The future
In an era of unprecedented global disruption, organizations are becoming increasingly aware of the significant levels of change they and their industries are being exposed to. This digitally punctuated evolution has altered the fabric of society and commerce to create heightened levels of uncertainty but unparalleled opportunities for reinvention. It’s a revolution of industries in the truest sense.
The ability for technology to support this level of change is not new as it was the driving force for each of the previous three industrial revolutions. However, the pace, scale, and impact of digital is truly extraordinary. Significantly, technology that is driven by consumer adoption, such as the proliferation of mobile devices and online shopping, has created societal and economic shifts requiring organizations to further adopt technologies as the response to change.
For organizations not born digital, adapting to the new competitive environments by digitizing existing products and capabilities provides only a limited lifespan. Instead, the imperative is to morph into becoming
digital. Achieving this upward digital mobility requires embracing technology-supported transformation initiatives that can move the organization to go beyond the traditional bounds of efficiency and cost, to become a driver of growth and innovation. However, for many organizations, the efforts have proven immensely challenging. Many digital transformation efforts have failed to achieve successful outcomes, with protracted costs weighing heavy on organizational balance sheets and employee morale.
A new approach is required, one that employs technologies that can incrementally change alongside the organization. They are technologies that can deliver incremental benefits throughout the journey, rather than providing the continual proverbial dreams of “jam tomorrow.”
Automation is just one of the many new technologies that have
emerged with the potential to energize new paths to digital transformation. The explosive growth of RPA has in large part been due to its ability to shift traditional notions of technology that were established during the information age. Solution accessibility has provided business functions with the potential to drive their own automation initiatives, minimizing the role of the IT function. These automation projects can be delivered in weeks as opposed to months and provide the immediate realization of highly tangible benefits.
However, behind this extraordinary growth, there is a more nuanced reality. Although RPA, the most mature of automation technologies, can be deployed rapidly, many organizations have predominantly failed to scale their initiatives beyond their automation incubators. The majority of organizations have been unable to align their automation capabilities to the wider transformation agenda of seeking out new value. Instead, they’ve created islands of automation limited to the replication of existing work practices.
This is because, like most technologies, performance in a vacuum is easy. However, an enterprise-class automation capability has to be tightly intertwined with the organization. This requires the adoption of significant change-management activity, organizational design, and operational management, as well as the development of key technical competencies.
The change mandate needs to engage senior leadership to actively support the initiative and provide direct linkage to the wider business strategy. A culture change, either driven by the transformation initiative or through the automation capability, needs to transition the organization and its employees from becoming spectators to advocates of automation. This is activated through management transparency and clarity of communication on why and how automation is an investment in people.
The Automation Culture Change Model discussed in chapter 5 provides us with a high-level model. However, given the nature of cultural change management, this will require significant adaptations. Cultural change is rarely a copy and paste activity. It requires a deep understanding of the automation journey the
organization needs to embrace, its change DNA, and the interlocking sets of goals, roles, values, and attitudes.
The organizational design of the automation capability needs to balance serving the wider enterprise with an internal urge for adopting new models of agility that can drive innovation. While each organization will have different experiences of adopting agile working practices, the flexibility of automation technology provides an ideal opportunity to experiment with new, innovative approaches to organizational design that can improve levels of productivity and employee engagement.
The optimum design for the capability will evolve alongside the changing automation mandate. Degrees of capability centralization, working practices, and third-party partnership models provide, to a greater or lesser degree, the opportunity to create new design configurations. Alternatively, fertilizing and upskilling existing organizational designs, such as an operational excellence unit or business improvement capability, may provide the organization with greater familiarity and established structures to drive the automation agenda.
Finally, the automation factory should be equipped with a set of defined skills, processes, and routines that equip the capability to perform, manage, and constantly optimize itself. The balance here, yet again, is on the selective inheritance of those operational processes that can add value to the overall capability while minimizing over-bureaucratic systems that may stifle innovation.
Focusing on the development of key processes, such as benefits management and support of digital workers, will help to establish early credibility. A pragmatic approach of adding processes and controls should be endorsed through data-driven capability performance information.
