What is hyperautomation? And how will it affect the life-science industry?
Ranked by Gartner as the number one strategic technology trend of 2020, hyperautomation is the end-to-end automation achieved by leveraging multiple advanced technologies such as artificial intelligence, machine learning, RPA, and data mining.
In its report, Top 10 Strategic Technology Trends for 2020, Gartner calls hyperautomation “the combination of multiple machine learning, packaged software and automation tools to deliver work.” In delivering this work, hyperautomation “deals with the application of advanced technologies including AI and machine learning to increasingly automate processes and augment humans.”
In simpler terms, hyperautomation represents the effective use of integrated technologies to complement people’s capabilities and enable them to complete processes faster and better.
This process typically combines process automation (standard software, like RPA) with artificial intelligence capabilities such as machine learning (ML), natural language processing (NLP), intelligent optical character recognition (OCR), and AI computer vision to amplify the effectiveness of work automation. Process mining, the application of data-mining algorithms to help analyse business processes, can also form part of hyperautomation.
Increasingly, hyperautomation looks set to help forward-thinking STEM businesses achieve optimal operational performance without neglecting employees.
In this article, we’ll explore how this novel technology can help life-science organisations successfully navigate the digital era via agility, people centricity and actionable insights.
Hyperautomation trends and use cases
In recent years, life-science businesses have begun to apply techniques such as robotic process automation (RPA) software to automate repetitive, task-based processes that once involved humans. RPA technology comes into its own when highly skilled teams are involved in running mundane, time-consuming, and low-value but important tasks on a repeated basis.
To underscore the difference between automation and hyperautomation, let’s take the example of creating an invoice — an important but mundane task within the accounts receivable process.
Through automation with RPA, scheduled invoices can be issued automatically at a specified time and date. In contrast, hyperautomation goes further and complements RPA with artificial intelligence — enabling business users to automate the entire end-to-end accounts receivable process, which typically consists of hundreds of tasks.
In this sense, hyperautomation represents the next phase in the evolution of automation models. Unlike the standalone application of technologies like RPA, hyperautomation’s collaborative intelligence and interoperability loops humans back into the process. With technology and humans working in tandem, employees can train automation tools to facilitate more AI-driven decision-making.
By automating key business processes and enabling better augmentation between integrated technologies and humans, hyperautomated solutions are helping organisations move towards a proactive, iterative and agile model — delivering a continuous automation journey with superior business outcomes. To be successful, these integrated tools should cover the stages of automation (from least automated to most automated):
To recap, hyperautomation looks to expand on automation. With hyperautomation, everything that can be automated will be automated. Hyperautomation focuses on moving from RPA and task-based automation to adding much more sophisticated AI-based automation, as well as building “digital twins” for a company.
Building the digital twin of an organisation
Although not the primary goal, one of the unique benefits of hyperautomation is the creation of a digital twin of an organisation (DTO). According to Gartner analysts Marc Kerremans and Joanne Kopcho, a DTO is a “dynamic software model of any organisation that relies on operational and/or other data.”
In other words, this digital twin is a simulated virtual replica of an organisation’s physical assets, products, processes, and services. The DTO also merges live data from its physical counterpart (the company itself) via an interactive visual interface — a level of visualisation that enables organisations to test new business opportunities and plan future scenarios.
The concept of digital twins has been around since the turn of the 21st century, when companies programmed replicas on their servers and input the necessary data to update the design or environment specifications. However, rendering digital duplicates in this way proved a slow and cumbersome process, especially given that information was rarely received in real-time.
By creating a DTO through hyperautomation, life-science organisations leave themselves better prepared to accurately forecast and flexibly respond to market uncertainty. Indeed, by 2021, Gartner predicts that half of large industrial companies will use digital twins — resulting in those firms seeing an estimated 10% rise in effectiveness.
More benefits of hyperautomation
- Workforce enablement
By performing time-consuming, tedious tasks in a quick and accurate manner, hyperautomation gives employees the time to focus on more meaningful, impactful work.
