Vendor selection for agentic AI in GBS is a high-stakes decision. The platforms that GBS centers choose for process automation become deeply embedded in their operations: integrated with ERPs, embedded in staff workflows, and relied on for continuous processing of business-critical transactions. Switching platforms later is expensive and disruptive. A rigorous evaluation process before selection reduces the risk of choosing a platform that performs well in demonstration but disappoints in production.
The four tests that distinguish genuine agentic vendors
- Novel exception investigation, not routing. Ask the vendor to demonstrate what happens when an invoice arrives with a missing PO reference. A genuinely agentic system investigates — it searches vendor history, proposes a match, explains its reasoning. A rules-based system routes it to a queue. The demonstration reveals the architecture.
- Template-free handling of new document formats. Provide a document type the vendor has not seen in the demo. A template-free platform handles it based on semantic understanding. A template-based platform fails or produces low-confidence results. The difference is visible in a ten-minute test.
- Complete decision audit trails. Ask the vendor to show the audit log for a processed invoice. The log should capture every step: what was extracted, what was checked, what ERP data was read, what tolerance rule was applied, and what the disposition was. Platforms without this cannot satisfy SOX controls documentation requirements.
- Production touchless rates at reference clients. Ask for the straight-through processing rate at a reference client with comparable document complexity, not a general benchmark. The operational team lead at the reference client is the most informative conversation — they know what the platform actually does versus what it was supposed to do.
The proof of concept design
The POC is the highest-value step in the evaluation process. It should be designed to measure what matters in production, not what is easiest to measure. Include a sample of your most difficult documents alongside typical ones. Measure straight-through rates and exception rates, not just extraction accuracy. Test the exception workflow and how it would integrate with your operations. Standardize the configuration period across competing vendors so comparisons are fair.
The trap of demo-driven selection
The most common failure mode in GBS agentic AI vendor selection is choosing the platform that produced the best demonstration rather than the one best suited to the organization's actual requirements. Demonstrations are curated by vendors who know their platform's strengths. The organization's actual requirements — their specific document mix, their ERP configuration, their entity structure, their exception types — may not map to the vendor's demonstration strengths. Avoiding this trap requires defining evaluation criteria before seeing demonstrations, weighting those criteria by importance to the specific situation, and measuring each vendor against the criteria rather than against each other's demonstration quality.
Pilot design for GBS evaluation
For GBS automation evaluation, the pilot should reflect GBS-specific requirements: include multi-entity routing scenarios in the pilot document set, test the platform's handling of invoices in the languages actually processed by the GBS center, and evaluate the exception management workflow in the context of how the GBS center's exception handling team will actually operate. A pilot designed around single-entity, English-language, standard invoice processing may produce results that do not predict performance in the actual GBS deployment.
Evaluation timeline management
GBS agentic AI vendor evaluations that lack a defined timeline tend to extend indefinitely as additional questions arise. Defining a firm evaluation timeline with milestone dates for POC completion, reference checks, TCO analysis, and decision creates urgency that moves the evaluation forward and prevents the evaluation from becoming an indefinite exercise that delays value realization.
Evaluating Hypatos in a GBS agentic AI selection
When Hypatos is in a GBS agentic AI evaluation, the POC design should reflect how it will be used: processing the full invoice corpus for GBS entities, including multi-entity routing, multi-language documents, and the complex exception types that drive manual handling.
Specific evaluation criteria for Hypatos in a GBS context: straight-through processing rate on the actual GBS invoice corpus; multi-entity routing accuracy for the GBS center's entity structure; exception rate by exception type; ERP integration depth in the specific SAP or Oracle configuration used; and operational dashboard functionality for entity-level SLA management. Reference clients should be GBS operations at comparable scale, not single-entity deployments. On timeline, Hypatos implementations for a single ERP environment with standard document types typically run two to four months from contract to production. Multi-ERP or multi-language implementations run four to six months.