Change management activity, organizational design adaptions, and the introduction of new operational processes need to constantly be assessed and modified against the evolving maturity of the capability. There may be a resurgence in demand for automation projects from a particular business function given a new imperative to achieve ambitious functional goals. The automation capability
may be transitioning towards greater process digitalization activity through end-to-end process improvement and automation. The inclusion of new intelligent automation technological capabilities such as chatbots may be on the roadmap, requiring upskilling of existing resources.
The progression from simply implementing automation technology to establishing an automated enterprise requires the application of an increasingly complex set of crafted changes in order to constantly fine-tune its performance and alignment towards key outcomes. Navigating through this increasingly intricate organizational landscape mandates the support of an automation strategy. This should provide the evolving capability with a clear roadmap and a supporting set of priorities that are aligned to supporting the digital transformation or overarching business strategy.
The Intelligent Automation Maturity Framework allows both the existing state and future state of the capability to be mapped across key dimensions, in turn driving the critical decision-making and scenario-setting required to establish clarity of purpose. Following the successful adoption of an automation incubator capability, organizations have typically gone on to develop an enterprise-scale automation capability in order to deliver a greater quantity of automation projects.
Avant-garde thinking leans towards the provision of many more options for an automation capability to mature, of which only one is scaled automation. The improved timelines within which RPA maturity can be established now, and the arrival of intelligent automation, provide organizations with the opportunity to fast-track initiatives in pursuit of higher-value automation projects—for example, the application of cognitive technologies such as intelligent document processing, AI, and analytics to drive improvements across existing supply chains.
Applying the Intelligent Automation Maturity Framework to an organization considering its transition options is critical. For example, a business may be considering extending its current automation capability, with a target of delivering 60 automation
projects over a 12-month period. Alternatively, it may want to align the automation capability with the existing digital strategic initiative, focused on supporting the end-to-end transformation of only a handful of key business processes that are forecast to generate in excess of $10m worth of new sales revenue over the next three years.
Each automation strategy is very different, requiring different approaches in terms of change management, organizational design, and operational models, and creating a unique set of risks and supporting mitigation options. Alternatively, the automation strategy may propose the adoption of a mixed product portfolio that delivers a consistent stream of low- to medium-complexity automation projects alongside enabling key strategic business initiatives.
The clarity provided by the automation strategy is critical in creating a longer-term view of the capability and supports in its evolution towards becoming a key technology enabler for the enterprise.
The potential for intelligent automation to transform the business processes that crisscross, merge, and intertwine our organizations is significant; the impacts are hidden but seismic. Redesigning the enterprise business process landscape has the potential to challenge the very roots of modern corporate design, with the ability to eliminate current notions of the front and back office. Digital has always been about integrating, not creating divisions.
The increasingly complex nature of organizations and their business processes, superimposed with unforgiving disruptive forces, has created an unprecedented need for change. While RPA elevated the profile of automation and the impact technology can have on work, materially it has only provided surface-level change. Retrospectively, however, it appears as though RPA was just a primer for intelligent automation. Technologically, intelligent automation is the game-changer; the range of tools it provides that can be applied across broad sweeps of the end-to-end business process are unparalleled. Never before in history has there been such an opportunity to drive large-scale productivity shifts and the
creation of new value through integrated digital–human solutions.
The experiences and lessons extracted from existing RPA programs will be crucial. The challenges in knowing how and when to apply cognitive automation will require building on an existing set of skills, merging traditional disciplines of business analysis and process improvement with a deep understanding of the applications of the various technologies. The emerging discipline of intelligent process design will need to leverage new tools and methodologies to help create the future of work.
To understand existing processes, new techniques will need to merge traditional approaches of user workshop sessions with data from AI-based process mining technologies. These technologies can be extended to simulate the impact of automation prior to creating detailed implementation roadmaps.
One of the key lessons learned from existing RPA initiatives over the last few years has been the impact of taking a technology-centric approach and negating the importance of change management. These risks are magnified considerably by intelligent automation for a number of reasons.
