Emerging AI Patterns
One of the top conversations that we have with businesses all over the world is how digital transformation is impacting every part of their operations. This wave of “Digital Transformation” impacts every business and every industry; from media to sports, from finance to healthcare, and from Fortune 1000 organizations to small businesses. This wave of transformation is being driven by the ongoing advances in computing building blocks of the last few decades: compute, storage, and networks. At the same time as companies are continuing down the path of digital transformation, there is a new generation of software building blocks that will drive even greater transformation for businesses in the future of which AI is one of the core drivers. AI will help to transform all industries, including transportation, manufacturing, retail, agriculture, and more and create opportunities we have yet to consider.
In this post I will talk about how Microsoft is helping businesses transform with AI. This moves beyond creating the AI platform, or using AI to enhance an existing application or service and into how people are starting to think about using AI to change core business processes. If you have been following along, this is the third and final part in a 3-part series on AI. If you want to revisit the other two posts before continuing, Part 1 provides some context on why this time is different for AI. It is followed up by Part 2 whichPart 2 looks at how Microsoft thinks about AI and the tools and services that we make available for you to create your own AI solutions and in turn how we use it to enhance our own products and services.
In this blog we’ll dive more into the patterns we see emerging for the use of AI across industries and experiences. As we talk about AI with business and industry leaders, we see 4 patterns emerging on how to apply AI to businesses and business scenarios that provide a useful frame for the conversation.
- Virtual Agents - The first pattern is the use of virtual agents to interact with employees, customers, and partners on behalf of a company. These agents can help answer questions, provide support and over time become a proactive representative of your company and your brand. Today Microsoft uses a virtual agent in customer support that will engage in close to 50 million conversations this year.
- Ambient Intelligence - The second pattern is anchored on tracking people and objects in a physical space. In many ways this is using AI to both map a physical space and activity to a digital space, and then allowing actions on top of the digital graph. Many people will think about “pickup and go” retail shopping experience as a prime example, but this pattern is also applicable to safety, manufacturing, construction scenarios and more. Think about a warehouse that can detect a person walking in one aisle and a forklift driving in another that are on a collision course, the AI can prevent the pending accident. We also showed this pattern applied to an office/meeting scenario at BUILD.
- AI-Assisting Professionals - AI can be used to help almost any professional be more effective. For example, we can help people in finance with forecasting. Large companies manage forecasts by having their front-line sellers predict what they are going to do, then having many layers of reviewers to help judge the forecast. Using historical data and global insights from Bing, LinkedIn, and custom data sources, an AI system can reliably forecast how a subsidiary will do while removing all of the middle layers of judgement and the time consumed doing that. We also see AI starting to assist doctors in areas like genomics and public health. There are great examples in sales, marketing, legal and practically every other profession.
- Autonomous Systems - The fourth pattern that we see is for autonomous systems. You might think of self-driving cars when you think about autonomous systems, but it also extends to robotic process automation and network protection. Threats to a network can be hard to identify when they are happening and the lag before responding can result in a lot of damage. Having the network automatically respond as a threat is happening can minimize the risk and free the team to focus on other tasks.
In this post I will cover these 4 patterns along with how Microsoft is helping businesses achieve their goals.
At Ignite 2017 we discussed how AI will be used to help large businesses with customer support. Since that time, the Microsoft AI Solution for Customer Service, which is being used by Microsoft, HP, Macy’s, Australian Government Department of Human Services, and others has shown tremendous success in using AI to transform customer engagement. Looking at the initial results from the early adopters we have seen great improvements for their businesses.
- Microsoft – where possible we are testing AI Solutions within our own business processes before releasing software for others to use (e.g. forecasting and customer care). We built this solution to solve our own problems first and it is trusted to handle one of the largest enterprise support organizations in the world. With the addition of a virtual agent, it has created some impressive results. Over a 6-month period we saw a 2x increase in users successfully being able to help themselves with a 3x decrease in transfers to agents. Users dislike having to repeat themselves as they get transferred between agents, but with this solution the state transfers with the call creating a better experience for customers and our call center employees, at scale with over 100,000 virtual agent sessions per day.
- HP – one of the early users of this solution has scaled up to handle 70% of support cases with AI and maintains a greater than 85% accurate dialog rate. To achieve this level of accuracy the service was initially trained on over 1 million chat logs and 50 KB of support articles to create a sophisticated dialog.
- Macy’s – gives an example of how an AI solution for customer support can evolve to become a brand ambassador for the company. The virtual agent is integrated into both the web and mobile web experiences for shopping, so users can use the agents where they are. Macy’s integrated their backend APIs for promotions, alerts, and more into the agent so that the agent could go beyond the corpus of data that it was trained on to pull up account details, shipping updates, and more providing broad and personalized information.
Last year at Ignite when we discussed the Microsoft AI Solution for Customer Service it was framed in the context of helping users get the support they need while also improving the experience for agents who would get to focus on more value-added support cases. Since then there has been an expansion into more personalized experiences as demonstrated by what Macy’s is doing. Looking forward, business agents will be moving towards conversational commerce and blending proactive conversation capability with the current reactive capability.
Today online commerce is primarily a self-help experience with the user either having to know exactly what they are looking for or spending a lot of time researching. With a virtual agent there is the opportunity to have a virtual personal shopper by your side who can help you with your online experience. As you search for products the agent can intelligently recommend relevant intents based on what you have been looking at. As you select options that are intelligently designed to guide you through the purchase experience, the information that you need is presented to you in adaptive cards that contains rich yet focused information so that the user can make better decisions without information overload. Since the agent can maintain historical information, a conversation could be resumed later, and transactions could be completed all with the use of the agent. This pattern is by far one of the furthest along and an area we see a lot of activity in today. Companies can choose from a set of tools in the platform today to get started with initial agents before moving up to a full customer care solution.
