Implementing AI-driven solutions is challenging because most stakeholders—whether decision-makers or end-users—do not think in terms of structured data or programming logic. Instead, they conceptualize information in broad, often imprecise ways that differ significantly from how AI systems process and utilize data.

To illustrate this conceptual gap, we can position terminology along a continuum from familiar but imprecise to less familiar but more precise:

  1. Things – A vague, unstructured way of referring to information.
  2. Documents – Where most non-technical users operate, thinking in terms of word processing files, PDFs, or printed materials.
  3. Files – A step closer to structured thinking, often where professionals like administrators and analysts operate, as they work with stored digital data.
  4. Records – A more structured term, used in compliance-heavy or data-centric fields.
  5. Transactional Records – A step further toward AI-relevant structuring, where data represents discrete events or actions.
  6. Institutional Knowledge – The most abstract but most valuable layer, where AI implementation can synthesize, organize, and extract meaningful insights from structured data.

 

Why This Matters for AI Implementation

AI solutions thrive on well-structured, well-labeled data. However, most organizations start from a position of loosely structured information—documents and files rather than records and transactions. The challenge for AI implementation service providers is to bridge this gap, translating unstructured or semi-structured data into machine-readable formats while educating stakeholders on the need for structured, contextualized information.

By framing AI implementation in these terms, service providers can better communicate why structuring data is a prerequisite for AI success and guide organizations toward a data-driven mindset.