💡 Why Not Every AI Use Case Needs Generative AI: What You Should Know

Introduction

OK, you have probably heard just about every man and his dog going on about Generative AI these days — particularly those fancy Large Language Models (LLMs). You would not want to be left out of the trendy new thing in the market that companies are jumping over themselves trying frantically acquire. I mean, who wouldn’t want to get into what appears to be the next future of everything, right?

The point here is though while LLMs are in fact pretty cool but they are not a magic bullet and their use should be critically evaluated. This is akin to attempting to leverage a hammer for all solutions strictly because it may be shiny and new. Spoiler alert: it failed to work well.

And, this is where knowing the correct AI use case comes in! (Yeah, you guessed it right — we mean “AI use case” a lot because yeah… that matters!)

This post — created to subtly (maybe with a joke or two) and gently persuade leaders who are on the fence about embarking themselves into urgent AI deployment – will helpfully make them think twice. Cool under fire: they might be, but that doesn’t mean LLMs are the best in every situation. So, join us in this AI adventure and see what works — and maybe what’s just a mirage.

I. Twelve AI Use Case Families: Where AI Really Shines (and Sometimes Fumbles)

Alright, folks, let’s talk about the different ways AI can make our lives easier—or at least more interesting. We’ve got twelve AI use case families here, each one with its little quirks. Think of them as the Avengers of the AI world, each hero (or technique) with its superpower.

1. Prediction / Forecasting

• What’s the deal? Imagine you’re trying to guess tomorrow’s weather or next month’s sales numbers. That’s where AI steps in, looking at past data and saying, “Hey, here’s what might happen next.”

• Example: You’ve got sales data from the past year. AI takes one look and says, “I bet you’ll sell a ton of umbrellas next week because it’s going to rain.”

  

2. Autonomous System

• What’s the deal? This is where AI goes all independent on us. Think of robots and drones doing their thing without human help.

• Example: Picture a drone buzzing around power lines, checking for problems, and you’re sitting back with a cup of coffee. Yep, AI’s got this.

  

3. Planning

• What’s the deal? Life’s complicated, right? Planning is where AI helps sort out the chaos, finding the best way to get things done with minimal fuss.

• Example: AI looks at traffic data and tells you when to schedule roadwork to avoid the morning rush. No more honking horns or angry commuters—well, fewer, at least.

  

4. Decision Intelligence

• What’s the deal? AI gives you the insights you need to make smarter choices. It’s like having a super-smart friend who’s always ready with advice.

• Example: Need to make a big decision at work? AI crunches the numbers and says, “Here’s what you should probably do.” You’re still the boss, though—AI just makes you look good.

  

5. Recommender System

• What’s the deal? Ever wonder how Netflix knows you love rom-com? That’s a recommender system at work, suggesting things you’ll like before you even know you want them.

• Example: You’re on Spotify, and suddenly it’s like the app is reading your mind, playing exactly the song you need to hear. AI magic.

  

6. Segmentation / Classification

• What’s the deal? AI helps you sort through a big pile of stuff and figure out what’s what. It’s like Marie Kondo, but for data.

• Example: You’ve got a bunch of loan applicants. AI says, “These folks are low risk, these are medium, and these… well, maybe don’t lend them money just yet.”

  

7. Intelligent Automation

• What’s the deal? AI teams up with automation to make your business run smoother. It’s like having a well-oiled machine that can think for itself.

• Example: AI predicts that one of your factory machines is about to break down. You fix it before it even becomes a problem, and everyone’s happy.

  

8. Perception

• What’s the deal? AI uses its sensors—vision, sound, whatever—to make sense of the world around it. Kinda like how you use your senses, but without the coffee breaks.

• Example: A camera catches someone running a red light, and AI sends them a ticket faster than they can say, “It wasn’t me!”

  

9. Anomaly Detection

• What’s the deal? AI spots the weird stuff—things that don’t quite fit. It’s like having a superpower for noticing when something’s off.

