The year 2020 marked a significant rise in automation adoption and propelled automation right to the top of CIO agendas across enterprise digital transformation plans. We witnessed an unparalleled enterprise demand for automation in the past year, coupled with multiple high-profile acquisitions, and a continued inflow of VC investments. While 2020 was a great year for Hyper Intelligent Automation (or HIA), we expect the momentum to not only continue, but also to amplify manifold as we enter 2021.
We, at Zinnov, have been at the heart of this HIA-led transformation, and we have been talking to both the demand side and the supply side ecosystems. One avenue where we did this is through our recently concluded, industry-first, one of its kind Zinnov CXO Automation Conclave to bring together the most prominent luminaries of the automation space from across the globe. The collaborative platform was leveraged to drive powerful, trailblazing perspectives as the CXOs engaged in purposeful discussions to unearth the biggest trends for 2021. The unique “Marvel’s Avengers”-themed event probed the attendees on the superpower technologies that can help enterprises unleash the true potential of automation and propel the automation space further ahead in 2021.
Here’s a comprehensive view on the top 10 trends that are poised to bring about a paradigm shift in the global automation landscape.
Enterprises displayed an obsessive focus towards adopting Automation for business resiliency during the pandemic, and they will continue to do so in the next year to future-proof or pandemic-proof their businesses. As per a recent survey, almost 50% of enterprises indicate that they intend to increase their spend on automation/RPA in 2021. In addition, enterprises are looking to engage with future-proofed platform vendors who have an architecture that allows new platform services and third-party services to be easily integrated, and the enterprises can extend the platform on their own.
Interestingly, RPA as a standalone technology is tactical and expected to phase out in the next 12 months. At the same time, we see a rapid gravitation towards a convergence of multiple complementary technology areas such as Process Mining, Task Mining, Document Processing/OCR, AI/ML, BPMS, Low Code No Code, and even API-fication in tandem with RPA, in order to drive maximum business value from automation. This is clearly the era of Intense Automation or Hyper Intelligent Automation (HIA).
Enterprises are now intently focused on optimizing processes and are open to leveraging the best in breed technologies across all Hyper Intelligent Automation elements. Additionally, as automation initiatives shift from department-level initiatives to enterprise-level objectives, we will see increased strategic focus on leveraging a combination of technologies to fuel automation.
“Enterprises need to understand automation broadly and holistically. Hyper Intelligent Automation is a set of capabilities that augment RPA – technologies such as Computer Vision, AI, and ML will unlock the true power of automation, and hence should be a priority for enterprises.”– Bobby Patrick, Chief Marketing Officer, UiPath
“It’s no longer about RPA, but Hyper Intelligent Automation. We are looking at providing end-to-end automation or a sequence of technologies offering digital transformation of processes, focusing on delivering meaningful and impactful business outcomes.”– Kaushik Bhaskar, Chief Technology Officer, Genpact
Co-opetition has remained a prominent theme within the automation space, and this will become even more prominent as we move ahead in 2021. Platforms seem to have realized that given the nature and complexity of use cases, no single vendor can win this automation race on their own. While there are platforms that are more attuned to manage low to medium complexity use cases, there are others that can effectively take care of the highly complex ones.
Another trend that has increasingly become par for the course is that enterprises do not wish to lock themselves in with a single vendor and want the flexibility of working with multiple vendors. All this has led to a greater reliance on a multi-platform strategy from the enterprises, with multiple vendors aligned to different use cases, based on their capabilities. As per Zinnov analysis, 30%+ of Fortune 250 enterprises are already working with two or more automation vendors, catering to different use cases. This has led to all the major platforms positioning themselves as having orchestration capabilities in order to provide a single platform layer that can help manage multiple platforms – or become the platform-of-platforms.
Enterprise demand for different automation platforms is a function of multiple factors such as the use case complexity and user persona. Platforms in turn need to adapt to the changing demand patterns and create targeted persona-based strategies.
This trend mirrors the broader trend that we see in the Cloud space, where, over the years as the industry matured, we witnessed cloud orchestration platforms that allowed an enterprise to manage multiple cloud vendors such as Azure, AWS, Google Cloud, IBM Cloud, etc., using a single common orchestration layer.
