What Will Be the Next Big Thing in AI?
For technology leaders, the important question is not which AI capability arrives first, but which capabilities justify enterprise-scale investment. Search, multimodal models, agents, robotics and self-monitoring systems create different value profiles, risk surfaces and integration demands. CIOs and transformation leaders should avoid treating them as one roadmap: some belong in customer experience, others in back-office automation, and others in long-horizon innovation bets.
The management challenge is portfolio discipline. Autonomous agents and more advanced models may unlock measurable productivity, but they also increase exposure to data quality failures, uncontrolled actions, regulatory scrutiny and model drift. That means funding decisions should be tied to use-case criticality, not vendor momentum. The next question for the portfolio board is simple: which AI initiatives can be bounded, tested and reversed if they underperform?
Operating model matters as much as capability. As AI systems become more multimodal and more autonomous, decision rights need to be explicit: who approves deployment, who monitors outcomes, who owns exceptions, and who intervenes when systems behave unexpectedly. Without clear accountability, organizations risk dispersing responsibility across architecture, security, legal and business teams while no one owns the full control loop.
Leaders should also ask what evidence will prove maturity. Useful metrics are not just model accuracy or usage volume, but cycle-time reduction, error rates, override frequency, incident recovery time and business-value realization. The next wave of AI will reward organizations that can combine experimentation with governance, and innovation with restraint.
1. Supercharged searches
Generative AI (GenAI) could soon replace traditional search methods as the primary way to find information, says Eric Bradlow, vice dean of AI and analytics at The Wharton School of the University of Pennsylvania. “GenAI not only allows for richer and more specific prompts, but also facilitates the use of multimodal inputs, such as text, voice, and video,” he explains. More importantly, it generates responses rather than simply retrieving them. “While search engines locate information, generative AI creates it.” GenAI is already here, and its capabilities will continue to expand, Bradlow predicts. “The scope of tasks that AI can perform is growing, making it an increasingly integral part of everyday life and work.”2. Advanced multimodal models
The next big thing in AI will likely be advanced multimodal models that can seamlessly integrate and process different types of data, including text, images, audio, and video, in more human-like ways, says Dinesh Puppala, regulatory affairs lead at Google. “We’re moving beyond models that specialize in one type of data toward AI systems that can understand and generate across multiple modalities simultaneously, much like humans do,” he notes. Related:Building a Culture of Cyber Resiliency with AI Advanced multimodal models will enable more natural and context-aware human-AI interactions. “They’ll be better at understanding nuanced queries, interpreting visual and auditory cues, and providing more holistic and relevant responses,” Puppala predicts. They could, for example, analyze a video of a person speaking, understanding not just the words but also the tone, facial expressions, and body language to grasp the full context of the communication, he says. In practical applications, advanced multimodal models could lead to more sophisticated virtual assistants, highly accurate content moderation systems, and AI that can generate multimedia content based on complex prompts.3. Autonomous AI agents
Ilya Meyzin, head of data science at Dun & Bradstreet, anticipates the widespread adoption and advancement of autonomous AI agents. “These are AI systems capable of performing tasks with minimal human intervention,” he explains. Related:FTCโs AI Washing Crackdown Hits Several Businesses A major upgrade from current chatbots, autonomous AI agents can use digital tools to access databases, deploy various software applications, and analyze real-time data. “This allows them to interact with different environments, adapt to new situations, and make a broad range of decisions autonomously,” Meyzin says. Current AI agents require some level of human feedback and oversight. “In the future, as agents become even more advanced, they will approach complete autonomy.” Autonomous AI agents will revolutionize various industries by automating complex manual processes, enhancing decision-making, improving efficiency, and reducing cost, Meyzin predicts.4. Smart robots
When it comes to robotics, Peter Stone, chief scientist and deputy president at Sony AI, says there are three things he’s most excited about. First is embodiment, the act of making physical robots more intelligent. Second is causal reasoning, which provides a way for AI models (and robots) to gain a better understanding of the world and what causes things to happen. Third is the amalgamation of strengths from different AI paradigms — symbolic, neural, and probabilistic. “Probabilistic AI is needed to enable AI agents to reason about likelihoods and contingencies,” he notes. Related:Is Explainable AI Explainable Enough Yet? Research related to embodiment will help to bring down the cost of robots and potentially get the technology to a point where the devices can become household appliances other than vacuum cleaners, Stone says.5. Metacognition arrives
Metacognition in AI — systems that can think about the way they think — is on the mind of Isak Nti Asare, co-director of the cybersecurity and global policy program at Indiana University. “This capability, often described as AI self-awareness, is a necessary frontier to cross if we are to build trustworthy systems that can explain their decisions.” Current AI systems, while advanced, often operate as “black boxes” where even their creators cannot fully explain their outputs. “Achieving metacognition in AI would enable these systems to self-reflect, evaluate their processes, and improve performance autonomously,” Asare says. “They could provide detailed explanations for their decisions, making their operations transparent and understandable to humans, which is critical in areas like healthcare, finance, and autonomous driving.” Metacognitive AI systems will also be able to identify and correct their own errors without human intervention, adapt to new situations more effectively, and incorporate ethical considerations into their decision-making processes. Asare estimates that such systems could become a reality within the next 10 to 15 years.Enjoyed this article? Sign up for our newsletter to receive regular insights and stay connected.