Navigating the challenges of AI integration in business practices

Recent discourse surrounding the integration of artificial intelligence (AI) within business practices has highlighted numerous obstacles that organisations face when trying to scale their AI initiatives to full production. Key issues identified include a lack of alignment within businesses, uncertainty regarding the trustworthiness of AI models, and ongoing regulatory concerns. According to experts, unified data and AI governance are deemed essential for navigating these challenges effectively.

During a recent webinar hosted by SAPinsider in collaboration with Collibra, Kat Cheng, Senior Director of Product Marketing for SAP Data & Analytics, stated, “Managing hybrid and multi-cloud environments, increasing data volumes, and the demand for trusted data are just some of the challenges faced by data stewards in large enterprises.” Cheng elaborated that the data needed for effective AI applications is derived from a mixture of existing and newly developed applications, presenting significant hurdles for organisations.

Cheng pointed to SAP Datasphere as a key solution, which offers a robust data foundation necessary for managing both SAP and non-SAP analytic applications. This platform supports a fabric architecture aiming for seamless and scalable access to business data while ensuring that critical semantics and business context are preserved.

SAP Datasphere incorporates features like data integration, data virtualisation, and self-service data access. Cheng noted that it is capable of harvesting detailed metadata lineages from various SAP sources, including analytics and cloud platforms, presenting this information comprehensively within SAP Datasphere. This level of integration is critical in providing clear visibility across an organisation’s data landscape.

AI governance has emerged as a natural extension of data governance, particularly when multiple stakeholders collaborate towards a common goal. Mike Robertson, Vice President of Field Alliances at Collibra, addressed the current complexities faced by data leaders, stating, “Some of the challenges that data leaders are facing with AI now is that there is a wider variety of semi-structured and unstructured data.” He emphasised the importance of utilising reliable data to bolster machine learning and AI initiatives while ensuring that all data is traceable to support various AI projects.

Robertson also described the fundamental aim behind the native integration of Collibra with SAP Datasphere: “The goal was to create a unified governance framework that supports both data and AI initiatives, ensuring compliance with regulations and mitigating risks.” This structured approach is pivotal for organisations wishing to navigate the intricate regulatory landscape while managing data-driven initiatives.

Additionally, Vasiliki Nikolopoulou, Principal Integrations Architect at Collibra, demonstrated the benefits users receive from the integration, which allows them to explore AI models and relevant use cases that align with business objectives, such as sales forecasting. “The integration allows users to filter and find relevant AI use cases, providing a business context around AI models and their associated data,” she noted, underlining the need for risk assessments and compliance tracking in the realm of AI governance.

These discussions underscore the significance of data quality in enhancing the predictive capabilities of AI technologies. By harnessing unified data and AI governance, organisations can foster a comprehensive understanding of their data environments while mitigating the associated risks and compliance requirements. This approach will be crucial as businesses continue to explore and implement AI technologies as a foundational layer in their operations.

Source: Noah Wire Services