The speed of digital transformation is continually accelerating due to advances in internet bandwidth, cloud computing and the explosion of internet enabled devices. This has been exacerbated in the post-pandemic era, where remote working and social distancing have made digital transformation critical for businesses. Many organisations are grappling to understand how to engage better with their own employees, with their clients and also with other businesses.
Intelligent Automation is emerging as one of the key technology drivers to achieve digital transformation in the post-pandemic era. Intelligent automation at its core brings people closer to digital technologies (AI, data sciences, APIs, digital interfaces and devices, bots) and the processes by which they can be adopted (low-code development, agile, design thinking). At SQA Consulting, we take a holistic view when defining the scope of intelligent automation to consider three key organisational dimensions – people, process and technology. You can read more about this in our recent publication, which explains how intelligent automation combines these three organisational dimensions and presents an opportunity to bring together both technological and process innovation initiatives within an organisation.
That said, we are seeing the Robotic Process Automation (RPA) market sliding down the hype curve into somewhat of a decline. Despite significant capital investment in this market over the recent years, the top RPA vendor shops are struggling to a profit. To survive, RPA vendors often position themselves as intelligent automation vendors. But this is far from the reality. Intelligent automation is not just RPA tools, it lies at the intersection of a number of technology markets; namely low-code development platforms, business process orchestration platforms, API integration and data analytics platforms, virtual assistant and artificial intelligence platforms. RPA constitutes less than 10% of this market.
Why is the RPA market in decline at the same time as IA on the rise? In this article we analyse the top 10 RPA challenges and the intelligent automation opportunities that this presents for adopters.
|Technological Differentiation/ Effectiveness||RPA is mainly focused on automating mundane, low-value repetitive tasks performed using desktop computers. RPA acts as an employee productivity tool, freeing up human resource to perform higher-value tasks. Automated scripts created using RPA tools are commonly termed as "bots", which operate predominantly via the user interfaces of enterprise and desktop applications in a non-intrusive way. This provides a useful automation utility to integrate data with legacy systems that would not necessarily expose APIs, or for automating smaller, simpler desktop tasks where integration via APIs may become complex and time-consuming.||Intelligent Automation is all about the convergence of people with process and technology, focusing not only on task automation but achieving end-to-end process automation by combining low-code development approaches with fuller artificial intelligence and data-focused technological capabilities and enabling this through the front door of new digital interfaces and experiences. Intelligent automation combines task, process, data, and experience-based automation initiatives by bringing together humans with apps, channels, interfaces, devices, intelligence, data, analytics, APIs, workflows, and bots into integrated low-code solutions to realise “to-be” processes and transformation outcomes.|
|Level of Innovation||Incremental Innovation - utilises UI interfaces to automate “as-is” repetitive tasks. UI automation has existed as a technological capability for more than 2 decades in the application testing space which is being reapplied in the business automation context predominantly by vendors who originally were building products in the test automation space but were late to that market.||Radical Innovation- characterised by digital transformation of business operations by “re-invention” both from a technological and process innovation standpoint. It implies the application of new disruptive technologies (AI, data sciences, APIs, digital interfaces, bots) as well as new production or delivery methods (low-code, agile, design thinking) creating fundamentally new experiences for both customers and employees.|
|Primary Focus Value Creation||Short term tactical outcomes. Quick return on investment for highly repetitive tasks especially in settings such as BPOs or shared service centers that operate standardised business processes often driven with legacy enterprise applications in departments such as finance, procurement, human resources, etc. to achieve outcomes such as cost savings in terms of hours back to business, cycle time reduction, quality improvements, and increased throughout.||Long-term transformation outcomes. Creating digital interfaces that enable multi-device, multi-channel, and multi-modal experiences for customers and employees underpinned by straight-through processing, workflow automation, data analytics, bots and artificial intelligence. Significantly greater outcomes in terms of profitability from new customer acquisition, increased customer retention, improved employee satisfaction, paperless office, significant reduction in operating costs.|
|Time to Market||4-8 weeks typically to develop and release an RPA bot into production use.||Re-engineering business process and end-to-end automation augmenting human workers with a combination of AI, bots, APIs, data analytics and digital apps may mean an implementation timeline somewhere between 8 weeks to 6 months depending upon complexity of the process.|
|Market Impact||RPA was fastest growing software market with 60%+ CAGR in 2019 but has started to plateau since 2020. Gartner predicts the RPA market size will reach $ 1.89 billion in 2021 expected to grow @19% but realistically this may end up somewhere between 10-15%. Overpromised technology hype and marketing spin created by RPA vendors to sell UI automation scripts as “robots” is now well understood by adopters, and with the key vendors facing significant challenge to sustain growth and to redefine their product offering.||Intelligent Automation lies at the intersection of a number of technology markets namely low-code development platforms, business process orchestration platforms, API integration and data analytics platforms, virtual assistant and artificial intelligence platforms, RPA and associated tools. Intelligent automation as such combines both process and technological innovation trends to offer a combined market value of around $20-25 billion in 2021.|
|Implementation Process||A problem-solving process characterised by process identification by applying techniques such as process mining, selection, refinement, automation development, execution and maintenance. Key success factors include change management, collaboration between business and IT.||A creative process characterised by ideation; research and experimentation carried out in a highly agile and iterative manner. Key success factors include adopting a culture of innovation, design thinking and fail fast approaches, ability to adapt and to invest in transformation outcomes for long term benefits.|
|Scalability||UI automation as a technological choice often proves to be brittle, resource intensive, and less reliable needing regular maintenance and troubleshooting due to the frequent changes that generally occur to the UI layer of the underlying applications. Almost 85% of RPA projects are small scale implementations of value less than $100k or under 50 bots. Most implementations complain about confusion around the scope of RPA being initially adopted as “Process Automation” technology to soon realise its suitability as a task automation capability instead.||With having to employ a much broader toolbox of technological capabilities (to include artificial intelligence, data analytics, machine learning, straight through data processing with API based connectors), and as well as production or delivery methods (low code development, DevOps, Agile, Design thinking), Intelligent Automation provides much more robust and scalable outcome-driven frameworks when re-engineering business processes, connecting the dots between people, process and technology.|
|Adoption Cost||Low for initial development and roll out but overtime a poorly managed RPA program can result in significant technical debt in having to maintain RPA bots impacted by constant UI changes and as well as spiraling operational cost in keeping the legacy systems running.||With most intelligent automation vendors now offering platform capabilities via cloud infrastructure (PaaS, SaaS), the barriers to intelligent automation adoption have significantly lowered over the recent few years, more so with low-code implementations to speed up delivery timelines, and Lego-style development that maximises component reuse using pre-built microservices architectures.|
|Skills Requirement||Many proprietary application UIs have become more dynamic and responsive over time, as such RPA scripts can quickly become complex for business users when undertaking UI-interface based automation development, due to the specific technical skills required needing good hands-on practical experience.||Intelligent automation vendors continue to invest significantly to provide low-code development platforms (as well as training and support) built around collaborative agile approach between Business and IT users, with visually aided authoring of end-to-end automation workflows to combine digital apps, devices, AI, API connectors, data analytics, and bots.|