AI Data Readiness of Organizations

Enterprise AI Data Readiness and Scaling Strategy Across Organizations Globally

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Situation

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Our client sought to understand the challenges of scaling Generative AI, specifically focusing on data fragmentation, customer pain points, and the shift from centralized to federated data models.

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Objective

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The objective was to explore the market opportunity and technical requirements for achieving "AI-ready" data architectures within large enterprises.

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Our Work

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10EQS assessed data readiness across key dimensions including architecture, metadata, governance, access, and organizational alignment. The study provided a global perspective on AI readiness, covering markets in the North America, Asia, and Europe. We synthesized findings into a clear maturity perspective and a set of “realities” defining AI-ready data organizations.

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Project team:

• Ex-McKinsey Management Consultant with extensive experience in AI and data/analytics
• Management Consultant with strong experience in thought leadership development
• 10EQS Delivery Operations (=PMO) providing quality assurance, process management and expert recruitment
• 15 industry experts

Industry experts (excerpt):

• Head of Digital & Data Science – Life Sciences Company (US)
• Group Finance & Headquarters Chief Data & AI Officer – Insurance Company (France)
• Chief Data Officer – Oil & Gas Company (Netherlands)
• Enterprise Data & AI Governance Leader –Natural Resources Company (Canada)
• Chief Al Officer – Global Bank (US)
• Director, Business Technology & Solutions – Healthcare Company (Japan)

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Results

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The research revealed that data fragmentation and lack of semantic consistency are the primary constraints to scaling AI, with most organizations remaining in amid-stage of readiness despite increasing AI adoption. It also showed that achieving AI readiness requires a fundamental shift in data architecture and governance.

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