The report identifies legacy system integration, fragmented data, and limited internal expertise as the main issues companies need to address to implement AI. The issue of fragmented data affects data governance frameworks, making the latter similarly piecemeal. The report’s authors cite complex data estates in many companies as the main reason that AI deployments are constrained in the sector.
Firms surveyed managed an average of 17 data sources, and a majority cite this as an issue, one that’s compounded after mergers and acquisitions.
The report’s authors imply AI will affect costs and scalability positively and could address some of the issues firms experience around manual error correction and mistakes in reconciliation processes. The report suggests decision-makers could target reconciliation processes for an initial proving ground for AI, given it’s a boundary-ed, rules-based domain where automation can yield fast positive results.
Any form of automation, AI or deterministic, placed on a fragmented architecture and a fractured data layer may not scale well without a rise in costs. The report highlights the potential for AI in structuring fragmented data sources, and suggests cloud-based, as opposed to in-house AI platforms may be an answer in that respect.
