Sometimes these failures happen sporadically within the application framework and often go undetected. Even if a user detects an anomaly, they need to decide how to react, which is time consuming.

To do this, we’re using Microsoft Azure Anomaly Detector to examine transactions across our SAP environment, which helps us identify issues before they become problems. In turn this enables us to proactively improve the performance, consistency, and reliability of our entire SAP landscape.

[Unpack how we’re optimizing SAP for Microsoft Azure. | Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel. | Explore how we’re upgrading Microsoft’s core Human Resources system with SAP SuccessFactors.]

Understanding the need for anomaly detection in SAP

At Microsoft, our SAP environment comprises many complex processes across multiple lines of business. To avoid having disparate environments and isolated monitoring and reporting data, we wanted to build a single codebase solution for monitoring and anomaly detection that each line-of-business can use with minimal code implementation.

We wanted to build intelligence to detect anomalies and inconsistencies in business process flow to improve platform health. Improved platform health improves engineering service-level agreements (SLAs) and reduces revenue loss by being proactive rather than reactive.

There were hundreds of areas that could benefit from anomaly detection in our SAP portfolio, but we wanted to identify a single area for our pilot project. In the Master Data Management (MDM) space, we create thousands of objects representing business entities such as customers and business partners.

Most of these objects are created by using an application programming interface (API), and no human interaction is needed. However, it’s extremely difficult to identify if issues related to MDM are occurring in upstream systems, so we needed a way to capture issues in advance, proactively and quickly.

In the MDM space, we have SAP Master Data Governance (MDG) background processes, such Customer Master data creation, which run without any user interaction. Across various batch and scheduled jobs, process runtime varies based on data volume, time of day, time of year, and resource availability.

Understanding the potential for issues in each process and the larger process environment involves several challenging questions, including:

• Is the transaction supposed to run that long?
• Is there a problem in an upstream system?
• Are there resources that reached their maximum capacity or that are creating a performance bottleneck?

Assessing Microsoft Azure Cognitive Services and Microsoft Azure Anomaly Detector

Detecting these issues by using human triage was difficult and time and resource intensive. Many issues went undetected, resulting in poor customer experience and the loss of potential revenue, in addition to lost capacity that could have been used for more productive purposes.

To solve this problem, we required a solution that was reliable, scalable, and easy to integrate with our SAP systems. The solution that we wanted would be process agnostic, implemented as a single codebase, and require no human intervention to detect issues.

The Microsoft Azure Anomaly Detector service, available within Microsoft Azure Cognitive Services, fits all our requirements.

The Anomaly Detector API enabled us to monitor and detect abnormalities in our data without having to know machine learning. The Anomaly Detector API’s algorithms adapt by automatically identifying and applying the best-fitting models to data, regardless of industry, scenario, or data volume, which greatly reduced our development efforts. Our primary steps were quite simple:

1. Provision a service instance for Anomaly Detector in Microsoft Azure Cognitive Services.
2. Start using the REST APIs in application code and interactions.

Using time-series data and data anomalies

For Anomaly Detector to identify anomalies, it requires time-series data, which is a series of data points indexed in time-based order.

For example, your car might have embedded sensors that send information regarding engine health, speed, tire pressure, and gasoline capacity. This information about your car is constantly updated over time and, as such, it can be used as time‑series data.

Most data received throughout time can be manipulated to be time‑series data if it’s a consistent data sequence with a time stamp. Time-series data with a single variable is considered a univariate series, while time-series data with more than one variable is considered a multivariate time series. Anomaly detector supports both univariate and multivariate series.

A data anomaly is outlying data that doesn’t fit within expected boundaries. The graphic below depicts the visual pattern of the time-series data with highlighted anomaly points in the time-series data. The graphic contains each of the time‑series data on the plot.

Data should be within minimum and maximum boundaries. In the figure, the boundary is filled with a light color. Most of the data points are within the expected boundaries. However, some data points that exceed the expected boundaries are highlighted in red in the figure, are data anomalies.

For example, a stock price that drops below the expected limit is a data anomaly. If the temperature reading of a power plant core exceeds the acceptable limit, the reading is a data anomaly, and the technicians at the power plant should be immediately notified so that they can act based on the anomaly.

Not all data anomalies are negative.

For example, if you have an article on your website that’s trending and experiencing larger traffic volume than normal, you likely want to be notified about the anomaly.

Or, if you have an e‑commerce website and receive a sudden spike in product demand, you, as the product supplier, should be notified so that you can act immediately. The graphic below contains examples of inputs and results for the Anomaly Detector service.

Using Microsoft Azure services to create a business solution

To enable integration with our SAP portfolio, we’ve implemented several decoupled software components. Each component has a specific use case, and we decouple business logic and the presentation layers to the extent possible. All application code is committed to a Microsoft Azure DevOps repository and is built as a Microsoft Azure-native solution.

