 2 cases and the others have 1.
This means that two pieces of “work” that was scheduled were so similar that they could be grouped. This is because, from a warehouse management perspective, the operation is identical. This is because the worker goes to one location, picks item(s) 1, goes to another location and picks item(s) 2, then put them away in “PACK”. Thus, it is two “Pick” operations and one “Put”, and therefore they will be grouped in this view.
Image: Process mining variants tab zone overview.
We can also change the Variants’ view by changing the Attribute selected. In this case, we will change the attribute from Zone to Order number. This will change our view, so that we see different Variants based on work type. It will in this case show us 5 variants, which at first can seem confusing. A new variant is displayed with these settings, since this now displays Variants by order number instead of zone, which means that we get one variant for each Sales order we created, since all of them were different from each other.
Image: Process mining variants tab order number overview.
In this instance, we can see the order numbers in the legend on the right side. This view tells us that we have 5 different order numbers, and the boxes below Variants Overview represents the number of work operations performed per Order Number. The Case Count per order number, in the case of “Variant 2” there has been a total of 6 operations performed (pick, pick, pick, pick, pick, put, as mentioned previously) and in the case of Variant 4 and 5, there has been a total of 3 case count (Pick, Pick, Put).
For this scenario, it can be helpful to see how much work we are performing per event. If we want a view where we can see how much work we do per event, we can switch Attribute to Work Quantity. This will in this instance allow us to see the quantity of work that needs to be performed for each event. In the example of “Variant 2” the interface tells us that 6 events have taken place, in 5 of the events quantity has been 1, and in one of the events quantities was 5. To put this into a warehouse perspective, this means that we have performed 5 of the events 1 time each, which for Variant 2 is “Pick item 1, Pick item 2, Pick item 3, Pick item 4, Pick item 5” and one event where we “Put” away these items 5 times. That single operation is performed 5 times and counts as one event because it is the same event occurring multiple times, whilst the other event, even though they are all “Pick” events, will count as individual events due to picking different products, which are all in different locations. When we “Put” away in “PACK” location, we don’t put the items in different locations, thus it counts as one event.
Image: Process mining variants tab work quantity overview.
If we select Attribute by Work type, this becomes clear:
Image: Process mining variants tab work type overview.
We might want to see the location where the events took place. To do that, we can set Attribute to Location, and the view will show us the locations of the events below the header Variants overview.
Image: Process mining variants tab work location overview.
In this image, we can see the variants based on location. To put this into context, “Variant 6” tells us 6 events have taken place, all in different parts of the warehouse. For “Variant 10”, we can see that one event took place in “LEGOLOC301” and one in “PACK”.
Now, after we have made ourselves comfortable within the report, we can start analyzing our process. To do that, press the Process Compare button below Variants.
A view similar to this one will appear:
Image: Process compare variants tab location map overview.
In the process map displayed on the screen, we have set the Mining attribute to Location, and the Metric to Total duration. This will allow us to see the total amount of time spent in each location.
By changing the Metric to Total count, we can see the number of times an event took place in each location, as the picture below displays:
Image: Process compare variants tab location map overview.
The total amount of time spent in one location and number of cases per location might be valuable, but a more telling metric could be how much time we spent on average per location.
By switching metric to mean duration, we can see the average time spent per location. This gives us yet another hint on which part of the process takes the most amount of time to manage. But, if we want to see how it looks from a proportional perspective, by toggling the percentage sign next to the Metric drop-down menu, we will achieve exactly that.
Image: Process compare variants tab location and mean duration map overview.
As we can see from the image above, LEGOLOC 201 is the location in which we spend the largest percentage of our time. If we want to further examine what is going on in that location, we can do so by pressing the bar. This will change the view slightly, and a card with detailed information will appear on the right of the screen.
Image: Process compare variants tab location map detailed view.
In the highlighted red box, we can see detailed performance data to further assess the performance in this location.
