The Future of ERP Implementations with AI: Enhancing Efficiency, Agility, and Ethical Practices

Artificial Intelligence (AI) is poised to revolutionise the future of ERP systems by enhancing automation, predictive analytics, and decision-making capabilities. The integration of AI in ERP systems is expected to streamline workflows, reduce manual tasks, and contribute to more agile and data-driven business operations. AI will enable ERP systems to adapt, learn, and continuously improve, making businesses more agile and responsive to dynamic market conditions. Customers of ERP stand to benefit greatly - but how will ERP implementation projects change??

In this article, we will explore how AI will transform ERP implementations in 2030, the benefits it brings, the skills SAP professionals should acquire to stay relevant, and the potential challenges and ethical considerations that need to be addressed.?

In 2030, AI-driven ERP implementations are likely to be more sophisticated and seamless. AI will play a central role in automating routine tasks, enabling intelligent decision-making, and providing predictive analytics. Advanced machine learning algorithms will enhance data analysis, leading to more accurate forecasting and personalised insights. Natural language processing (NLP) and chatbots may facilitate intuitive user interactions, making ERP implementations more user-friendly.?

AI is expected to make ERP implementations faster and better. Through automation, AI can accelerate processes such as data migration, system configuration, and other repetitive tasks, reducing manual efforts and associated timeframes. Additionally, AI-driven analytics can provide valuable insights, optimising decision-making during the implementation phase. This combination of automation and intelligent analysis contributes to more efficient and effective ERP implementations, ultimately enhancing the overall performance and adaptability of the systems.

How will AI change the way ERPs are designed?

AI is set to revolutionise the design phase of ERP implementations in several ways. Natural language processing (NLP) capabilities can be used to simplify communication during the design phase, allowing stakeholders to express requirements in natural language, which AI interprets and converts into actionable design elements. Through automated requirement analysis, AI can analyse and interpret business requirements, extracting key insights to inform the design phase. This could include analysis of existing ERPs and connected systems to validate “true” requirements and challenge requirements without justification - think of a Celonis on steroids. This approach will ensure the creation of a more accurate and tailored ERP system that aligns with organisational needs.?

Secondly, AI tools can assist in intelligent system design by recommending optimal data models, workflows, and integrations based on historical data and best practices. This helps in designing the architecture and structure of the ERP system effectively.

Additionally, AI enables predictive modelling, allowing organisations to forecast the impact of design choices on system performance, scalability, and user experience. This enables more informed decisions during the design phase. AI algorithms also analyse past customisation patterns and business preferences to provide intelligent recommendations for system customizations, striking a balance between flexibility and standardisation. AI also contributes to enhancing the user experience by designing intuitive and user-friendly interfaces through the system-based analysis of user behaviour and preferences. This is a big step up from the anecdotal soundbytes that go into design today.

Once design is clear, AI can be used for risk analysis and mitigation by identifying potential risks associated with the chosen design elements and proposing strategies to minimise issues during the implementation phase. The combination of data-driven insights, automating routine tasks, and facilitating more informed decision-making makes AI a very powerful aid to a better, faster, cheaper design.?

How will AI change the configuration process?

AI is set to revolutionise the configuration of SAP systems in several ways. Firstly, through automated configuration, AI can streamline the initial setup and configuration of SAP modules by automating repetitive tasks. This reduces the time and effort required for deployment, allowing organisations to quickly and efficiently configure their SAP systems.?

With the advent of technologies like text-to-speech and text-to-video, it's an opportune time to extend these capabilities to "text-to-configuration." This innovation has the potential to revolutionise the Systems Integration industry by transforming how end users engage with processes. By enabling flexible and dynamic modifications in response to evolving business needs, this approach could significantly enhance adaptability and efficiency. Such a development is not only promising but also increasingly feasible, promising a future where businesses can swiftly adapt their systems through intuitive, text-based commands.

