Kyndryl Provides Guidance On Building The Right AI Data And Foundation For Business

Kyndryl revealed the importance of building AI data and foundations for businesses (photo: Kyndryl)

JAKARTA - In a report entitled '5 Insights to Help Organizations Build Measurable AI', by Kyndryl and Ecosystem stated that there are still many companies that have problems with implementing Artificial Intelligence (AI) technology.

The study results found that at least 48% of research participants often faced challenges in integrating AI solutions with existing systems, 38% when collecting data from various internal sources, and 34% had difficulties with data quality.

Based on the study results, the Kyndryl report offers several important insights to guide organizations as they build scalable AI, such as the following:

Data Access Becomes Main Obstacle

The Kyndryl report revealed that the maturity level of data and AI adoption across ASEAN varied, and only 7% of research participants focused on building the right data and AI foundation. Building a data set requires key conditions, such as a focus on clean and reliable data, a data interoperability strategy, and building synthetic data to bridge data gaps.

Organizations Need Data Creativity

Organizations in ASEAN recognize that data-first organizations will derive more value from their data and AI investments. Over the next two years from 2023 to 2024, 77% of participants will increase their use of AI and data solutions for better customer experiences, 75% for human resources, and 72% for marketing. This will help identify and prioritize a number of business opportunities for data.

Governance Is Not Built into the Organization

According to the report, lack of internal policies and limited understanding of risks (36%) are the two biggest challenges to effective data governance policies in ASEAN. A data governance policy formulated by a data-driven organization should include accountability and ownership guidelines, regulatory standards, a dedicated data stewardship team, and a regular process for re-evaluating the policies created.

Lack of Upstream to Downstream Data Lifecycle Management

It's critical for organizations to have observation, intelligence, and automation capabilities built into the entire data lifecycle. Building a data infrastructure that is ready for today's needs may not be able to support future business needs because data continues to evolve considerably, indicating that an organization's outlook is not clear.

Democratization of Data and AI Must Be a Goal

The true value of bringing data and AI solutions to organizations is when the people who benefit from those solutions are the actual users who manage the solutions and run them. However, only 10% of organizations in ASEAN have a business team managing or maintaining AI solutions.