Now, we have enough information to draw some conclusions on our own. We have identified zone LEGOLOC 201 as our “time-thief”, and we know that more than 1/3 of the time was spent on picking items in this zone. To make the analysis process easier, Microsoft’s Copilot has been built into this feature. By pressing the Copilot sign in the top-right corner, you will open the dialogue box where you can create a prompt and ask the Copilot about your process. The Copilot will suggest some common prompts, but you can of course create your own. In this case, we will ask the Copilot to summarize our process.
Image: Process compare map and Copilot dialogue.Image: Process compare map and Copilot generated answer.
As displayed in the picture, the Copilot will give us a summary of the process. Because we have selected to compare our first part of the test vs our default value (the red locations), it also summarizes the default value’s process.
We do get some information on how many events took place etc., but we did not get the total case time, which was the value we wanted to find to confirm or deny our hypothesis. By asking the Copilot what the average case duration and the total case duration was, we received the answer that mean case duration was 4 minutes and 18 seconds, and total duration was 21 minutes and 31 seconds.
So, our answer in this case is that the Single order picking took 21 minutes and 31 seconds to complete.
Image: Process compare map and Copilot generated answer.
Now, we will compare the result to the cluster picking method, to see how they compare.
For context, cluster picking differs from single order picking in the sense that in cluster picking, workers pick multiple orders simultaneously and not one at a time. In this case, it means the worker will pick all 5 sales orders, then put them all away in the packing station at the same time, rather than picking an order, putting them away in the packing station, and repeating for next orders.
Image: Work clusters screenshot.
In this image, we can see the main difference between these picking methods. For cluster picking, we can see that the warehouse worker is tasked with picking 8 pieces of red Lego blocks (left image), and in the second screenshot (right) we can see how many and from which specific positions items should be picked.
Image: Work clusters screenshot with illustrations.
When all items have been picked, the Work status will be updated so all Cluster positions are “In process”.
Image: Work Cluster in progress.
Next task is to put all items in the packing station. When we have done that, all Cluster position Work statuses will be changed to Closed.
Image: Cluster Put screenshot.
As we can see in the image below, work status has been changed to Closed across the board.
Image: Work Clusters status closed.
Now, let’s jump back to the analysis. Start by creating a new process in the same way we did for single order picking and open the process map in Power Automate. In our test case, this is what we are shown on our screen.
Image: Process Compare map.
As we have already covered how choosing different metrics affects the process map and the information on display, we will not do that for this part of the test, since we know we need to compare location as the Mining attribute, and total duration as the Metric.
We will again use the help of the Copilot to evaluate the process map. Once again, we ask for a summary of the process.
Image: Process Compare map and Copilot generated insight.
Test Case Results
The summary from the Copilot tells us that this process started November 6th and ended after 8 minutes and 45 seconds.
This means we have successfully confirmed our hypothesis by using process mining and the process advisor. Now we know for a fact that for one picker with 5 sales orders constructed in this manner, cluster picking is a much more efficient picking method compared to single order picking, since identical amount of work took significantly less time to complete. Therefore, we can draw the conclusion that for all work with similar characteristics, we should prefer using cluster picking over single order picking, at least if we want to increase warehouse output.
Keep in mind, harnessing the power of Process Advisor requires an analytical mindset and a structured approach. The sheer volume of headers, variants, locations, and numbers can be overwhelming. To navigate this complexity, emulate the structured methodology illustrated in this example. By having a clear understanding of your comparison and measurement objectives, and a strategy to achieve them, you’ll significantly enhance the outcomes derived from Process Advisor.
Essential skills for effective process mining:
Use a fact-based approach with warehouse data as the base.
Use a strategic and tactical approach throughout the analysis.
Unlike this example, a great way of using process mining is by using continuous analysis, where you monitor something over time, rather than one-time analysis, which it can also be used for, as in this example.
Use quick data for immediate insights, and big data for continuous and conclusive analysis.
Master filtering to gain valuable insights and sort out what you believe is important.
Wealth of achievements made possible through process mining:
Identify areas in which processes can be improved.
Validate conformance of processes.
Do process simulation and predictive analysis.
