Problems Our Client Faced
- Unreliable AI outputs because of the underlying garbage data
- Conflicting records making it impossible for AI to learn accurately
- Significant time spent on manually cleaning data rather than acting on AI insights
A regional retail chain approached Digital is Simple after their AI initiatives repeatedly fell short of expectations. Predictions were unreliable, recommendations were missing the mark, and leadership had lost confidence in what AI could actually deliver for their business. Our AI development consultancy team conducted a data readiness assessment to identify why their AI was underperforming and what foundational work was needed before any meaningful AI development could succeed, all without disrupting ongoing operations.
Retail
AI Development Consulting Services
We reviewed the client’s existing data sources, pipelines, and reporting outputs to understand what their AI was actually being trained and fed on.
We traced the failures back to the data itself, duplicate records, inconsistent naming conventions, ungoverned data entry, and siloed systems producing conflicting inputs that no AI could reliably learn from.
We evaluated how prepared the organisation was to support effective AI development, across their data quality, internal processes, tooling, and team capabilities, and flagged the risks of building further on an unstable foundation.
We delivered a structured report outlining the exact gaps, their impact on AI performance, and a prioritised roadmap, with clear short-term fixes and longer-term recommendations to make their AI investments actually work.
Several departments had built their own data workarounds over the years and were protective of their processes. We made the case for document AI solution through business impact rather than criticism.
The deeper we looked, the more issues surfaced. We stayed disciplined about prioritising fixes that would have the most direct impact on AI performance rather than attempting to overhaul everything at once.
No single team felt responsible for data quality. A key part of our consultancy was helping the client recognise that successful AI development starts with organisational accountability for data, not just better models.
