Unlike generic models, custom AI refers to AI solutions tailored to specific business needs. It enhances decision-making and automation and is designed to align with an organization’s unique data and processes. In an interview with Fortune India, Sindhu Gangadharan, MD, SAP Labs India, and Head of Customer Innovation Services, SAP, explains the role of data in Custom AI’s success. From tackling supply chain bottlenecks to enabling personalized customer experiences, Custom AI leverages proprietary data and tailored algorithms to turn complexities into competitive advantages.

Edited Excerpts:

Q: What are the primary use cases for Custom AI and how does it differ from off-the-shelf AI solutions?

SG: The power of Custom AI lies in its ability to deeply align with a business’s unique DNA. Unlike off-the-shelf AI solutions, which are pre-designed for general applications, Custom AI goes beyond standardization to meet the specific goals and objectives of the business. Imagine an organization grappling with highly nuanced supply chain bottlenecks or seeking to create deeply personalized customer experiences— these are the challenges that generic AI cannot fully address. Custom AI, built on proprietary data and tailored algorithms, transforms these complexities into competitive advantages. Its use cases span industries, from enabling predictive maintenance in manufacturing plants to revolutionizing retail operations with hyper-personalized recommendations. It’s not just about solving problems— it’s about reimagining what’s possible.

We collaborate with leading customers globally to address their unique business challenges through Custom AI solutions. In the manufacturing sector, our AI-driven innovations provide competitive advantages by streamlining procurement processes and enabling Generative AI-assisted maintenance for industrial assets. In the finance sector, we address critical use cases such as automated dispute resolution, asset retirement obligation management, intelligent invoice processing, and AI-powered billing cycle automation. In the automotive industry, we leverage Generative AI to transform after-sales experiences, fostering innovation and operational excellence. Furthermore, in the healthcare sector, we are partnering with customers to revolutionize vaccine manufacturing through cutting-edge AI tools.

Q: What factors should businesses consider before investing in a custom AI solution?

SG: To ensure a successful investment in Custom AI and maximize its benefits, organizations should consider the following critical factors:

• Clearly Defined Objectives: Organizations must begin by identifying the specific business challenges that require a custom AI solution. It's crucial to ensure that the AI solution provides a competitive advantage and supports long-term growth. Custom AI goals should align with broader business strategies, and success criteria should be clearly established and measurable to track the impact over time.

• Data Readiness: The success of a custom AI solution is heavily dependent on high-quality, relevant data. Organizations must assess the availability of the data required for training the model, including its quality and completeness. It’s also important to account for additional efforts such as data cleaning, annotation, and integration with existing systems to ensure smooth implementation.

By focusing on these factors, businesses can effectively leverage Custom AI to create a competitive edge and unlock innovative solutions specific to their needs.

Q: How does Custom AI enhance scalability and competitiveness for businesses?

SG: In today’s fast-evolving markets, scalability is not just about size—it’s about agility, precision, and speed. Custom AI is uniquely positioned to deliver this. While out-of-the-box AI solutions are capable of handling every-day, standardized tasks efficiently, Custom AI drives deeper innovation and addresses the unique, high-value challenges businesses face. Custom AI contributes to a comprehensive AI strategy that provides both quick value and long-term transformation.

Customer AI helps businesses gain a competitive edge by

• Addressing unique business challenges arise that cannot be fully addressed with out-of-the-box solutions (e.g., specialized supply chain bottlenecks or advanced predictive analytics).

• Offering personalization at scale, such as creating hyper-personalized experiences for customers in industries like automotive or healthcare.

• Enabling complex integrations such as combining data from multiple systems to drive insights and outcomes.

Q: How can organizations successfully implement Custom AI?

SG: Implementing Custom AI presents valuable opportunities to enhance business processes, with a few considerations to ensure success:

• Data Integration and Quality: Integrating SAP and non-SAP data offers the potential for deeper insights. Ensuring the data is high-quality, accurate, and consistent across systems maximizes the AI’s effectiveness.

• Skilled Workforce and Initial Investment: Investing in skilled talent sets the foundation for long-term innovation. While the initial investment is necessary, it opens the door for AI-driven transformations across the business.

• Scalability and Long-Term Value: Custom AI is designed to grow with the business. Focus on scalability to ensure the AI solution continues to deliver value as your organization evolves and its needs expand.

• Data Governance and Compliance: Proper data governance is key, especially when dealing with confidential and sensitive business data. Compliance with relevant regulations is vital to ensure data is handled responsibly and ethically.

• Future-Proofing: Custom AI solutions should be flexible to adapt to emerging technologies, helping organizations stay ahead of industry trends and continue to innovate.

By addressing these key areas, businesses can implement Custom AI in a way that maximizes value while ensuring ethical, compliant, and sustainable growth.

Q: What role does data play in the success of Custom AI, and how can companies ensure its quality and security?

SG: Data is the backbone of Custom AI. The success of AI models is deeply tied to the quality, quantity, and relevance of the data used to train them. Without accurate and clean data, even the most sophisticated AI models will fail to provide valuable insights.

To ensure data quality, businesses should focus on establishing processes that clean, standardize, and validate their data. This involves removing errors, ensuring consistency, and integrating data from various systems to create a unified, reliable data source.

Data security and governance are equally important. Sensitive business and customer data must be protected through strong data governance frameworks. Companies should follow industry standards like GDPR and HIPAA, ensuring privacy and security while maintaining compliance.

Furthermore, ethical considerations must be at the forefront when dealing with data. This includes making sure that the data is collected transparently and used responsibly, with no bias influencing the AI’s outcomes.

To ensure the success of Custom AI initiatives, companies should continuously monitor their data quality, integrating automated tools for regular checks. By doing so, businesses can maintain data accuracy and security, ensuring that their AI systems continue to deliver impactful results.

Q: How does SAP tailor this to different industries? What role does R&D have to play to arrive at this innovation?

SG: SAP tailors Custom AI solutions to different industries by leveraging its deep industry expertise across various verticals, ensuring that AI is customized to address specific challenges unique to each sector. Whether it’s optimizing manufacturing processes, enhancing customer experiences in retail, or improving financial forecasting, SAP’s industry knowledge enables the creation of AI solutions that align with the unique needs and business goals of each customer.

The process of co-innovation plays a critical role, where SAP collaborates with customers to identify their challenges and co-develop solutions that truly add value. Additionally, visionary industry-specific use cases help SAP introduce the latest AI technologies to customers, allowing them to see the potential of emerging solutions and how they can revolutionize their operations.

Research & Development (R&D) is at the core of this innovation. SAP combines its built-in industry expertise with close collaboration between R&D teams and the SAP AI Product Engineering teams. This partnership helps in developing advanced AI technologies, ensuring that these innovations are aligned with the specific needs of different industries. By working together, SAP accelerates the development and deployment of AI solutions that provide real, measurable impact, positioning businesses for long-term success in an AI-driven future.