8 AI and machine learning trends to watch in 2025

AI agents, multimodal models, an emphasis on real-world results -- learn about the top AI and machine learning trends and what they mean for businesses in 2025.

Hype gives way to more pragmatic approaches

Since 2022, there's been an explosion of interest and innovation in generative AI, but actual adoption remains inconsistent. Companies often struggle to move generative AI projects, whether internal productivity tools or customer-facing applications, from pilot to production.

Although many businesses have explored generative AI through proofs of concept, fewer have fully integrated it into their operations. In a September 2024 research report, Informa TechTarget's Enterprise Strategy Group found that, although over 90% of organizations had increased their generative AI use over the previous year, only 8% considered their initiatives mature.

"The most surprising thing for me [in 2024] is actually the lack of adoption that we're seeing," said Jen Stave, launch director for the Digital Data Design Institute at Harvard University. "When you look across businesses, companies are investing in AI. They're building their own custom tools. They're buying off-the-shelf enterprise versions of the large language models (LLMs). But there really hasn't been this groundswell of adoption within companies."

One reason for this is AI's uneven impact across roles and job functions. Organizations are discovering what Stave termed the "jagged technological frontier," where AI enhances productivity for some tasks or employees, while diminishing it for others. A junior analyst, for example, might significantly increase their output by using a tool that only bogs down a more experienced counterpart.

"Managers don't know where that line is, and employees don't know where that line is," Stave said. "So, there's a lot of uncertainty and experimentation."Despite the sky-high levels of generative AI hype, the reality of slow adoption is hardly a surprise to anyone with experience in enterprise tech. In 2025, expect businesses to push harder for measurable outcomes from generative AI: reduced costs, demonstrable ROI and efficiency gains.

Generative AI moves beyond chatbots

When most laypeople hear the term generative AI, they think of tools like ChatGPT and Claude powered by LLMs. Early explorations from businesses, too, have tended to involve incorporating LLMs into products and services via chat interfaces. But, as the technology matures, AI developers, end users and business customers alike are looking beyond chatbots."People need to think more creatively about how to use these base tools and not just try to plop a chat window into everything," said Eric Sydell, founder and CEO of Vero AI, an AI and analytics platform.This transition aligns with a broader trend: building software atop LLMs rather than deploying chatbots as standalone tools. Moving from chatbot interfaces to applications that use LLMs on the back end to summarize or parse unstructured data can help mitigate some of the issues that make generative AI difficult to scale.

"[A chatbot] can help an individual be more effective ... but it's very one on one," Sydell said. "So, how do you scale that in an enterprise-grade way?"Heading into 2025, some areas of AI development are starting to move away from text-based interfaces entirely. Increasingly, the future of AI looks to center around multimodal models, like OpenAI's text-to-video Sora and ElevenLabs' AI voice generator, which can handle nontext data types, such as audio, video and images.

"AI has become synonymous with large language models, but that's just one type of AI," Stave said. "It's this multimodal approach to AI [where] we're going to start seeing some major technological advancements."Robotics is another avenue for developing AI that goes beyond textual conversations -- in this case, to interact with the physical world. Stave anticipates that foundation models for robotics could be even more transformative than the arrival of generative AI."Think about all of the different ways we interact with the physical world," she said. "I mean, the applications are just infinite."

AI agents are the next frontier

The second half of 2024 has seen growing interest in agentic AI models capable of independent action. Tools like Salesforce's Agentforce are designed to autonomously handle tasks for business users, managing workflows and taking care of routine actions, like scheduling and data analysis.

Agentic AI is in its early stages. Human direction and oversight remain critical, and the scope of actions that can be taken is usually narrowly defined. But, even with those limitations, AI agents are attractive for a wide range of sectors.

Autonomous functionality isn't totally new, of course; by now, it's a well-established cornerstone of enterprise software. The difference with AI agents lies in their adaptability: Unlike simple automation software, agents can adapt to new information in real time, respond to unexpected obstacles and make independent decisions.

Yet, that same independence also entails new risks. Grace Yee, senior director of ethical innovation at Adobe, warned of "the harm that can come ... as agents can start, in some cases, acting upon your behalf to help with scheduling or do other tasks." Generative AI tools are notoriously prone to hallucinations, or generating false information -- what happens if an autonomous agent makes similar mistakes with immediate, real-world consequences?Sydell cited similar concerns, noting that some use cases will raise more ethical issues than others. "When you start to get into high-risk applications -- things that have the potential to harm or help individuals -- the standards have to be way higher," he said.

Generative AI models become commodities

The generative AI landscape is evolving rapidly, with foundation models seemingly now a dime a dozen. As 2025 begins, the competitive edge is moving away from which company has the
best model to which businesses excel at fine-tuning pretrained models or developing specialized tools to layer on top of them.In a recent newsletter, analyst Benedict Evans compared the boom in generative AI models to the PC industry of the late 1980s and 1990s. In that era, performance comparisons focused on incremental improvements in specs like CPU speed or memory, similar to how today's generative AI models are evaluated on niche technical benchmarks.

Over time, however, these distinctions faded as the market reached a good-enough baseline, with differentiation shifting to factors such as cost, UX and ease of integration. Foundation models seem to be on a similar trajectory: As performance converges, advanced models are becoming more or less interchangeable for many use cases.In a commoditized model landscape, the focus is no longer number of parameters or slightly better performance on a certain benchmark, but instead usability, trust and interoperability with legacy systems. In that environment, AI companies with established ecosystems, user-friendly tools and competitive pricing are likely to take the lead.

AI applications and data sets become more domain-specific

Leading AI labs, like OpenAI and Anthropic, claim to be pursuing the ambitious goal of creating artificial general intelligence (AGI), commonly defined as AI that can perform any task a human can. But AGI -- or even the comparatively limited capabilities of today's foundation models -- is far from necessary for most business applications.

For enterprises, interest in narrow, highly customized models started almost as soon as the generative AI hype cycle began. A narrowly tailored business application simply doesn't require the degree of versatility necessary for a consumer-facing chatbot.

Notably, although AI and machine learning talent remains in demand, developing AI literacy doesn't need to mean learning to code or train models. "You don't necessarily have to be an AI engineer to understand these tools and how to use them and whether to use them," Sydell said. "Experimenting, exploring, using the tools is massively helpful."Amid the persistent generative AI hype, it can be easy to forget that the technology is still relatively new. Many people either haven't used it at all or don't use it regularly: A recent research paper found that, as of August 2024, less than half of Americans aged 18 to 64 use generative AI, and just over a quarter use it at work.


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