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Unlocking the power of AI in Microsoft Cloud for industries

AI is transforming industries by helping organizations in modernizing business processes and accelerating development. While global AI software revenue is forecasted to reach $62.5 billion in 2022, a 21.3 percent increase from 2021 (Gartner®),1 a common challenge is moving from experimentation to making AI part of standard operations.

Microsoft is deeply invested in AI solutions, engaging highly experienced researchers, data scientists, and thought leaders. Our goal is to help organizations move from reactive to proactive decision-making and bring the power of Microsoft AI to business outcomes while improving efficiency of operations and helping people be more productive.

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Microsoft’s pragmatic approach to solving real-world problems with AI, built on a foundation of responsible AI principles, empowers businesses to start implementing today. AI is core to the value we deliver and is inherently infused in all our industry clouds. Here, we share recent examples of new AI solutions we have delivered as part of Microsoft Cloud for Healthcare and Microsoft Cloud for Financial Services as well as give a preview of capabilities we are developing for our Microsoft Cloud for Retail customers and partners.

Healthcare: Reduce missed appointments

The annual cost of missed appointments in the healthcare industry is more than $150 billion in the U.S. alone.2 Missed appointments not only lead to a decline in patients’ health, but the economic effects of patient no shows significantly affect clinic operations and fixed cost calculations, resulting in overstaffing and unscheduled downtime, ultimately leaving healthcare providers struggling with everyday operations. At a time of rising demand for healthcare but funding shortfalls, the use of AI and machine learning promise much-needed efficiencies in healthcare, while maintaining or even improving the quality of patient care. These areas of concern present an opportunity to improve outcomes for both providers and patients.

Missed appointments prediction is a fully integrated AI solution available in Microsoft Cloud for Healthcare. The model is easily deployable and can be trained within just two hours, leaving the healthcare provider ready to use the solution within just one day. This offering benefits both clinicians and patients. With a user-friendly and familiar interface, missed appointments prediction empowers office staff and clinicians to predict patient no-shows without data science training or staffing. Patients in turn have peace of mind knowing their physician’s office is prioritizing appointments that work best for them, building trust and ensuring better patient-centric care.

The Microsoft Cloud for Healthcare missed appointment model uses Microsoft Dynamics 365 Customer Insights and is integrated in unified patient view to enable an out of box experience for the users. It can be shipped as a standalone model and potentially be integrated into other platforms.

Screen capture of Missed appointment prediction for a patient shows the reasoning behind why a patient is at risk and provides an option to contact the patient directly.
Figure 1: Missed appointment prediction assesses risk and explains reasoning

Various kinds of input data has been found to be significant in predicting missed appointments in the healthcare domain. Demographics, historic patterns, social determinants, and appointment data such as type and time of day are input examples that care teams can use to train the model. Based on these factors, the machine learning model predicts the probability for a patient to miss an appointment and surfaces the reasons that contribute to the score within the solution. A higher probability score means a higher risk of missing the appointment, allowing the healthcare representative to act upon— such as reminding the patient or better organizing the physician’s schedule.

Learn more about missed appointments prediction.

Financial services: Speed up the process with document intelligence

The customer onboarding process is thought to be one of the most critical aspects of the customer journey. Customers expect a secure and efficient process when becoming a financial services client. An awkward user experience or slow response time can degrade customer confidence and trust and potentially cost an institution a loss of business. Today, many onboarding scenarios are manually managed by financial institutions, resulting in high costs, long service times, and missed business opportunities. Whether the customer is onboarding as a new client or applying for a loan, manual processes are time-consuming and can be both inefficient and costly.

Our document intelligence AI solution within Microsoft Cloud for Financial Services improves these onboarding processes. Foundational to setting up a document journey for each document type, document intelligence enables institutions to automate various workflows depending on business context and scenario. The document verification scenario uses AI Builder with Azure Cognitive Services Form Recognizer AI model to evaluate whether a document fits a known template. Customers can train the model to process different document types and organizations can determine individual journeys for each document type with multi-tiered verification steps such as categorization and third-party validators.

Microsoft provides business administrators with low code tools to build the onboarding journey easily, accurately, and fast. The model is configurable to adjust to geolocation, business processes, and various kinds of documents. Several AI capabilities combine to validate documents uploaded by the customer (e.g., passport verification) and provide a predictive confidence score for document validation.

Microsoft industry AI solutions are also extensible and composable. Partners can build solutions on top of the AI model to include other steps that host third-party AI models or services that continue the customer journey. An external fraud detection service, for example, can be connected as part of checking whether the identity document is part of a fraud ID repository. From there, the pipeline business logic can determine the document status recommendation.

Financial institutions such as banks, insurance companies, and wealth managers can leverage the document intelligence model to implement various onboarding scenarios. From account opening, claims and policies applications, to loan and product applications, financial institutions can improve their processing time, the confidence in the accuracy of the process, and respond faster to their customers.

Prompt and accurate responses to customer queries are a critical part of successful customer onboarding—building customer confidence and trust in an institution’s infrastructure and commitment to customer care. With document intelligence used as part of the onboarding process, customers can receive the answers they need even quicker than before. This gives organizations a competitive advantage, drastically reducing the manual labor involved in processes, improving operational efficiency, and serving more customers, faster, while meeting their needs.

Retail: Bring the online experience to physical stores

Online shopping has grown and, in its wake, brick-and-mortar stores struggle to compete with e-commerce. Retailers with physical stores seek ways to optimize the shopper’s experience while managing operational costs. Understanding the customer purchasing journey becomes an important factor for success.

Our Microsoft Cloud for Retail and Industry AI engineering teams are exploring a scenario that uses anonymized data from smart store providers about shoppers’ in-store purchasing journeys to provide insights to help merchandizing managers optimize their smart stores. We would provide indicators for monitoring the performance of the store, including a built-in model that provides insight into items most commonly purchased together. The AI model would rely not only on checkout data to help understand shopper behavior, but also allow retailers to make decisions based on rich behavioral information uniquely available in smart stores—including shopper actions such as picking up items from the shelf and then returning them or lingering in front of the shelf.

Store managers are responsible for monitoring and optimizing the day-to-day operations of the store—ensuring products are stocked and placed effectively while also providing great customer service. Smart store insights would help managers assess the number of items moving from shelf to basket—monitoring window shopper data against paying customer data and assessing how quickly customers check out, all while maintaining individual customer privacy. Additionally, the shopper heatmap would help managers to understand which aisles are highly trafficked versus not. This way the store could avoid putting products that need clearing from the dead zone areas. Changes to complimentary product placement, ordering frequency, and promotional opportunities would also be made based on the recommendations provided by the AI model.

Read more about how Microsoft is thinking about smart stores.

Expanding our partner ecosystem

AI models such as document intelligence, showcase our approach to delivering responsible AI inside flexible, and reusable components. These allow our network of independent software vendors (ISVs) to build on top of Microsoft AI and integrate their own AI on top of Microsoft platforms to deliver composable solutions to serve their customers. Microsoft continues to invest in our global partner ecosystem to offer customers a network of trusted partners who build solutions tailored for specific industry and customer needs.

Learn more about our Microsoft Cloud for industries and explore apps offered by our partners.

1Gartner Forecasts Worldwide Artificial Intelligence Software Market to Reach $62 Billion in 2022, November 2021. Gartner is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 

2Missed appointments cost the U.S. healthcare system $150B each year, Healthcare Innovation.