Firstly, intelligent automation’s growing repertoire of technologies varies considerably from custom AI solutions that depend on vast swathes of clean, historic data, to intelligent OCR that could be operational in a matter of days. Secondly, where RPA simply removed the human from mundane tasks, intelligent automation design reconstitutes the employee through digital–human divisions of labor integrated into the automated solution.
Finally, RPA’s sweet spot of routine, simple processes for automation has been vastly expanded with intelligent automation’s appetite for application across the automation continuum, from digitized work replication to work improvement, optimization, and redesign through digitalization and transformation.
The successful adoption of intelligent automation across industry sectors to generate benefits, drive value, and support the business strategy is critically in need of new techniques and approaches that recognize these spectrums of complexity that exist across process, people, and technology.
These new approaches can be based on growing increasingly complex configurations of intelligent automation clusters—discrete combinations of people, process, and technology change. Automation plasticity provides us with a conceptual model that can be extended and applied to de-risk the delivery of intelligent automation to the enterprise. It provides libraries of intelligent automation clusters, tried and tested components of capability that can be built over time with increasingly complex configurations. These can be reused in automation solutions to augment the power and precision of machines with the experience, judgment, and creativity of employees. These intelligent automation clusters can be used to create semi-permanent capabilities formed by creating unique combinations of technology, people, and work. These combinations are highly malleable and can be applied to solve an increasing array of complex business challenges.
The automation software technology sector is experiencing remarkable levels of growth. The technical simplicity of RPA and an ever-expanding catalog of use cases across industry sectors has helped generate a rapid, widespread awareness of the tool. However, the challenges in adopting automation at the enterprise scale, or its application to solve more complex business processes, point to the need for RPA as a technology to develop further.
Instead, product vendors have looked to grow their offerings either across the project lifecycle, such as adding in tools to help manage the identification of new opportunities or expanding their RPA platforms towards intelligent automation. Current core RPA technology has been relatively stagnant over the last few years, with little sight of a true “RPA 2.0.”
The key challenge with RPA in its current form is its susceptibility to failure conditions, which can stop the automation from working. Digital workers are still highly brittle. The implications are considerable to an organization if there is a large digital workforce, with each automation typically requiring a detailed understanding of upstream dependencies to help identify an issue and the management of downstream consequences to manage the business impact of work that is not being done. Ramping up human support
resources in the control room in parallel to digital worker deployments can quickly become unsustainable, even at a ratio of 20:1 (20 digital workers for each human controller). This impacts the financial operating costs but, more importantly, impacts the business through halted or underperforming work.
Next-generation RPA tools need to support the development of self-healing digital workers. No longer in the realms of sci-fi, this functionality can leverage the principles adopted from AI and unsupervised machine learning to create automations that use error messaging and patterns of data to identify root failure causes and automatically generate new code to maintain the output of the digital worker. Extended technological capabilities may include the ability for multiple digital workers across the same business processes to communicate.
Analogous to a human workforce, multiple digital workers can demonstrate collaboration, sharing critical data across the business process to maximize throughput efficiencies and productivity. Further, new automation solutions can leverage principles adopted within smart manufacturing and the IoT (Internet of Things) to connect multiple devices and systems. Across the enterprise landscape, the IT systems and growing digital labor workforce can be integrated with data flows to create a new breed of automated enterprise. This can use hyper-interconnectivity to transform the organization to becoming almost self-aware, a combination of work activities and systems that can coordinate large swathes of work with new levels of digital fluidity and the agility to respond to increasingly disruptive business environments.
Beyond RPA, the richness of intelligent automation technologies will demand a new impetus between IT and the business to develop stronger alliances. Critically, with digital as the aligning force, IT will be required to level up to define and own new approaches of integrated intelligent automation systems architectures.
Skills within IT will be core in the creation of a new set of standards and approaches to platforms and tools that support the trends towards greater business autonomy. Innovative design approaches will be required that merge the new digital cloud with
the legacy systems of old, creating adaptable technology stacks that align to the data strategy and the transformation strategy to incrementally move towards supporting greater agility and resilience.
