AI Adoption Shifts for CIOs: Data Quality and Intelligent Retrieval Take Center Stage

The Shift in AI Adoption Priorities for CIOs

Artificial intelligence (AI) is evolving, especially with the rise of Agentic AI, changing how Chief Information Officers (CIOs) and IT teams approach adoption. Recent research from Sinequa and ChapsVision reveals a significant shift in priorities. Cost, once seen as the main obstacle, is now a minor concern, with only 10% of leaders citing it as a major issue.

The real challenge lies in data accuracy and quality, which 19% of decision-makers now identify as their top concern. This shift reflects the growing complexity of AI projects and the need for reliable data to fuel them.

The study surveyed 100 enterprise leaders in the UK and US. It found that 66% expect returns on their Agentic AI investments within five years. Also, 82% agree AI is already improving their operational intelligence and efficiency, while 62% feel ready to implement Agentic AI. Yet, nearly 61% admit their data readiness requires significant improvement.

Data Readiness: A Core Focus for IT Teams

Agentic AI depends on accessing accurate, contextual insights in real-time. This puts pressure on IT teams to ensure data is not only accurate but also easily accessible. The survey highlights three main challenges:

• Data security and compliance concerns (67%)
• Interdepartmental data silos (47%)
• Managing the volume and speed of data (37%)

These issues limit organisations from fully scaling AI solutions. For CIOs, improving data management is now a top priority to enable successful Agentic AI adoption.

The Importance of Intelligent Retrieval Systems

Enterprise search tools, or intelligent retrieval systems, are becoming essential. They help IT teams extract and connect knowledge from diverse data sources, smoothing AI integration. According to the research, 66% of leaders see expertise in intelligent retrieval as key to overcoming AI adoption hurdles.

Benefits of these systems include:

• Improving data accessibility across departments (46%)
• Enhancing AI model training (43%)
• Supporting processing of high-volume data (42%)

Addressing these data challenges requires ongoing investment in data infrastructure and retrieval technologies. While quick returns on AI are important, sustained focus on data quality and access will deliver deeper transformation.

For management professionals looking to stay ahead with AI, prioritising data readiness and intelligent retrieval capabilities is critical. Building strong data foundations enables organisations to leverage AI effectively and stay competitive.

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