Surveys consistently show a large share of enterprise AI pilots never reach production, and the common thread in stalled initiatives is rarely the model—it is the surrounding architecture: fragmented data platforms, absent integration layers connecting AI systems to systems of record, and identity and access models never designed with AI workloads in mind.
The unglamorous work that makes AI possible
Enterprise architecture for AI adoption means establishing consistent APIs so AI systems can reliably reach business data and take action, data platforms that make relevant context available without duplicating pipelines for every new use case, and clear ownership for the integration layer connecting AI to existing systems rather than each AI project rebuilding its own bespoke connections.
This foundational work rarely shows up in an executive AI strategy deck, which is precisely why it gets underfunded relative to headline-grabbing pilot projects, even though it is what determines whether pilots become durable production capabilities.
JIG helps enterprises build the architectural foundation—data, integration, identity—that turns AI pilots into production systems rather than permanent proofs of concept.