A Journey Guide to Delivering AI Success Through ‘AI-Ready’ Data

Urgent demand for AI-ready data to support AI initiatives (including GenAI applications) requires evolutionary changes to data management and upgrades to the architecture, platforms, skills and processes. D&A leaders can use this guide to successfully deliver AI-ready data for AI use-case success

Strategic Planning Assumptions

• Through 2026, those organizations that don’t enable and support their AI use cases through an AI-ready data practice will see over 60% of AI projects fail to deliver on business SLAs and be abandoned.
• Through 2026, 30% of generative AI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs or unclear business value.
• Through 2026, data management tool providers failing to leverage GenAI will exhibit 10% revenue erosion annually.
• Through 2026, GenAI will reduce manually intensive data management costs up to 20% each year while enabling four times as many new use cases.

Analysis

Data and analytics (D&A) leaders are under extreme pressure to support their organization’s urgent and mission-critical AI efforts. While most organizations have invested over the years in traditional data management architectures and practices, those that fail to realize the differences between AI-ready data requirements and traditional data management will endanger the success of their AI efforts.

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