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Intelligent automation (IA), also known as hyperautomation, is a set of technologies and methods for automating the work of white-collar professionals.
Here, I lay out a framework for explaining its power in terms of four main capabilities — vision, execution, language, and thinking and learning — and how they can enable business transformations with people and business goals at the center.
Applications of computer vision in a physical environment include robots recognizing objects and self-driving cars interpreting signs and road markings. Computer vision supports intelligent character recognition (ICR), digitizing, and analyzing documents in a digital environment. Finally, computer vision can automate the analysis of images and videos. It has a vast range of applications, including medical diagnostics, business process documentation, biometrics, and retail store automation, such as Amazon Go.
As businesses begin considering how to adopt computer vision, they should understand that a large amount of training data may be needed, especially for applications like medical diagnostics or biometrics. The technology behind Amazon Go is also still costly and out of the reach of most small and medium enterprises.
Execution involves doing things — accomplishing tasks in digital environments. The execution capability acts as a glue to connect the other capabilities in a streamlined way. For example, with the help of the language capability, it can collect sales data using the vision or language capabilities, automatically convey the data to the thinking and learning capability for analysis and compile and send out a report about the findings.
The key technologies supporting the execution capability are intelligent workflow, low-code platforms, and robotic process automation (RPA). Smart workflow platforms help to automate predefined standard processes. Low-code platforms allow business users without coding skills to develop automated programs. RPA is used to automate any tasks that a human can do on a computer, such as opening applications, clicking menu items, entering text, or copying and pasting. It learns by recording the actions of the human user and automating them.
My aim is that this conceptual framework will equip you with the information you need for selecting vendors and choosing technologies for your IA journey, and fitting them into your existing organizational IT landscape.
To decide between RPA and automated workflow platforms, consider whether you're building a well-defined automation platform from scratch or integrating it with existing legacy systems. Low-code and smart workflow platforms offer a superior foundation for your overall automation platform.
They provide predeveloped, ready-to-use modules for particular industries and business functions, making them quick to implement. They can be linked to each other and external systems using robust connectors. On the other hand, if you have bespoke systems you need to integrate with, you could benefit from the greater flexibility of RPA, even though the implementation will be slower. The data connections may be less robust.
The challenges involved in adopting low-code platforms and RPA are often psychological rather than technological. Even though learning to use these technologies is easier for business users than learning to code, there is still a learning curve. There may also be conscious or unconscious resistance or resentment. Upper management must create buy-in and galvanize enthusiasm across the organization.
The language capability enables machines to read, write, listen, speak and interpret the meaning of natural human language. This capability can extract useful information from unstructured documents, categorize text (for example, in spam filters), and perform sentiment analysis. It enables text-to-speech, speech-to-text, and predictive text keyboards and powers chatbots, such as ANZ Bank's Jamie or Google Duplex, and machine translation, such as Google Translate.
Businesses should carefully monitor customer satisfaction when launching a chatbot and allow the bot to escalate queries to a human where needed. Chatbots have their strengths as user-friendly interfaces for looking up data and making bookings, but they usually cope badly with more complicated or unusual queries, leading to customer frustration.
The thinking and learning capability are about analyzing data, discovering insights, making predictions, and supporting decision-making.
The key technology behind this capability is machine learning — most of all, deep learning, the newest and most potent component of machine learning. Deep learning uses neural networks with multiple layers, each processing and interpreting the data at a different level, inspired by how the human brain works.
It learns autonomously from large amounts of training data, spotting patterns and correlations without explicitly teaching or programming any rules. It excels when faced with complex, unstructured data with numerous features, making it useful for image classification, natural language processing, and speech recognition.
There's a large and fragmented market for open-source, traditional, and cloud-only solutions for machine learning platforms. I recommend that smaller companies with limited budgets use open-source platforms (such as Scikit Learn, Keras, PyTorch, or R-Project) because they cost nothing, and you're allowed to modify them. Still, they can be less reliable and lack dedicated support.
Larger and more established companies may prefer traditional platforms (such as SAS or Matlab), licensed and offer dedicated customer support. Finally, companies with high growth objectives can benefit from the scalability of cloud-based platforms (such as AWS, Microsoft Azure, and Google Cloud), which are priced based on usage and offer the broadest range of connectivity with other technologies.
My aim is that this conceptual framework will equip you with the information you need for selecting vendors and choosing technologies for your IA journey, and fitting them into your existing organizational IT landscape.
Beyond the value that these four capabilities can deliver individually, though, the combination of them unlocks more impact than the sum of their parts. Combining the technologies broadens the power of automation, from isolated tasks to continuous, automated, touchless end-to-end business processes.
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