Are Autonomous Agents the Next Big Leap in AI Efficiency?

So, according to Gartner, by 2027, about 40 percent of generative AI solutions will be multimodal, meaning they'll handle text, image, audio, and video all together. That’s a big jump from just 1 percent in 2023. This shift toward multimodal AI enhances how humans interact with AI and helps companies create more unique offerings.

Erick Brethenoux, a VP Analyst at Gartner, explains that as these models are trained to understand different types of data at once, they’ll get much better at capturing the relationships between them. So, for example, AI will be able to handle multiple tasks involving various forms of media more effectively. This will also make AI more helpful across many fields, not just in one specific area.

Multimodal generative AI is one of two key technologies Gartner highlighted in their 2024 Hype Cycle for GenAI. They believe early adoption of these technologies will give companies a competitive advantage, especially when it comes to getting new products and services to market faster. Alongside multimodal AI, open-source LLMs are expected to have a massive impact on businesses over the next five years.

Another interesting point Gartner made is that the GenAI ecosystem is rapidly evolving, and it’s pretty overwhelming for businesses to navigate. Arun Chandrasekaran, another Gartner VP Analyst, mentioned that right now, GenAI is in what they call the "Trough of Disillusionment." This means there's a lot of hype, but not all of it is delivering just yet. Once things settle down, we’re going to see some big advancements in what GenAI can actually do.

Now, let’s break down the technologies Gartner highlights:

Multimodal GenAI will completely transform enterprise applications. It’s going to add new features and capabilities that weren’t possible before. The cool part is that it’s not limited to one specific industry; it can be applied anywhere AI interacts with humans. Right now, we mostly see multimodal AI working with two or three types of media (like text and images together), but this will grow to include more.

Open-source LLMs are another game-changer. These are large language models that are open for developers to customise and optimise. They make it easier for businesses to use AI in specific ways without being locked into one vendor. Plus, they allow for innovation since developers can tweak them for better privacy, security, and task-specific uses.

Then we have domain-specific GenAI models, which are designed to work in particular industries or for certain tasks. Since they’re specialised, they tend to be more accurate and secure, and they can save companies a lot of time and effort. Think of them as a more tailored version of AI, where you don’t need to give them as much guidance for complex tasks.

Finally, autonomous agents are a big leap. These are systems that can achieve goals without needing humans to intervene. They’re smart enough to observe, make decisions, and act on their own, learning and improving over time. This could really change how businesses operate by automating more complex tasks and reducing costs.

In short, we’re on the verge of some major breakthroughs in generative AI, and over the next few years, these technologies are expected to change how businesses operate, innovate, and compete.