Artificial intelligence (AI) and machine learning (ML) improve VIM in two areas today: extraction accuracy and workflow reduction. On the capture side, ML models improve field recognition and handle layout variation that fixed templates miss, and the capture engine combines its ML models with an invoice knowledge base that grows as users correct results, so recognition improves with use across your specific vendor mix.
Our caution from delivery experience: that improvement depends on a feedback loop rather than arriving automatically. Vendor invoice layouts change over time, so capture accuracy is sustained through consistent correction by users, monitoring, and deliberate training on new layouts. A capture layer that is deployed and never maintained degrades. On the workflow side, ML can learn which exceptions are routinely approved and reduce the share of invoices that need human attention. AP automation is moving further in this direction: predictive general-ledger and cost-object coding learned from posting history, exception handling that suggests or applies a resolution when confidence is high, vendor-behavior scoring that surfaces duplicates and anomalies, and payment-timing recommendations that weigh discounts against working capital. Treat those as roadmap-stage capabilities to evaluate against current OpenText releases rather than assumptions for your business case, and validate any specific accuracy expectation against your own invoice population rather than vendor benchmarks.