AI Agents and the business world: Emerging Use Cases and Trends for 2025

Looking back on the whirlwind of AI developments over the past year, it’s clear that 2024 was a game-changer. We’ve seen a mix of excitement and hurdles, but overall, the progress in AI has left many of us feeling hopeful. It’s wild to think about how we’ve woven various AI models into our everyday lives.
But let’s be real: we’re always hungry for more. It’s just how we are. So, let’s dive into some ongoing challenges and trends that I’m sure the big tech companies are tackling right now.
Just to be clear, I’m not here to make predictions — that’s not my thing. Instead, I want to point out areas where, like me, you’ve probably noticed that AI still has some growing up to do.
  1. AI Agents Are the Future
    Lately, there’s been a huge buzz around AI agents. But what exactly are we talking about? These are smart systems that can think, plan, and take action. They’re like problem solvers, breaking down complex issues and creating step-by-step plans to get things done. Most folks see the value in a solid AI agent.
But here’s the kicker: many of these models still struggle with logical reasoning and consistency. They can handle simple tasks like champs, but throw in a complex scenario with multiple variables, and they can trip up, making decisions that don’t quite fit.
The real magic of AI agents is in their ability to give us tailored, context-aware responses. The challenge? Finding the sweet spot between their independence and the quality of their answers. To close that gap, we’ll need even more advanced models as we head into 2025.
  1. Rethinking Human-in-the-Loop AI Systems
    Remember that fascinating study from last year where a chatbot outperformed doctors in clinical reasoning? In that study, 50 doctors were asked to diagnose medical conditions from the same case reports, and the chatbot scored higher.
What’s wild is that some doctors were randomly assigned to use the chatbot as a helper during the study. Surprisingly, this group scored lower than the chatbot working solo. This shows a breakdown in both the AI system and the human-augmentation process. Ideally, an expert and a solid AI should work better together than either could alone.
Rolling out LLM-powered chatbots is still tricky. It’s all about crafting the right prompts — asking for things just the right way. We need better systems that let professionals integrate AI tools into their workflows without needing to become AI whizzes themselves.
  1. The Rise of Ultra-Large AI Models
    We’ve seen large language models built with an astonishing number of parameters, fine-tuned during training. The models we saw in 2024 typically had between 1 and 2 trillion parameters. Now, as we move into 2025, the next generation could blow that out of the water, potentially exceeding 50 trillion parameters.
We’ve already seen launches like Gemini 2.0 and o3 from ChatGPT, which show where things are headed. It’s no surprise that these advanced models are paving the way for new business opportunities to pop up alongside them.
  1. The Potential of Compact AI Models
    While we’re talking about big models, there’s also a growing chance for smaller ones. These models, with just a few billion parameters (which still sounds like a lot), don’t need massive data centers filled with GPUs to run. They can work on laptops or even smartphones.
Take IBM’s Granite 3 model, for example. With just 2 billion parameters, it can run on a laptop without needing heavy computational power. Looking ahead, we’re likely to see more models like this designed for specific tasks, offering efficient solutions without demanding a ton of resources.
  1. The Path to Near-Infinite Memory in AI
    I still remember the first time I used generative AI to help draft an email. Back then, the context window for the LLM was only 2,000 tokens. Fast forward to today, and we have models that can handle context in the hundreds of thousands or even millions of tokens, aiming for near-infinite memory — where bots can remember everything about us at all times.
We’re entering a time where customer service chatbots can recall every conversation they’ve had with us. At first glance, this might seem like a fantastic development — but is it really?
  1. Evolving AI Applications
    So, what were the most common business uses for AI in 2024? According to a Harris survey, AI was mainly used to enhance customer experience, improve IT operations and automation, power virtual assistants, and boost cybersecurity.
As we move further into 2025, we can expect to see even more advanced use cases. With increasingly sophisticated multimodal capabilities, customer service bots will likely tackle more complex issues instead of just generating support tickets. We might also see AI systems that proactively optimize entire IT networks or security tools that adapt to evolving threats in real-time.
  1. The Role of Inference Time
    During inference, the model processes real-time data, comparing the user’s query to what it learned during training. New AI models are extending their inference capabilities, taking a moment to “think” before generating a response. The time this takes can vary: a simple query might only need a second or two, while a more complex request could take several minutes.
What makes inference time so interesting is that we can fine-tune and improve reasoning without needing to retrain or change the underlying model. This opens up two key opportunities for enhancing reasoning in LLMs: during training — by using better-quality data — and now during inference, by refining the chain-of-thought process.
This dual approach could lead to AI agents that feel significantly “smarter” and more capable.
In conclusion, as we look ahead to 2025, it’s clear that the AI landscape is evolving rapidly. While we’ve made significant strides, there’s still plenty of room for growth and innovation. Whether it’s through developing more advanced AI agents, rethinking how we integrate AI into our workflows, or exploring the potential of both ultra-large and compact models, the future of AI is undeniably bright. As we navigate these changes, I’m genuinely excited to see how these advancements will shape our lives and industries in the years to come.

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