Skip to main content

Jumping the last hurdle in business adoption of AI: unstructured data

Robotic process automation (RPA) is a market which is expected to go from $1.7bn last year to $2.3bn this year, with this figure predicted to more than double by the year 2022. Despite these huge growth projections, more than 40 per cent of RPA projects fail to deliver expectations due to factors such as implementation time/cost and its benefits to analytics. One possible alternative to traditional RPA that can not only match its functionality but also offer more is Cognitive Machine Reading (CMR), an AI-based automation capability.

CMR is able to read structured, unstructured, image and inferred data, whether it is printed or handwritten text, or simply image data such as notary stamps or signature verifications. It can also undertake natural language modelling, processing and generation. 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 means that businesses deploying CMR can fully automate operational processes whilst also significantly reducing 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.

The question is, why is CMR important to your business?

Automation industry on the cusp of going mainstream

Unless you’ve been living under a rock for the past 10 years, you should know that automation is on the brink of going mainstream across the spectrum of business. One aspect of automation is robotic process automation (RPA), a market which is expected to go from $1.7bn last year to $2.3bn this year, with this figure predicted to more than double by the year 2022.

The trouble is that vendors using these technologies have fallen behind on their ability to deliver end-to-end business processing. This isn't a surprise as RPA has been touted as one of the most exciting, new emerging technologies tipped to revolutionise business processes and the overall operational efficiencies of organisations across various sectors. Despite the technology’s promise to revolutionise the way businesses operate for decades to come, reservations still remain around how effective it may be for the business proposition.

There are various points of confusion when it comes to RPA, as even though companies know the benefits of the technology, they don’t always know when to start RPA deployment or what data they should be analysing within their organisations to yield maximum ROI. It's often the latter issue that has become somewhat of a barrier in adopting RPA. Although the AI and automation industry could be worth $5tn by 2025, it can only be as good as the data it runs on.

Unstructured data: ignoring the obvious problem

Companies from a variety of different sectors that currently use RPA systems are unable to process crucial unstructured data which includes images, web pages, legal documents, medical records, mobile content etc. 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.

In fact, we believe that 80 per cent of all business data will remain completely unstructured in the near future, and if the number is even close to that figure, businesses will never be able to use AI and automation to their full potential. It is difficult to say whether AI and machine learning will be utilised to their full capability, but if not, it is hard to say whether these emerging technologies will even last beyond the next couple of years as there will naturally be a decline in demand.

This is why it is so important for companies intending to implement AI and automation technologies to address the problem of unstructured data. You wouldn’t mix-up documents from several different departments in one filing cabinet and it’s the same with data. Companies and organisations need to get to grips with the idea that automation will only work properly if their digital filing cabinets (data) is in order first.

AI adoption: getting it right the first time

Most unstructured data is information that lacks any pre-defined data-model or properties and is usually difficult to analyse and process. As unstructured data makes up 80 per cent of enterprise data, companies need to be able to identify the right type of AI tools to deal with it. 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 a lot of traction over the last few years, but yet still fall short of the level performance expected for business purposes.  Businesses are in the market for RPA tools that can effectively eliminate repetitive administrative processes so that they can focus more on their core business activity. In order to do so, they need to find the right AI tools that not only automate some functions within the business operations, but the entire process.

Many companies 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 not only ensures that certain tasks are automated but the whole process, unfortunately RPA’s capabilities are limited to just that. It is, therefore, not true automation and it cannot be scaled to meet the growth of these businesses. This is where fractal science-based automation can give a company the edge to meet this need.

If you want to reap the proper benefits of AI for your business, RPA which is based on neural science, or that is not CMR enhanced, is not going to be sufficient when it comes to organising and making the best use of your unstructured data. Instead, focus your energies on finding solutions that are based on fractal science, and that have a clear CMR element. Only then will your company truly excel in the automation era.

Asheesh Mehra, Co-Founder & CEO, AntWorks (opens in new tab)

Asheesh Mehra is the Co-Founder & CEO, AntWorks, an artificial intelligence (AI) and intelligent automation company.