What is Cognitive Automation

If you ever experienced a daunting task at work that you believe should be automated in the AI era, then you already know Cognitive Automation. Cognitive automation refers to the use of AI in automating repetitive tasks and processes that are alternatively performed manually be a person or even a whole team.

There are three component that come together to make this happen:

1. AI: To perform the core task that require human intelligence such as comprehending and analyzing text

2. Automation: To execute a task or a workflow based on the AI outcomes

3. Application: The system where this task or activity should be performed such as ERP, CRM, or a ticketing system

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How To Evaluate Your Cognitive Automation Use Case

The value of cognitive automation is clear in terms of completing tasks faster, saving time, and reducing operational costs. Evaluating your use case is a great way to measure your progress towards that goal and use it as a checkpoint to refine your approach further. Hence, it is best to do the evaluation based on the three components mention above; AI performance, Automation integration, and Application setup.

Evaluating the AI Performance

Evaluating the AI performance is the first step in your evaluation process. Let's take document classification as an example which is useful for many tasks such as customer service ticket routing. The task here is to assign the right category or label to a document like an email. The AI model would analyze the text and assign a category to it based on the text context as illustrated in the diagram below.

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Evaluating the AI model requires a very pragmatic approach. This is the case for a simple reason which is that AI model performance keeps increasing with more examples just like humans. Another point that AI models have in common with humans is performing a task with decent quality by seeing a few examples but it take significantly more time and effort to build that additional 10% knowledge and become the domain expert. With DeepOpinion proprietary technology an AI model requires significantly less examples but follows a similar trend.

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Therefore, the pragmatic metric of evaluation is when the AI model accuracy starts becoming useful to your application. This varies from one use case to the other. For example, at what AI accuracy would you speed up your resolution time by 70% or eliminate your mis-routed tickets by 50%. Once you reach to this point you can release a model and start realizing its value to your business process. From our experience, most applications can start realizing positive business value at a 70% accuracy. Our customer success team can work closely with you to define your go-live ready accuracy based on your application and business case.

An important question here is how do I get closer to a human-level accuracy or even a domain expert. DeepOpinion Studio is a learning system and continues to improve after going live in multiple ways. Some examples can be Active Learning where an AI model would ask you to validate its learning or by getting feedback from your users correcting its prediction on your application such as a ticketing system. This way the faster you go live the sooner your AI model can learn from new examples just like having an on-job training.

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Evaluating the Automation Integration

This part is straight forward. The thing to test here is that the integration works and executes the right task on your application. For example, you should see the ticket priority field gets assigned with a priority once you activate your automation and receive a new ticket. This way you can confirm that the setups works properly and the task is executed. It is advisable to do such tests in a sandbox environment so that you do not interfere with your current setup until you are ready to go-live.

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Evaluating the Application Setup

This point is specific to workflow automation. In some cases you might be performing a task manually while in others you might have a system in place that automates some of the tasks to a certain level. In this case you would ensure that the cognitive automation plays nicely with what you have in place already. For example, you might have 2 rules in place; the first one will search for the keyword "Delayed" in the body of an email and the second one will assign the priority to "Urgent". Once you integrate cognitive automation you would replace the first rule with a ticket priority classification AI model to have a better accuracy and then feed it to the second rule. In some cases, you might have a few dozen rules and it is important to configure them tightly so that your workflow can get the best of both and enhance your productivity.

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We Are Here For You

Cognitive Automation is a new technology and you are one of the innovators and early adopter to harness this technology. We are here to guide you on your journey for any question or idea you might have and help you refine your cognitive automation use case. We have a shared goal here, which is to successfully implement your use case and improve your productivity. All you have to do is to get in-touch with our customer success team.

Question 2

How Do I Connect PowerBI to DeepOpinion Studio Automation

Accepted Answer

Why Connect PowerBI to DeepOpinion Studio

As a DeepOpinion Studio user you are automating your text based processes. Depending on your work this can be intelligent routing of customer support tickets or customer review analysis or even insurance claim processing. In some cases, you might be interested in building reports and live dashboards out of this data. Many users prefer to customize their own dashboard or perhaps feed this data to an existing dashboard to have a single source of truth. PowerBI happens to be the BI tool of choice for many of our customers. Therefore, this integration makes it super easy to pull data to PowerBI to make you able to tell your data story.

How to Connect PowerBI to DeepOpinion Studio

There are two main steps to complete the PowerBI integration:

Create an automation on DeepOpinion Studio with PowerBI as the output step
Use PowerBI Query to get the data from DeepOpinion Studio

Step 1 Create an Automation

The first step is to create an automation. An automation consists of an input that has the raw data, a DeepOpinion AI model to analyze the data, and an output where the analysis results will be automatically sent. For example, if you would like to monitor your customer support, an input might be your customer support tickets from Zendesk, your model can be opinion mining, and your output can be sent to PowerBI. Alternatively, you can choose to trigger this automation manually which allows you to upload data on-demand and it will be directly reflected on PowerBI.

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Once you select PowerBI as an output a screen will slide in explaining the instructions. Here you just have to copy the query that you will later add to your PowerBI account to get the results of this specific model.

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Step 2 Create a PowerBI Customer Query

Now move to PowerBI, click on "Get Data" on the top menu and choose "Blank Query" from the drop down list.

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An empty window will pop up, click on "Advanced Editor" at the centre of the top menu and another small window will pop up. Here on the advanced editor is where you will paste the query you copied from DeepOpinion Studio and then click on "Done" on the bottom of this tiny window.

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Now by this, all the analysis that has been performed by this specific model will be pulled from DeepOpinion Studio. Then click on "Close & Apply" on the top right. All future analysis will be updated here as well when you open PowerBI again.

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Now you are ready to do your magic with data visualization on PowerBI. We look forward to hearing about it!

How can I evaluate my cognitive automation

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