In today’s rapidly accelerating digital transformation, artificial intelligence (AI) technology is reshaping the global business landscape at an unprecedented speed. Both tech giants and traditional enterprises are actively exploring how to integrate AI into their business systems to enhance their core competitiveness. At the same time, in the pursuit of long-term strategic goals, enterprises also face multiple challenges such as market uncertainty, low operational efficiency, and rapidly changing customer demands.
In this context, artificial intelligence is not only a technological tool, but also seen as an important “empowering engine” to drive the implementation of corporate strategies. Taking SAP as an example, its generative AI assistant Joule has been embedded in multiple modules such as SAP S/4HANA Cloud, SAP Ariba, and SAP SuccessFactors, achieving over 400 practical application scenarios and significantly enhancing the company’s strategic execution capabilities.
In order to successfully implement these advanced capabilities, SAP implementers (such as system integrators and consultants) play a crucial bridging role throughout the entire AI import process. They can not only help enterprises choose suitable AI scenarios, but also complete customized implementation and process reconstruction based on business logic, ensuring that AI technology is truly transformed into strategic value.
This article will explore how AI, especially generative AI represented by SAP Joule, can assist enterprises in achieving strategic goals at different levels, providing feasible path references and forward-looking thinking for enterprises.
Artificial Intelligence (AI) is a technological system that simulates human intelligent behavior, aiming to enable machines to have the ability of “perception understanding decision-making execution”. It demonstrates human like thinking, learning, and reasoning abilities in specific tasks through algorithms and data-driven approaches.
AI includes multiple key technologies:
Machine Learning: Using training data to enable the system to automatically learn and optimize performance.
Natural Language Processing (NLP): Enabling machines to understand and generate natural language, achieving intelligent text processing and semantic understanding. SAP Joule is a typical representative based on NLP, capable of understanding business languages and completing tasks directly in the SAP system.
Computer vision: recognizing and processing images and videos, widely used in fields such as security, retail, and healthcare.
Deep learning: a machine learning method based on neural networks, which has significant effects in fields such as speech recognition and image recognition.
The application of AI has penetrated into various aspects of enterprise operations, including customer service automation, intelligent recommendation systems, supply chain management, market trend forecasting, intelligent manufacturing, etc., becoming a key tool for enterprises to improve efficiency and enhance competitiveness. Among them, SAP has promoted the full process intelligence from financial automation to procurement category management and customer response by integrating Joule AI assistant.
AI helps businesses target high potential customer groups and achieve precise market expansion through intelligent marketing and sales forecasting. For example, SAP Joule analyzes CRM data and automatically generates customer insight reports in customer relationship management to assist salespeople in seizing opportunities and improving conversion rates.
By automating processes and optimizing operations, AI can significantly reduce labor costs and improve resource allocation efficiency. SAP Joule can identify inefficient processes and suggest optimization paths in S/4HANA, and assist procurement personnel in automatic negotiation and contract optimization in SAP Ariba.
AI drives intelligent design and simulation of products from concept to implementation, accelerating innovation speed and achieving technological breakthroughs. SAP Joule supports low code developers to generate code and processes through natural language in SAP Build Code, accelerating prototype iteration and release speed.
By leveraging AI’s insights and real-time response to customer behavior, enterprises can provide more personalized and real-time service experiences. SAP Joule implements semantic understanding and task assignment in SAP Field Service Management, enabling frontline service teams to respond to customer requests in real-time.
AI helps enterprises achieve automation and real-time monitoring in risk warning, compliance review, and sustainability assessment. For example, SAP Green Token uses AI to analyze images of supplier statements, achieving automatic identification and compliance recording of ESG data.
Enterprises utilize AI to integrate structured and unstructured data, achieving deep mining and value discovery. SAP Joule provides personalized “My Home” and contextual decision prompts in S/4HANA to assist management in real-time data-driven judgment.
It is worth noting that SAP implementers can develop the optimal AI scenario application roadmap based on the enterprise’s data structure, industry attributes, and system status, and coordinate the data interfaces and algorithm deployment between various systems during the implementation process to avoid the problems of “data silos” and “algorithm blind spots”, thus truly achieving data-driven strategic decision-making.
One of the biggest obstacles that enterprises face in improving operational efficiency is “process rigidity” and “human resource dependence”. The combination of AI and RPA (Robotic Process Automation) to achieve automatic execution of daily processes is an important method to solve this problem. How to operate it specifically? The following are the landing steps:
Step 1: Process Identification and Mapping
Enterprises use process modeling tools such as SAP Signavio to identify highly repetitive and error prone processes, such as invoice review and procurement approval.
Step 2: Connect to AI analysis engine
Automatically integrate existing business data of enterprises through SAP Joule, AI identifies bottlenecks and inefficient nodes, and provides optimization suggestions.
Step 3: RPA Automated Deployment
Integrate SAP BTP platform and third-party RPA tools to automate tasks such as generating reports and invoice entries.
Step 4: Continuously optimize the feedback mechanism
AI continuously learns process operation data, proposes iterative improvement suggestions, and gradually optimizes algorithm parameters and business rules with the assistance of implementers.
The role of SAP implementers in this process is crucial, as they are typically responsible for process organization, system integration, automated script design, and post operation and maintenance, ensuring a smooth and seamless process from “problem discovery” to “implementation improvement” for the enterprise.