For more complex areas that can’t be left entirely to AI, AI algorithms can still provide intelligent recommendations for optimal SAP configuration settings. By analysing historical data, user preferences, and system performance, AI can offer insights and suggestions to enhance system efficiency. This ensures that organisations can make informed decisions when configuring their SAP systems, maximising their performance and effectiveness.

As configuration is happening, AI-driven tools can detect configuration errors or inconsistencies in real-time. By offering suggestions for resolution, AI minimises the risk of misconfigurations and helps organisations maintain a stable and reliable SAP system. Moreover, the natural language processing (NLP) capabilities of AI simplify communication with the SAP system. NLP allows users to define and update configurations through natural language commands, making the process more intuitive and user-friendly. SAP has been on this path for a number of years, moving away from IMG and SPRO to guided configuration in simple English - this is the logical next step.

Incorporating AI into SAP configuration processes means agility, accuracy, speed and robustness. AI automates configuration tasks, provides intelligent recommendations, enables adaptive configuration, detects and resolves errors, and facilitates intuitive communication. This ultimately leads to more efficient and effective SAP system configurations that align with business needs.

How will AI change the customisation process?

Customisations are often both the longest lead time activity and the highest risk activity of large ERP programmes. They consume high volumes of resources, take significant effort and have material impact if not done well. SAP has already set out a path to “low code / no code” with modern tools such as BTP. AI will take this to the next level by 2030.

Firstly, through automated code generation, AI can assist in generating code snippets or even entire modules, reducing the manual effort required for coding WRICEFs and extensions. This streamlines the development process and increases efficiency. Increasing incorporation of Natural Language Processing (NLP) in the coding process means the technical barrier to entry is also reduced.

During WRICEF build, AI tools can provide intelligent design assistance by analysing historical data and best practices to provide design recommendations. This helps developers create more efficient and effective solutions, ensuring that WRICEFs and extensions are designed in a way that maximises performance and meets business requirements.

Additionally, AI enables predictive analytics for performance, allowing developers to identify potential performance issues in WRICEFs and extensions before they occur. By proactively addressing bottlenecks and optimising the system, AI helps ensure that the implemented solutions perform optimally and deliver the desired outcomes. Similarly AI-driven testing tools automate the testing process, ensuring that WRICEFs and extensions function correctly and efficiently. This leads to higher quality implementations and reduces the risk of errors or issues arising during operation.

AI for customisations means efficiency, robustness, error prevention at source and better adherence to architecture principles such as “keep the core clean” and “make the core easy to upgrade”. This ultimately leads to more efficient, intelligent, and business-aligned implementations.

How will AI change the Master Data processes?

Harnessing AI for automated master data management unveils a revolutionary path for synthesising new data with pre-existing datasets through the lens of Generative AI. This innovative approach substantially enriches and diversifies reference datasets, representing a pivotal evolution in the realm of data management.

The integration of automatic duplication detection signifies a pivotal moment in broadening the functionalities of Generative AI. Incorporating conversational interfaces and logic-based data enhancement facilitates the improvement of data quality by eradicating redundancies, duplications, and inaccuracies. This perpetual refinement of data, fueled by insights from processes and actual financial transactions, heralds new opportunities for breakthroughs.

To illustrate this concept, let’s delve into two specific examples:

Automated Customer Creation and Validation Process:

Picture a streamlined operation in which a new customer is autonomously established and authenticated against authoritative sources such as Companies House and Know Your Client (KYC) protocols. Following this, the system automatically evaluates the customer's creditworthiness and establishes credit limits within the ERP framework. The procedure for authorising the customer for general use is managed through a sequence of automated verifications. This methodology could drastically shorten the time frame from several days to just moments, markedly boosting operational efficiency.

AI-Enhanced Product / Material Creation:

The generation of new materials or Products for sale, detailed with attributes like weight and dimensions, benefits immensely from the GenAi driven automated comparison with similar or related items. This process adeptly assigns international shipping harmonisation codes and supplements any missing information, thereby ensuring compliance and accuracy while conservatively using resources.

Building the best of AI into master data processes at source underscores the significant potential of Generative AI in streamlining and enhancing data management practices, promising greater operational efficiency and precision.

