Find out if your company is ready for AI


A 5-minute checklist to help you assess your organization’s readiness to implement AI across 10 key areas of data infrastructure.

Alterdata

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    Why most new AI projects fail to deliver results

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      Data you can’t trust

      Disparate systems, a lack of integration, and inconsistent definitions make the data unsuitable for AI and models learn from their mistakes.

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      Manual and inconsistent data processes

      Pipelines based on scripts and manual tasks are not scalable, every AI implementation ends up causing operational issues.

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      Lack of AI-ready infrastructure

      Without environments for training, deploying, and monitoring models, AI remains nothing more than an experiment.

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      Decisions made belatedly

      The lack of real-time processing means you react too late and AI has no impact on operations.

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      Lack of governance and control over data

      Unclear data ownership, a lack of documentation, and insufficient access controls hinder the development of AI and increase risks.

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      Chaos in analytics and reporting

      Different teams have different figures without a single source of truth, AI only exacerbates the problem.