As artificial intelligence (AI) continues to dominate industry conversations, the importance of “getting AI ready” becomes increasingly clear. However, many organisations are finding that the excitement surrounding AI is tempered by a stark reality: the information and data needed to power these AI systems is sprawled in lots of places and is untrustworthy. In a recent report The Art of AI Maturity, Accenture identified that companies aptly stated, “Data quality is our largest barrier to AI adoption.”
To address this challenge, it’s essential to shift the focus from AI itself to the quality of the information and data feeding it. This brings us to the concept of building AI systems from the ground up, rooted in structured and reliable information. In this context, the 3 C’s—Connectivity, Confidentiality, and Curation—play a pivotal role in ensuring AI’s success.
The 3 C’s of AI Readiness
1. Connectivity: AI can only deliver value if it has access to the right information and data from all data sources. Connectivity ensures that AI systems can “see” and interact with the all the necessary information resources. Without this access, even the most advanced AI models will fail to provide real-world benefits.
2. Confidentiality: AI must operate within the bounds of an organisation’s data and information security protocols. This means AI should be permission-led, accessing only the data it is authorised to use. Maintaining confidentiality is crucial to preventing data breaches and ensuring that sensitive information is handled appropriately.
3. Curation: Perhaps the most critical aspect, curation refers to the consistency and accuracy of data. The quality of AI’s outputs is directly linked to the quality of the data it ingests. Poorly curated data leads to unreliable AI outputs, often referred to as “hallucinations.” Therefore, ensuring that AI systems only access relevant and up-to-date information is essential for generating trusted results.
The Role of Metadata
Metadata-driven systems are central to achieving the 3 C’s. As generative AI continues to evolve, it is solidifying the superiority of using metadata over traditional folder-based structures for organising and managing information. Metadata offers automated tagging, improved searchability, and greater flexibility—key components that ensure AI systems can access and process information effectively.
By embedding these principles into document, information and process management solutions, organisations can overcome the hurdles of data quality and unlock AI’s full potential. Rather than treating AI as a separate entity, it becomes an integrated part of the information ecosystem, drawing on well-structured and reliable data sources to deliver meaningful insights.
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