Your business already uses AI. Why is your team still doing everything by hand?

The demo impressed everyone. On Monday, the spreadsheet was still there

Picture this: a business buys an artificial intelligence tool. During the demo, a few instructions are enough to draft a customer reply. The team leaves excited. On Monday, a real order arrives: the customer record is in one system, pricing in a spreadsheet and the commercial terms in the manager’s email. Someone gathers everything, checks the details and types them in again. AI can write the reply. But who prepares the path to that point? This is an illustrative scene, yet its question guides a recent discussion about business AI. On November 12, Rodrigo Kede Lima shared lessons from Microsoft Frontier Company after nearly 500 projects with more than 200 customers. The post examines how AI experiments can lead to operational changes. One case helps show where that work begins: in the process people need to carry out every day.

Sources: Microsoft · Rodrigo Kede Lima · From AI ambition to business impact

What changed when the team looked at the whole process

According to a Microsoft account published on November 9, Northern Trust brought finance specialists and engineers together to work on client onboarding. Data preparation fell from roughly two and a half weeks to three days. The solution incorporated business rules and retained human review. That outcome belongs to its particular context; it is not a forecast for other businesses. For Sync360, the case opens a useful conversation: before choosing a tool, we need to understand where information travels. Does the order arrive complete? Does the customer record have an identifier that works in both systems? Who handles a mismatch? An integration can move structured data between applications. An automation rule can route a predictable task. AI can help interpret a document or prepare a suggestion. The choice depends on the problem at each step. Another Microsoft post, also dated November 12, emphasizes redesigning workflows, tracking results and retaining control over decisions. In practice, this requires an end-to-end view: from the incoming request to a deliverable someone can check.

Choose a recurring task and follow its path

Start with a frequent activity: creating a customer record, preparing a proposal or checking an order. Follow a real execution and note where information arrives, who completes it and which systems require someone to enter it. Measure the time spent and record errors or returned work. Then discuss a small pilot with your technical team. Define which data it may access, which actions need approval and how incomplete information will be handled. Ask for a comparison between the current process and the pilot using the same criteria: time, rework and output quality. Involve the person who performs the task; they know the exceptions a demo often leaves out. Without an internal technical team, gather an example with no personal or confidential data and the names of the systems involved. That material can already help identify where to start.