Artificial Intelligence adoption continues growing
The ability to think and learn in humans is called natural intelligence, therefore in machines, we call it artificial intelligence (AI). There are two primary types of AI: traditional and generative. Traditional AI can recognize patterns, but generative AI creates new patterns based on the recognized historical patterns and any additional data that's provided. Machine learning is a subset of AI, focused on teaching computers to learn from data and improve with experience, instead of being explicitly programmed to do so. In machine learning, algorithms are trained to find patterns and correlations in large data sets, and to make the best decisions and predictions based on these analyses. Machine learning applications improve with use and become more accurate the more data they have access to.
The adoption of AI and machine learning continues to accelerate in the business world. McKinsey's State of AI in 2023 global survey found generative AI (gen AI) to have "explosive growth" less than a year after the technology debuted. One third of survey respondents said their organizations are using gen AI regularly in at least one business function, which means 60% of organizations already using AI have used gen AI. What's more, 40% of respondents said their organizations are planning to increase their investments in AI overall because of the gen AI advances. McKinsey predicts that gen AI could add $2.6-$4.4 trillion incremental value annually to the global economy. They also explain AI and gen AI are most commonly used in the business areas of marketing and sales, product and service development, and service operations.
What experts are saying about SAP & Artificial Intelligence
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Watch this video to get an overview of SAP Business AI.
SAP Business AI is embedded across the portfolio
SAP Business AI is a product that embeds artificial intelligence (AI) across SAP's entire portfolio of solutions. We also provide business process-specific AI services that our customers can adapt to their own workflows; for example, business document processing, data attribute recommendations, and Robotic Process Automation templates.
Data is the core of what makes business AI valuable, and SAP provides both the foundation (SAP Business Technology Platform) and services (SAP Datasphere, Analytics Cloud, Integration Suite) that make it possible to easily connect and model data across different applications. SAP also has strategic partnerships with other leaders in the industry where we embed their technology into our products (Microsoft Teams, Google Workspace, and others), or we enable customers to build custom machine learning models on SAP data in a secure platform.
At the end of the day, we want our customers to have a comprehensive cloud ERP that enables them to optimize and automate processes, and gain insights through data so they can make the most informed decisions possible for their businesses. Learn more about SAP Business AI here.
Examples of SAP Business AI in real customer lives
Let's take a look at some examples of SAP Business AI in the lives of real customers.
GoodYear Proactive Solutions offers advanced telematics and real-time monitoring of tires to help fleet managers improve vehicle performance and safety, in addition to reducing the total cost of ownership (TCO). Their customer base was growing and they were having difficulty responding to the increased volume of inquiries. GoodYear needed a solution to streamline the handover of inquiries between different teams that provided monitoring and tracking of issues and their resolutions. They purchased SAP Service Cloud and SAP Business AI to anticipate, automate, and personalize every customer interaction across commerce, sales, service and marketing. GoodYear Proactive Solutions increased the transparency of issue management with progress dashboards for key requirements in service-level agreements and saw a 10% faster resolution of service tickets. They found their agents were better-informed by having a 360-degree view of customer and ticket history and access to documentation and instructions, which resulted in more efficient inquiry handling across multiple communication channels and overall faster ticket resolution.
Aspen Pumps example:
Aspen Pumps is one of the leading suppliers in the installation and maintenance of air conditioning systems. They had many repetitive administrative tasks that took a huge amount of time for employees to complete to the point that employees' unplanned time off caused disruptions in the day-to-day operations of the business. Aspen Pumps needed a solution to streamline their administrative processes and reduce the workload on their employees. They purchased SAP Business by Design and SAP Build Process Automation (previously, SAP Intelligent Robotic Process Automation) with SAP Business AI to streamline and automate processes and improve employee experiences. Aspen Pumps easily identified the repetitive processes that could be automated, including entering and approving invoices, and set up bots to automate the processes. As a result, 2,200 documents are processed automatically every month, which saves up to 350 monthly employee hours that are now used for higher-value tasks. Employee time off no longer affects the business, and their employees are happier with the new streamlined, straightforward processes.
ZF Friedrichshafen example:
ZF Friedrichshafen supplies systems for passenger cars, commercial vehicles, and industrial technology that keeps the automotive industry innovative and focused on the future. However, their existing solutions did not provide a unified system for them to understand actual demand and plan accordingly. ZF needed a demand planning solution that had intelligence to support predictive planning and forecasting. They purchased SAP Integrated Business Planning (IBP) and SAP Supply Chain Control Tower with SAP Business AI to predict customer demand reliably with AI-powered demand forecasting, improve quality with intelligent anomaly detection, and streamline operations with predictive maintenance. ZF connected SAP IBP with their ZF Aftermarket ERP systems to create one environment for demand planning. They now have an accurate and unified view of demand and with the AI-powered insights, gained a massive 92% increase in forecasting speed. The forecasting data has informed their marketing activities that drive additional customer demand on social media.
Accenture is a global professional services company that delivers consulting services to more than 7,000 customers in 120 countries. They issue more than half a million client-facing invoices each year from 200 locations globally, and were struggling with their existing system not matching incoming payments to the corresponding invoices and client accounts accurately. To resolve the low matching rate, Accenture had an extremely high volume of manually-intensive tasks (~250,000 annual manual entries) to correctly match payments with invoices and client accounts. When client invoices hadn't cleared yet, their collections team would incorrectly call clients asking for payment, when most of the clients had already paid in full. Accenture needed a cash application with automated clearing and machine learning-generated proposals to reduce manual processing. They purchased SAP S/4HANA and SAP Cash Application, which runs on the SAP Business Technology Platform to automate and increase the rate of invoice matching and reduce losses with anomaly detection. Accenture found a significant increase in automatic matching with 54% of invoices matched with payments automatically, which in turn reduced the open accounts receivable balances and reduced the time needed for processing during peak times (month-end, quarter-end, and year-end closing).
Generative AI Digital Assistant
Joule is described as the next-generation digital assistant "copilot", that uses generative artificial intelligence (AI) and replaces the previous digital assistant, SAP Conversational AI. With the old conversational AI, users would input information when having a conversation via typing or speaking, and the digital assistant would use the interaction to create responses. With generative AI, Joule uses data about a user's past historical patterns in addition to their new inputs to generate fresh content, information, and recommendations.
Joule is currently being embedded in the backend of all SAP applications with a phased approach. Because it will live in the backend of each solution, Joule has access to a huge volume of data it can use to provide holistic support and feedback to end users. An employee can simply ask a question or frame an issue in plain language and receive answers that take into account data across the entire portfolio of SAP solutions the customer uses. For example, a Sales Manager could ask Joule for help understanding their past sales performance with a specific product, or for all products. Joule can identify underperforming regions, link to other data sets that reveal a supply chain issue, and automatically connect to the supply chain system to offer potential fixes for the manager's review. New scenarios will continue to be released over time. Joule is also compliant with all SAP product standards, certifications, AI ethics, and GDPR (General Data Protection Regulation in the European Union).
Joule can be accessed from the diamond icon in the top right corner of any solution where it's available. Joule can run in any of the SAP Business Technology Platform (BTP) environments, and requires some initial set-up that needs to be completed during implementation. Because the service runs in SAP BTP, a customer needs to first subscribe to the service, then set up authentication between SAP Cloud Identity, SAP BTP, and the target system (for example, SAP S/4HANA Cloud, SAP SuccessFactors). Detailed information about setting up and enabling Joule is in the SAP Help Portal here.