What kind of AI should companies focus on? Rather than choosing between attractive technologies, business leaders should start by identifying important problems and determining how best to solve them. AI strategies should be a means to an end, not the end itself and the objective of the business should drive the technology, not the technology driving the business.

What is Predictive and Generative AI ?

Predictive AI, often called Machine Learning or even predictive analytics, is a subset of AI that focuses on developing algorithms and statistical models that enable computers to learn from and make predictions based on data.

Generative AI (GenAI) is a class of AI that can create new content, such as text, images, music, or code, by learning from existing data. Unlike Predictive AI, which focuses on forecasting outcomes based on input data, GenAI aims to produce original and creative outputs.

In recent years, GenAI has significantly propelled AI into the mainstream with innovations like ChatGPT, which is great; however, this attention has overshadowed other AI areas, such as Predictive AI, which can offer greater value, especially to business operations.

• Higher Returns: Predictive AI often delivers higher returns by improving an enterprise's large-scale processes. For example, UPS saves an estimated $35 million annually by optimising package delivery planning with predictive AI. This technology can significantly enhance customer experience and operational efficiency by focusing on systematic, high-volume tasks that benefit from predictive optimisation.
• Autonomy: Predictive AI can operate autonomously, making decisions without a human in the loop. In contrast, GenAI usually requires human oversight to review and approve outputs. For instance, predictive AI in banking systems instantly decides whether to allow credit card charges, while GenAI helps write content but needs human review for accuracy.
• Cost Efficiency : Predictive AI is cheaper and has a smaller footprint than GenAI. Predictive models are lighter, often consisting of dozens to hundreds of parameters, whereas generative models can have up to a trillion parameters. This makes predictive AI more energy-efficient and easier to deploy.
• Practical Applications: Predictive AI drives millions of operational decisions daily, such as predicting fraudulent transactions or determining optimal marketing targets. Its practical applications cover marketing, manufacturing, fraud prevention, and more. This technology’s ability to predict outcomes from existing data makes it invaluable for enhancing business operations.

GenAI’s novelty shouldn’t overshadow predictive AI’s growing adoption. GenAI offers remarkable capabilities, but predictive AI drives substantial operational improvements and efficiencies. Organisations should leverage both technologies to address specific operational problems and generate the greatest value.