AI EXPLAINED: Generative, Predictive and Agentic AI for Business
W hen most people hear “AI,” they picture robots or ChatGPT spitting out essays. The truth is a lot less dramatic and a lot more useful. At its core, AI is a toolbox of mathematical models that help businesses work faster, smarter and with less waste. The challenge is knowing which type of AI solves which problem. Three categories are shaping how companies get things done today: generative AI, predictive AI and agentic AI. Each does something different, and confusing them is where businesses waste money, frustrate teams and lose trust in technology. Generative AI: Creating at scale Generative AI is what everyone recognizes. It drafts emails, builds marketing visuals, writes code or produces product descriptions in seconds. Under the hood, tools like large language models (LLMs), diffusion models or generative adversarial networks (GANs) learn patterns from huge datasets and create something new that fits those patterns. In business, generative AI works when you need to create text, images or even synthetic data quickly and at scale. A food manufacturer can use it to auto-draft compliance reports from raw data. A retailer can spin up personalized ad copy for hundreds of products without needing extra staff. The productivity gain is clear: less time on repetitive work and more time on decisions that matter. But generative AI is not a silver bullet. Left on its own, it invents information and misses context. It only performs well if you feed it structured, accurate inputs. Think of it as an accelerator, not an autonomous decision-maker. Predictive AI: Seeing what’s next If generative AI creates, then predictive AI forecasts. It looks at historical data, finds patterns and uses those patterns to predict what is likely to happen next. Manufacturers use predictive AI to spot material shortages before they hit production, recommending substitutions that keep operations running. Call BY TRACY GROMNISKI, CHIEF PRODUCT OFFICER, MODE40
PHOTO BY CHANTELLE DION
Tracy Gromniski
centres use it to forecast peak demand and schedule staff effectively. Finance teams use it to flag anomalies before they turn into fraud. The strength of predictive AI is foresight. It gives leaders a chance to prevent problems before they happen. Done right, it saves time, costs and reputation. The limitation is data quality. If your systems are siloed or your naming conventions are inconsistent, the predictions will not be accurate. Garbage in, garbage out still applies. Agentic AI: Getting work done This is the next frontier: agentic AI. Where generative creates and predictive forecasts, agentic acts. These are systems that plan, decide and execute tasks autonomously, often coordinating across multiple systems. Think of a digital operations manager. It notices a production line drifting off target, recalculates the schedule and pushes the update across planning, supply chain and quality systems without a human
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WINTER 2025
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