10 Tips for Using AI in Automation: How to Avoid Common Pitfalls

If there’s no ready-made AI model for the process you want to automate, you’ll need to prepare plenty of training material for the AI. For example, UiPath’s Document Understanding tool requires at least 30 examples, while the Communications Mining tool needs at least 100. Insufficient training material makes the teaching process much more challenging.

You should also consider that business changes can affect an AI model’s performance. For instance, the performance of a Communications Mining model may degrade over time if a new product is launched, altering communication patterns not covered in the training data. In such cases, the model will need to be retrained. In other words, AI models require maintenance, and you need to use them carefully. When retraining an AI, ensure that no changes conflict with the original implementation.

Some AI models are like black boxes, making it difficult to adjust parameters to achieve specific results. If training doesn’t yield the desired outcomes, revising your goals later may require significant effort and be costly. Therefore, it’s crucial to understand from the start what the input data can achieve. Low-quality data cannot produce high-quality results, so you must ensure your training material is of good quality and aligned with the model’s desired outcomes.

1. Understand the problem thoroughly:
   Spend time clearly defining the problem you want to solve. Ensure that AI is truly the best solution for your needs. Clear goals from the beginning will keep the project on track.

2. Quality data ss essential:
   Data quality is critical. Poor or inaccurate data will lead to poorly performing models. Invest time in obtaining high-quality, relevant data.

3. Iterate and adapt as needed:
   Don’t settle for the first version of the model. Test it, evaluate its performance, and make adjustments as needed.

4. Test in a controlled environment before launching:
   Test your model in a controlled setting before deploying it. This allows you to analyze its performance, identify issues, and avoid unpleasant surprises.

5. Understanding builds trust:
   As models become more complex, ensure all stakeholders have at least a basic understanding of how the model makes decisions. This helps build trust.

6. Choose the right model for the task:
   For example, an LLM (large language model) is ideal for text-related tasks, so the task should involve text processing or generation.

7. Leverage open-source models:
   You can use and modify open-source models (e.g., from Hugging Face). These models are often pre-trained for general tasks but can be fine-tuned for more specific applications. For instance, a model trained to recognize objects in images can be adapted to specialize in identifying specific objects or categories.

8. Ensure adequate resources:
   Running a model, especially one with many parameters, can require significant computing resources. If your company lacks sufficient systems, consider using remote hosting services or cloud platforms like Google Colab.

9. Match the operating environment to the training context:
   A model trained to process contracts, for instance, might perform poorly if applied to literary texts. Keep the operating and training contexts aligned.

10. AI models shine in handling irregular inputs:
    AI excels at tasks involving unstructured or irregular inputs, such as processing documents with non-standard formats or information in varying orders or structures.