Preeti
By 2026, more than 80% of enterprises are expected to deploy AI APIs or generative AI applications, leveraging AI models and data to enhance customer experiences and drive business value. The abundance and quality of data play a crucial role in the success of AI initiatives, as seen in examples like the Master’s generative AI-driven golf fan experience.
However, the exponential growth of data poses challenges in terms of access, governance, and management. Organizations need to adapt to evolving data landscapes by embracing hybrid cloud deployments and open data architectures to facilitate seamless data integration and access for AI applications.
To scale AI use cases effectively, a balance of technology, people, and processes is essential. Organizations must align their data strategies with their evolving business objectives, leveraging AI to automate data management tasks and enhance data quality and governance.
Establishing a foundation of trust through data quality management and governance is crucial for the success of AI projects. By prioritizing data quality and governance, organizations can build trust in their AI systems, minimize risks, and maximize the value of their data.
With an open and trusted data foundation in place, organizations can unlock the full potential of their data by building data products, AI assistants, AI applications, and business intelligence solutions. By leveraging AI and data platforms, organizations can create value, drive innovation, and make data-driven decisions to propel their business forward.
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Source link: https://www.ibm.com/blog/data-ready-for-ai-for-business/
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