By Johan Steyn, 19 April 2023
The fast-paced and data-driven world of today has made artificial intelligence (AI) an essential tool for businesses to gain a competitive edge. As the race to harness the power of real-time AI data continues, business leaders must keep up with the latest trends and advancements.
Real-time AI data refers to the ability to analyse and act upon data as it is generated. This requires the integration of AI algorithms and machine learning techniques into a company's operations and decision-making processes. The benefits of real-time AI data are numerous, including faster and more accurate decision-making, increased productivity, and improved customer experience.
According to a recent survey by Gartner, real-time data analytics is a top priority for businesses in 2023. The survey found that 92% of organisations plan to increase their investment in it over the next three years.
However, building a real-time AI system is not an easy task. It requires a significant investment in technology, data infrastructure and talent. For business leaders who want to build a vision for real-time AI, some considerations need to be kept in mind.
First, it is important to define your goals and identify how real-time AI data can help you achieve them. By doing this, you can prioritise your investments and ensure that your real-time AI strategy aligns with your overall business strategy.
Secondly, investing in the right technology is crucial for implementing real-time AI. A robust data infrastructure and the right technology tools are necessary for real-time analytics. Business leaders should invest in scalable, flexible and reliable data platforms that can support real-time analytics. Investing in AI tools that can analyse and act on data in real time is essential.
Thirdly, building a strong data team is necessary for real-time AI. Business leaders should invest in hiring and training data scientists, machine learning engineers and AI developers to build a team with the right skills and expertise. Fostering a culture of data-driven decision-making and innovation is also important for creating a successful real-time AI system.
Lastly, ensuring data security and privacy is a critical consideration. Real-time AI requires access to large volumes of data, which can pose security and privacy risks. Business leaders need to ensure that their real-time AI systems comply with data protection regulations and implement strong security measures to protect sensitive data.
Real-time AI can be particularly useful in industries where timing is critical, such as finance, health care, and transportation.
For example, in the finance industry, real-time AI can be used to analyse market trends and make investment decisions based on the most up-to-date data. This can lead to faster and more profitable trades. Similarly, in the health care industry, real-time AI can be used to monitor patient data in real-time and alert health care providers to potential issues or complications, allowing them to respond quickly and provide better care.
Real-time AI can also be used to automate decision-making processes, such as fraud detection or supply chain management. By using it to analyse data and make decisions automatically, businesses can save time and improve efficiency. This can lead to cost savings and increased profitability.
Perhaps most crucial is that leaders focus on real-time AI as a source of support to humans and that they do not aim to replace the value of human experience in the process.
Andrew Ng, a prominent AI thought leader said, “Automated decision-making with AI is not about replacing humans, but rather augmenting their decision-making capabilities. By combining the strengths of AI and human intelligence, businesses can make better decisions and achieve better outcomes.”
• Steyn is on the faculty at Woxsen University, a research fellow at Stellenbosch University and founder of AIforBusiness.net