• Microsoft Azure Web Apps. We host the front-end (presentation layer) application in an Azure Web App, from which the user can call the anomaly-detection service by using the prepared time-series data. Microsoft Azure Web App Service gives our developers the option to work in their preferred language, which can be .NET, .NET Core, Java, Ruby, Node.js, PHP, or Python. We protect the application endpoint with Microsoft Azure Active Directory for user authentication and authorization.
• Microsoft Azure Function Apps. We host all business-logic functionality in Azure Function Apps. We use two Azure Function Apps. The first is used to connect to Microsoft Azure Application Insights and capture SAP telemetry, such as customer or business-partner processes that need anomaly detection.
  The Function App transforms the data into JavaScript Object Notation (JSON) format with time-series subformatting. The second Function App captures the precompiled time-series data from the first Function App, makes a call to the Anomaly Detector service, and then retrieves the result. The Web App presentation layer displays the results in a graph format. Function App endpoints are protected with access tokens.
• Application Insights. We store all SAP log data in Application Insights. This log data is posted from various business processes, including Customer Master Data creation, Business Partner Creation and updates, and batch program logs. These logs are the source for all anomaly detection.
• Microsoft Azure Anomaly Detector. Anomaly Detector uses the Anomaly Detector API to detect and return all anomaly points based on time-series data that the Function Apps send. While there are two options for interacting with Anomaly Detector, our developers chose to call the HTTP REST API directly for the Anomaly Detector rather than use the client SDK to integrate Anomaly Detector directly with their application. Using the API removes the limitation of using a single codebase and enables simple integration with any modern language that supports calling REST APIs through HTTP.

Implementation architecture

As depicted in the graphic below, various SAP applications post their business-process logs into the Application Insights instance. The Web App hosts the core application, including the presentation layer and user interaction. The two Function Apps perform extract and process data from the Application Insights service and control interaction with the Anomaly Detector service. The Function Apps send the final results from the Anomaly Detector service for display and consumption in the Web App.

Business implementation and benefits

One of our key business processes that we onboarded to the Anomaly Detector–based solution was the Master Data Management (MDM) business-partner creation that uses SAP Master Data Governance (MDG).

We constantly create and update business-partner data in our SAP system via API calls from various upstream tenants and front-end systems. Based on incoming telemetry sources, the Anomaly Detector solution detects if there is a sudden drop in creation or update processes because of API failure or network issues.

The detection algorithm can detect these issues automatically, in real time, which helps our system users to take corrective action. This simple addition to the issue-detection process helps us supply a better customer experience and eliminates major negative effects on revenue.

We’re planning to implement the same solution design across many other business processes, such as batch-job monitoring.

Currently, we have several hundred batch jobs that range from a runtime of a few seconds to several hours. It’s extremely difficult to monitor them manually and individually.

Sometimes, due to system issues or transaction locking, these jobs take more time, further affecting downline processes. Anomaly detection will play a critical role in detecting those issues, creating automatic alerts, and reducing manual monitoring.

This application has many potential use cases across multiple business scenarios. We’re planning to explore several of these use cases, including:

• SAP batch job monitoring, evaluating long running jobs and triggering alerts.
• Business-document processing and creation, such as sales orders, purchase orders, financial postings, and work orders.
• Any set of data that has time-series patterns. Data sets such as these can be evaluated and monitored for anomaly detection on a case-by-case basis.

Using Microsoft Azure Anomaly Detector has enabled us to quickly and efficiently build a solution to detect abnormalities in our SAP processes without having to know machine learning. The Anomaly Detector API’s algorithms help us to identify issues before they become problems, thereby proactively improving the performance, consistency, and reliability of our entire SAP landscape.

• Unpack how we’re optimizing SAP for Microsoft Azure.
• Discover how we’re protecting Microsoft’s SAP workload with Microsoft Sentinel.
• Explore how we’re upgrading Microsoft’s core Human Resources system with SAP SuccessFactors.
• Learn more about our best practices for using Anomaly Detector API.

The post Examining Microsoft’s SAP transactions with Microsoft Azure Anomaly Detector appeared first on Inside Track Blog.

It’s clear that AI brings immense value to our IT infrastructure.

“This is a fascinating time to be working in IT,” Sherwood says. “We’re using AI across all of our services, and now we get to take that investment to the next level. Now it’s all about seeing what we can do with it.”

Aligning IT infrastructure innovation with the rest of the organization

The strategy for AI transformation in core IT infrastructure is one part of a larger vision for the impact of AI across all of Microsoft Digital.

“The potential for transformation through AI is nearly limitless,” says Natalie D’Hers, corporate vice president of Microsoft Digital. “We’re evaluating every service in our portfolio to consider how AI can improve outcomes, lower costs, and create a sustained competitive advantage for Microsoft and for our customers.”

We’re hyper-focused on our employee experience, and AI will be instrumental in shaping the future of how Microsoft employees interact with customers, the organization, and each other.

