There is much chatter around artificial intelligence (AI) and the subfield of machine learning (ML), which can be confusing for SMME owners who may believe that they need to climb on the bandwagon. That’s why it’s time for a reality check.
When SAP first introduced the concept of the intelligent enterprise, it was defined as: “An intelligent, sustainable enterprise is one that consistently applies advanced technologies and best practices within agile, integrated business processes.”
“ERP systems play a crucial role in enabling the intelligent enterprise,” says Heinrich de Leeuw, Managing Director, SEIDOR in South Africa.
“An intelligent enterprise is one that leverages data, analytics, and digital technologies to optimise its operations, but does this mean that AI is needed in the business?
ERP systems are designed to help SMMEs manage their operations and processes more efficiently by integrating various departments, automating routine tasks, and providing real-time data insights. While AI and ML can enhance these capabilities by analysing large volumes of data and predicting outcomes, their implementation can also be complex and expensive.”
Advanced technologies like AI, ML and Internet of Things (IoT) are powerful tools that can be used to solve a wide range of problems, from predicting consumer behaviour to identifying potential disease outbreaks.
“But to effectively leverage these technologies, it is critical to first have a solid ERP foundation in place to integrate data, infrastructure, and business processes,” says De Leeuw. “Without the basics in place, any business challenges that the organisation is trying to address will not be resolved.”
Before SMME’s think of looking at AI, they need to build the basics which include centralised data, automated tasks, technology integration and real-time insights that enable SMMEs to grow and be profitable.
Here are three reasons why advanced technologies are useful and appropriate only when the basics are in place:
- Quality data is essential: AI and ML algorithms rely on large amounts of high-quality data to learn and make accurate predictions. If the data is incomplete, inconsistent, or inaccurate, the results of the AI or ML model will be similarly flawed. That’s why it’s crucial to have a robust data collection, management, and quality assurance process in place to ensure that the data is clean, reliable, and suitable for use in machine learning.
- Infrastructure and computational resources: AI and ML require a significant amount of computational power and infrastructure to run efficiently. Without proper infrastructure, including hardware and software, the algorithms will not be able to run quickly or accurately. Moreover, this can result in increased operational costs and decreased accuracy in decision-making.
- Business processes: Sophisticated technologies must be integrated into existing business processes to be truly effective. Organisations must have a clear understanding of their business goals, the problems they are trying to solve, and the metrics they use to measure success. Without these foundational elements in place, AI and ML may be unable to provide meaningful insights or actionable recommendations.
“AI and ML are terms that refer to the use of technology to model human intelligence,” De Leeuw adds. “They are the current buzzwords, just as the cloud once was. That’s not to suggest that they are not powerful technologies, but simply to underline that they will not solve business issues if they are not deployed on top of an existing infrastructure that works. Much like ChatGPT, they will not provide all the answers people are looking for if they are not applied correctly, on top of operations that are running optimally, and in harmony with a well-designed ERP system.
He adds that there’s no doubt that businesses across all sectors will continue to embrace AI and ML technology over the coming years, transforming their core processes and business models to take advantage of machine learning for enhanced operations and greater cost efficiencies.
To make the best use of this technology, he suggests beginning by spending time on developing a use case that defines and articulates the problems or challenges that the business would like AI to solve, and then to ensure the processes and systems already in place are capable of capturing and tracking the data needed to derive real value from the technology.
“Without ensuring this, the organisation will gain bragging rights with no value add. If the company does not have the processes and systems to drive efficiencies it will be unable to leverage the promise of the technology to grow the business and that means the project has failed,” De Leeuw cautions.