The increasing levels of awareness of RPA and its benefits has paved the way for existing product vendors to use this growing recognition as a catalyst for intelligent automation, supporting a strategy of organic and acquisition-led additions to extend the capabilities of their automation platforms. Importantly, however, the intelligent automation tech explosion over the last few years has a serious risk of confusing and disillusioning customers.
The range of complementary and sometimes competing technologies infused with marketing-hyped “automation-washing” (vendors claiming their offerings exist within the automation space, when at best they are minimal) has created a tech solutions landscape that is proving increasingly challenging for customers to navigate.
Alliances with reputable consultancies and third-party organizations with expertise in automation solutions will become more valuable. Genuine partnership approaches beyond short-term tactical implementations and towards the development of long-term sustained relationships will become increasingly important to customers looking to navigate the ecosystem and develop their competencies.
The automation strategy should support a phased approach to the addition of intelligent automation technologies and the adoption of a structured pilot innovation process that will help organizations select the right tools based on their technical feasibility, risk profile, and potential business value. Following independent proofs of value, the automation plasticity model can be used to create intelligent automation clusters—increasingly complex, reusable combinations of technology, processes, and people that can be merged to create new capabilities that can adapt with the organization.
At its core, the nascent intelligent automation sector does offer an exciting paradigm shift for many organizations and a path to transformation and digital reinvention. However, in an era where
technology has been the primary force for disruption, organizations need to go beyond viewing these tools with such awe and wonder. Instead, each potential addition to the intelligent automation portfolio needs to be considered with regard to its ability to act in concert with existing investments as a business-value multiplier, not to mention the ease with which the technology can be embedded within the existing automation existing capability to support wider strategic objectives.
The expanding use of software automation and AI over the last decade has opened the future of work’s Pandora’s box. The topic has increasingly attracted debate amongst industry leaders, academia, and governments on the impact of changes in work through automation and the social and economic impacts this may have. These discussions are, as yet, far from sharing common ground. The highly emotive nature of work based on our deep-seated relationship to it as humans and varying levels of understanding of these technologies has led to a spectrum of viewpoints. Extremes across both sides range from The Terminator
myths of AI intent on world domination to human liberation and the arrival of the four-hour working week and the promise of vast swathes of leisure time.
Creating an automation capability within the organization obliges leadership to maintain an active understanding of how the future of work may impact its employees, its organization, and its wider role in society. Recognizing that these are complex issues, interlacing society, humanity, economics, and commerce, they coalesce across several key, interdependent themes; namely, the capability of technology to substitute work, and the resultant impact this has on the volume and nature of that work.
As discussed at the outset, the relationship between technology and humans has been one of symbiosis, with mutual benefits for both players. Automation in its current form has been focused on the removal of routine, low-value work that has emerged due to inefficiencies with which current IT systems transact and manipulate data. The benefits of this level of automation have been glaringly obvious, allowing organizations to utilize the technology to own the high-volume, monotonous, repetitive work activities previously
done by employees, in turn freeing them up to focus on solving more complex, non-routine activities within the organization; a win–win.
Rather than with RPA, the future of work discussions relate to the emergence of AI, a key component of intelligent automation. With AI, its ability to imitate human “intelligence” has been correlated to its gradual encroachment towards tasks that demonstrate greater cognitive functions. These go beyond routine, low-value activities to what humans consider skilled activities, such as medical disease diagnosis using imaging, driving a car, or reviewing the damage on a submitted image of a car to assess the insurance claim. This gradual encroachment will, may, or will not—depending on your viewpoint—mean changes to the amount of work left to do, and importantly the complexity of the work that remains. Future of work discussions are based on these relatively simple extrapolations of technology and its growing capability to do more work.
While intelligent automation will progressively have the technological capabilities to displace specific activities, its ability to make entire jobs redundant is as yet unproven, with organizations currently continuing to use the additional capacity automation provides to improve productivity and optimize their workforces. In the digital age, like all of the industrial revolutions before it, there is the opportunity for this new level of disruption to create new jobs and activities that we cannot, as yet, comprehend.