Discover the most optimal paths for automatization.
Conclusion:
The power of Process Advisor extends far beyond what we’ve explored in this blog. It’s a versatile tool that can be adapted to a myriad of scenarios, and this guide merely scratches the surface of its potential. We’ve used it here to streamline warehouse operations, but the possibilities are truly limitless.
We encourage you to dive in and experiment with Process Advisor. Use the scenario we’ve outlined as a starting point, but don’t stop there. Input your own warehouse data and see firsthand how Process Advisor can illuminate opportunities for efficiency and growth. The journey towards optimizing your warehouse output begins with the Process Advisor.
This article is contributed. See the original author and article here.
The finance department is the heart of the organization, juggling a myriad of critical, yet complex tasks—from quote-to-cash processes like credit and collections to risk management and compliance. Financial teams are not only responsible for these mandatory, labor-intensive operations, but are increasingly tasked with real-time insights into business performance and recommendations for future growth initiatives. In fact, 80% of finance leaders and teams face challenges to take on more strategic work beyond the operational portions of their roles.¹On the one hand, teams are poised and ready to play a larger role in driving business growth strategy. On the other hand, however, they can’t walk away from maintaining a critical and mandatory set of responsibilities.
Microsoft is introducing a solution to help finance teams reclaim time and stay on top of the critical decisions that can impact business performance.Microsoft Copilot for Finance is a new Copilot experience for Microsoft 365 that unlocks AI-assisted competencies for financial professionals, right from within productivity applications they use every day. Now available in public preview, Copilot for Finance connects to the organization’s financial systems, including Dynamics 365 and SAP, to provide role-specific workflow automation, guided actions, and recommendations in Microsoft Outlook, Excel, Microsoft Teams and other Microsoft 365 applications—helping to save time and focus on what truly matters: navigating the company to success.
Copilot for Finance
By harnessing AI, it automates time-consuming tasks, allowing you to focus on what truly matters.
Leveraging innovation to accelerate fiscal stewardship
Finance teams play a critical role in innovating processes to improve efficiency across the organization. As teams look to evolve and improve how time is spent to support more strategic work, it’s evident there are elements of operational tasks that are more mundane, repetitive, and manually intensive. Instead of spending the majority of their day on analysis or cross-team collaboration, 62% of finance professionals are stuck in the drudgery of data entry and review cycles.² While some of these tasks are critical and can’t be automated—like compliance and tax reporting—we also hear from majority of finance leaders that they lack the automation tools and technology they need to transform these processes and free up time.¹
With the pace of business accelerating every day, becoming a disruptor requires investing in technology that will drive innovation and support the bottom line. In the next three to five years, 68% of CFOs anticipate revenue growth from generative AI (GenAI).³ By implementing next-generation AI to deliver insight and automate costly and time-intensive operational tasks, teams can reinvest that time to accelerate their impact as financial stewards and strategists.
Microsoft Copilot for Finance: Accomplish more with less
Copilot for Finance provides AI-powered assistance while working in Microsoft 365 applications, making financial processes more streamlined and automated. Copilot for Finance can streamline audits by pulling and reconciling data with a simple prompt, simplify collections by automating communication and payment plans, and accelerate financial reporting by detecting variances with ease. The potential time and cost savings are substantial, transforming not just how financial professionals work, but how they drive impact within the organization.
Users can interact with Copilot for Finance in multiple ways. It both suggests actions in the flow of work, and enables users to ask questions by typing a prompt in natural language. For example, a user can prompt Copilot to “help me understand forecast to actuals variance data.” In moments, Copilot for Finance will generate insights and pull data directly from across the ERP and financial systems, suggesting actions to take and providing a head start by generating contextualized text and attaching relevant files. Like other copilot experiences, users can easily check source data to ensure transparency before using Copilot to take any actions.
Copilot for Finance connects to existing financial systems, including Dynamics 365 and SAP, as well as thousands more with Microsoft Copilot Studio. With the ability to both pull insight from and update actions back to existing sources, Copilot for Finance empowers users to stay in the flow of work and complete tasks more efficiently.
