Databricks, DataIQ report maps deliberate enterprise AI choices

The report sits against a backdrop the body describes as rising expectations and spreading experimentation, with organizations under pressure to show meaningful returns. Early AI success, it notes, can raise expectations faster than an organization can absorb, and pilots can multiply before teams have the capacity to assess, govern and support them.

The leadership disciplines that emerged from the DataIQ Guild 2026 discussions are presented as an alternative to two extremes: slowing down until everything is certain, or committing to every promising use case. The body frames the goal as deliberate, evidence-based choices that preserve the ability to adapt, and it ties that to the four foundations of context, control, cost and choice, reflected in Databricks capabilities such as Lakebase and Genie.

Quoted practitioners point at the practical edges of the problem. Thomas Gebetsroither of Axpo Trading & Sales warns that security, support, adoption, governance and the operating model around a tool can leave a business case incomplete if those costs are invisible. Gavin Goodland of National Grid notes that not every investment needs to pay back the same way, and Bharathi Rajan of Swire Coca-Cola says prioritized use cases need a business accountable for impact on the long-range plan.