“We have enterprise customers with mature automation initiatives, who at times are working with more than 10 different automation platforms! They assess the complexity of the process, identify the use case, and then align the capabilities required. Hence, the advent of multi-platform environments is imminent, and it is becoming absolutely critical to have orchestration capabilities.”– Eric Johnson, CEO, Nintex
There has been a massive influx of Low Code Application Platforms (LCAP) within the automation space, that came in with the promise of simplifying app development and making it 10X faster to build new apps – targeting both professional developers and citizen developers. We saw Low Code platforms such as Appian and Microsoft (Power Apps) acquire RPA capabilities (with Jidoka and Softomotive acquisitions respectively); in fact, even No Code platforms such as Nintex acquired Enablesoft (Foxtrot RPA) to bolster their automation capabilities. Tech majors such as Google also went ahead and acquired No Code application start-up AppSheet earlier last year. Clearly, there is immense play for Low Code, No Code technology to simplify new application development and enable workflow automation scenarios (while completely eliminating the need for deploying RPA or bots). If in case RPA is required, this can be done by partnerships or native RPA capabilities downstream.
Hence, Low Code, No Code technology has emerged as a serious threat to RPA bots, and has a play – both working in tandem with RPA, and even as a standalone solution for workflow automations. Even in terms of market size, Low Code, No Code represents a bigger opportunity with more than USD 10 Bn in market today, which is expected to exceed USD 45 Bn by 2025 (compared to USD 4.7 Bn for HIA, growing to USD 42 Bn+ by 2025)! This is the reason why we see immense focus by traditional RPA incumbents such as UiPath come up with their native Low Code solutions like UiPath Apps, which was announced recently.
Typically, enterprises target the low hanging fruits of automation to kick-start their journeys and show quick wins. These are generally identified using manual techniques such as crowdsourcing, internal workshops, hackathons, etc. We estimate that a majority of these enterprises will run out of these low hanging fruits quickly and look towards more scientific techniques for process discovery such as process mining and task mining. 2021 will be the year of Process Discovery, and enterprises will realize that this is an effective way to tie their automation deployments to outcomes and ROIs, and thus scale their automation practices. Currently, Process Discovery is estimated at a USD 200 Mn market size and is expected to grow at 70%+ CAGR to exceed a massive USD 3 Bn in the next five years! (This is, in fact, among the fastest-growing categories within the HIA framework.)
Enterprises view data as the new DNA for digital transformation. Hence, it is important that techniques such as Process Mining and Task Mining can unlock a new layer of analysis and insights by giving the real picture of where data is present across the process flow, in which format, and in turn, point towards the right set of technologies required to optimize it. Consequently, we see automation platforms bolstering their process discovery and intelligence capabilities – either natively or through acquisitions. However, Process Discovery alone would not be sufficient, and we will increasingly see analytics and insights-led Process Intelligence also becoming important. Process Intelligence includes What-if scenarios for identifying bottlenecks within the process, delayed activities, demand spikes, and potential outcomes.
“Process Discovery is emerging as the most critical tool for automation. Our enterprise customers who are facing scaling related challenges, are able to build a healthy pipeline of use cases using task mining.”– Harel Tayeb, CEO, Kryon
“Expectation setting is extremely important with enterprises. It’s about understanding the process – anything that you do around the process will help augment its intelligence. A technology like Process Discovery is well poised to become a core component within Automation.”– Pankaj Chowdhry, CEO & Founder, FortressIQ
The first wave of growth within the RPA and automation space was led by large enterprises. Zinnov analysis reveals that more than 75% of the Fortune 250 enterprises have already started their automation journeys and are investing in automation in a meaningful way. Consequently, all the focus and energy of the leading platforms were hitherto focused here. However, with large enterprises already getting saturated, and with Cloud gaining prominence in the wake of COVID-19, the space is ripe for Small and Medium Businesses (or SMBs) to take the baton and drive the next wave of growth within automation.
