Many organisations already have the ideas. They know where AI could make a difference. It could be in workflows involving citizen letters, translations, notes, screening or first drafts of documentation.
What is often missing is a realistic path from idea to solution in operation.
That was exactly the journey we focused on in our webinar about AI apps on Promte: how to go from a concrete process to a live AI app without having to establish the entire operational foundation yourself.
Here is a practical way to think about that process.
The first question is not which model you want to use. The first question is which task needs to be solved.
The best AI apps often start with a workflow that already exists in the organisation and is concrete enough to be made more consistent and less time-consuming.
In the webinar, we gathered ideas for processes that could become apps. Suggestions included citizen letters, citizen service apps, knowledge lookup, route planning and other administrative workflows.
It is a good exercise because it shifts the focus from "what can AI do?" to "where does AI make sense for us?"
If a process repeats often, requires the same types of input each time and needs an output in roughly the same form, that is a strong sign that the task is well suited to an AI app.
The next step is to decide whether the solution should actually be an app.
An open chat format is good for tasks where the user needs flexibility and free dialogue. But if the workflow is based on fixed information, specific choices and a clear output, it is often better to build an app with a guided flow.
We saw several examples of that in the webinar. Recruitment work and impact assessments are good cases because they are based on known inputs and a result that needs a specific quality and structure.
The same applies to many types of citizen enquiries. If the goal is to help employees create a first draft of an email or letter, it can be a major advantage to let the user choose case type, tone, target audience and central facts in fixed fields instead of writing everything freely.
The more concrete the process is, the stronger the case for an app.
Once the task has been chosen, the work is about translating the workflow into a user experience.
This is where many good ideas either become stronger or weaker. An AI app does not succeed only because the model is good. It succeeds when the employee can easily understand what needs to happen.
A simple example from the webinar demo was an email or letter generator for citizen responses. Here, it makes sense to think in a flow such as:
It is a small but important shift. The AI is not a free conversation space, but an engine in a workflow that the employee can complete calmly and clearly.
Once the flow is clear, the next question is where the app should live.
A central point in the webinar was that many organisations can get to a prototype quickly, but getting the solution into operation is significantly harder. Hosting, backend, AI models, user management and distribution all need to be handled.
That is why it makes sense to build the app on top of an existing operational layer if the goal is real use and not just a demo.
On Promte, this means the app can be connected to the platform the organisation already uses or can access. Core elements such as hosting, models, access control and distribution are included, without having to establish new infrastructure for every individual solution.
That makes the path from idea to use shorter and more realistic.
Another practical point is that a good AI app is rarely relevant in only one place.
If you build a solution for citizen letters, translations or document-heavy processes, others will often be able to use something similar. That is why it is worth considering early whether the app should only solve a local need, or whether it should also be shareable more broadly in the organisation or with others.
In the webinar launch, we showed exactly how apps can be installed from a library and shared further. It is an important part of mature AI adoption because it prevents the same development work being repeated unnecessarily many times.
Sharing also makes it easier to start small. You can build a first version for a limited need and later decide whether the solution should be rolled out to more users or shared with other organisations.
The biggest barrier for many organisations is not a lack of ideas. It is that the journey from idea to operation can feel heavy and unclear.
That is why it is important to break the work down:
It is not always the most spectacular method. But it is often the most effective.
AI apps rarely become successful because they can do everything. They become successful because they solve a concrete task in a way employees can and want to use.
If you already have a process in mind, or if you would like help assessing whether an idea is suitable for an AI app on Promte, you are very welcome to contact us or get started with the examples and apps that are already ready.
