AI Scaling Challenges Persist

Despite increased adoption, many AI projects struggle to scale beyond limited deployments due to data quality and legacy infrastructure limitations.

Themes

AI-Driven Transformation

Timeframe

near-term

Subcategories

Artificial Intelligence Corporate Strategy Innovation Management Operational Efficiency

Impact areas

Data management IT Infrastructure Project Management

Detailed Analysis

While AI adoption is growing, many organizations face significant hurdles in scaling their projects. The report reveals a bottleneck in the AI project pipeline, with a substantial number of projects stuck in limited deployment. This inability to scale effectively hinders organizations from realizing the full potential of their AI investments. "The average organization has 10 projects in the pilot phase and 16 in limited deployment, but only six deployed at scale."

Context Signals

Average organization has more projects in limited deployment than at scale. 42% of organizations identify data quality as a top-three barrier to AI production. Legacy data architectures are a major contributor to scaling difficulties.

Edge

Organizations prioritizing data modernization and robust data pipelines will gain a competitive edge. Demand for data management solutions and expertise will surge. The development of automated data quality tools and techniques will become increasingly important.

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