At the 19th Annual Shared Services for Finance & Accounting Conference, practitioners and leaders from across North America revealed experiences and insights from their organizations’ shared services journeys.
Three key lessons stood out:
1. The importance of the business case
Before setting out on the journey to implement a shared services center, leaders stressed the importance of developing, documenting and sharing a clearly defined business case. This should include the vision and mission of the new model, the goals, the key performance indicators (such as expected costs and savings), anticipated benefits and the overall governance structure.
2. Don’t short cut change management
Several speakers mentioned that they regretted discounting the importance of ongoing change management when migrating to a shared services delivery platform. While it is tempting to cut out change management activities when looking for ways to streamline an implementation effort, doing so will ultimately delay acceptance from internal customers and employees. Adequate training from the process subject matter experts for those who will operate the shared service center is crucial for a smooth transition and ongoing customer satisfaction. Clear communication to internal stakeholders throughout the transition process will help with managing expectations, fully understanding the end goals and minimizing resistance to the changes.
3. The opportunity to optimize and automate
Moving a bad and inefficient process to a shared service environment will not make it a good one. Several shared services leaders pointed out that the migration from a decentralized to a centralized model provides an opportunity to assess processes. This will ensure that they are efficient and eliminate manual steps through automation and business process re-engineering.
Effective leaders of shared service centers recognize the importance of continuously improving processes and looking for opportunities to leverage process improvement programs, Robotics Process Automation (RPA), machine learning and Artificial Intelligence (AI).