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BPM, RPA, and the intelligent automation future

Nolan Greene, Connectez-vous pour vous abonner au blog

If you work in the operations, process improvement, or robotic process automation (RPA) spaces, chances are you’ve seen the phrase “intelligent automation” pop up many times. While this term seems like it encompasses a lot, the lack of clarity around it can render it meaningless. Is intelligent automation a rebranding of RPA? Is it RPA enhanced by AI? Or, is it simply a catch-all term for process automation software that aims to make technologies like business process management (BPM) sound new again?

Intelligent automation is real. It starts with technologies we know and love, like BPM and RPA, and brings them together in a unified toolkit so that an enterprise’s automation needs can be addressed by the most relevant tool. More importantly, intelligent automation orchestrates complex processes across a seemingly disparate application and systems landscape. Processes are not only automated in the most optimal way, but directly tracked to the outcome being sought.

True intelligent automation starts with a low-code platform that allows business users to collaborate with IT to rapidly develop and deliver business process automation (BPA) applications. Intelligent automation encompasses RPA, email bots, chat bots, artificial intelligence (AI), natural language processing (NLP), and process intelligence on a unified platform with case management and orchestration at its core.

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RPA is key

RPA is an important part of real intelligent automation, but to the surprise of many, it’s not the intelligent part! When embarking upon digital transformation, many enterprise IT leaders find that legacy applications create bottlenecks in common enterprise processes and often cannot be digitized quickly through the creation of relevant APIs. RPA provides a great, and temporary, way to more rapidly capitalize on those applications by weaving them into broader enterprise processes that involve both human and AI elements. When used effectively for an appropriate use case, RPA is shown to save enterprises considerable time and generate quick ROI. But RPA needs orchestration to make sure that the work performed by robots is handed off to the right person or system. The BPM-based orchestration capabilities of intelligent automation do exactly this and, working with low-code development and case management, allow for RPA bots to be modularly replaced with APIs as priorities and resources dictate.

What about AI?

The strategic use of AI is a necessary feature of intelligent automation. However, some have fallen in love with the idea of possessing AI technologies without necessarily having defined a clear, outcome-driven use case. Others are buying into the hype that by adding AI to RPA bots, you end up with “intelligent bots.” Let’s unpack these things. AI has some fantastic applications within intelligent automation, such as using AI to make better decisions and work with NLP to route work from inbound chats and emails. But AI will not make your bots smarter, and if you focus on this, you will get little of the critical re-usability and learning capabilities from this siloed AI. It is best to think of AI and RPA as complementary tools within the intelligent automation toolkit, with RPA performing rote work, such as data entry, and AI scaling across all forms for digital transformation (email, conversational [human and chatbots], documents, interactions, IOT, cross-selling, decisioning, and rules, for example.)

Knowing what to automate

Intelligent automation provides all the tools needed for today’s business process automation, but only if those tools are applied correctly. Consider that EY has reported up to 50% of RPA projects fail. There are many factors contributing to the failure of RPA and other automation projects. Two that we commonly see out in the field are poor selection of processes to be automated and poor selection of automation techniques.

This is where process intelligence comes in. Process intelligence refers to various tools used to understand the steps and cycles of business process work. This can include process mining, desktop analytics, and other related tools. Organizations embarking upon intelligent automation must evaluate today’s processes to identify variations and inefficient tasks embedded within these processes so that they know the right intelligent automation tools to use. An ideal process intelligence framework will not point to just one technology (like RPA, for example) to solve process automation needs, but will identify areas of optimization that can be addressed across the intelligent automation suite. Moreover, process intelligence should also be able to point to areas where humans could be working more effectively.

BPM is still the foundation of the future

Regardless of where you get started with intelligent automation, you need orchestration to tie together all of the bots, systems, and people to ensure you get to the right outcome. Orchestration requires case management that is built upon a solid BPM foundation. Wait, isn’t BPM an old technology? How can it be part of the future, you ask? While BPM has been around for a few years (long enough for Pega to have been named a leader in 12 consecutive Gartner iBPMS Magic Quadrants!), it continues to evolve to power the intelligent automation future, supporting bots of all kinds alongside AI. By enabling the unification and orchestration of intelligent automation tools, BPM will carry the world into the intelligent automation future.

Learn more on how to succeed with RPA:

Balises

Défi: Automatisation des processus numériques Défi: Excellence opérationnelle Groupe de produits: Automatisation robotique Groupe de produits: Pega Platform Groupe de produits: Plateforme Industry: Inter-secteur Thème: Automatisation robotique Thème: BPM Thème: Transformation numérique

À propos de l'auteur

Nolan Greene is an industry analyst and marketer focused on digital transformation.

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