13/11/2019 Three tips to embrace these new technologies and accelerate your digital transformation.
Robotic Process Automation (RPA) has already facilitated much of our everyday life both at work and outside of it, that we might not always realise it. Why? Because RPA makes use of software robots which mimic human-like behaviour to automate mundane processes, thus allowing businesses and individuals to focus their time and resources on more value adding activities.
RPA has been mainly used to process structured data in high volumes and high-intensity processes. The reason is that these bots are developed to always work with the same type of inputs and within a pre-defined fixed set of rules to perform activities quickly and repeatably.
However, about 80% of enterprise data is unstructured (documents, notes, chats, images, logs, etc.) and this type of data is much more difficult to codify. Thus, the market is seizing this opportunity and gradually moving towards “cognitive” or “intelligent” automation. We are now combining RPA (“hands”) with other advanced technologies (“brain”) in order to process unstructured data or analyse big data on-the-go.
This new type of automation holds a lot of promise. According to Reuters, the Global Intelligent Process Automation Market is expected to grow at a compound annual growth rate (CAGR) of about 40%, reaching an estimated USD 8 billion by the end of 2023(1).
This poses one important question: how can businesses make the most of this “augmented” automation and reap the benefits?
2 key technologies that will make RPA “intelligent”
To correctly address the rise of cognitive automation, it is important to understand that it would not be possible without the combination of several other key Artificial Intelligence (AI) technologies.
1. Machine learning
Processing unstructured data is difficult for a standard RPA. These more traditional bots are developed to work with data following the same pattern, based on a pre-defined template. If there are several patterns, then the templates followed by the bot must be configured for each and every single one individually.
However, if there is a need for tens or hundreds of these templates, the required effort is often too high. That is where machine learning (ML) comes into the picture. ML can identify the required data across various input structures by gradually learning where to find these data. By providing the bot with enough data, it can achieve very high success rates.
A very common example is the invoice booking process. Companies usually receive invoices in a multitude of different formats (or templates). Although the set of data to be retrieved from these documents is the same, it is located in many different places. ML technology identifies and reads these data, and organises them in a consistent structure, which it gives to the bot so that it is able to process them in a standardised way.
2. Natural language processing
Another source of inputs for automated processing is language. Various notes, written requests or call transcripts, which are recorded as unstructured text, are a rich source of information and inputs.
Natural language processing (NLP) technology is able to extract the key information from the text and hand it over to the bot for further processing. This allows the RPA to be used on a large number of use cases. Imagine bots being able to process e-mail requests to update customer data, record insurance claims or read through CVs or contracts to classify and sort them!
How will cognitive automation impact digital transformation?
Digital transformation is of strategic importance to all board rooms. “Intelligent RPA” is a very important element in this case, as it can be applied in many ways and provides a myriad of opportunities for doing things “better, cheaper and quicker”. The difference between the leaders and laggards in the market will lie in the ability of organisations to embrace these new kinds of technologies. Knowing where the benefits and risks are and how to leverage those begins by understanding the impacts.
For instance, we have read the headlines and the negative sentiment they convey: “Intelligent bots will destroy our jobs”. Indeed and of course, intelligent bots will impact the work that we do. For executives, management and workers in administrative and operational positions, things will change. But how exactly?
Digital transformation is something that needs attention and careful management, but at the same time is not a theoretical exercise. Organisations need to move quickly, be prepared to invest and learn, and create a digital and change culture in their organisation to support this. More than ever, this will come down to visionary leadership at the top of organisations.
We see 3 key principles for adopting intelligent robots and reaping the benefits:
1. Apply a “think big – start small’ strategy:
Once the decision to embrace digitisation has been made, having a roadmap in place that articulates a vision on how intelligent RPA impacts strategic planning and business goals is key. This involves not only assessing the opportunities which intelligent RPA provides, but also the challenges in getting there. Analysing external factors such as what employees and clients expect and what the competition is doing should also be considered. Without a clear vision, the chances of executing poorly implemented digitisation plans are increased. Thinking big, but starting small is one way to improve chances of success.
2. Introduce a “Stop talking & start acting” environment:
Small projects with limited budget and impact, and short timelines work better than ones involving many workshops, meetings, working sessions, etc. Applying and using new technologies such as intelligent RPA can help create the required momentum to move forward with digitisation plans. But itis easy to get caught up in the bigger technology picture instead of focusing on which elements of new technology and digitisation can work best for your company. Setting up a group of internal and external digitisation ambassadors to work on small pilot projects with limited budget and impact, against short timelines can often provide better insights and improves the chances of moving successful projects to full implementation. This also helps to avoid an “all talk, no action”backlash caused when projects do not move forward.
3. Manage people & change:
The challenge is not in the technology; it lies in managing the people, the business and the process changes in the organisation to make the technology truly deliver the desired value. It is important, therefore, that organisations create awareness and understanding of what the benefits of digitisation are to everyone, alongside the everyday impact of such changes, especially in the case of Intelligent RPA which can be perceived negatively at first glance. It requires vision from the top to the bottom to create a change culture that supports all aspects of the company’s digital transformation.
This paper is part of the #InnovatorsAtMazars series. If you would like to learn more about this campaign, please visit our website.
(1) Reuters, 20 May 2019, Intelligent Process Automation (IPA) Market 2019 Global Trends, Size, Industry Segment, Regional Study and Growth by Forecast to 2023 https://www.reuters.com/brandfeatures/venture-capital/article?id=111140
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Marc Engel Partner | Digital Transformation & IT Consulting - Rotterdam, Pays-Bas