Revised September 19, 2026 to remove unsupported market-wide performance claims and focus on a testable pilot design.
A useful AI pilot begins with a bounded task and evidence of how that task is performed today. Company size alone does not establish whether automation will work. Integration, permissions, data quality, and the cost of mistakes all affect the result.
Specify the workflow
Choose a task with observable inputs and outputs. For example, extracting fields from a document and preparing a record for human approval has a clearer acceptance test than a broad instruction to automate operations.
Document which actions the system can take, which need confirmation, and when it must stop. Include missing documents, ambiguous values, duplicated records, and changed instructions in the evaluation.
Establish the baseline
Compare the proposed system with the existing workflow and a simpler technical baseline. Count time spent reviewing outputs, correcting errors, handling exceptions, and maintaining integrations.
A model-cost estimate alone is not a measure of business value. A fast output that requires extensive repair may increase total work.
Expand on evidence
Run the pilot on representative tasks with appropriate access controls. Keep a held-out set for acceptance testing and track regressions when models, prompts, or tools change.
For teams handling regulated or sensitive work, define acceptance conditions with the people responsible for the workflow. A benchmark result does not establish that a specific deployment meets those conditions.
OmniManas focuses on business workflows and permissioned agents. Multivon provides tools for examining AI behavior and comparing runs. Our technical report connects visual understanding, agent workflows, and outcome verification.