THE GIST
A practical guide to asking whether the information behind a proposed AI workflow is ready to use.
A perspective from Lognetics Editorial · 2 min readFollow the information to its source
A promising AI use case can depend on information that is scattered, inconsistently recorded or difficult to access. Before discussing a model, identify where the relevant records come from, who maintains them and which parts people already distrust.
For a hypothetical customer-assistance workflow, that might mean comparing the knowledge base with recent conversations and the actual process used by staff. A document can be well written and still describe a workflow that has changed.
Examine a representative sample
Look at ordinary cases alongside exceptions. Check whether the sample includes incomplete records, conflicting updates and the language people actually use. A clean demonstration set may hide the conditions that will shape everyday performance.
Write down which fields are required for the proposed task and which gaps would change the answer. This creates a more useful discussion than a general claim that the organisation has plenty of data. Quantity cannot answer questions about relevance or ownership.
Give maintenance an owner
An intelligent workflow needs a way to stay connected to changes in the underlying business. Decide who can update the source material, how corrections reach the system and how the team will notice when the information becomes stale.
Our engineering perspective is that data readiness belongs inside product planning. A focused improvement to the information flow may be the most useful first step toward AI, even when it is less visually impressive than an early demo.