Transforming and securing our network and infrastructure

AI holds enormous potential across all of Microsoft Digital, but within IT infrastructure, the benefits of AI-enabled transformation play out across several specific pillars where we’re focusing our efforts: Device management, network infrastructure, tenant management, security, and the IT support experience.

Security

We can’t transform without adequate security. Properly implemented security controls and governance provide the secure foundation on which our engineering teams build solutions, and that security is especially relevant as we incorporate AI into our services and solutions.

Securing our network and endpoints is imperative, and our Zero Trust Networking efforts across our IT infrastructure provide essential protection against threats to our network security. AI will enhance the security and compliance of these efforts in our cloud and on-premises environments.

AI-based network assignment for devices will simplify network classification and provide more robust risk-based isolation to isolate risky devices and reduce unwanted movement across the network.

We’re automating access controls for our wired and wireless networks to improve security effectiveness. AI-infused processes for analyzing device vulnerabilities, detecting anomalous firewall traffic flow, and diagnosing other network incidents will play a critical role in our continued shift toward the internet as our primary network transport.

We anticipate that AI-supplemented capability in Microsoft 365’s multi-tenant organization feature will help us meet our ever-changing network segmentation needs by maintaining tenant separation and enabling secure tenant cross-collaboration when required.

AI will help us manage third-party app access and revolutionize how we understand user interactions with applications across managed devices or SaaS platforms. We’ll increase access efficiency and reduce costs by capturing third-party app usage and needs more accurately, using AI to determine the how, why, and when of user access.

Intelligent infrastructure

Software-defined networking and infrastructure code are already transforming how we approach networking, but AI amplifies the benefits radically.

AI enables us to build data-driven intelligence into network infrastructure, engineering, and operations. AI-driven processes will help us eliminate configuration drift, comply with security policies, reduce operator errors, and efficiently respond to rapidly changing business needs.

We’re implementing AI-driven automation to simplify resource management and deployment, capitalizing on the flexibility provided by software-defined networking and infrastructure as code.

AI will assist with generating code designs, defining and managing network configurations, managing deployments, conducting pre- and post-deployment verifications, and assisting with change management over time. Near real-time streaming telemetry from network devices will form the foundation to guide operation and continuous improvement.

We’re improving network self-healing capabilities by using AI to detect and remediate network issues, creating a more reliable, resilient, and elastic network environment and reducing human intervention and potential for error.

One of our current projects is creating an AI-based assistant app for our direct engineering teams that mines and analyzes our current network infrastructure catalog, providing an advanced set of capabilities that supplement our engineers’ expertise in the field. The assistant app improves productivity and mitigation time for network infrastructure incidents. The AI component is trained on more than 200,000 prior incidents for anomaly detection and predictive analytics. We’re confident it will lead to a considerable reduction in network outages and maintenance costs.

Device management

With more than 1 million interconnected devices, AI-powered capabilities will significantly benefit our device management practices with a focus on user and administrator workflows.

We’re implementing intelligent device recommendations to ensure our employees have the best tools to do their work. Building AI into a centralized device lifecycle management tool will create efficiencies in procurement, tracking, and responsible device recycling.

We’re designing AI-powered predictive maintenance and intelligent troubleshooting to reduce device-related issues significantly. AI-enabled device maintenance schedules and tasks will automate the device management process and reduce the load on our IT help desk by correcting device issues before they become user problems, reducing device-related helpdesk incidents.

Across our vast scope of device management, many alerts and tickets contain information or fixes that our helpdesk engineers can use in other situations. We’re employing AI to generate device insights by analyzing a massive set of signals, including device configurations, network traffic, vulnerabilities, and user behavior. These insights will power more informed decisions across the device management portfolio, including device replacement, software updates, and capacity increases.

We have more than 100,000 IoT devices on our corporate network. AI-automated IoT device registration will create more robust and efficient IoT device management, tracking, and security.

AI and machine learning will help us to perform aggregated meetings and call data for device monitoring across personal devices, Microsoft Teams meeting rooms, networks, IoT devices, and Microsoft 365, improving and safeguarding the user experience.

Tenant management

Our cloud tenants in Microsoft Azure, Microsoft 365, Dynamics 365, and the Power Platform are among those platforms’ largest and most complex implementations. Our internal implementation includes more than 205,000 Microsoft Teams, 534,000 SharePoint sites, 430,00 Microsoft Exchange mailboxes, 93,00 Power Apps, 5,000 Viva Engage communities, and a massive 25,000 Microsoft Azure subscriptions.

It’s a lot to manage, and AI will improve how we do it.

In tenants of our size, unmanaged assets can lead to unnecessary costs. Our asset compliance and lifecycle management processes will include an AI-powered compliance assistant that informs tenant users and owners, recommends assets for deletion, and proactively identifies areas of high risk for the tenant. Through the assistant, tenant admins gain an all-up view of compliance status and can investigate and resolve issues more granularly.

AI is also simplifying and streamlining our license management processes. We adhere to precise rules and regulations, which result in complex access scenarios across different countries and regions. AI will bolster our ability to detect and remediate non-compliant tenants amidst this complexity.

