The current landscape

AI is transforming the current business landscape at an accelerated rate with capabilities like predictive analysis, process automation, and real-time, data-driven insight. AI-powered tools provide great potential on their own but when combined with ERP systems they can create a truly transformative force for businesses at scale.

ERP systems provide the digital infrastructure needed to industrialize AI capabilities at scale, enabling organizations to implement AI across their organization – resulting in commercially powerful solutions that drive efficiency and growth.

However, AI implementation within ERP still faces challenges such as deeply established workflows, poor data quality and lack of expertise, causing bottlenecks for implementation in most large organizations. Despite these challenges, AI-powered ERP systems are already addressing these gaps. For instance, predictive analytics can enhance demand forecasting, enabling organizations to optimize inventory levels and avoid stockouts. Similarly, AI-driven automated invoice processing can streamline accounts payable, reducing manual errors and administrative burdens.

AI-integrated ERP systems

Today’s traditional ERP systems can be used as a springboard for scaling AI solutions.

The road of AI implementation at scale requires infrastructure, standardized process, AI design and workforce empowerment. While proofs of concept (PoCs) don’t require full maturity in these areas, scaling AI sustainably demands all above mentioned dimensions. Without addressing these – AI won’t be able to scale and be a sustainable technology within large organizations.

For example, SAP has introduced its generative AI assistant Joule. This AI integrates across software environments to enhance processes like supply chain management and finance and exemplifies how AI can be embedded into ERP systems to drive comprehensive digital transformation.

What is required to make this AI transformation?

The first step in this transformation is to overcome the mentioned initial challenges in implementing AI capabilities.

The first challenge is that existing processes are often deeply ingrained within the organization, making it difficult to restructure them to fit these new technological possibilities:

1. Integrating AI into established processes usually meets this type of challenge. Existing workflows may be deeply established, making it difficult to introduce new technologies without disrupting the old way of working – causing a need for a thorough approach to change management.
2. Another challenge is the data quality. Poor data quality does not make it possible to feed the AI solution with the necessary inputs needed to enable its potential.
3. The third main challenge is to find the right professionals and expertise within the AI area to solve some of the unique challenges that appear in such a project. Being able to bridge this AI-ERP skills gap is critical.

How can organizations meet these challenges?

a. Adopting a holistic approach

A holistic approach in this context would involve viewing processes not only as end-to-end but as connected rather than isolated tasks. This perspective encourages organizations to evaluate and improve the workflows themselves, not only separate tasks within the flows. To adopt a holistic approach in process design enables leveraging the most from AI capabilities. It’s advantageous to rethink the processes, end-to-end, to avoid the approach of trying to put in pieces of a puzzle where they don’t fit. By instead rethinking processes through an end-to-end perspective, the overall workflow can be designed effectively, leveraging AI capabilities and enhancing overall operational efficiency.

b. Data quality from the start

The foundation of any successful AI integration lies in high-quality data. Ensuring data quality means that the information collected is accurate, relevant, and timely. Prioritizing data integrity from the beginning will help to set a solid foundation for AI applications.

Vital dimensions to consider is firstly the importance of clean data. Data must be free from errors, duplicates, and inconsistencies to ensure that AI algorithms can process it effectively. Dirty data can skew results, leading to misguided strategies and wasted resources. To achieve this, it is important with regular data cleansing routines—such as deduplication and standardization.

Furthermore, implementing strong data governance practices is essential for managing data. This includes defining roles and responsibilities for data management, establishing policies for data usage, and ensuring compliance with relevant regulations.

c. Vital expertise

Integrating AI into established ERP systems requires professionals with specialized skills in both advanced technologies and business transformation. This presents a significant challenge as organizations often face skill gaps and difficulties in managing organizational change. Finding the right blend of both technical and business value understanding is essential for implementing AI technology with a high ROI – as up to 80% of AI initiatives fail.

Key takeaways

Integrating AI effectively into ERP systems ensures that organizations can move beyond isolated proofs of concept and truly scale their capabilities. By treating data with care, reimagining processes end-to-end, and bringing in the right expertise, companies can unlock sustainable growth and build a competitive edge for the future.

• End-to-end process thinking avoids fragmented solutions and drives efficiencies
• High-quality data and strong governance provide a solid foundation for AI
• Balancing technical skills with business acumen ensures that AI investments deliver lasting value

Authors

Johan Valeur & Mattias Lindeborg