How should I budget for AI readiness?

Cherry Seed

Spend on the data foundation before you spend on models. The expensive, hard-to-undo work is getting clean, complete, consistently-structured events flowing into a warehouse you own; off-the-shelf AI is comparatively cheap and getting cheaper. Gartner predicts at least 30% of generative AI projects are abandoned after the proof-of-concept stage, most often because the data underneath was never ready. Budget so that data capture, identity stitching, and warehousing come first and AI tooling comes second. A modest, durable data layer beats an expensive model sitting on broken inputs.

Full Answer

AI budgeting usually fails because the money chases the visible thing. Models, dashboards, and demos are what stakeholders can see, so they get funded first, while the unglamorous data plumbing that determines whether any of it works gets whatever is left over. Gartner found that, on average, only about 48% of AI projects ever reach production, and poor data quality is a recurring cause of the projects that stall.

A more durable split puts the larger share of early spend into the foundation: server-side event capture so you are not losing a third of your data, identity stitching so events join to real customers, and a warehouse you control so the history accumulates in a usable shape. These are one-time and slow-to-build, which is exactly why they belong at the front of the budget. Model and tooling costs, by contrast, are falling every quarter and can be added once the data exists.

The practical rule is to fund what is hard to redo before funding what is easy to swap. A clean, owned dataset keeps its value no matter which AI tool you adopt next year. A clever model trained on fragmented inputs has to be rebuilt the moment the inputs change. Treat the data layer as the asset and the AI as the consumer of it.

Cherry Seed

Quick Answer

Full Answer

Sources