IT support

We’re poised to transform how Microsoft employees interact with our support services using generative AI.

Our employees interact with Microsoft support services in a complex, global hybrid environment. Our self-help solution using Microsoft Azure OpenAI will enable contextual and human-like conversation and support in the employee’s local language. Our chat and incident summarization tools will use AI to summarize incidents and provide context when assisted support is necessary.

We’re infusing our support ticketing systems with AI capability for forecasting support requirements and proactively checking the health of devices to reduce issues and improve resource planning and response times.

Transforming our IT infrastructure as Customer Zero

As Customer Zero for Microsoft, we pilot and deploy new products and capabilities in our IT infrastructure before releasing them externally. Our scale, size, and knowledge of our products and services enable us to envision connected experiences across large enterprises, manage complex combinations of product use cases, and engineer solutions on top of our product platforms.

AI improves our role as Customer Zero by accelerating insights and improving time-to-value. We’re using AI capabilities to capture, review, analyze, and report on the most important and actionable insights from the Customer Zero experience. We’re also using AI to redevelop processes, regulatory compliance, security reviews, and deployment practices within the Customer Zero environment.

Looking forward         

It’s almost impossible to envision a future for corporate IT infrastructure without AI. Our active planning for AI in our infrastructure is continually evolving, and we’ve only just begun our implementation. We’re positioning Microsoft to be a catalyst for innovation, and we’re committed to innovating with AI to streamline our IT operations.

“We will continue to infuse AI into every dimension of our enterprise portfolio,” Sherwood says. “We’ll continue to identify new opportunities for building AI-powered applications and services that improve how we deliver IT services to the company.”

By showcasing our progress with AI capabilities, we aim to transform our approach to AI internally here at Microsoft and to fuel a similar transformation across the IT sector.

Here are four important steps you can take to transform your IT infrastructure with AI:

• Make device handling smarter with AI. Use AI to manage all devices better, helping to fix problems before they affect people and easing the workload for your IT team.
• Use AI to improve the network. Integrate AI into the network system to make it more intelligent and more adaptable, which helps reduce downtime and facilitates faster and easier changes.
• Manage cloud services better with AI. AI can help keep track of cloud services, ensuring everything is used properly and securely.
• Boost security and helpdesk with AI. Enhance safety and helpdesk services using AI, leading to better network protection and quicker, more effective support for employees when they need it.

Click here for a free trial of Entra ID (formerly Azure Active Directory) to unlock Microsoft 365 Copilot and other digital transformation tools.

• Discover moving Microsoft’s global network to the cloud with Azure.
• Unpack reinventing Microsoft’s employee experience for a hybrid world.
• Explore deploying Microsoft 365 Copilot internally at Microsoft.

• Want more information? Email us and include a link to this story and we’ll get back to you.
• Please share your feedback with us—take our survey and let us know what kind of content is most useful to you.

The post Transforming Microsoft’s enterprise IT infrastructure with AI appeared first on Inside Track Blog.

Just ask the 25,000 people who pitch the complex array of products and services that the company sells to customers across the globe.

Those sellers used to wade through more than 30 homegrown applications to get their jobs done, often spending more time filling out forms and cross referencing tools than talking to customers.

“We needed to modernize our toolset,” says Kim Kunes, who leads the Microsoft Digital team that provides the tools and experiences that the sales and marketing organizations use to sell the company’s wares. “We needed to turn what had been our sellers’ biggest headache into an asset that would help them flourish in a connected, cloud world.”

A complete overhaul of the company’s tools and processes is fully underway, says Kunes, but it’s not complete.

“We’ve winnowed a disconnected, heavily seamed group of tools down to a core group of critical experiences connected in ways that make sense for our sellers and marketers, but there is still work to do,” she says. “We’re in year two of a multiyear journey to revamp our sellers’ toolset.”

Kunes says her team, like all of Microsoft Digital, is shifting its focus from working for internal partners in a traditional IT manner to building experiences in partnership with the business that make sense for users. In this case, those users are the heart and soul of the company’s revenue-generating selling community.

“Now, just like any other product team at Microsoft, we operate with a baseline budget that funds a group of FTE (full-time employee) engineers and a continuous prioritization and planning process to deliver functionality most critical to our users and businesses,” she says. “Now we’re thinking, ‘What should the seller experience be from start to finish? Are we doing everything we can to make their experience as seamless as possible?’”

This transformation has Microsoft Digital’s Commercial Sales and Marketing Engineering Team working in new ways. It’s centralizing and standardizing the many channels of feedback and data to derive a picture of users’ unmet, unarticulated needs. The shift is built around a new focus on how Microsoft Digital approaches customer research. It’s adopting a fluent, modern look and feel that’s consistent with how the rest of the Microsoft is approaching design. It’s using DevOps and other agile engineering principles that truly keep the team focused on the user’s end-to-end experience as it moves, fast and flexibly.