Built for finance professionals
Copilot for Finance is well versed in the critical and often time-consuming tasks and processes across a finance professional’s workday, providing a simple way to ask questions about data, surface insights, and automate processes—helping to reduce the time spent on repetitive actions. While today’s modern finance team is responsible for a litany of tasks, let’s explore three scenarios that Copilot for Finance supports at public preview.
Copilot for Finance can also help financial analysts to reduce the risk of reporting errors and missing unidentified variances. Rather than manually reviewing large financial data sets for unusual patterns, users can prompt Copilot to detect outliers and highlight variances for investigation. Copilot for Finance streamlines variance identification with reusable natural language instructions in the enterprise context. A financial analyst can direct Copilot to identify answers for variances, and Copilot will gather supporting data autonomously.
Audits of a company’s financial statements are critical to ensuring accuracy and mitigating risk. Traditionally, accounts receivable managers were required to pull account data manually from ERP records, reconcile it in Excel, and look for inaccuracies manually. With Copilot for Finance, these critical steps are done with a single prompt, allowing AR managers to act on inconsistencies and any delinquencies found with Copilot suggested copy and relevant invoices.
“Finance organizations need to be utilizing generative AI to help blend structured and unstructured datasets. Copilot for Finance is a solution that aggressively targets this challenge. Microsoft continues to push the boundary of business applications by providing AI-driven solutions for common business problems. Copilot for Finance is another powerful example of this effort. Copilot for Finance has potential to help finance professionals at organizations of all sizes accelerate impact and possibly even reduce financial operation costs.”
—Kevin Permenter, IDC research director, financial applications
The collections process is another critical responsibility as it affects company cash flow, profitability, and customer relationships. Collection coordinators spend their time reviewing outstanding accounts and attempting to reconcile them in a timely manner. This often means phone calls, emails, and negotiating payment plans. With Copilot for Finance, collection coordinators can focus their time on more meaningful client-facing interactions by leaving the busy work to Copilot. Copilot for Finance supports the collections process end-to-end by suggesting priority accounts, summarizing conversations to record back to ERP, and providing customized payment plans for customers.
Copilot for Finance can also help financial analysts to reduce the risk of reporting errors and missing unidentified variances. Rather than manually reviewing large financial data sets for unusual patterns, users can prompt Copilot to detect outliers and highlight variances for investigation. Copilot for Finance streamlines variance identification with reusable natural language instructions in the enterprise context. A financial analyst can direct Copilot to identify answers for variances, and Copilot will gather supporting data autonomously.
Copilot will suggest financial context contacts and will provide auto summaries for streamlined tracking of action items and follow ups. Copilot for Finance can generate fine-tuned financial commentary, PowerPoint presentations, and emails to report to key stakeholders.
Our journey with Microsoft Finance
Microsoft employs thousands across its finance team to manage and drive countless processes and systems as well as identify opportunities for company growth and strategy. Who better to pilot the latest innovation in finance? For the first phase, we worked closely with a Treasury team focused on accounts receivable as well as a team in financial planning and analysis—who need to reconcile data as a part of their workflow before conducting further analysis. After trialing the data reconciliation capabilities in Copilot for Finance, the initial value and potential for scale for these teams was clear.
“Financial analysts today spend, on average, one to two hours reconciling data per week. With Copilot for Finance, that is down to 10 minutes. Functionality like data reconciliation will be a huge time saver for an organization as complex as Microsoft.”
—Sarper Baysal, Microsoft Commercial Revenue Planning Lead
“The accounts receivable reconciliation capabilities help us to eliminate the time it takes to compare data across sources, saving an average 20 minutes per account. Based on pilot usage, this translates to an average of 22% cost savings in average handling time.”
—Gladys Jin, Senior Director Microsoft Finance Global Treasury and Financial Services
Microsoft Copilot for Finance availability
Ready to take the next step? Microsoft Copilot for Finance is available for public preview today. Explore the public preview demo and stay tuned for additional announcements by following us on social.
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