In the era of customer experience, precise outreach, instant response, and continuous optimization are the core elements to enhance customer value. The typical path for AI to enhance customer value is as follows:
① Building customer profiles: By integrating CRM, marketing systems, and sales behavior data, AI identifies different customer preferences and lifecycle characteristics.
② Activate personalized recommendations: With the support of Joule, platforms such as SAP Emarsys dynamically generate marketing content to achieve personalized product recommendations for thousands of people.
③ Intelligent customer service system launched: With the help of Joule semantic recognition technology, SAP Field Service can quickly analyze customer requests and automatically generate response task orders.
④ Tracking customer satisfaction: SAP implementers can also integrate external NPS tools to automatically collect and score customer feedback as input for subsequent optimization.
These four steps are connected to form a customer closed-loop management mechanism of “cognition recommendation service optimization”, which safeguards the “customer satisfaction” goal in the enterprise strategy.
How can enterprises use AI to drive products from “conceptual design” to “rapid iteration”? You can do it through the following methods:
A Intelligent prototype design: Through SAP Build Apps, business personnel describe product functions in natural language, and Joule automatically generates UI layout and logical flow.
B Quick online experiment: Connect to SAP BTP platform, simulate deployment and conduct small batch testing based on existing system data.
C Real time collection of market feedback: SAP implementers assist in configuring a testing feedback collection system, which directly inputs user feedback into the next round of product iteration recommendations.
This process consists of four stages: concept development experiment feedback, with AI participation in each stage to enhance innovation response speed. SAP implementers are like innovation incubation consultants here, assisting in building an “agile innovation mechanism” that is suitable for enterprises.
The application of AI in compliance and risk control is becoming increasingly widespread, especially in key areas such as finance, procurement, and supply chain. Joule can achieve intelligent prevention and control through the following methods:
Detecting abnormal transactions: Similar to anti fraud systems, AI continuously monitors transaction flows and identifies patterns that do not conform to historical patterns.
Intelligent Audit Document: In the SAPS/4HANA system, AI can automatically identify whether invoice fields and contract terms are compliant.
Automatically triggering compliance reminders: Once potential violations are discovered, the system pushes tasks and processing suggestions to the responsible person through Joule.
The configuration, integration, and operational monitoring of these capabilities are typically carried out by SAP implementers in collaboration with enterprise legal and internal control teams to ensure compliance is embedded in every business action.
The value of AI comes from deep mining of data, but this also brings privacy and compliance risks. Enterprises need to manage data security from the following dimensions:
Who manages data: clarify the division of data responsible persons and role permissions;
Where to store data: choose a reliable local or cloud platform;
How to protect data: using encryption, auditing, access control and other means.
SAP implementers typically assist enterprises in developing AI data governance frameworks and integrating them with SAP BTP security modules to implement “data controllability and security traceability”.
AI has high requirements for composite talents in technology and management, but internal teams often find it difficult to adapt quickly. Implementers can act as external think tanks, providing:
Employee training plan;
Internal AI Culture Construction Workshop;
Support for management strategic discussions.
Many companies can see the value of AI, but cannot figure out the implementation path. We should adopt the approach of “taking small steps and running fast”:
Clarify business scenarios → design AI solutions → pilot deployment → continuous optimization.
During this process, SAP implementers can help control progress and risks through a dual perspective of business and technology.
In the coming years, generative AI will continue to develop rapidly, and companies will see the following trends in using AI to achieve strategic goals:
Intelligent strategic planning: AI assists management in obtaining real-time market dynamics and dynamically adjusting goals and indicators;
Fully automated business processes: SAPJoule will become a business execution assistant in more modules;
Ecological collaboration intelligence: SAP implementers will become the “connector” between enterprises and AI, assisting in building AI driven business ecosystems.
AI is transforming from a “technology testing ground” to a “propeller” for corporate strategy. It demonstrates profound influence in multiple strategic dimensions such as decision-making, operations, customers, products, and risks. The practice of SAP Joule shows that generative AI not only makes enterprises “smarter”, but also makes them “faster” and “more stable”.
For enterprises that hope to quickly deploy AI and truly integrate it into their strategic execution path, partnering with professional SAP implementers is the best choice to reduce trial and error costs and improve landing efficiency. In the future, only companies that truly understand the potential of AI and strategically implement it can stand out in the competition, go further, and be more stable.
Acloudier is a SAP Platinum Partner, GROW with SAP Certified Partner, and member of the United VARs Global SAP Partner Alliance, specializing in SAP public cloud ERP solutions. Driven by the dual engine of “AI+Global Services”, we have created a “cloud native+scenario based” digital engine for 300+enterprises in 8 industries including Qingdao Huadi and Naiyou Biotechnology, providing a one-stop cloud solution from business process reconstruction to AI innovation applications. We have a large number of successful SAP cloud service cases in industries such as automotive parts, medical equipment, high-tech, e-commerce, equipment manufacturing, discrete manufacturing, and engineering services. As one of the first SAP cloud native service providers in China, we have reconstructed the digital gene of enterprises with SAP’s best business practices and the “1+X” innovation matrix, empowering enterprises to quickly unlock the core value of SAP public cloud. We have been selected as the “SAP Best Cloud Partner” multiple times.
This article "How can artificial intelligence (SAP AI) assist in achieving corporate strategic goals?" by AcloudEAR. We focus on business applications such as cloud ERP.
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