“All of our sellers’ regular tasks need to be in one place and arranged so that it’s efficient and virtually seamless to flow through them,” Kunes says. “Everything has to be intuitive. There should be no big learning curves. They shouldn’t have to figure out how to use a new application every time they want to get something done.”

This laser focus on the customer experience has required the team to think and work differently.

“I’ve seen our team’s culture shift,” Kunes says. “In the past we were focused on incremental improvements to make the process and tools better. Now we’re thinking bigger. For example, we’re beginning to use AI and machine learning to curate the gold mine of valuable data we have to surface critical next best action insights to our sellers and marketers.”

This transformation is driving results that are paying dividends, says Siew Hoon Goh, the Microsoft director of sales excellence in charge of making sure the tools and experience that Microsoft Digital is building meet the needs of the company’s digital sales force.

“Our sellers do recognize that there has been lots of progress,” Goh says. “Technology is one of the best enablers for us to scale to bigger and better things and increased revenue for the company.”

Microsoft’s umbrella tool for sales is Microsoft Sales Experience. Known as MSX, it’s an integrated solution built on Dynamics 365, Microsoft Azure products, Office 365 productivity and collaboration services, and Power BI. In July, MSX was upgraded to the new modern Microsoft Dynamics 365 for Sales user experience. It includes a simplified user experience and integration into LinkedIn, Microsoft Teams, and several internal tools.

“Our MSX instance is one of the largest implementations of Dynamics 365 in the company,” says Ismail Mohammed, a principal program manager on the Microsoft Digital team working to make life better for the company’s sales field. “Ultimately, we want to make our tools more intuitive and help our sellers get their time back so they can focus more of their time on selling.”

MSX is the gateway to several important seller experiences that you’ll read about here:

• Portal, a second generation of MSX meant to be a true single pane of glass for sellers to work from
• Account Based Marketing, a transformed approach to sifting through marketing sales leads to find the ones that are worth pursuing
• Daily Recommender, a machine learning-based discovery engine that advises sellers on the specific leads they should pursue next
• Account 360, an aggregated view of customer content that helps sellers find the right customer information before they reach out to leads

Charting the evolution of MSX

When MSX launched in 2015, it replaced eight on-premises instances of Dynamics CRM 2011, each of which was highly customized and complex. Built on Azure Cloud Services, MSX brought all those experiences into one cloud-based platform.

Though it was a big improvement, it was still just a beginning.

“For perspective, MSX started out as a collection of links,” Kunes says. “It was nice to have a place where you could get to everything, but it really wasn’t the seamless, single-pane-of-glass experience that we are working toward.”

The team has continued to refine MSX, pushing hard to evolve it into an experience in which sellers feel more productive. They felt less so when they pieced their sales story together with their own offline Excel spreadsheets, PowerPoint decks, and secret contact lists (the latter of which are no longer allowed anyway, because of GDPR, or General Data Protection Regulation).

The original MSX solution is gradually making way for MSX Portal, a new, transformative experience that is being rolled out to the company’s sellers role by role, says Steffie Hofmann, Microsoft Digital’s lead MSX Portal program manager. MSX Portal debuted in February, when the experience was provided to the company’s customer-success managers, a specialized sales role at Microsoft.

MSX Portal helps sellers figure out what the next best action they can take. They get suggestions on their homepage and in context-driven ways within their workflow.

“Each time we ship MSX Portal to a new group of sellers, the experience improves dramatically,” Hofmann says. “They no longer have to leave the tool to get their work done.”

The goal has long been to have MSX provide sellers everything they need as they reach out to customers to sell the company on a daily basis , says Steve Thomas, Microsoft Digital’s lead software engineering manager for MSX Portal.

“We built MSX Portal with the idea of making it a great place for our sellers to start their day, to get their work done,” Thomas says. “We wanted to get past the notion that it was something they had to work around.”

The rollout of MSX Portal is expected to be complete by the end of the 2019 calendar year.

Sifting through the noise

Sales leads pour into a company the size of Microsoft from all directions, at massive scale. After their interest gets piqued by the company’s wide-ranging marketing efforts, leaders at other businesses watch webinars and make decisions:

• Should CIOs invest in Microsoft’s stack?
• Should CEOs ask Microsoft to see how the company can help them digitally transform?
• Should IT pros ask for Microsoft’s help via product websites and customer-service lines and at conferences?

[Read this case study on how Microsoft uses a bot to improve basic lead qualification to see how millions of potential sales leads received each year are qualified down to thousands. Read about how we use AI to serve up the next best lead to sellers.]

All those many thousands of leads get funneled into the Microsoft Global Demand Center.

“Before we move a lead to one of our sellers, we nurture them in the Global Demand Center,” says Prabhu Jayaraman, a group engineering manager who helps lead Microsoft Digital’s marketing effort. “They don’t go to our sellers right away—first we need to make sure our leads are high quality and have a high propensity to result in wins before we transfer them.”

