There's a lot of buzz surrounding the subfields of artificial intelligence (AI) and machine learning (ML), which can be confusing for small business owners who think they have to jump on the bandwagon. That's why it's time for a reality check.
When SAP first introduced the concept of an intelligent enterprise, it was defined as: “An intelligent, sustainable company is one that consistently applies advanced technology and best practices within agile, integrated business processes.”
“ERP systems play a key role in enabling intelligent enterprises,” said Heinrich de Leeuw, Managing Director of SEIDOR in South Africa.
“An intelligent company is one that leverages data, analytics, and digital technology to optimize its operations, but does this mean your business needs AI?
ERP systems are designed to help small and medium-sized businesses manage their operations and processes more efficiently by integrating different departments, automating daily tasks, and providing real-time data insights. AI and ML can enhance these capabilities by analyzing large amounts of data and predicting outcomes, but their implementation can also be complex and expensive. ”
Advanced technologies such as AI, ML, and the Internet of Things (IoT) are powerful tools that can be used to solve a wide range of problems, from predicting consumer behavior to identifying the likelihood of disease outbreaks.
“However, to effectively leverage these technologies, it is important to first have a solid ERP foundation in place to integrate data, infrastructure, and business processes,” says De Leeuw. “If the fundamentals are not in place, the business challenges that organizations are trying to address will not be solved.”
Before small businesses can consider considering AI, they need to lay the foundations such as centralized data, automated tasks, technology integration, and real-time insights that will enable small businesses to grow and profit.
Here are three reasons why advanced technology is only useful and appropriate if 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. When data is incomplete, inconsistent, or inaccurate, the results of an AI or ML model are similarly flawed. Therefore, it is important to have robust data collection, management, and quality assurance processes in place to ensure that your data is clean, reliable, and suitable for use in machine learning.
- Infrastructure and compute resources: AI and ML require large amounts of computing power and infrastructure to run efficiently. Without the appropriate infrastructure, including hardware and software, algorithms cannot be executed quickly and accurately. Additionally, this can increase operational costs and reduce decision-making accuracy.
- Business process: To be truly effective, advanced technology must be integrated into existing business processes. Organizations need a clear understanding of their business objectives, the problems they are trying to solve, and the metrics they will use to measure success. Without these basic elements in place, AI and ML may not be able to provide meaningful insights or actionable recommendations.
“AI and ML are terms that refer to the use of technology to model human intelligence,” adds De Leeuw. “Just like the cloud once was, these are buzzwords today. This is not to suggest that they are not powerful technologies, but simply to emphasize that they cannot solve business problems unless deployed on top of a functioning existing infrastructure. Like ChatGPT, they cannot provide all the answers people are looking for unless applied correctly, in harmony with a well-designed ERP system, in addition to a well-run operation.
He added that there is no doubt that companies across all sectors will continue to adopt AI and ML technologies over the next few years, leveraging machine learning to transform core processes and business models to enhance operations and improve cost efficiency.
To get the most out of this technology, he suggests that companies start by spending time developing a use case that defines and articulates the problem or challenge they want AI to solve, then ensuring that the processes and systems already in place can capture and track the data needed to derive real value from the technology.
“If you don't secure this, your organization will end up with bragging rights that don't add value. If your company doesn't have processes and systems in place to drive efficiency, you won't be able to leverage the promise of technology to grow your business, and that means your project has failed,” warns De Leeuw.
