Start with the problem you need to solve. Explore team training, agents, automation, internal knowledge, custom models and alternatives to costly software.
After a private AI application fails, employees want their documents and unfinished work back. Restarting the model alone does not restore that workspace. When commissioning backups, establish what the team needs to recover and how much recent work it may need to reconstruct.
After several pilots, a company may have private models and applications with overlapping purposes. IT still maintains them, but may not know who needs each tool. Before expanding further, establish which services support real work and which could be combined or retired.
Reports are faster to prepare after training, but an automated data import launched in the same month. L&D wants to explain the program's value, while finance needs to understand what caused the change. A simple before-and-after time comparison cannot separate those causes. When commissioning training, agree on the work you will be able to assess afterward.
Several companies expect work from the same shared service center, and each sees only its own deadline. The team manager negotiates priorities by phone and copies the result into a spreadsheet. A shared plan can show what the team has actually committed to and which requests still need a decision.
Two products look convincing in a presentation, while a custom software proposal promises a close fit with your team’s work. Before choosing, consider commissioning a focused assessment using real work. The exercise should resolve an important purchasing uncertainty before the company commits to migration or development.
An assistant's subscription and application support are easy to spot in a budget. Less visible is the time someone spends updating a guide after a product change, answering an editor's questions, and checking the revised explanation. If the team has no capacity for that work, its knowledge library can grow faster than its ability to maintain it.
A company wants its own language model, but that phrase can describe different needs: running it in a chosen environment, adapting its responses, or controlling its future development. Defining the need makes it possible to compare adapting an existing model with training one from the beginning.
After a company assistant launches, some questions still go to experts. A label saying “unanswered” gives the person developing the library little to work with. They need to know what the employee could not accomplish and whether the gap calls for a shared explanation or information about one particular case. That determines what is worth adding.
An application needs to assign text to an agreed category. Yet the team uses a large language model that generates an answer, which software then parses for a label. It is worth asking whether the task needs that flexibility and whether a simpler classifier could provide a useful result with less operational work.
Employees copy data from a form into another application, so the company starts considering AI. Yet the form already contains the necessary fields, and the team can explain where each one belongs. Before choosing a model, it is worth checking whether a straightforward systems connection can solve the problem and whether the existing tools support it.
Salespeople do not update the CRM, their manager keeps a separate spreadsheet, and the company is considering building a new system. A custom application may inherit the same problem if the team has not agreed which information matters and who records it. Before buying, establish what is actually making the current product difficult to use.
A manager does not need to sit beside employees for every attempt in an AI tool. Their participation may matter when the group cannot agree on a useful result or what it can change about the work. Attendance should follow those questions. A name on the participant list does not explain why the manager is there.
Your company maintains a custom extension built to cover a missing product capability. The vendor now offers something similar, and the team wants to know whether the extra application can be retired. Treat this as a change in its own right: the extension may also embody working agreements that a short feature description does not reveal.
Your company wants to move away from a SaaS product, but most of the team still needs it for daily work. The first stage needs a clear scope: which work moves to the new application, where its data comes from, and what stays in the current system. Otherwise, a partial replacement can leave people managing the same case in two places.
A custom application is live, but when its ERP connection first fails, the team does not know whom to call. A developer wrote the code, an administrator manages the server, and the ERP provider supports its own product. A SaaS replacement purchase should establish who takes responsibility for the service employees actually need.
An employee uploads a file for an audit, but the coordinator opens it and finds that the evidence request is still unanswered. They need to explain what is missing and go back to the person gathering the material. An AI agent can compare the document with the request, identify the missing information, and draft a specific follow-up question.
An employee asks to return later from a business trip, but their hotel stay ends a day earlier. The travel coordinator needs to clarify whether the request includes another night before checking booking options. An AI agent can connect the message to the right traveler’s itinerary, identify the unanswered question, and draft a follow-up. The coordinator then receives a complete request to work with.
The report is ready and the branches have replied, but head office still cannot explain why the backlog grew. Some comments repeat the figures in the table; others describe the previous month. The report owner has to read them again and return to their authors. An AI agent can bring each question together with the branch's reply and draft a request for the missing explanation.
The author has uploaded a revised guide and marked the task complete, but the project manager still needs the client to review that version. An AI agent can find the original comment, connect it to the revised file, and draft a specific review request. The project manager can then see whether the next step belongs to the author or the person reviewing the changes.
After the urgent response, a property manager gathers the documents for a damage report. The tenant's report and inspection note identify one suite, but the contractor's estimate names another. Before passing the material on for review, the manager needs to clarify the discrepancy. An AI agent can bring those records together, show their sources, and draft a question for the contractor.