In our current educational and professional livelihoods, it can take a lifetime for an individual to develop the necessary skills and knowledge to be considered an expert in his or her field, such as a heart surgeon or teacher. AI provides an opportunity to rapidly shrink these timespans, disperse knowledge, and critically integrate previously siloed disciplines to start tackling some of the most complex issues faced by humanity, from climate change to disease prevention, initiating a new era of discovery.
For the organization, these link into a number of key considerations based on an evolving automation capability. Firstly, the medium- and long-term skills strategy for the organization. The automation strategy will need to help define the types of activities within current roles that can be replaced with technology. The
aligned HR strategy would then need to look at how this may impact hiring new talent, what skills and competencies will be of greater value in the future to the organization, and how it can actively upskill the workforce.
For example, the introduction of intelligent automation to the front office through assisted agents that support the adviser to resolve customer queries may reduce overall call-handling times. The organization could then decide to reinvest these time savings in further staff training on upselling sales techniques, supporting the generation of top-line growth.
Employee performance has always been a key precursor to organizational performance, and intelligent automation provides the opportunity to rethink and apply new approaches to improve the levels of employee performance; for example, leveraging automation to adapt to changing work-time patterns and working locations, which is expected to stay relevant beyond the global pandemic crisis.
Intelligent automation will increasingly augment employees with technology. With greater volumes of work completed by machines, algorithms will determine allocation, placing humans in new roles of validating outputs, dealing with expectations, and coaching both humans and systems. In these emerging roles, AUX (Automation User Experience) design will be increasingly important for new automation technologies to support better employee interfacing and systems interactivity.
As digital disruption continues to shift our currently held views of work, it raises new, challenging questions. For example, in only a few years what seemed like perfectly reasonable activities such as systems data entry have been demoted to the category of “low-value work,” while, in fact, many of our roles consist of combinations of low-value, routine work and more complex activities. With technology continuing to displace routine work, what then is our capacity as humans to engage in sustained periods of “deep work,” the antithesis to low-value work? And how could this impact the health and wellbeing of employees in the long-term?
These questions take us back again to our symbiotic relationship with technology. Ardipithecus ramidus
is one of the oldest identified species with lineage to modern humans. It existed about 4.4 million years ago, and looked part-human and part-ape. Had we asked “Ardi” to relax and not spend so much effort on deep-work activities such as standing upright, would we be who we are now, to ask the question? The topic is thought-provoking, requiring expertise in a range of diverse fields such as psychology, neurology, and change management, to name but a few. What is clear is that the ability of an organization’s employees to consistently re-skill will become an essential competitive advantage. This resonates yet again with one of the key aims of business transformation, to demonstrate increasing levels of adaptability.
As we enter a period of unprecedented disruption, the demand for organizations to change has become crucial. It will determine whether companies cease to exist given the challenges, or thrive in the opportunities presented. Digital is transitioning from proliferation into deep adoption, defining new levels of customer experiences, competitive threats, and wider social changes. In parallel, the global pandemic has incapacitated global economies, driving immense pressures on supplier networks, costs, and employee wellbeing. Never have the hopes placed in technology been so great, yet been so opportune.
As organizations emerge from the pandemic from states of disorientation to action, many are accelerating their digital transformations as a means to amplify their response to the disruptions now faced. This requires them to urgently establish new levels of continuity, agility, and innovation—key traits that will help in navigating the tumultuous period that lies ahead. Yet beyond the spotlight of digital transformation, within the shadows lurk spiraling costs, timelines, and the specter of failed, zero-sum initiatives. A new approach is required that both recognizes the urgency for change yet sympathizes with the differing capabilities and stages of digital mobility each organization is at.
Of the many technologies that have emerged over the last few years, automation provides the capability to both drive immediate tactical relief and enable the organization to achieve the wider
change initiative. Automation and its transition to intelligent automation provide the next generation of technological tools. Integrated with the organization’s wider capabilities, these tools can be systematically applied to an increasingly complex set of business challenges, progressively and consciously transitioning towards digital enablement.
The gradual conversion to intelligent automation extends the reach of technology further, penetrating deeper into the organization to go beyond digital business process replication to optimize and redesign work. intelligent automation will not only free up teams from repetitive, routine activities but provide the organization with the capacity to invest in its real intelligence: its people.