The leading platform players bolstered their Cloud/SaaS play over the last year and are now turning their attention to these SMBs which are more conducive to Cloud-based deployments. These SMBs are also open to transition their mission-critical workloads onto Cloud, while large enterprises are still limited in their cloud adoption due to security and confidentiality concerns. Platforms’ aggressive focus on SMBs through cloud deployments will intensify over the next 12 months, and we estimate SMBs to account for ~20% of the total platform spend over the next 12-18 months.
“Services partners hold the key to unlocking the true potential of Cloud. In fact, smaller and mid-sized Service Providers are aggressively targeting SMBs by pushing innovative Cloud solutions specifically tailored to the needs of SMBs.”– Chris Huff, Chief Strategy Officer, Kofax
Similar to Moore’s Law, we are postulating an Automation Law where we are witnessing automation power and capabilities provided by platforms almost doubling every year. And AI is at the heart of this transformation.
While this started with RPA and simple automation targeting simple, low complexity use cases, we gradually saw AI making its way to tackle more complex use cases. We saw AI, powered by Optical Character Recognition (OCR), Natural Language Processing (NLP), and Computer Vision (CV), help understand and process unstructured documents, which provided better capabilities to automate complex use cases. Post that, deep AI algorithms also found their way into discovery techniques such as process mining and task mining, in a bid to scientifically identify the next best use cases for automation. There were, of course, AI-powered chatbots already in play, that could use conversational AI to understand human queries and automatically complete actions by triggering bots downstream. It’s evident that AI has transformed the entire automation value chain end to end, and enhanced capabilities of bots over the years.
And this will continue with AI transcending the boundaries of what is possible and making the deployments even more seamless. One such scenario is the focus on AI-powered self-healing bots. While writing RPA scripts or configuring RPA bots has been relatively simple, enterprises have struggled with maintaining these bots. Given that RPA operates on the UI layer, this makes it brittle because with every change in the screen layout, the shift of a button, or change of an input field, the automation malfunctions. This leads to increased downtime and maintenance costs. This problem further amplified during the ongoing pandemic, where software has shortened upgrade cycles in a huge way. New-age platforms are trying to overcome this challenge of fixing bot failures with self-healing bots, without human intervention. These self-healing bots leverage AI to detect UI (or process) changes and then work with bot authoring tools to fix themselves. Machine Learning also updates the AI model to improve the detection of failed bots over time. Several leading vendors are currently exploring this use case seriously.
“The role of AI is becoming more real and explicit in 2021. For the last 5-10 years, AI has stayed within the labs, because it was a model (or a concept). But now, we will see its true potential unleashed. 2021 will be the year of true AI!”– Ankor Rai, Chief Digital Officer, EXL
Platforms showed an immense focus on taking the inorganic route to acquire new capabilities throughout the last year. The most noteworthy ones included Tech Giants making big bang acquisitions, with Microsoft setting the tone with its Softomotive acquisition, and then IBM following suit with its acquisition of WDG. In addition, we saw Low Code Application Platform Appian formally enter the automation space with its Jidoka RPA acquisition. Even the No Code Application Platform Nintex acquired K2 to bolster its BPM and workflow automation capabilities. Others included Hyland-Another Monday and Celonis-Integromat acquisitions, which were some of the most significant.
A majority of these acquisitions are focused on acquiring newer capabilities such as RPA capabilities, or bolstering their existing capabilities. The new year will see more such acquisitions with the goal of acquiring depth in the industry and specific use case coverage. For instance, there are tons of specialist platforms that focus on specific use cases such as AP Automation within F&A – such as AvidXchange, Yooz, Esker, Corcentric, etc., and these will be the prime targets for acquisition by the larger automation vendors in order to acquire deep capabilities.
“Consolidation is already in motion, and we expect to see a higher rate of consolidation in the automation market going ahead. Eventually, it will be a few major automation platforms with a wide set of capabilities sitting within enterprises.”– Eric Johnson, CEO, Nintex
2020 was clearly when the technology majors – especially Microsoft and IBM – made their automation strategy abundantly clear with extravagant acquisitions. And given their focus on AI/ML, NLP, OCR, etc. modern tech capabilities, they have emerged as leading contenders within the Hyper Intelligent Automation space. In addition, we also see others such as Google, ServiceNow, and Adobe already eyeing the automation space.