• Example: Your electricity grid is acting funky, but you don’t know why. AI says, “Hey, there’s something strange going on with these ten generators.” Mystery solved.

  

10. Conversational User Interfaces

• What’s the deal? AI chats with you, answering questions and helping out, just like a friendly customer service rep—minus the bad hold music.

• Example: Need help with your order? A chatbot pops up and says, “What can I do for you today?” No human is needed, unless things get complicated.

  

11. Content Generation

• What’s the deal? AI gets creative, whipping up text, images, videos—you name it. It’s like having an artist and writer rolled into one, but way faster.

• Example: You need a blog post by tomorrow. AI’s got you covered with a draft in minutes. Just add your human touch, and you’re good to go.

  

12. Knowledge Discovery

• What’s the deal? AI digs through mountains of data to find hidden gems—patterns and insights you might have missed. It’s like panning for gold, but with data.

• Example: AI analyzes a ton of patient data and says, “Hey, I think we’ve found a new way to treat this condition.” Who knew?

  

So, there you have it—twelve AI use case families, each with its special skills. Pick the right one, and you’re on your way to AI greatness (or at least a lot fewer headaches).

II. Six Common AI Techniques: The Toolbox for Every AI Use Case

Well, prepare yourselves — we’re going in DEEP on the six fundamental algorithms behind just about every AI application there is. These are the faithful companions in the AI toolbox, each one having its special abilities (imagine the superpowers of your superheroes). How about let’s break them down shall we?

1. Non-Generative Machine Learning

• What’s the deal? Think of this as the bread and butter of AI. These are the classic methods that have been around for a while, like the wise old grandparent of AI techniques.

• Examples: Ever heard of linear regression, clustering, or decision trees? Yep, that’s what we’re talking about. They’re simple, effective, and they get the job done—kinda like a good old-fashioned cup of coffee.

2. Simulation

• What’s the deal? Ever wondered what would happen if you changed one little thing? Simulation is your “what-if” machine. It’s like AI playing a giant game of ‘what happens next?’ with real-world scenarios.

• Example: Imagine you’re trying to optimize a process—like figuring out the best layout for a factory. Simulation lets you test different setups without moving a single piece of equipment. Way less hassle, right?

3. Optimisation

• What’s the deal? This technique is all about finding the sweet spot. Whether you’re balancing a budget or fine-tuning a marketing campaign, optimization helps you get the most bang for your buck.

• Example: Let’s say you’re running a sale. You don’t want to discount so much that you lose money, but you also want to attract customers. Optimization helps you hit that perfect price point where everyone’s happy—especially you.

4. Rules / Heuristics

• What’s the deal? Sometimes, you don’t need fancy algorithms; you just need a good set of rules. This technique uses predefined rules to make decisions. It’s like AI with a cheat sheet.

• Example: Think of a rule-based system that makes decisions based on expert knowledge. It’s like having a little voice in your head that says, “Hey, remember what the expert said—do this!” No guesswork is involved.

5. Graphs

• What’s the deal? Graphs aren’t just for math class. In AI, they’re super useful for mapping out relationships between different pieces of data. It’s like connecting the dots in a complex puzzle.

• Example: Need to understand how different data points are connected? Graphs map it all out, showing you relationships that might not be obvious. Imagine you’re building a social network—graphs help you see who’s friends with whom, and how those connections matter.

6. Generative Models

• What’s the deal? Here’s the one you’ve been waiting for: Generative Models. These guys are the artists of the AI world, creating new content from scratch. Text, images, videos—you name it.

• Example: Ever used ChatGPT? That’s a generative model in action, whipping up text like it’s got a million ideas just waiting to be typed out. Pretty cool, right?

So, there you have it—six AI techniques that are the backbone of every AI use case. Each one has its strengths, and knowing when to use it is like having the ultimate AI superpower. Choose wisely, and you’ll be the hero of your AI project.

