The artificial intelligence landscape is shifting, and agentic AI is set to redefine business intelligence in 2025 by shifting from mere generation to true decision-making and reasoning.

According to theCUBE Research’s latest analysis, as discussed in the new “The Next Frontiers of AI” podcast, the industry is moving beyond generative AI’s hype and into an era defined by agentic AI, reasoning and trust. In a recent discussion, Scott Hebner (pictured, left), principal analyst at theCUBE Research, and Tim Sanders (pictured), vice president of Research Insights at G2 Inc., laid out six major AI predictions that will define the year ahead.

Their insights reveal a clear pattern: While large language models have dominated AI adoption, their limitations are becoming increasingly apparent. The next wave of AI innovation will demand more than just text generation — it will require decision-making, reasoning and deeper explainability. Here’s what to expect in 2025.

1. LLMs alone won’t deliver ROI.

For the past two years, businesses have raced to implement generative AI. More than 70% have deployed at least one use case, but according to a recent survey, only 18% report high ROI. The reason? LLMs are correlation-based — they generate responses without true understanding or adaptability. To create enterprise-grade AI, companies will need to layer decision intelligence, planning and reasoning capabilities on top of LLMs.

2. AI reasoning becomes accessible.

One of the biggest limitations of AI today is its inability to reason. In 2025, theCUBE Research predicts that reasoning capabilities — like chain-of-thought processing, semantic understanding and causal AI — will be democratized. This shift will allow AI to break down complex problems, plan more effectively and refine its decision-making process, making it a more reliable tool for businesses.

3. AI explainability will drive adoption.

Explainability is no longer optional — it’s the currency of innovation. Businesses won’t trust AI-driven decisions unless they understand how they are made. Regulatory pressure, enterprise risk concerns and ethical considerations are forcing AI vendors to prioritize explainability. Expect to see new frameworks, tools and standards that provide transparency into AI-generated outcomes.

4. Generative AI enters the ‘browser phase.’

Hebner compares generative AI’s trajectory to the rise of web browsers — once a dominant focal point, now an infrastructure layer. The industry is shifting away from standalone LLMs toward integrated AI ecosystems. Much like web browsers eventually faded into the background as enablers of innovation, generative AI will become just another enterprise tool, with value shifting toward applications built on top of it.

5. Software developers must become data scientists.

In 2025, software engineering and data science will converge. AI-powered development tools and intelligent coding assistants will force developers to acquire data science skills. Businesses will increasingly expect software teams to build AI-driven applications, rather than just traditional software.

6. AI talent augmentation will be critical.

The demand for AI talent far outstrips supply. With a 2.3x gap between open AI jobs and available talent, businesses will be forced to augment their workforce with AI-powered agents and external experts. Companies will invest in AI talent platforms and upskilling programs to bridge the skills gap.

The road ahead

The key takeaway from theCUBE Research? AI is evolving — fast. As the industry moves beyond generative AI into agentic, decision-making AI, businesses must prepare now or risk falling behind.

For a deeper dive into these predictions, check out the full analysis from theCUBE Research here.

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