Channel partners face AI infrastructure limits

Channel partners are struggling to move generative AI pilots into production because legacy storage, inaccessible ‘dark’ data, limited network throughput and rising cloud costs are creating infrastructure bottlenecks.

Over the past two years many resellers and systems integrators helped customers build generative AI proofs of concept and demonstrations. A small number of early-stage deployments followed, but most pilots ran in isolated environments and did not require the sustained performance or data access that production systems need.

Production AI requires sustained throughput, fast access to large datasets and an IT stack built for continuous training and inference. On-premise storage arrays can reach performance ceilings during model training. As datasets grow, cloud expenses can rise quickly. Network capacity, data centre power and cooling limitations become visible when workloads scale beyond proof-of-concept levels.

Most of the technical friction is in the data layer rather than model tooling. Industry analysis finds roughly 80% of the AI infrastructure challenge relates to data. A large volume of enterprise information exists as ‘dark’ data-unstructured, unclassified or effectively unreachable-so GPU clusters and other compute resources cannot deliver expected results when data cannot be located or processed at scale.

A recent study showed 67% of UK businesses now cite high-quality data as the primary factor for AI success, up from 41% the previous year. Organisations with strict legal and privacy rules report the tightest constraints. Financial firms running decades-old core systems and hospital networks with fragmented patient records, including paper files, face complex unification tasks before enterprise-wide AI can be deployed.

Commercial models also create barriers. Many channel partner programmes remain oriented to one-off hardware sales and capital expenditure cycles. Customers increasingly request consumption-based, pay-as-you-grow contracts that support repeated model training and scaling. Where partners cannot provide flexible procurement and ongoing data services, buyers pursue other suppliers for infrastructure work.

Some enterprises are contracting directly with hyperscalers or with specialist system integrators that offer end-to-end AI infrastructure services, from data ingestion and classification to high-performance storage and hybrid cloud orchestration. Research also indicates that 85% of UK businesses have specific data sovereignty requirements that affect where AI workloads run.

The main constraints reported by organisations are storage performance, network throughput, data architecture and the ancillary needs of power and cooling. Channel firms report gaps in those areas when attempting to expand pilots into production environments.