Keynote
Your AI Is Standing on Broken Data
The $12.9M bill your board has never seen, and the foundations your AI ambitions need before they can return anything.
Every board wants the AI win, but few have seen the bill underneath it. Poor data quality costs the average enterprise $12.9 million a year, and it arrives disguised as stalled pilots, duplicated spend, and decisions made on numbers nobody trusts. 63% of organisations do not have the data practices to support the AI they are already funding.
This talk opens the third door of the Track: Build. Build comes last for a reason. You sequence the foundations before you fund the features, because building on data you cannot trust just moves the money to the bench. This is the unglamorous layer everything else stands on: data architecture, enterprise AI design, AEO/GEO, and the order of operations that decides whether AI investment returns anything. Foundations before features.
Leave able to
- Spot the $12.9M data-quality bill before the board approves the next AI line item.
- Test whether their data practices can actually carry their AI ambitions, using the questions the 63% never asked.
- Price AI discoverability as a revenue line, not an afterthought: a 5% shift in AI-driven discovery is worth tens of millions to a large consumer or publishing business.
- Sequence the build by root cause, with the right specialist on each problem and none of the budget sitting idle.
Sources: Gartner, on the average annual cost of poor data quality to organisations.
More keynotes