It used to be that all those marketing-driven leads would get dumped on sellers, tossed over the fence with little vetting or insight.

“Sellers would look at these queues, they’d see 25 pages of leads, and randomly say, ‘This looks interesting, let me go talk to them,’” Jayaraman says. “The problem was the lead they picked out of the 10,000 options might not be the next best lead to pick.”

To help sellers get to the right lead, Microsoft adopted a new approach to how it markets to larger customers by infusing AI into its Account Based Marketing (ABM) program.

“ABM is not a tool, it’s a concept,” Jayaraman says. “It’s about stitching these opportunities together in ways that make sense—when one company contacts us in five different ways, we will connect those together into one opportunity.”

To Vinh Nguyen, ABM is about bringing marketing and sales closer together—something it does by weaving relevant contacts and insights together in ways that help sellers be more effective.

“It may sound simple, but it hasn’t been,” says Nguyen, the senior program manager leading Microsoft Digital’s efforts around Account Based Marketing. “We’re trying to use machine learning and automation to optimize when sellers should engage with a customer on products that their employees have shown interest in.”

The team has been working for more than a year on getting it right.

“We’re using Marketo marketing software to listen to our customer interaction signals,” Nguyen says. “When signals come into the Global Demand Center, we feed them into our machine-learning models.”

Those ML model-fueled recommendations are fed into the Daily Recommender, where sellers use them to decide which leads to pursue on a daily basis.

Finding the best leads with Daily Recommender

Until recently, Microsoft’s most successful sellers were those best at finding gold nuggets of customer information hidden in the company’s many sales tools. That was when star sellers were known for maintaining their own offline databases and sales pitches more than they were for building close relationships with customers.

“Why should our most successful sellers be the ones who are the best at navigating complex systems?” Kunes says. “Why shouldn’t success be about having intelligent, human connections with customers?”

This culture was fed by the fact that the company’s sales strategy was built around educated guesswork—each quarter, SWAT team-like groups of sellers would gather, discuss the indicators that each of them were seeing, talk it out, and use that war-room discussion to set sales targets for the upcoming quarter.

All of this made selling more art than science.

The team looked to change that when it developed Daily Recommender, a machine-learning tool that makes individualized recommendations for each seller, says Hyma Davuluri, principal program manager in Microsoft Digital.

“With Daily Recommender, we’re pushing the envelope on using AI to influence large-scale selling at Microsoft,” Davuluri says. “It’s also helping us accelerate our digital transformation journey across the company’s sales organization.”

Launched three years ago, Daily Recommender has been rolled out to about 1,000 sellers and, as it has learned and matured, is starting to show very promising results. So says Salman Mukhtar, the director of business programs who leads the selling community’s use of Daily Recommender.

“It’s Microsoft using Microsoft,” Mukhtar says. “We’re using SQL Server, Azure Fabric, Azure Machine Learning—we’re using a lot of our own technology together and connecting it on top of Dynamics.”

Microsoft started small with the intent to prove the value of an AI-enabled discovery engine that would improve targeting of new business while reducing the preparation efforts by sellers. So far, the results have been promising—one in four recommendations pursued by sellers result in a customer opportunity or engagement.

“Machine-to-human AI requires a mindset change,” Mukhtar says. “It requires legacy processes to be enhanced and new habits to be formed across the sales force.”

For example, sellers must give up their personalized Excel spreadsheets and PowerPoint decks. “The sponsors and developers of our legacy toolkits and processes need to be bold and decommission where necessary,” he says.

The needed changes are happening but are not complete yet.

“Digital transformation is a journey—for us it involves data, tools, processes, and people all enabled by AI,” Mukhtar says. “We are scaling up our enablement efforts to transform Daily Recommender into the primary discovery engine for the business.”

Account 360 stitches the customer story together

Historically, it has been a challenge for sellers, as they reach out, to understand what relationship a customer has with Microsoft.

“The key challenge for sellers was to gather consistent insights in order to have a productive conversation,” says Alioscha Leon, Microsoft Digital’s program manager for Account 360, a new MSX sales tool that seeks to stamp out that legacy of opaqueness. “They would have to go to several tools with different interfaces and search functionality in order to get the information required to have a productive conversation, and there still was no guarantee that they were getting the full picture.”

To change that, Microsoft Digital rolled out Account 360 in May 2019.

It was introduced in beta form to an initial wave of sellers from Microsoft Inside Sales. Built into MSX Portal, it aggregates multiple tools into one, with a consistent user interface, giving sellers a comprehensive view of their customers. More than 1,300 sellers volunteered to try out the tool, exceeding the goal of 800.

“We allow sellers to very quickly prepare for an interaction with a customer,” Leon says. “We’re making it easy for them to have relevant conversations without having to do huge amounts of research, increasing the seller productivity and interaction quality.”

Account 360 allows sellers to see Microsoft’s agreements across modern and legacy systems, revenue across products, marketing interactions, partner association, and account profiles. It also shows what opportunities and leads are already being pursued, and what products and services the customer is already consuming. The insights are available and delivered in a fast and consistent manner, using an interface tailor made for sellers.

