Keep It Simple: How AI Can Help Us Simplify Workflows

Is Everything Really AI?

These days, it feels like everything is being called AI. Whether it’s sending emails at the perfect time, calculating lifetime customer value, or even predicting when to water your plants, the term AI is everywhere. And let’s be honest—sometimes it’s hard not to cringe.

Why is this happening? Well, AI is a hot topic, and using the term can make even simple features sound futuristic and advanced. But the problem is that this overuse confuses people. What really counts as AI? And how do we separate the hype from what’s genuinely useful?

In this article, we’ll talk about how AI, especially tools like large language models (LLMs), can be used not just as solutions but as tools for discovery. The goal isn’t to rely on AI forever. Instead, it’s to use AI to help us learn, simplify, and build systems that are stable, efficient, and easy to understand.

What Is AI Actually Good For?

Before we get into how to simplify workflows, let’s take a step back and ask: What is AI good for?

Right now, tools like ChatGPT and other LLMs are great at helping us with creative and exploratory tasks. For example:

• Generating Ideas: Need help brainstorming names for a project or writing a first draft? AI can give you a head start.
• Filling Gaps: If you’re stuck on a process or don’t know what’s next, AI can suggest the next steps.
• Handling Complexity: AI can analyze large amounts of information and summarize it in a way that’s easier to understand.

But here’s the thing: AI is most useful in the messy, early stages of figuring something out. Once you know what works, you can usually replace AI with simpler, more stable systems. This is where we move from exploration to optimization.

How AI Helps Us Discover Better Workflows

Think of AI as a partner for solving problems you don’t fully understand yet. Here’s how it works:

1. Exploration: Let’s say you’re building a system to predict when customers are most likely to buy. In the beginning, you might use an LLM to analyze past purchases, customer reviews, and email open rates. The AI helps you see patterns you might not have noticed on your own.
2. Refinement: Over time, you start to see what’s working. Maybe you find out that purchase times are closely tied to specific holidays or that email timing doesn’t matter as much as the subject line. Now, you can simplify your system to focus only on these key insights.
3. Simplification: Finally, you replace the AI with a simpler, more focused system. Instead of using an LLM to process every email, you write a bit of Python code that automates the process. It’s cheaper, faster, and more predictable.

Why Simpler Systems Are Better

It might sound strange, but “dumber” systems are often smarter in the long run. Why? Because simpler systems are:

• Easier to Maintain: Simple code is easier to test and debug. If something breaks, you can fix it quickly.
• More Secure: Static systems are less vulnerable to attacks. Unlike AI, they don’t have unpredictable behaviors that hackers can exploit.
• Cheaper to Run: AI models require a lot of computing power. Simpler systems can do the same job with fewer resources, which is better for the environment.

For example, let’s say you’re running a customer support chatbot. In the beginning, you might use an LLM to answer questions. But over time, you notice that most questions are about a handful of topics, like shipping times or return policies. Instead of using AI for everything, you create a simple FAQ system for common questions and save the AI for more complex cases.

Why AI Is Still Important

Even if the long-term goal is to simplify, AI still plays a critical role. Here are a few ways AI fits into the bigger picture:

1. Input Validation: AI can check that the data going into your system is accurate and consistent. For example, it can flag typos or unusual patterns that might cause errors.
2. Exception Handling: When something unexpected happens, AI can step in to make decisions. For instance, if a customer asks a question your FAQ doesn’t cover, the AI can provide a thoughtful response.
3. Discovery: The most powerful use of AI is helping us learn. By analyzing patterns and testing ideas, AI helps us build better systems and workflows.

A Long-Term Vision for AI Workflows

So what does the future look like? In my view, AI will become less central as we refine our workflows. Instead of relying on large, complex AI models for everything, we’ll build systems that are:

• 90% Traditional Code: Most tasks will be handled by simple, efficient code.
• 10% AI: Small, specialized AI models will handle validation, exceptions, and edge cases.

This shift will make systems more stable and accessible, reducing costs while maintaining flexibility. It’s like using a reliable car for everyday errands and saving the race car for special occasions.

Why We Should Keep AI Simple

At the end of the day, the goal of AI isn’t to replace humans or take over every process. It’s to help us work smarter by simplifying the way we do things. Here’s why that matters:

• Accessibility: Simpler systems are easier for everyone to use, not just tech experts.
• Sustainability: Reducing the size and scope of AI models makes them less resource-intensive and more environmentally friendly.
• Innovation: When systems are simple, they’re easier to build on, improve, and adapt to new challenges.

The mantra for the future should be: Keep it simple. Keep it dumb. Let AI guide us toward smarter, leaner workflows.

Use AI to Move Beyond AI

AI is an amazing tool, but it’s not the destination. It’s a way to explore new ideas, understand complex systems, and create workflows that are better for everyone. The ultimate goal is to take what we learn from AI and use it to simplify, stabilize, and strengthen our processes.

So the next time you hear about AI being used for something simple, ask yourself: Is this just hype, or is it part of a bigger journey toward clarity? The answer might surprise you.

#AI #SimplifyAI #Innovation #LoFiAI #Workflows

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