Workflow-Design

How to use AI to detect edge cases, trigger the right escalations, and keep human judgment in the loop when standard workflows are no longer enough.

TL;DR for operators A model that is highly reliable at applying one explicit rule transition is not necessarily reliable at executing an entire procedure built from those transitions. In Reasoning Capabilities of Large Language Models. Lessons Learned from General Game Playing1, the strongest evaluated model, Gemini 2.5 Pro, achieves 95.6% exact success on one-step next-state generation. At five dependent state transitions, exact success falls to 73.4%. When the model must also choose actions during those five steps, it falls again to 65.3%. ...

TL;DR for operators Month-end close is not where small firms discover their love of manual labour. It is where invoices arrive half-labelled, clients reply with attachments named final_final_real.xlsx, and a senior accountant spends expensive hours doing work that is intellectually closer to sorting laundry than advising a business. The practical AI opportunity for small accounting and professional service firms is not “give everyone a chatbot and hope the profession becomes futuristic by Friday.” The better architecture is a cost-aware, privacy-first workflow: classify the task, remove or mask sensitive data where possible, retrieve the right firm knowledge, route the easy work to cheap or local tools, escalate uncertain cases to stronger models, and keep humans in charge of outputs that affect filings, financial statements, tax positions, or client advice. ...

TL;DR for operators Small teams should stop asking whether they need “AI automation” and start asking what kind of human agency each task deserves. Full automation is attractive when the work is repetitive, low-value, easy to verify, and not politically radioactive. Semi-automation is better when the task depends on context, judgment, interpersonal trust, creative control, or reversible-but-annoying decisions that still consume human attention. ...