The goal is for the sellers to get all the info they need to enable a productive customer interaction in the Account 360 interface. But if they need to go deeper, a linking strategy allows them to navigate to additional resources.

A first version of Account 360 went live in July for all seller audiences. “We continue to have exponential growth in both monthly and weekly unique users, with 3,000 unique monthly users and a run rate of 1,300 weekly unique users in August,” Leon says.

Dynamics 365 is the backbone of selling at Microsoft

MSX’s heavy use of the Dynamics 365 platform is very helpful, says Linda Simovic, principal group program manager for the Dynamics 365 product group.

“I think the way we’re drinking our own champagne inside the company is amazing,” Simovic says. “With 25,000 sellers or more in the company, it gives us a lot of great ways to test out our products and services.”

Showcasing the way Microsoft uses Dynamics 365 products also helps other companies understand what they can do with the platform, she says.

Simovic says the Dynamics team continuously talks with the Microsoft selling community and Microsoft Digital, weaving their steady stream of feedback into Dynamics 365 as fully and quickly as possible.

“We actually say to the MSX team, ‘We’re thinking about building this—what do you think?’” she says. “We want them to use it and to let us know if it works. It’s a litmus test to see if what we’re thinking is a good idea or not.”

The recent decision to upgrade MSX to the latest version of Dynamics 365 helps with this—now the Microsoft Digital team can try out new features as soon as they’re ready for testing.

“We want to be able to cover their needs out of the box as much as possible,” Simovic says. “The better we can support the company’s complex sales motion, the better we can support our external customers.”

Mohammed agrees, calling out how the two teams have worked together to bring new enterprise-level capabilities into Dynamics 365.

In fact, he says, the teams are working so closely together that in some cases the Microsoft Digital Commercial Sales and Marketing Engineering Team is co-developing directly with the Dynamics team to add features that the sales teams need.

“That’s a big change from our historical approach of building in-house bridge software,” Mohammed says. “This is a pretty major leap forward for us—we’re working hand-in-hand with the product group to build new capabilities for customers.”

For Kunes and her Microsoft Digital team, the successful partnership with Dynamics is just one more signal that their new, transformed approach to supporting the company’s complex sales motion is working.

“We’ve laid the groundwork for us to finally get this right for our sellers,” Kunes says. “Now we just need to go finish what we started. It’s an exciting time to be working on this team.”

• Read this case study on how Microsoft uses a bot to improve basic lead qualification to see how millions of potential sales leads received each year are qualified down to thousands.
• Read about how we use AI to serve up the next best lead to sellers.

The post Retooling how Microsoft sellers sell the company appeared first on Inside Track Blog.

Both are part of being a good steward of the environment, says Elizabeth Willmott, carbon program manager at Microsoft, but they are only a beginning. There is a much bigger opportunity within reach.

“Microsoft is in a unique position with its enormous network of customers, partners, and suppliers,” Willmott says. “We have an incredible reach with our software and services, and with our devices. If we can use that reach to drive positive change for the environment, then we can really start to help the planet.”

[Read this case study on how Microsoft is using machine learning to minimize its carbon footprint and reduce its energy consumption.]

Employees are asking about and encouraging Microsoft’s efforts all the time, and the company’s leaders are also pushing to find ways to do more.

“This is definitely a year when we’re on the move in terms of doing a lot more on sustainability,” says Brad Smith, Microsoft’s president and chief legal officer, speaking to company employees at a recent internal event. “It starts with getting our house in order, but then it ultimately connects to how we can help everyone on the planet use technology to drive sustainability goals.”

To mitigate its impact on the climate, the company’s efforts have been in three main areas: decreasing its carbon emissions through energy efficiency and conservation; moving to renewable energy for its datacenters and buildings; and offsetting the carbon emissions of business air travel.

“We’re on a leadership path in these areas,” Willmott says. “Now we are leaning in to encourage and help our suppliers, our partners, and our customers to do the same.”

Levying a tax for good

Microsoft charges internal teams a tax of $15 per metric ton on all operational carbon emissions, a fee that just went up from the approximately $8 per metric ton charged previously. The money is paid into a sustainability fund that is used to achieve carbon neutrality via efficiency, renewable energy, and offsets. From there it is granted to Microsoft internal teams and external organizations to address climate change and other environmental sustainability priorities. This process is transparent, and the investment areas are tracked publicly on Microsoft’s Sustainability Fund Power BI Dashboard.

Among those investments to accelerate progress is Microsoft’s AI for Earth program, a commitment by Microsoft to spend $50 million over five years to support projects that use Microsoft’s AI and machine-learning technology to tackle environmental challenges, says Bonnie Lei, AI for Earth program manager at Microsoft.

“We’re supporting individuals and organizations that are building AI models that are broadly useful for the environment,” Lei says. “We want to provide exponential impact with our investment, and so we work with our partners to make these models available on the AI for Earth website to the wider public.”

