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AI is transforming industries by helping organizations modernize business processes and accelerate development. While global AI software revenue is forecast to reach $62.5 billion in 2022, an increase of 21.3% from 2021 (Gartner®),1 a common challenge is moving from experimentation to making AI part of standard operations.
Microsoft is deeply involved 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 results while improving the efficiency of operations and helping people be more productive.
Microsoft’s pragmatic approach to solving real-world problems with AI, built on a foundation of responsible AI principles, allows companies to start implementing today. AI is core to the value we deliver and is inherently infused into all of our industry clouds. Here, we share recent examples of new AI solutions we’ve delivered as part of Microsoft cloud for healthcare Y Microsoft Cloud for Financial Services as well as giving a preview of the capacities that we are developing for our Microsoft Cloud for Retail clients and partners.
Health Care: Reduce Missed Appointments
The annual cost of missed appointments in the healthcare industry is more than $150 billion in the US alone.two Missed appointments not only lead to a decline in patient health, but the economic effects of patient absences significantly impact clinical operations and fixed cost calculations, resulting in excess staff and downtime. unscheduled downtime, ultimately leaving healthcare providers struggling with daily operations. At a time of rising healthcare demand but funding shortages, the use of AI and machine learning promises 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 Appointment Prediction is a fully integrated AI solution available at Microsoft cloud for healthcare. The model is easily deployed and can be trained in just two hours, getting the healthcare provider up and running in just one day. This offer benefits both doctors and patients. With a familiar, easy-to-use interface, Missed Appointment Prediction enables office staff and clinicians to predict patient absences without data science training or staff. Patients, in turn, have peace of mind knowing that their doctor’s office is prioritizing appointments that work best for them, building trust and ensuring better patient-centered care.
The Microsoft Cloud for Healthcare missed appointment model uses Microsoft Dynamics 365 Customer Insights and is integrated into the unified patient view to enable an out-of-the-box experience for users. It can be shipped as a stand-alone model and potentially integrated into other platforms.
Various types of input data have been found to be significant in predicting missed appointments in the health care domain. Demographics, historical patterns, social determinants, and appointment data such as type and time of day are examples of input that care teams can use to train the model. Based on these factors, the machine learning model predicts the likelihood of a patient missing an appointment and exposes the reasons that contribute to the score within the solution. A higher probability score means a higher risk of missing the appointment, which allows the health care representative to act accordingly, such as reminding the patient or better organizing the doctor’s schedule.
learn more about missed date prediction.
Financial services: speed up the process with document intelligence
The customer onboarding process is believed to be one of the most critical aspects of the customer journey. Customers expect a safe and efficient process when they become a financial services customer. An uncomfortable user experience or slow response time can degrade customer trust and potentially cost the institution lost business. Today, financial institutions manually manage many onboarding scenarios, leading to high costs, long service times, and lost business opportunities. Whether the customer is onboarding as a new customer or applying for a loan, manual processes are time consuming and can be inefficient and costly.
Our document intelligence AI solution inside Microsoft Cloud for Financial Services improves these onboarding processes. Critical to setting up a document journey for each document type, document intelligence enables institutions to automate various workflows based on context and business scenario. The document verification scenario uses AI Builder with the Azure Cognitive Services Form Recognizer AI model to assess whether a document conforms to 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-level 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 quickly. The model is configurable to accommodate geolocation, business processes, and various types of documents. Multiple AI capabilities combine to validate customer-uploaded documents (eg, 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 can be connected, for example, as part of checking whether the identity document is part of a fraud identification repository. From there, the pipeline business logic can determine the recommendation of the document state.
Financial institutions such as banks, insurance companies, and wealth managers can take advantage of the document intelligence model to implement various onboarding scenarios. From account opening, policy applications and claims, to loan and product applications, financial institutions can improve their processing time, confidence in the accuracy of the process, and respond faster to their customers.
Quick and accurate responses to customer inquiries are a critical part of successful customer onboarding, building customer confidence in an institution’s infrastructure and commitment to customer service. With document intelligence used as part of the onboarding process, customers can get the answers they need even faster than ever before. This gives organizations a competitive advantage, drastically reducing the manual work involved in processes, improving operational efficiency and serving more customers, faster and satisfying their needs.
Retail: Bring the online experience to physical stores
Online shopping has grown and as a result, physical stores are struggling to compete with e-commerce. Retailers with physical stores are looking for ways to optimize the shopper experience while managing operating costs. Understanding the customer’s buying 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 purchase journeys to provide insights that help merchandising managers optimize their smart stores. We would provide metrics to monitor store performance, including an integrated model that provides insights into the items that are most frequently purchased together. The AI model would rely not only on payment data to help understand shopper behavior, but also enable retailers to make decisions based on rich behavioral insights available only in smart stores, including shopper actions, such as picking items off the shelf and then returning them or lingering in front of the shelf.
Store Managers are responsible for monitoring and optimizing the daily operations of the store, ensuring products are stocked and stocked effectively while providing excellent customer service. Insights from the smart store would help managers assess the number of items moving from shelf to basket, monitoring data from window shoppers against data from paying customers and assessing how quickly customers move. customers pay, all while maintaining individual customer privacy. Additionally, the shopper heat map would help managers understand which aisles are heavily trafficked and which are not. In this way, the store could avoid placing products that need cleaning in the dead zone areas. Changes to companion product placement, order frequency, and promotional opportunities would also be made based on the recommendations provided by the AI model.
Read more about how Microsoft thinks about smart stores.
Expanding our partner ecosystem
AI models such as Document Intelligence showcase our approach to delivering responsible AI within flexible and reusable components. This enables our network of Independent Software Vendors (ISVs) to build on Microsoft AI and integrate their own AI into 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 create solutions tailored to specific customer and industry needs.
Learn more about our Microsoft Cloud for Industries Y explore apps offered by our partners.
1Gartner forecasts the global AI software market to reach $62 billion by 2022, November 2021. Gartner is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the US and internationally and is used herein with permission. All rights reserved.
twoMissed appointments cost the US healthcare system $150 billion each yearHealthcare Innovation.
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