Qlik has published research identifying what it calls “the hard part of AI” in enterprise deployments. The framing is self-serving — Qlik sells data integration and analytics software — but the underlying observation is accurate and underreported.

The hard part of AI is not the model. It is everything the model depends on.

Enterprise AI projects fail at a predictable set of choke points. Data that exists in the organization but is not accessible to the model. Data that is accessible but inconsistent, unlabeled, or structured in ways that the model cannot parse reliably. Outputs that are technically correct but operationally useless because they do not map to the decisions the organization actually makes. Governance requirements that make deployment legally or politically untenable. Integration costs that exceed the value of the capability being deployed.

Turing has launched Turing Frontier, a platform that connects AI laboratories with domain experts for evaluation, fine-tuning, and validation work. The product category is modest. The structural position it occupies is not.

What Turing Frontier is building is the interface layer between AI systems and the specialized human judgment those systems cannot reliably replicate. This is not a novelty. Every serious AI deployment in high-stakes domains already has a version of this layer — it is just typically ad hoc, expensive to staff, and impossible to scale. Turing is betting it can systematize and productize that function.