Find the work AI agents can handle, what they still get wrong, and where a person needs to stay in charge.
It is difficult to separate real workflows from empty promises. Teams waste time testing tools that cannot handle edge cases or hallucinate at scale.
Three ways this can help. Illustrative scenarios, not promised results.
Support queue is overwhelmed with simple status inquiries, hiding urgent technical issues.
Agent tags, categorises, and answers tier-1 questions based on docs. Escalate complex issues to human agents with a summary.
Finance team spends hours daily copying invoice details into the ERP system.
Agent extracts line items from PDFs and maps them to standard ERP fields. Flags low-confidence extractions for human review.
Sales reps spend 30 minutes researching each prospect before a call.
Agent scrapes recent news, earnings calls, and LinkedIn, summarising key talking points in a 1-page brief.
We are starting small so each entry can be tested and maintained. The first collection will focus on research, support, sales operations, document work, and internal reporting.
We are choosing the first workflows now. Share the repetitive task you want examined, and we will use the requests to set the testing order.