TL;DR

What: AI depends on data, so how you collect data matters

Overcoming The Greatest Obstacle in Business Adoption of AI

Thinking of implementing AI in your business? Consider how you're going to feed it with data.

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RPA can only be as good as the data it runs on.

The adoption of intelligent automation and artificial intelligence-powered solutions for businesses are increasingly growing across the globe. Singapore, for instance, has recently invested over SGD 20 billion in the Tuas mega port project that will incorporate new technologies, making it the largest fully automated terminal in the world. Similarly, Deloitte’s 2018 global RPA survey revealed that 53% of organisations have started incorporating Robotic Process Automation (RPA) into their business operations to improve efficiency and productivity. This is especially relevant to the healthcare, manufacturing, banking, financial services and insurance (BFSI) business verticals, to name a few, who turn to RPA as a quick fix to automate their otherwise labour-intensive processes.

Despite that, more than 40% of RPA projects fail to deliver expectations due to factors such as implementation time and cost. It is also vital to consider RPA’s incapability when it comes to reading unstructured data, which poses a challenge when it comes to handling tasks such as data curation. With the projection of over 80% of enterprise data to consist of unstructured data by 2025, businesses need to look at alternatives to RPA now. One possible alternative to traditional RPA that can not only match its functionality but also offer more is Cognitive Machine Reading (CMR)

CMR is capable of reading both structured and unstructured, as well as image and inferred data…

CMR is capable of reading both structured and unstructured, as well as image and inferred data – data consisting of printed or handwritten text, or simply image data such as notary stamps or signature verifications. It can also undertake tasks such as natural language modelling, processing and generation that is essential to helping business improve operational efficiency. For example, a system using cognitive recognition can understand that the phrase ‘patient’s name’ is the same as ‘name of the patient’ when processing documents, and as a result can manage that data far quicker than other tools.

The significance of this is that businesses deploying CMR can fully automate operational processes while also significantly reduce human error, enabling workers to focus on the more strategic and creative tasks. This makes CMR a tangible, all-in-one solution that can rival any other RPA tool and revolutionise the AI and automation industry.

This leads us to the question, why is CMR important for your business?

Automation is in a time of transition

Despite RPA’s promise to revolutionise the way businesses operate for decades to come, reservations around its effectiveness to businesses still linger.

The adoption of automation to improve business operations is becoming a standard across the spectrum of business globally. One aspect of automation is RPA, a market which is expected to go from USD 1.7 billion last year to USD 2.3 billion this year, with this figure predicted to more than double by the year 2022.

This isn’t a surprise as RPA has been touted as one of the most exciting, new emerging technologies that looks set to revolutionise business processes and the overall operational efficiencies of organisations across various sectors. However, vendors using these technologies have fallen behind on their ability to deliver end-to-end business processing. Despite RPA’s promise to revolutionise the way businesses operate for decades to come, reservations around its effectiveness to businesses still linger.

There are various points of confusion when it comes to RPA. Even though companies know the benefits of the technology, they don’t always know when to start RPA deployment or what data they should analyse within their organisations to yield a maximum return of investment (ROI). It’s often the latter issue that has become a barrier in adopting RPA. Although spending on AI systems in the Asia-Pacific region is expected to hit USD 5.5 billion this year, RPA can only be as good as the data it runs on.

Unstructured data: A major obstacle

organisations only benefit from analysing structured data in the form of standardised code text or categorised fixed field text.

Among the advantages that companies can gain by integrating RPA into their current processes, there are limitations to the capabilities of the technology. It is important to note that RPA systems are unable to process crucial unstructured data which includes images, web pages, legal documents, medical records and mobile content. Instead, organisations only benefit from analysing structured data in the form of standardised code text or categorised fixed field text. These specific use cases for data generally aren’t difficult to analyse or process, and only make up a small percentage of overall business data. 

We believe that 80% of all business data will remain unstructured in the years to come. Hence, businesses will never be able to use AI and automation to their full potential should they adopt an RPA system to process unstructured data. Therefore, it is essential for companies intending to implement AI and automation technologies to first identify the type of data that they are working with and address the problem of unstructured data. You wouldn’t want to mix up documents from several different departments in one filing cabinet, and it’s the same with data. Companies and organisations need to understand that automation will only work properly if their digital filing cabinets (data) are in order.

Hitting the home run with AI adoption

Unstructured data is difficult to analyse and process without the appropriate set of tools as they consist of information that lacks any pre-defined data model or properties.

Unstructured data is difficult to analyse and process without the appropriate set of tools as they consist of information that lacks any pre-defined data model or properties. Together with the fact that 80% of enterprise data will soon be made up of unstructured data, companies must begin familiarising themselves with the AI solutions that are available and identify the right type of solutions that will help them reach their objectives. Currently, businesses are utilising RPA tools that can only analyse the structured form of data which can be described as only the tip of the iceberg.

RPA tools that are based on bots have gained much traction over the last few years, but yet still fall short of the level of performance expected for business-wide use. Businesses are in the market for solutions that can effectively eliminate repetitive administrative processes so that they can focus more on their core business activity. Many who are looking to automate aspects of their billing logistics, tax, analytics, accounting or human resources function, will need to rely on an AI tool that automates the whole process, not just specific tasks. Unfortunately, RPA’s capabilities are limited to only tasks. It is, therefore, not true automation, and it cannot be scaled to meet the growth of these businesses. 

To reap the full benefits of AI, businesses must invest in the right AI tools that are not just capable of automating some functions within the business operations, but the entire process. RPA (which is based on neural science) or one that is not CMR enhanced is not going to be enough when it comes to organising and making the best use of your unstructured data. Instead, businesses should focus their resources on finding solutions that are based on fractal science with a clear CMR element. Only then will your company truly excel in the automation era.

Featured image from Futurama


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I am an ardent advocate of artificial intelligence. My passion for Artificial Intelligence fuels my entrepreneurial spirit and helps my drive for total global automation adoption. As a tech-entrepreneur, I founded AntWorks™ with only one goal in mind – to become a rock-steady voice for innovative thinking and enterprise automation. I am 1 part unconventional, 1 part scuba-lover, and all parts for automation.
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