Tech majors are poised to emerge as serious challengers to the incumbent vendors in 2021. As a case in point, IBM made almost USD 44 Bn from its Global Business Services (GBS) unit in FY2019. Even if they could cross-sell automation within 5% of this portfolio (at a conservative estimate), their revenue numbers would be far greater than any of the major automation platform players today! This is the scale at which the tech majors could dent the automation market.
Enterprises’ automation has progressed from targeting tactical cost savings to more strategic outcomes such as Customer Experience and Employee Experience. While product and technology continue to be pivotal in defining the right set of capabilities and solutions required to solve enterprise challenges, ensuring higher experience for customers must become the cornerstone of an automation platform strategy. To achieve this, significant investments need to be made in relationship management and customer success which will transform the traditional sales strategies of platforms. For enhanced employee experience, platforms are building persona-based automation solutions that directly cater to the specific needs of the employees and focus on reducing complexity across processes.
“We live in an experience-led economy. We are investing more in relationship management and focusing heavily on customer experience which must be the key to winning the space now. Customers are looking for low-effort digital buying experience – where the ease of experience is super important, while the ease of product use is table stakes!”– Chris Huff, Chief Strategy Officer, Kofax
Mainframes are the backbones of the IT architecture in all major large enterprises. In fact, as per statistics, more than 80% of the global enterprise data continues to reside on mainframes, which further emphasizes their strategic role. At the same time, with rapid tech advancements and digital transformation, the one critical challenge till now has been – how to unlock the data housed in these mainframes and integrate it with newer applications and architectures? This is where RPA came in as a band-aid solution to help integrate with these legacy systems.
Traditionally, there was focus on leveraging mainframe emulators in order to access mainframes using APIs such as HLLAPIs (High Level Language APIs) or .NET to allow screen scraping by RPA vendors. However, this route is time-consuming, inefficient, erroneous, and not scalable. The modern approach to mainframe automation is to leverage Web Services such as REST or SOAP which bypasses the need for a terminal emulator and is lighter weight, maintainable, and scalable service. This is the area where we saw Rocket Software recently acquiring ConnectIQ (from ActiveOps) which provides scalable access to mainframe applications by turning the native mainframe protocol into web services. ConnectIQ in turn has partnerships with UiPath and Blue Prism to implement automation use cases downstream.
While automation/RPA has provided one way to access the data from mainframes, and also automate processes using a band-aid approach, it is becoming abundantly clear that the way ahead is through mainframe modernization. As per industry surveys, almost 50% of senior IT leaders have identified mainframe modernization as a top priority for their enterprises, with many already underway. In addition, there has been an increased focus on automation and AI on the mainframe. This trend of mainframe modernization and then exploring automation opportunities is expected to amplify over the next 12 months.
“Mainframe systems are still heavily prevalent across enterprises. While earlier, RPA emerged as a band-aid solution using web scraping, it wasn’t efficient or scalable. To unlock the true value of automation, multiple elements need to come together including web services using REST or SOAP APIs, RPA, AI, Low Code No Code etc.”– Mohandoss T., Zinnov Senior Advisor, and CEO CompassMet Consulting
Hyper Intelligent Automation (HIA) is clearly a fast-moving space, with innovations happening at break-neck speeds. While the outlined trends emerged predominantly through our numerous discussions with industry leaders and the who’s who of the automation world, they were further refined during the Zinnov CXO Automation Conclave. However, they will evolve further in the coming months. One thing is for certain – Automation is definitely the number one priority for enterprise CIOs today and is steadily becoming an important lever to help enterprises scale their digital transformation efforts.
We thank all the participating CXOs for making the first of its kind CXO Automation Conclave a huge success and in crystallizing these trends further.
To outline your enterprise’s automation agenda and how that can accelerate your digital transformation efforts, write to us at email@example.com.