RPA in the Public vs Private Sector

It is fair to say that Robotic Process Automation (RPA) is nothing new. Having emerged back in the early 2000s, the market for RPA continues to grow exponentially, with Forrester predicting that the market will grow to $22 Billion by 2025. The Covid-19 crisis has put a staggering amount of pressure on organisations to drive down costs and is one of the main reasons for the increased interest in RPA. In fact, the Intelligent Automation Network argue that much of the market growth is due to organisations expanding on their existing RPA portfolios as opposed to investing in the tool for the first time, highlighting just how prolific the technology has become.

Whilst it is increasingly common for organisations to be utilising RPA in some way, every organisation has its own motivations, with Gartner recognising efficiency and cost reduction as being two of the core drivers. Some organisations will focus solely on process automation, whilst others view RPA as being just one element of a wider process optimisation strategy.

Having worked in RPA across various organisations, it is interesting to observe the differences in their Digital Transformation journeys and to reflect on how their position in either the public or private sector can have an influence on the approach taken.

Is it all about Cost Reduction and Efficiency Savings?

The short answer is that it depends on the organisation, its vision, and its strategic objectives.

As we know, the pandemic has forced organisations across the public and private sectors to minimise costs. The combination of relatively inexpensive RPA licenses and the ability to automate processes quickly, means organisations can often expect to see a return on their investment within months of deploying the technology.

Having worked in organisations, public and private, where the RPA function was very much in its infancy, this has enabled me to draw comparisons in how priorities have shifted over time. Automation opportunities were initially being prioritised based on what would deliver the highest Full-Time Equivalent (FTE) saving and in the quickest time, although these priorities soon diverged.

Take law enforcement, where efficiency needs to be balanced against factors such as risk mitigation and quality improvement. An example of this is an automation that enabled a team dealing with at-risk, vulnerable members of the public, to quickly distinguish the level of risk associated with each reported incident. Whilst this automation led to minimal time savings, it enabled relevant bodies to respond more quickly to individuals most at risk, ultimately improving the quality of operations.

This holistic approach was also reflected in the conversations that took place post-RPA implementation . Whilst public bodies are under pressure from ever-tightening budgets, it is understood that the FTE attributed to a manual process will not necessarily lead to an equivalent cost saving once automated. Certainly, in public services, teams are so under-resourced and over-stretched that staff are often using overtime to complete processes that sit outside of their job roles. So, automating these processes can reduce the need for this overtime and free these employees to complete their assigned job roles, which can ultimately improve employee satisfaction.

In contrast, my experience of RPA in private firms is that the focus very much remained on driving efficiencies, even once the initial investment in RPA was offset. Unlike in the public sector , there was the expectation that the agreed FTE saving from an automation was either deducted from the department’s budget or the equivalent number of employees redeployed elsewhere.

However, what did evolve was the approach to implementing RPA. Whilst it was initially tactical, identifying and automating siloed processes across the organisation, this transformed into a more strategic vision of process optimisation, focusing on end-to-end processes.

Which approach is best - Automation or Optimisation?

Like automation, process optimisation can enable organisations to minimise waste and increase efficiencies. So, what is the difference?

Process optimisation involves making improvements to a process to make it as streamlined and effective as possible, before introducing automation and other Business Process Management technologies. This can be done by:

• Identifying and removing obsolete tasks
• Removing bottlenecks
• Re-arranging the order that tasks are completed
• Identifying under-utilised resources

The approach adopted by an organisation will depend on both the Digital Transformation strategy and its stage on this journey. Organisations often focus on automation initially to benefit from efficiency savings and may then evolve into optimisation as the RPA function matures, a shift which Forbes argues is essential to achieve the best results from the Digital strategy.

However, the mantra ‘improve first, automate second’, isn’t necessarily a viable option for all organisations. Let’s again consider law enforcement, where optimisation over automation may simply be biting off more than they can chew. With teams being so under-resourced, there is a willingness to automate processes in their current, as-is state, to relieve some of the pressure on staff as quickly as possible.

In public organisations where the RPA pipeline is rife and teams in dire need of capacity, the speed at which processes can be automated in their current state may outweigh the benefits from initially improving the process. However, HBR argue that the increased time and cost spent improving a process prior to automation could actually lead to a greater ROI in the longer-term.

So, what’s next?

The future of RPA is unique to each organisation and its Digital Transformation strategy. For some, the focus will remain on expanding the RPA portfolio, whilst others are already looking ahead to Intelligent Automation (IA), combining RPA with Machine Learning and AI capabilities.

Equally, the growth in no/low-code platforms cannot be ignored. According to Forbes, the current demand for technical resources outweighs the limited number of skilled developers available. Whilst some organisations are offering higher salaries to attract these resources, it is no surprise that public bodies simply cannot compete in this way. Instead, with no/low-code platforms empowering non-technical staff to quickly develop and deploy their own applications at low-cost, it is likely a trend that is here to stay.