AI in Enterprise Software: Start by Removing Repetitive Work

AI has been the most discussed technology of the past two years, and also the easiest to misuse. Our view is simple: start with work that is repetitive, time-consuming and rule-based, and let AI share it — instead of pursuing intelligent decision-making from day one.

Three pragmatic starting points

• Content organization: turn meeting notes, customer feedback and tickets into structured information to reduce manual sorting;
• Customer service assistance: generate reply drafts from the knowledge base, confirmed by a human before sending;
• Data queries: answer common report questions in natural language to lower the barrier for frontline staff.

Boundaries and human confirmation

Enterprise scenarios tolerate errors far less than consumer scenarios. Steps involving pricing, contracts, permissions and external commitments must keep human confirmation; AI output must be traceable and able to show which source the answer came from. Writing boundaries into the process is more reliable than fixing things afterwards.

How to evaluate the results

Do not only look at how much content was generated. Look at how much time was saved, how many repetitive operations were removed, and whether the error rate went down. We usually run a small pilot and use two or three weeks of real data to decide whether to expand.

The value of AI in enterprise software is not replacing people, but freeing people from repetitive work.

Technology will keep evolving, but the criteria stay the same: does it make the business more stable, more efficient and easier for frontline staff to accept.