We upgraded 15 Timefold Solver quickstarts to 2.0 in under 10 minutes using an OpenRewrite recipe. Here’s exactly what that migration looked like.
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It seems like you can’t scroll through a tech feed these days without hearing about Generative AI. From ChatGPT writing blog posts, to AI-generated art taking over Instagram, GenAI is everywhere. And just to be clear, we’re hardcore users ourselves. But there’s a misconception we want to clarify. Not every AI falls under the GenAI banner. Our DevRel Tom Cools already wrote a compelling blog post on the different kinds of AI, and how each kind has different capabilities. However, in this blog post, we’ll explain 6 major differences between PlanningAI and GenAI. Both powerful, both AI, but fundamentally different.
# 1. Black box vs. explainable
- Generative AI often relies on deep neural networks that are incredibly complex. The model’s inner workings can be difficult to interpret, hence the “black box” label. Even developers can struggle to explain exactly how the system arrived at a certain output.
- Planning AI, on the other hand, uses methods that are more transparent. It’s built on well-established algorithms (like constraint programming, linear optimization, or rule-based systems) that are easier to trace. Users can see how decisions are made, step by step, which is especially important in industries where compliance and explainability are paramount.
In many business applications, stakeholders demand to know why a decision was made. Planning AI delivers that explainability. Generative AI is incredible for creative or less regulated contexts, but in scenarios requiring a clear rationale (think supply chain, workforce scheduling, or compliance-heavy fields), an explainable system wins every time.
# 2. Non-deterministic vs. deterministic
- Generative AI is non-deterministic. That means it can produce different outputs even when given the same prompt. This variability can be great for creative applications, but it can also lead to inconsistencies.
- Planning AI is deterministic. Give it the same inputs, and you’ll get the same outcome every single time. This consistency is crucial for businesses that rely on predictable results, like production scheduling, logistics planning, or financial forecasting.
Predictability is often the key to operational success. When you need reliable, repeatable results, deterministic models are more appropriate. If you’re aiming for creative inspiration or novel outputs, non-deterministic models shine.
# 3. New tech vs. proven tech
- Generative AI feels like the “new kid on the block.” While deep learning has been around for a while, the explosion of large language models and image generators is relatively recent. This newness excites people but also means there’s still a lot to figure out: best practices, regulations, ethical frameworks, etc.
- Planning AI has been around for decades. Methods like optimization, constraint programming, and rule-based systems are proven in countless real-world scenarios. They’ve been tested and refined over time, offering reliability that’s hard to beat.
Mature, proven technologies often come with extensive documentation, robust user communities, and well-established integration paths. If your business requires stability and a track record of success, Planning AI can offer peace of mind.
# 4. Generating new content vs. optimizing what exists
- Generative AI is about creating. Be it text, images, or even code snippets. It’s an amazing fit for brainstorming, content creation, and personalized marketing materials.
- Planning AI focuses on optimizing existing resources and processes. Instead of inventing a new image or paragraph of text, Planning AI schedules your workforce more efficiently, optimizes complex routing problems and helps allocate resources across multiple projects.
If your biggest challenge is to streamline operations, cut costs, or improve your workflow, Planning AI is designed for those optimization tasks. Generative AI can create fresh ideas, but it might not be the best fit for strictly operational goals that need a laser focus on efficiency.
# 5. Large training datasets vs. manually programmed algorithms
- Generative AI: Creating an LLM requires massive amounts of training data. These large datasets consume enormous computational power (and, yes, electricity) to reach impressive accuracy and capabilities.
- Planning AI uses optimization algorithms that are preprogrammed by experts. While some data may be involved, it doesn’t require training. Instead, Planning AI relies on logical constraints, heuristics, and well-defined optimization strategies.
Collecting, cleaning, and maintaining huge datasets can be expensive and time-consuming. If your business doesn’t have that kind of data. Planning AI can be far more practical to implement and maintain.
# 6. Environmental impact: “Burns” vs. “Saves”
- Generative AI is computationally heavy. Training large models can consume tons of energy, which raises environmental concerns.
- Planning AI requires less compute power. It’s more about applying targeted, efficient algorithms rather than brute-forcing through billions of data points. As a result, it tends to have a smaller carbon footprint. Additionally, planning AI is used to optimize issues like vehicle routing, which has a massive impact on CO2 emissions.
Sustainability is becoming a core metric for many organizations. While it’s not always a direct apples-to-apples comparison, if your company values a lower environmental impact, Planning AI is often more resource-friendly.
# Where does Timefold fit?
We’ll be the first to admit we’re big fans of Generative AI, some of us are even using it to help write code or jump-start content. However, our product is built on PlanningAI principles. It’s explainable, deterministic, and designed to optimize existing processes rather than create new content. This makes it ideal for businesses that need reliable, repeatable, profitable, and environmentally conscious solutions.
Our takeaways
- Generative AI and Planning AI are both “AI,” but they solve very different types of problems.
- Generative AI shines at creating new content and inspiring innovation, whereas Planning AI excels at optimizing complext processes at scale and delivering predictable results.
- Both have unique strengths, but if you need transparency, consistency, and a smaller environmental footprint, Planning AI may be the better fit.
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