For example, Microsoft is supporting SilviaTerra, a California company that is using an AI for Earth grant to create a national forest inventory, which is now being piloted in an effort to help people who own small private forest land receive payment for keeping that land forested.

“They created machine-learning algorithms that were scaled through Microsoft Azure to create the first map of every single tree in every forest in the continental US, down to the tree’s species and size,” Lei says.

Landowners can use those maps to better manage their land, and, more importantly, they are testing a new approach to qualify their lands as viable carbon offsets. This means that, for the first time, these owners can be paid to keep their land forested by companies looking to offset their carbon emissions.

“Previously, they were not able to enter the carbon market due to the high cost of overhead and monitoring,” Lei says. “Now they have more incentive and a way to value keeping their forest stands standing.”

Landowners are also leveraging the maps to improve how they manage their land, including better preparing themselves for fire danger and drought.

Easing the environmental cost of buildings

One of the core ways Microsoft aims to reduce its carbon footprint is by transforming how it constructs and manages its buildings.

The company is currently rebuilding part of its headquarters in Redmond, Washington, and it’s seeking to do so in ways that slash the amount of carbon released into the atmosphere that is typical of new construction, says Katie Ross, global sustainability program manager for Microsoft Real Estate and Security.

“Traditionally, the building sector has been focused on operational carbon,” says Ross, referring to the carbon associated with the energy used to run a building. “But that’s only half of the carbon problem in the building sector—the other half is embodied carbon, or the carbon that is emitted when building materials are manufactured.”

Think about the latter as “upfront carbon.”

“It’s the carbon you expend before you even flip the switch to turn on the building,” Ross says.

Unlike operational carbon, which you can reduce to zero over the life of the building by implementing energy-efficiency programs and by sourcing renewable energy, embodied carbon was emitted to make that concrete—to pull the raw materials out of the ground, to process them, and so on.

“That’s a carbon footprint number you can only reduce when you pick the material, and once the building is built, you cannot change it,” Ross says.

Microsoft is constructing 17 new buildings on its east campus—a total of 2.5 million square feet of new space.

“We knew we wanted to tackle both sides of the carbon equation, with the aim to build zero-carbon buildings,” Ross says. “To get there, we’re focusing on reducing our energy usage, we’re sourcing 100-percent carbon-free electricity, we’re removing natural gas—including for cooking—in our cafes, and we’re using a new tool, Embodied Carbon Calculator for Construction (EC3), to track and reduce our embodied carbon.”

Microsoft is partnering with the University of Washington’s Carbon Leadership Forum and the global project-development and construction company Skanska to pilot EC3 on its new campus. This open-source, free-to-use tool is helping the Microsoft Real Estate and Security construction team assess the embodied carbon within construction materials it considers for the project.

“A lot of this is unchartered territory,” Ross says.

Efforts by corporations to track and reduce the embodied carbon impact of their buildings are in their infancy, she says.

“So far we are on target to reduce our embodied carbon emissions by 15 to 30 percent,” she says. “We are learning a lot about what’s possible by piloting this tool and hope to create a roadmap to support the industry targeting embodied carbon reductions in future projects.”

Energy and airplanes

Willmott says that one of the most notable ways Microsoft has made an impact on the environmental sustainability side is by procuring renewable energy to power the company’s datacenters.

So far, Microsoft has procured enough renewable energy to power 60 percent of its datacenter load by the end of this calendar year. The goal is to continue on a path to power 100 percent of its datacenters with renewable energy.

One area the company is exploring to further shift behavior and reduce carbon emissions is by encouraging employees to skip carbon-intensive airline trips in favor of using Microsoft Teams to meet and collaborate.

When employees do fly, Microsoft offsets the associated emissions by investing in verified carbon-offset projects, such as a first-of-its-kind forest conservation project in King County, Washington. The company’s environmental sustainability team vets all offset projects closely to ensure that they are having a measurable impact.

“These investments have supported the protection of 5.1 million acres of sensitive land worldwide,” Willmott says. But even though scaling up the impact of these “natural climate solutions” investments is meaningful, the team is very aware that avoiding carbon emissions altogether is the first and best line of action.

It’s all part of Microsoft’s commitment to sustainability, Willmott says.

“We’re using Microsoft tools and purchasing decisions to prove what our research has told us—that AI and other technologies can help usher in a low-carbon transition and protect the planet from catastrophic degradation,” she says.

If there were significantly greater adoption of AI in key sectors, greenhouse gas emissions could be reduced enough to zero out the annual emissions of Australia, Canada, and Japan combined.

“There is so much we can do if we all work together on this,” Willmott says. “Let’s go do this.”

Read this case study on creating business intelligence with Azure SQL Database and this case study on using machine learning to develop smart energy solutions to see how Microsoft is using technology to minimize its building footprint and reduce its energy consumption.

The post Tackling environmental sustainability from the inside out at Microsoft appeared first on Inside Track Blog.