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      Shared Service Centres (SSCs) are currently under considerable pressure. This is due to rising costs, a shortage of skilled workers and the rapid advancement of generative AI. Traditional measures such as process harmonisation and automation are no longer sufficient to successfully address these challenges.

      Only the targeted use of artificial intelligence (AI) opens up new possibilities: processes can increasingly run autonomously, unstructured data becomes easier to use, and self-service offerings can be significantly improved.

      Artificial intelligence as a key driver for increasing maturity

      In recent years, process automation and digitalisation have significantly increased the maturity of shared service centres. At the same time, staff costs have risen sharply – even in traditional low-wage locations. This is putting shared services organisations under increasing pressure. Furthermore, there is a growing call for services to be brought back to Germany, albeit with a higher degree of automation. Rising expectations regarding speed, service quality and a greater value-adding role for shared service centres are also creating a need for further action.

      This is where AI offers a decisive lever: deployed in the right way, it can trigger a significant efficiency boost over the next three to four years. AI reduces complexity, recognises patterns and significantly shortens processing times. It is thus becoming a central component of modern shared services organisations and is bringing about lasting changes to their structures and working methods.

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      Economic management, scaled AI utilisation and digital transparency determine whether initiatives achieve measurable impact.
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      Areas of application for AI in shared services organisations

      AI needs data. Shared service centres already have large volumes of historical transaction data from systems such as SAP, Oracle or Workday – supplemented by unstructured information from emails, PDF files, scans or support chats. This diversity of data is a major advantage, but its quality is often inconsistent.

      Before AI can deliver real benefits, data should therefore be cleaned, harmonised and managed centrally. Once these foundations are in place, however, the existing data provides an excellent basis for training machine learning models or deploying large language models for knowledge management and assistance functions.

      Typical areas of AI application include, for example:

      • Automation of repetitive processes

        Patterns are identified, documents are read and decisions are made using AI-powered processes. Relevant information is automatically extracted, sorted and processed, leading to a significant reduction in staff costs.

      • Support at the Service Desk

        Ticket content is recognised and automatically assigned to the correct department. Standard enquiries, such as “How do I apply for leave?”, are answered automatically based on the company’s accumulated knowledge. This significantly simplifies and, above all, speeds up the operation of self-service portals using AI-powered navigation.

      • Role in the Finance Shared Service Centre

        AI can do far more in the finance function than simply automatically posting invoices and credit notes. It detects discrepancies and anomalies much more quickly and reliably – including automatic suggestions for comments. When it comes to fraud and anomaly detection in particular, manual processing often reaches its limits in terms of time, whereas AI can analyse large volumes of data in real time.

      • Role in the HR Shared Service Centre

        A well-trained, context-aware AI significantly speeds up the screening of application documents and provides reliable support in pre-selecting candidates based on defined criteria. It can also generate standardised HR documents, such as employment contracts, which employees then simply need to review.

         
        AI also provides valuable insights into forecasts regarding staff turnover, absences or future capacity requirements – thereby enabling significantly more forward-looking workforce planning.

      • Role in the IT Shared Service Centre

        An IT system failure has serious consequences for the entire organisation. To prevent this, AI can predict and initiate upcoming system maintenance tasks or create self-healing IT processes (automatic fault rectification). AI-supported IT security, featuring anomaly detection and threat analysis, can protect the organisation from significant damage.

      Benefits and potential

      Although the implementation of AI solutions in companies requires an initial investment, there are often significant economic benefits, particularly within the finance function. Depending on the scope of the AI applications deployed, total cost savings of 20 to 40 per cent are realistic – with a return on investment after around eighteen months.

      AI maturity curves provide a more detailed assessment where required: they show the extent to which time and cost expenditures can be reduced whilst simultaneously improving quality – for example, by relieving staff of routine tasks, enabling faster response times or providing a service that is available around the clock.

      Key factors for the successful implementation of AI solutions

      The introduction of AI in shared services organisations should follow a planned and structured approach. This is always based on the interplay between processes, technologies and staff, whilst taking the necessary governance into account. 

      The following factors must be taken into account prior to implementation:

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      Definition of a clear vision, as well as potential use cases and pilot projects

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      Review of the system architecture

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      Review and professionalisation of the database

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      Training and development of SSC staff

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      Consideration of risk and compliance issues

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      Development of a change and communication strategy

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      Modernisation of process design

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      Conclusion

      The introduction of AI is not a one-off IT project, but a fundamental transformation of the shared services function – particularly in the initial phase. Organisations that do not adopt AI will gradually lose the efficiency gains they have achieved so far.

      With good organisational preparation and targeted deployment at the right points in the process, AI can significantly strengthen the shared services model: it noticeably boosts efficiency, raises the organisation’s maturity level and delivers a positive value contribution.

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      Your contact

      Heiko Schwarzer
      Heiko Schwarzer

      Senior Manager, Performance & Strategy

      KPMG AG Wirtschaftsprüfungsgesellschaft