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The augmented workforce

The augmented workforce

Catia Davim and Chris Foster outline four key areas for financial institutions to consider when pursuing intelligent automation for greater value and productivity.

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Workforce composition is changing rapidly. Beyond human factors such as demographics and the rise of the gig economy, digital automation is transforming organisations' workflows and processes – and the nature of work itself.

Automation technologies

Automation technologies can be broadly grouped into three categories:

  1. Robotic Process Automation (RPA): The simplest form of automation, RPA technology automates repetitive rule-based processes. This technology cannot learn, adapt or make decisions; an RPA bot simply applies a consistent set of rules to a process to deliver quick and efficient outcomes. Many manual administrative processes can be streamlined in this way.
  2. Machine learning: At the next level is machine learning, where a computer is able to use large volumes of data to understand and predict the desired course of action, with performance improving over time. Chat-bots are a good example of machine learning being used today in the financial sector. These bots use technologies such as natural language processing (NLP) to communicate in real time with human customers, use data from past interactions to understand the nature of the customer's query, and provide the desired information or response.
  3. Cognitive augmentation: Cognitive augmentation is the closest we currently have to true artificial intelligence. Cognitive computers, such as IBM's Watson, are able to handle unstructured data and provide answers to complex queries, enabling them to complete tasks that could once only be performed by humans.

Though these categories denote levels of complexity, it is better not to think of automation technologies as stages through which an organisation must progress. Instead, each technology is best suited to particular types of work and may be used in concert to achieve larger goals.

Intelligent automation (IA) is a term increasingly applied to this concept of combining multiple automation technologies to solve complex business issues. For example, organisations are looking to use RPA with machine learning, NPL and digital character recognition to help address regulatory compliance challenges and process high-volume, low-complexity insurance claims.

Jobs changing, not necessarily replaced

Doomsday predictions about automation's impacts to the workforce have been in the headlines for the past few years, with total job loss a significant concern. For example, in 2014 Gartner research director Peter Sondergaard stated, “Gartner predicts one in three jobs will be converted to software, robots and smart machines by 20251.” Yet while there are pockets of extensive automation within the industry, generous estimates cannot put the average rate of automation above 5% – significantly behind the rate required to achieve replacement of a full third of the workforce in 7 years.

While initial predictions were for automation to result in wholesale replacement of human workers, that is not what we are seeing play out in immediate timeframes. Instead, these technologies are being used to enhance or support the work of human employees. Automation capabilities can help remove the burden of repetitive administrative work or provide information to help individuals make better decisions, allowing employees to focus on value-added tasks. For example, many of the basic contact center transactions can be completed by RPA, while a machine learning process can provide a human employee with suggested responses to customer complaints in real time. This enables the employee to focus on solving more complex customer issues, while reducing the risk of error.

There is no question that the number of employees required to perform certain functions will decline as a result of digital automation. However, automation will also create and increase demand in other job areas. One obvious area of growth is in deploying and training these systems to work in unique business contexts. Individuals will also be required to manage teams of AI, perform quality assurance, and address errors or complex issues as they arise.

Automation is not the only way

The technology exists to achieve the type of workforce transformation predicted in years past – yet other barriers stand between organisations and the vision of a wholly automated office environment. Human behaviour, as well as the speed at which organisations can invest and adapt to significant changes in both technology and process, form roadblocks. Many financial institutions are also still making the shift from a strategic plan with a 1-3 year timeframe to a broader strategic plan with the longer time horizon needed to engage in meaningful technological innovation and transformation.

While automation has been viewed as a way to increase efficiency and reduce costs, especially in the back office, it is not the only route to achieve these goals. Many financial institutions have instead been prioritising areas such as standardising and centralising processes, addressing the challenges of an aging workforce and looking to broader questions of digital innovation.

Creating an automated future

Digital automation in its many forms will increasingly contribute to workplace productivity. The current question is not whether automation will affect the workforce, but how, to what degree, and at what point will we reach the equilibrium between human and robotic workers.

Financial institutions pursuing the use of intelligent automation are encouraged to consider four key areas during the transition:

  1. Lead with the vision. Too often organisations are starting with the technology and looking to apply that technology to the business. But intelligent automation is not about replacing people with technology, or finding quick wins through a technology solution. Instead, organisations need to take the end-to-end view, creating a vision for a wholly new customer experience or other process, and then leveraging new automation technologies to achieve that vision.
  2. Consider the human component. When developing an IA strategy, businesses need to consider not only how automation will shape workforce composition and capabilities in years to come, but also what change is required to meet the long-term organizational objectives. As a starting point, transform hypotheses about future needs into workforce scenarios that can guide decision-making. Key areas to consider include the balance between automation technologies and people, the organisational structure to support that balance, workforce hiring and training needs, and compliance considerations.
  3. Transparency is critical. Change management must be a priority as your people need to understand both the reason for transformation and the path forward. Employees will understandably think of an automation strategy as a ticking time bomb for their job prospects, resulting in inaccurate assumptions and resistance. Do not feed into their fear. Instead, be transparent about the purpose for the changes, their potential effects and how your employees will contribute to the transformation. This is especially important as you move into more complex automation strategies, as machines need to learn by watching humans complete tasks and make decisions.
  4. Invest in your people. The way that organisations manage the transition to a hybrid automated workforce will be critical to success. In the short term, organisations need to provide the skills and training necessary for employees to effectively tap into and work with these new technologies as part of their daily jobs. Over the longer term, the question then becomes how to upskill employees into new or evolving roles, or provide support to move on from the organisation.

As the industry transitions toward greater automation, it is important to remember that this is not an 'all or nothing' proposition: there is no need to choose between an all-machine or all-human workforce. Instead, organisations need to seek ways to integrate automated operations with legacy people processes so as to achieve the greatest value and productivity from both types of resources.

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