When introducing any new technology initiative, digital transformation leaders need to build a strong business case that justifies the investment in technology, in terms of its expected commercial benefit. However, calculating ROI for intelligent automation projects – which combine process automation with artificial intelligence (AI) capabilities – is by no means straightforward.
This is largely due to the fact that intelligent automation is a digital strategy for transforming an entire portfolio of business processes rather than one easily definable software deployment. Additionally, the benefits of intelligent automation stretch far beyond time and cost savings, which are easy to quantify; to advantages such as increased agility and ease of use, which are tricky to measure.
Hopefully, the following points for consideration will provide some guidance.
Understand the technologies
Each technology adds value in a unique way – and these benefits are compounded when all three approaches are combined.
Robotic process automation:
RPA software bots can be configured to automate routine and repeatable tasks, mimicking the same actions that human users execute on existing systems, with minimal impact on existing IT assets. Once tasks are automated, they can run quickly, accurately, and consistently to reduce error and improve compliance.
Digital process automation:
DPA is the evolution of traditional business process management (BPM) suites. With low-code DPA platforms in place, organizations can custom-build new process management applications in a fraction of the time it would take using conventional methods. These solutions, which can include automated workflows and digital forms, work on any device, and connect with line-of-business systems, providing exceptional mobility and data accessibility.
AI is a broad term for all intelligent technologies and cognitive services, including machine learning (ML), machine vision, and natural language processing (NLP). AI capabilities can, for example, help to unlock new value from data and extend RPA bots’ process automation abilities. This way, businesses can solve a much wider range of problems while liberating employees from monotonous work.
Take a qualitative view
While a focus on cost savings is essential, this will not provide a holistic picture of intelligent automation ROI. Working together, DPA, RPA, and AI have the power to do much more than ramp up operational efficiency. To understand the true value of intelligent automation, it’s important to take the following outcomes into consideration – all of which play a key role in driving revenue growth.
Human resources are optimized and redeployed
RPA augmented with AI capabilities can liberate employees from a wide range of routine processes. Simultaneously, DPA keeps processes running seamlessly – ensuring human-led tasks are routed to the right user, at the right time, along with the relevant data and tools, even when they’re out in the field. The result? Teams are more efficient, and people can finish projects faster to take on more high-value work.
Process bottlenecks are reduced
With the DPA platform automatically managing the process, delays are quickly flagged and issues or exceptions to the rule are promptly escalated to the relevant human expert. This prevents process bottlenecks from disrupting business – and also provides insights to inform ongoing improvement.
The risk of human error is decreased
With the maximum number of tasks digitized and RPA bots handling data-intensive processes, the organization can substantially reduce its exposure to the risk of human error. Considering how costly mistakes can be – in terms of their impact on product and service quality, processing time, and compliance – this is a vital contribution to ROI (albeit one that’s difficult to quantify).
At the same time, every action taken by the digital or human workforce is tracked by the DPA platform, helping to improve visibility and governance and provide evidence of regulatory compliance.
Integration is effortless
For intelligent automation solutions to deliver real business value, the technology platforms that power these solutions need to integrate well with existing line-of-business systems. These include the content management, ERP, and CRM platforms that many organizations rely on. With RPA and DPA working together, developers can quickly build process applications that integrate with virtually any system. For systems that have APIs, DPA offers excellent connectivity; and for legacy systems that lack APIs, RPA can perform the task at the presentation layer without the need for integration points.
Ease of use
RPA and DPA technologies are engineered for ease of use, both inside and outside the IT department. If a low-code DPA platform is selected, this can provide tools for building new, custom applications, digital forms, and workflows with minimal coding. The result is shorter development cycles and less need for costly developer skills to manage the ever-growing demand for process applications.
The intelligent automation environment enables organizations to innovate and take advantage of new technologies while continuing to derive value from existing technology stacks and legacy systems. There is no need for an extensive rip and replace every time the company wants to modernize its operational approach or incorporate a new AI tool into a process.
The customer experience
All the factors mentioned above add up to an improved customer experience. The business case needs to explore what this could mean for the business. Value could be derived from reaching and winning more customers, personalizing customer journeys more effectively, and/or increasing loyalty through excellent service and product quality – to name but a few examples.
Consider a staged approach
Often, a staged approach to intelligent automation projects is best. Sometimes this involves focusing first on the “quick wins” (i.e. the rapid cost savings or productivity gains) that can be achieved through the application of automation and AI capabilities and then iterating from there. Once a solution has been developed to satisfy these issues, ROI can be measured and the solution can be scaled to meet more needs, with the added value of being able to incorporate user feedback.
There are many ways to manage a staged approach:
A minimal viable product (MVP) approach allows developers to create and deploy a pared-down version of an intelligent automation solution so that critical pain points are addressed while the solution’s value is monitored and analyzed in the business environment. Once in use, developers and process optimization experts can identify the next priority areas, with guidance from users.
Another option is to build a proof of concept (POC), which involves building a model of a small portion of a conceptual solution, to test assumptions and evaluate the feasibility of developing the entire solution.
Alternatively, developers can build a prototype – a working model of the product to test its usability and functionality, as well as identify bugs.
Establish a center of excellence
- A Center of Excellence (CoE) that capitalizes on both technological and process optimization expertise encourages a more holistic and structured approach to intelligent automation across the enterprise.
- The CoE can be a seat of leadership, research, knowledge sharing, collaboration, and guidance, to ensure that every project is planned, resourced, developed, and deployed in line with best practices; and in a way that maximizes ROI.
- A CoE also provides business users with the opportunity to contribute ideas. This adds immense value, as these are the people who understand how processes work in the real operational setting; and what capabilities are required to improve them.
The technologies that underpin the intelligent automation environment are not only geared towards solving immediate problems, but also future business challenges. In other words: they drive an agile and ever-evolving platform for digital transformation. Therefore, when evaluating intelligent automation ROI, it’s important to account for this long-term value.