It seems like everyone today is talking about the latest generative AI trends, from the rise of new small language models to multimodal solutions. It’s easy to see why. Ever since the launch of ChatGPT in November 2022, Gen AI has taken the world by storm.
This innovative technology is impacting every business, industry, and consumer. With Gen AI, companies can enhance productivity, improve customer service, transform data analysis, and more. It’s little wonder that McKinsey research suggests generative AI will add up to $4.4 trillion to the global economy each year.
As adoption of generative AI grows, the technology itself is evolving too, creating new opportunities for businesses to explore. Here are the top generative AI trends worth watching this year.
1. Multimodal Shapes Generative AI Trends
Back when ChatGPT was first introduced, generative AI was primarily seen as a tool for analyzing, understanding, and creating text. Now, model frameworks are evolving, allowing users to process and create a wider range of content, from images and videos, to code, and audio.
Multimodal solutions, like GPT-4o, and Google’s Gemini aren’t just influencing generative AI trends in content creation, marketing, or advertising either. Companies are creating tools to understand video feed images to provide more intimate, personalized customer service.
We’re also seeing the rise of solutions that can improve security by identifying biometric data in a person’s face, eyes, or voice or pinpointing suspicious factors in an image. The rise of multimodal models will lead to endless new use cases for generative AI in every business.
2. Small Language Models Make a Big Impact
2023 was the year when large language models stepped into the spotlight. They offered valuable insights into how generative AI solutions worked and how powerful they could be. However, many companies struggled to take advantage of LLMs due to the computing power and data required.
In 2024, small language models, like Microsoft’s Phi-3, began gaining more attention. These models are smaller in parameter count, storage, and memory requirements, which means they can run on less expensive and powerful hardware. However, they still produce content of comparable quality to some of their larger counterparts.
Plus, many vendors offering access to “SLMs” are allowing enterprises to fine-tune their SLMs to specific tasks and functions. This could help companies create customized applications and tools more rapidly while keeping costs low.
3. Autonomous Agents Grow More Advanced
Autonomous agents are another key factor shaping generative AI trends. Not to be confused with Gen AI chatbots for the contact center, autonomous agents offer companies a way to build AI solutions that can accomplish specific objectives.
These software programs use AI to perform a range of tasks without human input. For instance, they can use tools, like information stores or knowledge bases to surface information, and plan and execute tasks. With advanced algorithms and machine learning, the agents can adapt to new situations and evolve over time, becoming more efficient.
Already, various frameworks have emerged for developing autonomous agents, such as LangChain and LlamaIndex. In the years ahead, we’ll likely see new frameworks that can take advantage of multimodal AI capabilities, and new algorithms. Gartner even predicts that by 2028, one third of interactions with Gen AI services will involve the use of autonomous agents.
4. Demand for Flexibility Drives Generative AI Trends
The rise of small language models and autonomous agents are just two generative AI trends that indicate a growing demand for customization and flexibility in the industry. There are plenty of other examples of vendors looking for unique ways to democratize access to AI, and enable companies to tailor their tools to their needs.
For instance, open-source generative AI models have become increasingly popular this year, such as Falcon 180B, and Claude 2. These models offer endless flexibility to enterprises, allowing them to build the tools best-suited to specific tasks, and host them either in the cloud, or on-premises.
Elsewhere, “Bring Your Own AI” (BYOAI) is also driving faster enterprise adoption. Many UCaaS, CCaaS, CPaaS, and other software leaders are enabling companies to bring their preferred pre-built, or custom AI models into existing platforms. This approach enables greater customization and more control over models for business leaders, reducing security risks.
5. Conversational AI and Gen AI Join Forces
Conversational AI and generative AI have become more closely connected in the last couple of years. Both solutions offer exceptional benefits to businesses. Conversational AI makes it easy for users to have natural interactions with AI tools using machine learning and natural language processing.
Generative AI empowers accelerated content creation and transforms creativity. Used together, these tools can be even more valuable. Many popular generative AI tools, like Microsoft Copilot, use a mixture of Gen AI and conversational AI to enhance user experiences.
Gen AI enhances conversational AI, allowing tools to not only comprehend human input and context, but produce coherent, personalized, and valuable responses. The combination of these tools will be extremely valuable going forward, particularly in the realm of customer service.
6. Industry-Specific Generative AI Trends Evolve
Aside from giving companies more freedom to build and customize their own generative AI tools, vendors are also increasing their focus on industry and vertical-specific solutions. Rather than simply offering one-size-fits-all solutions, companies like Microsoft are tailoring bots to certain use cases.
For instance, since announcing Microsoft Copilot, the tech giant has introduced specific solutions for sales teams, customer service, collaboration, and security. Other innovators like Salesforce are following in those footsteps, taking a more granular approach to AI design.
The introduction of use-case-specific models doesn’t just make it easier for companies to choose the right tool for their specific needs. It can also help businesses adhere to emerging compliance standards. For instance, tools like Morgan Chase’s IndexGPT adhere to financial regulations. Other solutions are specifically designed for the healthcare or manufacturing industry.
7. New AI Governance Standards Emerge
Finally, perhaps one of the most complicated generative AI trends worth focusing on this year is the emergence of new governance standards. Many companies are already familiar of the risks associated with Gen AI tools. Though these solutions are powerful, they also rely on large amounts of data, which creates privacy, security, and ethical concerns.
As a result, countless governments and industry regulators are introducing new policies for AI governance. The EU has already introduced an AI Act focusing on transparency, explainability, ethics, and data security. The US has a similar policy: the Executive Order of AI Safety.
Since generative AI is evolving at such a significant pace, these governance standards will likely change significantly in the years ahead. Companies in all industries will need to ensure they’re prepared to adapt to new rules about building, deploying, and monitoring AI tools.
Stay Ahead of the Latest Generative AI Trends
Generative AI has undoubtedly transformed the world we live in, and it’s impact is far from over. As innovators continue to explore new algorithms, training strategies, and models, the potential of generative AI will continue to grow.
Most businesses will rely on generative AI to some extent in the years ahead. Developing your AI strategy now, based on the latest trends and emerging dynamics in the landscape, is how you stay one step ahead of the competition.
