Reasons 1: Insufficient or missing comprehensive digital transformation plan with a well-defined targeted goal.
Businesses frequently understand where there is room for improvement and where automation is required. While automating these discrete processes improves the productivity of the automated process parts, it only uses the potential that can be achieved with an overall strategy for digital transformation to a limited extent. Only a few companies take the step from a first trial via pilot projects to an integrative and comprehensive end-to-end automation only with such a system that hidden inefficiencies and inconsistencies can be identified and addressed. This necessitates a well-defined goal that serves as the foundation for a digital transformation plan. An approach like this includes more than just automation specs. If you know where you want to go, the path often follows itself.
Reasons 2: Inadequate or no integration of digital solutions and processes in the goal operating model.
The target operating model identifies the critical framework characteristics for a company's efficient and successful operation for RPA, in the long term. When starting a first pilot project, the topic of the operating model is usually not considered in detail, which changes as the successful pilot projects progress and the more extensive RPA activities (roll-out) planned afterward in the company. However, an incomplete or non-existent operational concept endangers the RPA scaling in the long term, since questions such as the location of responsibility for the technical operation of the solutions, the definition and best possible location of roles and responsibilities in the company, up to company-wide RPA governance as well as to be answered with an adequate billing system. The ability to orchestrate automation sustainably across the enterprise is a key differentiator between advanced RPA platforms and niche software solutions. It is recommended that the activities should be largely parallelized, which should ideally be done from the start of pilot projects so as not to overlook the RPA operating model's basic definition With the RPA piloting expected to be a success, The topic may be swiftly disseminated throughout the organization, allowing it to expand sustainably.
Reasons 3: Lack of technological democratization to enable "self-service" automation (citizen-developer).
Highly depend on third-party service providers for process automation, but also required changes to established processes are cost drivers and often slow down the implementation of automation or adaptations. In enterprises, first, a competency Center of Excellence (CoE) should be established, and users in companies (citizen developers) should be trained. The recording, structuring, and automation of complex processes can also be internalized if necessary. However, this is only worthwhile from a company size whose procedural complexity ensures constant utilization of the Center of Excellence.
Reasons 4: A lack of end-to-end process models/considerations and a lack of global process ownership as efficiency drivers.
The basis of the holistic, digital transformation should be a corresponding target image at the process level. In doing so, companies often find that the process models no longer meet the current requirements or simply no longer correspond to the current, lived processes. About cross-functional process responsibility and the associated, simplified management of interfaces across functional silos, end-to-end process digitization is of great value. An end-to-end view of the processes makes it easier to see the potential and identify concrete use cases in the discussion between employees. Well-designed end-to-end process management has an effect far beyond RPA use cases.
Reasons 5: Use cases don't fit one size (RPA tool/software).
These days, markets are becoming more networked and complex. Similarly, the RPA solutions required to deal with this complexity need a more sophisticated approach. To build a resilient, secure, and scalable platform, it's essential to choose and set up the correct RPA infrastructure setup. It enables robots to work smoothly no matter the volume of work, inspires confidence in the technology among executives and users, and enables companies to take full advantage of automation. Companies are increasingly relying on combinations of available technologies (e.g., RPA tools combined with AI tools or even different RPA tools) to be able to respond to different process requirements in a differentiated way. RPA infrastructure setup can vary significantly depending on the technology chosen and the automation model to be implemented. As a result, an appropriate selection process and a well-communicated, well-understood target image backed by stakeholders, which processes and use cases are to be digitized and how are essential for scaling success.
Reasons 6: Expandable change management and long-term employee upskilling.
Employee resistance can emerge during any transformation process. Digitization initiatives are particularly vulnerable to this, especially if buzzwords like "automation" and "robots" are not adequately explained, as in the case of RPA. RPA's goal is to help overloaded employees who spend too much time on hated repetitive activities and have too little time to focus on their real job. Automation is intended to be a helper for employees to use in order to free up their time for more challenging and interesting tasks. To overcome this resistance, a thorough explanation of what is being automated, why it is being automated, and how it will benefit employees is required. As soon as the automation has been implemented, experience shows that the initial resistance of the employees usually dissipates quickly, and in many cases, the employees themselves make suggestions for further automation. Once this stage is completed, a company's transformation plan begins to take shape, and it may typically be implemented more rapidly and comprehensively than originally anticipated.
