Unless you have lived under a rock in the past year, you know that artificial intelligence is a popular topic. Whether it is Copilot, ChatGPT, Bard, or Open AI, artificial intelligence is here to stay. It is time to embrace the utilization of artificial intelligence and learn how to embrace it for our benefit.

But surely, the use of artificial intelligence inside of Power Query is brand new, right? Wrong! I remember seeing it available five years ago and it is a powerful tool to integrate with your models! I am not going to be able to go into all of the details of artificial intelligence in this article, but rather help you understand how to integrate it within your existing queries.

What Is Artificial Intelligence?

In the fast few years, Artificial Intelligence (AI) has become a force in society. It permeates the world we live in and has become an integral part of how things are done. We see examples of it in our lives through things like chat bots, self-driving cars, and even social media. While some may find it intimidating, the reality of AI is that it has the power to make our lives easier.

Artificial Intelligence is used to describe technologies that perform tasks that once required human intelligence to complete. When properly deployed, AI models can recognize patterns, solve problems, make decisions, and understand natural language. These models do not organically exist. They are created through extensive training activities to ensure the results you receive are valid. The work we perform with Power Query may result in the creation of a model while others will leverage one.

While generative artificial intelligence (GenAI) is all the rage right now, we will not be using it for our queries. Instead, we will be integrating models that have been trained to provide a response. You might be the creator of these models while others are creating them for you. The result is the opportunity to bring back a predictive response based upon your query.

How to Get Started?

While Power Query is available in both Microsoft Excel and Power BI, this functionality is only found in Power BI. While you can leverage similar AI models in Microsoft Excel, it is done outside of Power Query. This is an important item to note if you prefer using Power Query inside of Excel.

Second, you must have some form of a premium workspace to enable this functionality. This can be in the form of a Premium Per User, Premium Capacity, or Azure Power BI Embed environment. If you already have one of these environment at your disposal, you are set. If not, check out my article on licensing strategies around Power BI. I recommend spinning up an Azure Power BI Embed SKU or activate a Premium Per User trial if you want to try these features at a minimal cost.

What if you do not have a Power BI environment available to you? Unfortunately, you will not be able to take advantage of this functionality inside of Power Query. I recommend you work with your organization to spin up a trial to explore this functionality.

Pre-Built Models in Power Query

Power Query allows us to leverage pre-built models. Leveraging Azure Cognitive Services, we can perform text and image analysis on our queries. We can do this from the add column ribbon inside of Power Query:

Screenshot of the add column ribbon inside of Power Query with an arrow pointing to the text analytics function.
Activating the Text Analytics function

If you are doing this for the first time, you will be prompted to allow the analysis of your data. Once you have done that, you can select which model you wish to use:

Screenshot of the Text Analytics screen with options to select the detect language, extract key phrases, and score sentiment models. Next, you can select the column to analyze and language associated with the text.
Selecting a Text Analytics model for your analysis

As you can see, you can select multiple pre-built models to perform your analysis. With text based analysis, you should consider both the text and the language being used. This helps ensure the proper analysis is being performed. By default, it will use your tenant’s default language. If your column is using the same language consistently in the query, you can hard code it in this screen. However, if you have multiple languages, you might need to to use the detect language model to bolster your analysis.

You can also identify tags for images you ingest with Power Query. This is not easily performed, but is ideal for small images. Because of the practicality of this, you will likely not use the vision model. However, you can try it out for yourself:

Screenshot of the tag images model in the vision module from Azure Cognitive Services.
Configuring the tag images model

These models can provide quick analysis and provide meaningful results with minimal effort. I find that combining these results with other dimensions it allows me to sort through results and find the most meaningful responses in surveys.

Custom Artificial Intelligence Models

If you have created a custom model using Azure Machine Learning, you can leverage it within Power Query. Leveraging a public dataset around diabetes, I created a custom model with Azure Machine Learning. With an endpoint created, I can access it within Power Query with a few clicks:

Screenshot of the home ribbon in Power Query with an arrow pointing to the Azure Machine Learning function.
Activating the Azure Machine Learning function

Once I have selected Azure Machine Learning, I can select which endpoint I wish to use. I have already deployed my model and selected it below:

Screenshot of an Azure Machine Learning endpoint being configured for Power Query.
Configuring my predictive model in Power Query

As you can see, there are multiple inputs I must identify for my endpoint to return a predictive response. I can identify the different columns in my query and return a result. We will need to identify values for each field to allow this to work.

It is important to consider that Azure Machine Learning uses a consumption pricing model. Every time you refresh your query, it is seeking new predictions. Keep this in mind when building out your queries as you could rack up some extra costs without realizing it!

Any Other Options?

If you are a user of R or Python, you can leverage custom algorithms and models inside of Power Query as well. You need to deploy software on your device to make this work and can be prohibitive if you do not have admin access on your machine. I do go into detail on these tools in Chapter 11 of my book, but decided not to discuss them in this post. Primarily, I wanted this to be about out of the box and easy implementation.

Conclusion

It is amazing how artificial intelligence has infiltrated Power Query. For many, they have no clue it was there or what is needed to get it activated. With a little effort, you can really transform your queries with a few clicks. I recommend you try it out to see how you can harness the value of artificial intelligence in your queries!

So did you know about this feature in Power Query? Have you used it before? Are you going to try it out? Tell me about it in the comments below!