Will AI replace humans in ERP implementations?

AI is unlikely to entirely replace humans in designing and implementing ERP systems. While AI can automate certain aspects of coding, configuration, and offer design suggestions, human involvement remains crucial for several reasons. Firstly, humans are needed to make strategic decisions aligning ERP implementation with overall business goals. Factors such as organisational culture, industry specifics, and long-term objectives require human judgement and decision-making.

Secondly, customisation and adaptability are essential in ERP systems. Humans possess the contextual understanding and creativity necessary for tailoring solutions to unique business requirements. They can analyse complex needs and design customised solutions that meet specific organisational needs - using AI to analyse, guide and even recommend in key areas.?

Thirdly, ethical oversight is crucial in the use of AI in ERP systems. Humans provide the moral and ethical judgement necessary to guide AI decisions responsibly, ensuring the avoidance of biases and addressing ethical concerns. Moreover, human designers play a critical role in creating user interfaces that align with the expectations and preferences of end-users. User experience (UX) design focuses on aspects such as usability, accessibility, and overall user satisfaction, which require human expertise.

While AI can enhance efficiency and automate repetitive tasks, the collaboration between AI and human expertise is likely to yield the most effective, ethical, and adaptable solutions in complex business environments. SAP professionals should consider acquiring skills in AI and machine learning, data analytics, cloud technologies, cybersecurity, integration and APIs, user experience (UX) design, change management, and a commitment to continuous learning to stay relevant in the SAP landscape in 2030.

In conclusion:?

AI is set to transform the future of ERP implementations by enhancing automation, predictive analytics, and decision-making capabilities. It will optimise processes, offer real-time insights, and improve overall efficiency in areas like supply chain management, finance, and human resources. The integration of AI in ERP systems is expected to streamline workflows, reduce manual tasks, and contribute to more agile and data-driven business operations.?

However, it is crucial to address the potential challenges and ethical considerations associated with AI in ERP to ensure a positive and sustainable digital future. By embracing responsible AI practices, fostering human expertise, and promoting continuous learning, we can harness the full potential of AI in ERP and drive meaningful innovation in the enterprise landscape.

The future of ERP implementations with AI is promising, with three key takeaways. Firstly, AI-driven ERP implementations in 2030 will be more sophisticated and seamless, automating routine tasks, enabling intelligent decision-making, and providing predictive analytics. This will make businesses more agile and responsive to market conditions. Secondly, AI will enhance the efficiency of ERP implementations by automating processes, accelerating data migration, and providing valuable insights for decision-making. This will result in more efficient and effective ERP systems. Thirdly, AI will transform the design and configuration processes of ERP systems, providing intelligent recommendations, automating code generation, and simplifying communication through natural language processing.?

Human involvement remains crucial in strategic decision-making, customization, ethical oversight, user experience design, problem-solving, and change management. SAP professionals should acquire skills in AI, data analytics, cloud technologies, cybersecurity, integration, user experience design, change management, and continuous learning to stay relevant in the evolving ERP landscape.

SAP professionals should also double down on their business transformation skills, recognising that their “added value” over AI is to deliver critical business outcomes through technology. Competing with AI to deliver a technology quickly and efficiently is to set up for failure, but becoming the “voice of the business” is a route to success. As revolutionary as AI will be, it is hard to imagine AI alone solving for the type of challenge faced by large organisations today - fragmented and complex intercompany processes, tax inefficiencies and compliance risk, transfer pricing challenges, rigid data structures that aren’t fit for future disruptors like Pillar 2…. - there is a key role for humans to play, even if it isn’t the one we have played so far.

Here at PwC we frequently work with organisations to work through these complex decisions and identify the right pathway to S/4HANA enabled transformation. We bring a robust approach, proven tools and templates and hard-earned experiences of doing this across industry sectors. To find out more, please contact Jonathan R. or Rohit Chandrasekhar - alternatively please visit https://www.pwc.co.uk/issues/transformation/finance.html.