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### AI@FRE Blog Series:
AI@FRE (Part 1) - Enabling Enterprise AI Adoption through the BTP FRE Engagement Model
AI@FRE (Part 2) - Accelerating Business AI Transformation with the FRE Value Framework
AI@FRE (Part 3) – Guardrails for Enterprise AI Adoption
AI@FRE (Part 4) - Guardrail Enterprise AI Adoption – AI Architecture Guardrails
AI@FRE (Part 5) - Guardrail Enterprise AI Adoption – AI for People and Team
AI@FRE (PART 6) - Guardrail Enterprise AI Adoption – Establish AI Ethics, Security & Privacy
###
In this blog, we will explore the challenges of adopting Business AI; what use cases are for AI and not for AI; and how enterprises can effectively roadmap their Business AI Adoption Journey with foundational BTP AI Services.
With Business AI, our goal is to maximize business value for customers by providing Relevant, Responsible, and Reliable intelligent capabilities through AI and GenAI. These beneficial applications are evident across various SAP LoBs functions, showcasing the transformative potential of AI in accelerating and reimagining business processes.
SAP Business AI sounds great. Why I haven’t started implementing it yet?
Adopting Business AI involves navigating both technical and non-technical challenges.
Organizations must first develop a clear understanding of how AI can be effectively utilized within their operations. This includes identifying appropriate business use cases for AI, such as automation, analytics, and personalized recommendations; while recognizing that AI is less suitable for tasks requiring high levels of creativity and innovation, mission-critical decision-making, compliance, and handling complex matters, as well as random or overly simple scenarios.
Business Use Cases FOR AI
Business Use Cases NOT for AI
On the non-technical side, one of the primary challenges is incorporating a human element into AI deployments. This means understanding which tasks should remain human-centric and ensuring that AI complements rather than replaces human capabilities. Furthermore, effectively bridging the skills gap is crucial. Organizations must invest in training and development to equip their workforce with the necessary skills to work alongside AI technologies. This balanced approach ensures that AI adoption enhances business operations without undermining the human contributions that are essential for creativity, critical thinking, and complex decision-making.
I Understand the Challenges. What Are the Recommended First Steps for AI Adoption?
The advantage of SAP Business AI is that it seamlessly integrates the right AI capabilities into the appropriate business processes, embedding AI into Cloud LoB applications to make customers' business operations more intelligent. SAP AI Business Services, developed through strong ecosystem partnerships with leading AI model and cloud platform providers, help organizations overcome the complexities of their initial machine learning projects. These services offer easy-to-use, enterprise-ready intelligent solutions that address specific business problems across various processes. By leveraging these capabilities, organizations can further solidify their AI foundation and prepare for more advanced applications.
When organizations begin their journey with AI, focusing on quick-win use cases can set a positive trajectory. For instance, infusing AI into enterprise functions, such as deploying AI co-pilots and digital assistant applications like SAP Joule, can deliver immediate value and demonstrate AI's potential to enhance productivity and efficiency. Digital assistants platform, such as SAP Conversational AI, can improve customer service by providing instant responses to common inquiries, reducing wait times and improving customer satisfaction. These quick wins can build confidence and pave the way for more comprehensive AI integration.
Establishing guardrails is essential to guide AI adoption and ensure long-term success. Standardization is key; without a solid foundation in data management, UX, security setup, and a clean core infrastructure, AI integration efforts are likely to falter. For instance, inconsistent data formats or poor data quality can severely hinder AI performance. A standardized approach ensures that data is clean, well-organized, and secure, providing a reliable foundation for AI applications. Additionally, building AI systems that are responsible, ethical, and explainable is critical. This means ensuring that AI operates transparently, making decisions that can be easily understood and justified, thereby fostering trust and compliance with regulatory standards. For example, in healthcare, an explainable AI model used for diagnosing diseases should provide clear insights into how it arrived at a particular diagnosis, allowing doctors to verify and trust the AI's recommendations. These guardrails help integrate AI responsibly and sustainably, aligning with broader business objectives and setting the stage for long-term success.
Guardrails for Future Ready Business Transformation with AI
Hmmm… That’s good to know but I am a bit lost again in my AI Adoption Journey. Any structure framework to guide me?
Embracing change with new innovation is usually challenging for customers operating within complex environments. The Future Ready Enterprise Engagement (#FRE) Model is designed and developed specifically to help customers Embrace Change, Operate at Pace, and Protect their Core. (To revisit FRE and its value lens to partners / customers, please visit this SAP Blog on #FRE Engagement Model).
The #FRE model also provides a structured approach for adopting AI in their platforms, guiding them from getting started to becoming mature, proficient users of Business AI.
The FRE Engagement Model for Platform Maturity
For organizations looking to adopt AI, #FRE offers a comprehensive roadmap. The journey with BTP Business AI starts by infusing / embedding AI capabilities into operations, focusing on enhancing business functions and standardizing security architecture. As organizations progress, the FRE model guides them in driving business process excellence, standardizing UX architecture, and developing a robust data strategy with ethical and secure AI use. Once these foundations are established, organizations move towards scalable execution, optimizing business processes with reliable AI. The final stage involves creating an Enterprise AI Center of Excellence and implementing Change Management strategies for sustainable AI adoption.
In Part 2 of the AI@FRE Blog Series, we will discover how customers can start with BTP foundational capabilities, to accelerate GenAI capabilities in their business processes.
For more information on BTP FRE Engagement Model and how FRE can establish foundational capabilities for customer's AI Adoption, please feel free to reach out to our team at BTPFRE@sap.com