- Employee upskilling
Hyperautomation democratises the automation process, not only freeing up the IT department but also giving any business user the opportunity to become an automation leader within their organisation.
- Systems integration
Interoperable software speeds up processes and allows for more effective use of data throughout the organisation.
- Improved performance
Robust data sets and more accurate, automated processes improves the consistency and efficiency of a company’s people, products and services.
- Return on investment
Through advanced analytics, hyperautomation gives companies the means to measure and demonstrate the exact ROI of automation and its impact on key business outcomes.
By augmenting the work of humans with automated technology, hyperautomation promotes sustainable business development while also cutting costs and generating more revenue.
Hyperautomation in the life-science industry
By 2024, Gartner predicts organisations will cut operational costs by 30% through adopting hyperautomation technologies and redesigning their operational processes. However, though certain players in the life-science sector have been quick to integrate hyperautomation into business processes, the industry at large has been slow on the uptake.
Given the heavily regulated nature of the sector — within which organisations must report and process all products, adverse events, and complaints — hyperautomation could revolutionise complaints-management. And with process optimisation, regulatory compliance, patient service and supply chain improvements all key priorities in industries such as pharmaceuticals, hyperautomation represents a great opportunity for life-science companies to deliver realisable business value and drive measurable cost reduction.
But what is hindering the digital scale-up efforts of many organisations? For one, businesses frequently start implementing RPA programmes as an experiment and then get mired in the pilot stage. IT issues, the complexity of processes, and unrealistic expectations also keep them from fully integrating their digital workforce.
A number of difficult-to-shift digitisation roadblocks in the industry have hindered the full-scale move towards hyperautomation. Owing to poor standardisation and poor governance of process automation initiatives, the approach is often to deploy technology within teams without considering the wider business impact. If technology is adopted in silos, it is less likely to be scaled up unless stakeholder support is able to convince leaders of its benefit to the business as a whole.
In healthcare, widespread digital transformation has also been held back by the continued presence of high-cost and inefficient legacy systems. Though many life-science CIOs recognise the game-changing potential of hyperautomation, the dearth of existing automated processes and the associated costs of automating has caused them to err on the side of caution.
An agile approach is needed. Before hyperautomation can be considered, businesses first need to focus on device connectivity and find solutions to data collection challenges. Once these first steps are in place, the next step is to utilise AI and analytics to reveal business insights hidden within the data, as well as to begin automating basic tasks with technologies like RPA.
When businesses have these capabilities in place and have built a robust digital-first culture, only then can hyperautomation be properly implemented.
The Industrial Revolution 4.0 is causing unprecedented disruption to traditional business, prompting leaders to fundamentally alter their business models. In this new world of digitisation, pervasive connectivity, the Internet of Things (IoT) and AI, being able to effectively leverage the latest tech is the difference between success and failure. With digital transformation on the strategic agenda, firms are reinventing themselves as technology companies to stay ahead of the curve and retain a competitive advantage.
Moreover, software is no longer harnessed to primarily record and store data. Thanks to technologies like advanced analytics, interoperable software programs are now being deployed as systems of action, insight and engagement — enabling businesses to optimise processes and maximise employee capability. As the 2020s unfold, the winners will be those organisations that use blended technologies like hyperautomation to become fully autonomous digital enterprises.
Though hyperautomation is set to be a game-changer in the life-science industry, first, a word of warning. In this era of artificial intelligence and the rampant application of technologies, the most important consideration for businesses adopting hyperautomation is knowing what question they are trying to answer. What, exactly, are your expectations for the technology?
While hyperautomation initiatives can improve productivity and reduce waste, they will not create new processes. To be truly effective, such initiatives need to engage stakeholders across the business to determine the best opportunities for automation and who will be responsible for governance. Ultimately, they need to be targeted towards tangible business outcomes that address business needs, organisational readiness, and, most importantly, have clear strategic direction.
Discover more about the future of work
For a more comprehensive overview of the changing world of STEM and what your business can do to thrive in these uncertain times, our report on the Future of Work in the Life Sciences Industry provides actionable advice on how to futureproof your organisation.
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