How much does an AI solution cost?

Since a customized AI solution is always individual, we cannot give a general estimate of the costs. We never implement the same project twice.

However, we can point out which factors are decisive for the costs. And we have some best practices on how to minimize the costs of an AI project and get value from it as quickly as possible so that your investment pays off and project risks are minimized.

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  1. Training Data
  2. Model Accuracy
  3. Integration

The Main Cost Factors

1. Quality and Quantity of Training Data

Since AI models only learn based on training data, the necessity of data cannot be overestimated. The more consistent and correct training data is available, the faster a model can be used in production to add value.

Therefore, data collection and data preparation is a prerequisite to actually train a model and subsequently take up a very large portion of an AI project. Often, the data accumulates during your current business process and only needs to be collected and processed appropriately in order to be usable for training.

You could hire third parties to label training data to speed things up. However, depending on the use case, it may be a better choice to use in-house expertise to label data. Sometimes it is also possible that we can create synthetic training data.

Once we know your goals you want to achieve, your boundary conditions and your existing data, we can advise you on which way to go to get the first model into production as quickly as possible.

The takeaway is: if your data situation doesn't align with your goals, costs occur to level up your data.

2. Required Model Accuracy

The desired model accuracy is of course 100% (model predictions are always correct), but this is usually neither required nor realistic. So the question is, what is the minimum accuracy required to add value to your business?

The higher the accuracy required, the higher the cost of the project is likely to be. This is due to the fact that there are higher demands on the data and the model.

Furthermore, the higher the required accuracy should be, the more difficult it is to actually achieve this accuracy (Pareto principle), which in turn means higher costs.

3. Integration Complexity

The complexity of your technology stack and the processes into which the trained model is to be integrated also affects project cost. There are different requirements for a model, whether it is to be deployed on-edge, on a laptop, or in a cloud cluster.

Planning and establishing a continous data collection and correction cycle is critical if it is intended to improve the model accuracy over time based on new and more correct training data. This includes monitoring the performance of the deployed model.

Monitoring an AI model in production enables us to get feedback based on real-life data and usage and to take measures to improve the performance of the model in the next training iteration.

All mentioned aspects are part of integrating AI models into your environment. The complexity of the integration depends heavily on your specific use case.

How to Minimize Project Costs

To summarize, the main cost factors in an AI project come from the data situation (quality and quantity of training data), required model accuracy and the complexity of integration into your processes (establishing continous data collection, monitoring model performance etc.).

To reduce project risk and investments, we always recommend the following approach: Pick low hanging fruits first. Tackle the goals that are easily achievable first.

We have found that this is the best way to start an AI project. In practice, we first try narrow down the vision to define and implement a Minimum Viable Product (MVP), i.e. the smallest possible AI use case that already adds value to your business.

Advantages of this approach are:

  1. You can start with little training data and collect more data as the project scope increases.
  2. You can test the AI on a small scale and we can identify improvements faster. Later, you can expand to more use cases.
  3. You will see the ROI early on. This will get the decision makers on your side and facilitate further funding of the project.
  4. You reduce the risk of failure. The faster the AI solution comes into contact with reality (the users), the lower the risk of expensive misdevelopments.
  5. If you have use cases you want to improve with AI, just contact us. Our AI experts will find the right solution for your use case.

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