III. The Matrix: Matching AI Use Case Families with the Right Techniques

Alright, let’s get into the nitty-gritty of pairing AI techniques with the right AI use case. Think of it as a matchmaking service but for AI. We’ve got this handy matrix that tells you which techniques are most likely to succeed with each use case—and which ones might leave you scratching your head.

So, what’s this matrix all about? Simple: it’s a tool to help you figure out which AI techniques are best suited for specific AI use cases. It’s like having a cheat sheet that says, “Hey, this method works great here, but maybe not so much over there.”

1. Stability Ratings: Low (L), Medium (M), High (H)

Each technique gets a rating for how well it fits a particular use case. High means you’re golden—go ahead and use it with confidence. Medium? You might want to think twice before pulling the trigger. And if it’s Low, well, that’s AI’s way of saying, “Nope, not a good idea.”

2. Guidelines for Use:

• High Suitability: When the matrix gives a technique a High rating, it’s telling you, “Go for it!” This is your green light to confidently apply that technique to your AI use case.

• Medium Suitability: A Medium rating is like a yellow light—proceed with caution. It might work, but you’ll want to carefully consider whether it’s the right tool for the job. Think of it as a “maybe, but don’t blame me if it doesn’t work out” kind of situation.

• Low Suitability: And then there’s Low. This is the matrix’s way of politely suggesting you look elsewhere. Using a Low-rated technique for your AI use case is like trying to use a spoon to cut steak. Sure, you could try, but it’s probably not going to end well.

So there you have it—the matrix is your go-to guide for making smart choices in AI. Just match your AI use case with the right technique, and you’ll be well on your way to AI success (or at least avoiding some headaches). And remember, just because something’s shiny and new doesn’t mean it’s always the right fit—sometimes, the classics are classics for a reason!

IV. Generative Models: A Deeper Look into This AI Use Case

Alright, let’s talk about generative models—the AI use case that’s like the creative artist in the AI family. These models are the ones you’ve probably heard about, the ones that make cool stuff like text, images, and even music. But before you start thinking they can do it all, let’s get real about where they shine and where they might, well, not.

1. The Role: When to Call in the Generative Models

Generative models are like that friend who’s great at whipping up something new on the spot. Need some text written? They’ve got you covered. Want to generate some code? No problem. But just because they’re good at creating doesn’t mean they should be asked to predict the future (leave that to your horoscope, maybe?).

2. Caution: Don’t Ask Your LLM to Predict the Weather

Here’s where things get a little tricky. While generative models like LLMs (think ChatGPT) are awesome for tasks like content generation, asking them to predict your next quarter’s sales is like asking a painter to fix your car. Sure, they might give it a shot, but don’t be surprised if things don’t turn out as planned. Predictive tasks are better left to those other AI techniques we talked about earlier.

3. Proper Use: Let’s Stick to What They’re Good At

So, when should you use generative models? Think of them as your go-to for anything creative. Whether it’s generating text, coding scripts, or even creating art, these models excel. Need a blog post in a pinch? Generative AI has your back. Just don’t expect them to give you accurate sales forecasts—stick to content generation, and you’ll be in safe hands.

4. Misuse Example: Don’t Blame ChatGPT for Bad Predictions

A classic misuse example? Asking ChatGPT to predict your company’s future sales. Spoiler alert: It’s not going to end well. Generative models are great at spinning out creative content but trying to use them for number-crunching predictions is like using a hammer to cut paper—it’s just not what they’re built for.

So, the takeaway here? Generative models are amazing for the right AI use case, but make sure you’re using them for what they’re designed to do. Stick to content generation, and you’ll be on the right track.

Conclusion

So, here’s the deal: we’ve covered twelve AI use case families and six core AI techniques, all with their quirks and superpowers. The key takeaway? It’s all about matching the right AI technique to the right AI use case—just like you wouldn’t use a spoon to cut steak (unless you’re really into challenges). And while generative models like LLMs are the new rockstars in town, remember—they’re not the solution to every problem. Before diving headfirst into AI investments, it’s worth taking a step back and thinking critically. Because, let’s be honest, AI is cool, but only when it’s used wisely.

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