Much of the teams' daily work is spent reading messages, sorting through information, writing matching responses, and preparing reports. Artificial intelligence can do part of this; As long as we know exactly what we want from it and how the result should be checked.
First, I know the process and data, and then I go to the model and tools. Because an impressive response in the demo is not necessarily reliable in everyday use. Input, errors, cost, human control and method of measuring the result should be known from the beginning.
How does this change help?
- Less time is spent reading, categorizing and manually recording information
- Answers and preliminary reports are prepared with a uniform structure
- The necessary information is placed in front of the responsible colleague faster
- Outputs can be checked and ambiguous ones go through the human path
Where should we start?
The best starting point is something that is repetitive, the input is digital, and the correct output can be explained. Categorizing messages, extracting information from forms and documents, summarizing a conversation, or preparing a draft response are good examples.
For sensitive decisions or tasks that do not have reliable data, full automation is not a good choice. There, artificial intelligence is better to be the assistant of the team and make the final decision by one person.
What goes on behind a good output?
There isn't just one prompt behind the scenes. Information must be received and verified correctly, sensitive data must be protected, the output must have a specific form, and if the result is uncertain, the work must be referred to someone.
Service outages, slow responses and increased costs are part of reality. Therefore, from the beginning, I determine how the system should behave in this situation and how the team will be informed of the problem.
Some real use in sales and operations
For example, a lead's information is read from a form or message, matched with CRM, and a ready summary is provided to the sales professional. Or scattered reports become a distinct structure and unusual cases are flagged for investigation.
In relation to the customer, every reply is not supposed to be sent unattended. The draft can be prepared automatically, but sensitive or uncertain items should reach the relevant colleague so that speed does not take the place of precision and the right tone.
Where does cooperation begin?
In the first conversation, we'll walk through what you're doing today: who starts it, where the information comes from, where time is wasted, and what the exceptions are.
Then I choose a limited part for example. If it is useful in practice, we will make it more complete and leave a clear way for error, access, warning and documentation.
Frequently asked questions
What exactly does AI automation do?
Depending on the process, it can read text, extract specific information, categorize messages, create summaries, or prepare drafts. The continuation of the work is done with clear rules and, if necessary, with the approval of one person.
Is artificial intelligence going to replace humans?
This is not my goal. The goal is that the team's time is not spent on copying and repetitive tasks. Important decisions, exceptions, and sensitive communications still require human judgment.
What do you need to get started?
A few real examples of input and output, an explanation of the current workflow and common problems are enough to get you started. From there, you can understand whether the idea is worth implementing or not.
Do you want to see if this solution is suitable for your work?
It is enough to write briefly how the work is done today and which part is annoying or time consuming.