TechWyns is focused on research and development in the areas of Robotics and AutoML.
A Digital Workforce for Every Enterprise? Actualisation of Hyperautomation by Combining Robotic Process Automation (RPA), Artificial Intelligence (AI) and Machine Learning (ML).
What is the difference between Robotic Process Automation (RPA) and Artificial Intelligence (AI)? This question in itself isn’t too difficult to answer, since there are clear distinguishing characteristics between RPA, which mimics repetitive processes
that human workers carry out and AI, which mimics and predicts decisions
that human workers would make or usually make. That is the difference in a nutshell, notwithstanding the plethora of available industry jargon that varies in the scope and coverage of meaning. So let me break down a few of the key terminologies you may have to hand already, but will definitely experience in the early stages of your organisation’s Intelligent Automation Powered Digital Transformation Journey.
What is Robotic Process Automation?
Going deeper into the original question: RPA, or Robotic Process Automation
, is when a software robot (Digital Worker) carries out tasks using business rules to execute a combination of processes and activities that were formally carried out by human workers. This may or may not include processes where some of the steps require human inputs or interventions. So, RPA is when a robot/Digital Worker replaces a human for activities and tasks that are repetitive or process driven. This means the Digital Worker can repeat the same task again and again with little to no human intervention.
Artificial Intelligence & Machine Learning
AI, or Artificial Intelligence
, is a combination of many things, including, but not limited to; cognitive automation, machine learning (ML), reasoning, computer vision, natural language processing and intentional algorithm mutation. AI produces insights and analytics at or above human capability. In laymens’ terms this means that the greater the volume of data that an AI algorithm has to hand, the better it gets at making decisions that mimic, or improve human decision making. Hence, AI is when a computer/Digital Worker simulates a human’s intelligence and completes tasks just as a human would… or should!
As Artificial Intelligence is applied to more of the same type of process, it produces data about the decision, analysis or recommendation that the AI workflow made. This data can then be analysed, tested and benchmarked to improve decision making speed and accuracy. Basically, it means that this data is used by machine to learn, i.e. Machine Learning (ML). So ML is the subsection of AI which fulfils the “learning” aspect of AI, whereas the rest of AI concerns the “thinking” aspect. Once these two are built on top of RPA, the scope for Hyperautomation becomes huge.
80% of Enterprise Data is Unstructured – What Does This Mean for Automation?
Gartner stated that 80% of Enterprise Data is unstructured. This means that rules-based RPA can be applied to tasks associated with 20% of Enterprise Data, but with the remaining 80%, there is thinking involved, so a Digital Worker is not able to execute tasks that are not rules-based. So, if we take a standard and widely-used business process that commonly has unstructured data components, such as invoicing (many invoices will be in PDF format, or will be scanned versions of paper documents, so need (i) “thinking” and “vision” to identify and extract data that is relevant to executing invoicing processes), as well as (ii) knowledge of how and when to route payments to a specific supplier, i.e. “learning”. On the surface, it may seem as though this process cannot be handed over to a Digital Worker, as thinking, “vision” and “learning” are human traits. However, this is not the case when RPA, AI and ML are stitched together by a team of qualified, experienced and certified Intelligent Automation and Artificial Intelligence professionals, such as the specialists at Venturiq.
In order to execute end to end automation of Invoice Processing within a client organisation, our team first identifies those aspects of an organisation’s Invoicing protocols that are
rule-based. For example, the bulk of emailed invoices may come into one mailbox… This is rule-based and can be looked after by a Digital Worker that acts as a mail monitor. The Digital Worker can be trained to recognise who a Supplier is by looking at the email ID of the sender. If this email ID is saved in the organisation’s Accounting, CRM or ERP system, the Digital Worker can complete standardised follow-ups for processes, such as saving the Invoices in dedicated folders and placing the processing of the Invoice in a task queue.
It may be that your organisation is dealing with a large volume of invoices, the processing of which is a tedious and mundane task. If this is the case, it is highly likely that there will be large volumes of unstructured and semi-structured data that needs to be extracted, in order to process invoices. Venturiq can combine the power of AI, ML and RPA to help you to eliminate invoice processing from your list of tasks, allowing your team members to be more strategic in what they do and, therefore, more productive and competitive in an environment where your competitors will be looking at how to digitise. However, invoices do not form all 80% of unstructured Enterprise Data. This means that there are many other processes that we can explore as candidates for combined AI, ML and RPA, in order to help you in your drive towards hyperautomation.
- Automated Machine Learning (AutoML) is important because it allows data scientists to save time and resources, delivering business value faster and more efficiently
- AutoML is not likely to remove the need for a "human in the loop" for industry-specific knowledge and translating the business problem into a machine learning problem
- Some important research topics in the area are feature engineering, model transparency, and addressing bias
- There are several commercial and open-source AutoML solutions available now for automating different parts of the machine learning process
- Some limitations of AutoML are the amount of computational resources required and the needs of domain-specific applications
In recent years, machine learning
has been very successful in solving a wide range of problems.
In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars.