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Introducing new technology into ERP systems requires detailed action planning as well as building a solid foundation for the changes being introduced. This is especially true when we’re talking about a technology as complex and dynamically evolving as artificial intelligence.
Based on the 2023 Gartner ERP, Procurement, HCM and Finance Apps Survey, findings present a forecasting analysis on the future of ERP systems and offer data-backed recommendations.
The authors identified the following key forecasts for ERP systems:
It’s becoming clear that broad AI implementation is and will remain organizationally problematic, time-consuming, and demanding significant effort from employees at every level of the organization.
Management technology is evolving rapidly, and companies, following changing realities, are opting for simpler ERP systems. Moving away from personalized solutions toward standardization makes it more convenient and cost-effective to implement various innovations on this basis.
87% of respondents to the 2023 Gartner ERP, Procurement, HCM and Finance Apps Survey declared plans to replace or update their ERP applications within the next three years to adapt them for easier and faster adoption of new features.
However, new features must be introduced thoughtfully in line with a well-developed strategy, implementation procedure, and testing process. Apparent innovativeness should not be the sole argument for introducing solutions. The strategy should be thorough, containing answers to every “how?” and “why?” sufficient to form a solid basis for development activities. Stakeholders should participate in the decision-making process, so that the solutions being designed align with business goals and expectations.
Although applications for generative artificial intelligence have been found in the context of ERP systems, the leading technology best suited to this environment’s needs is Agentic AI. This type of AI uses machine learning (ML) algorithms, large language models (LLMs), and other advanced technologies. Among other things, it uses them to create autonomous AI systems designed to make decisions and perform actions based on output data from the environment. In short, generative AI creates content, while AI agents perform tasks based on rules.
GenAI has proven useful for working with unstructured data within CRM (Customer Relationship Management) systems and HCM (Human Capital Management) solutions. However, in other areas of ERP operations, generative AI has less value. By 2027, less than 30% of AI-based features contained in ERP systems will be based on GenAI. Using AI agents allows for the automation of everyday tasks. This gives employees more time for value-adding activities, increasing the company’s overall efficiency. It should be noted, however, that ultimate success depends on data quality and the system’s ability to effectively operate on highly complex data.
To maintain full control over the situation, it’s worth stipulating in the contract with the ERP provider a requirement to disclose the use of GenAI. This covers the system, the specifics of its application, the resulting risks, the safeguards used, and possible additional costs.
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Investments made as part of AI transformation serve as a safeguard for the future, but there’s no denying that they require substantial financial outlays and effort from employees.
By 2027, only 30% of enterprises will have data of sufficient quality to fully leverage the advanced capabilities of AI solutions. Investment in data management solutions will continue to grow.
Data quality management alone isn’t the only challenge; companies also face significant demand for systemic solutions such as storage, data cleaning, integration, and management. As a result, internal organizational culture can delay the adoption of new technologies through resistance from employees and management, especially in situations where AI doesn’t quickly deliver measurable benefits.
45% of respondents stated that the main obstacle to introducing flexible architecture in 2023 was technical debt and/or internal resistance to the changes being introduced.
Technical debt is the result of short-term thinking, which should be avoided. When a development team makes compromises in order to deploy a product as quickly as possible, at the expense of quality standards, one can expect long-term consequences from every error and, ultimately, greater work effort down the line. A new type of technology, whose capabilities and limitations aren’t yet fully understood, is unpredictable and requires a degree of caution. That’s why the right implementation tactics training employees and communicating changes in the technology being used serve to protect the investment. All of this is meant to ensure employees receive the changes positively, even if the benefits don’t appear immediately.
A factor significantly affecting the pace of change in the market is uncertainty about the profitability of implementing AI solutions within ERP systems. Clear-cut case studies are still lacking, and the anticipated effects of the transformation are not yet fully established.
By 2027, less than 10% of entities that have introduced agentic AI within their ERP systems will experience significant and measurable gains.
To ensure the highest possible ROI, decision-makers must define clear and measurable goals before introducing AI agent support. The benefits promised by technology providers must align with the company’s business goals. Ideally, new features should be tailored to the company’s needs and capabilities, automate standard processes, help the company stand out from competitors through early adoption, and, above all, perform better than the conventional solutions previously used.
If you're interested in improving how your company operates, get in touch with us to learn more.
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