Computing is often thought of in the context of devices but with the use of sensors a physical space can be digitized which creates an environment where people, objects and activities can be detected and tracked. When AI is added to the digitized environment you then make it possible to reimagine how a room interacts with the people and objects in it. For example, using facial recognition to know when a person has entered a room is valuable not just for personalization but also for safety. If the fire alarm goes off, knowing who was in the building and has left, or if anyone is still inside can be quite useful. The next level beyond just tracking people is to track people’s interaction with objects around them. This capability makes it possible to create several interesting retail experiences including; grab and go shopping, personalized offers, immediate and on the spot assistance when needed, and more. In manufacturing we can use this type of capability for health and safety scenarios that can include keeping a person safe by flagging when they are going to pick up something that might too heavy, or to make the problem of losing things less burdensome by flagging where a lost item might be with instructions on how to find it and more. There are many interesting scenarios enabled by the ambient pattern that are just starting to be explored.
We see real-world examples of this today using mixed reality to keep mission critical systems up and running. Preserving perishable goods is a great example. Cows produce 6 gallons of milk daily, so it is important that the milk is packaged on time to avoid waste. However, when a milk packaging line fails because of a faulty part, it can take several days to get up and running resulting in a lot of spoiled milk. Tetra Pak uses cloud-connected predictive analytics to analyze packaging lines data to predict maintenance issues to reduce down time. This is done by using mixed reality headsets to cut down fixing time by having remote experts guide service engineers.
AI Assisting Professionals
AI presents a great opportunity to augment human ingenuity by providing proactive timely support to people so that they can focus their energy on their most important tasks. This shows up in a variety of ways including digital assistants like Cortana, Alexa, Siri, and Google Home that many of us use today to do tasks for us. In business settings we have seen many early uses of AI helping busy professionals including Bing for Business which will take your companies organizational chart, internal sites, and cloud documents, and then integrate them into your search experience so that you have the public and private data that you need. AI helps professionals in other ways more specific to different industries. Attorneys who need to assess the risk of an event will spend a lot of time going through contracts to find the impact of any exposure to a negative event. Machine reading techniques can be used to understand the details of all their contracts and then surface the problems. Journalists can use AI to serve as a virtual editor that will look beyond rules-based spellchecking to make writing suggestions. Finance professionals can also use AI to do sales forecasts to make better forecasts or sellers can use AI to target sales prospects and how to close a deal. Marketers can use AI to predict new trends in customer interest before investing a lot of money to experiment. We are working on a variety of areas where we can apply AI to assist professionals and one of my favorite examples is the work we are doing to help medical professionals.
Microsoft Research is known for world class advances in computer science, but they also focus on medical, health, and genomics. Their approach is to apply novel computational tools and analytical techniques to make healthcare more impactful and to assist patients. One application of AI to healthcare is Project InnerEye.
Today expert medical practitioners spend a lot of time analyzing 3D images to identify tumors versus other healthy tissue. Using years of Microsoft AI research in computer vision including deep decision forests which are used in the Kinect and HoloLens, and machine learning, and then applying it to more medical images than the average medical practitioner could analyze on their own, Project InnerEye can assist in identifying the presence of a tumor. To ensure that the medical practitioner is in charge, the results can be adjusted by the experts until everyone is comfortable with the result. Since we are a platform company we are also making the technology available to third-party medical software companies so that medical practitioners can use the tools they are most comfortable with today.
Most people when they think of autonomous systems, visions of self-driving cars come up but there are so many other ways to apply this technology as well. Networks when they are under attack can take a long time for someone to notice and even longer to determine the root cause and then address it. Machine learning does a great job of detecting patterns in a vast amount of data, so it can be used to quickly identify attacks in real-time and then AI can be used to find the most effective approach for addressing the problem. The Network Admin will be notified and can control the process if needed but speeding up this process will limit the amount of damage that an attacker could cause. At the RSA 2018 Conference, Mark Russinovich shared how Microsoft’s investment in AI has created new security capabilities for protecting all of our customers.
For security, autonomous systems can help create secure products by finding security flaws during development. Microsoft Security Risk Detection is our offering for customers to use AI to find security flaws.
DocuSign is a company that enables people to sign documents from any device which means that they take security very seriously. Like most software developers, DocuSign uses a variety of software components in their products including ones from third parties and the security of their product is only as strong as all of the components put together. Using Microsoft Security Risk Detection, DocuSign can automatically check the security of all their components across multiple virtual machines with no extra work.
“We were able to automatically run millions of test cases across multiple virtual machines, entirely automated, with no extra work …. We’re always looking for services that add value, and that scale of automation is a great added value.” — John Heasman, Senior Director, Software Security, DocuSign
It is an exciting time in the industry as artificial intelligence is not just a topic of conversation but becoming the starting point for thinking about what will become the next wave(s) of digital and/or AI based transformation. The examples presented here across the four patterns are just a few examples, but we see the patterns as an interesting point to start the conversation. Three weeks I ago I started this series discussing why this time is different for AI compared to other times of AI excitement in the New Generation of Software Building Blocks post. Last week we focused on Microsoft’s approach to AI and how we are making it available to others across the platform, our products, and solutions. Today we presented four patterns for the use of AI in businesses and industries that we hope provide a useful framing around the topic. As you think through your own use cases think about how these patterns can apply to your business. Thank you for taking the time to read the series and if there are any other topics that we should explore please let us know.