Generative AI 2025 Predictions: Part 2
Small Language Models: The Future of AI
Large language models (LLMs) are, well, big. But in 2025, businesses will find that there’s a lot of benefit to running small language models, says Hao Yang, the vice president of artificial intelligence at observability firm Splunk.
"Today’s LLMs know everything. But why do you need that? If you reduce the model to a reasonable size that fits your specific use case, you can reduce the cost significantly," Yang says. "This is why we’ll see a rise in domain-specific small language models (SLMs), which will deliver unprecedented accuracy while significantly reducing operating costs and environmental impact."
Agentic AI: The Next Frontier
2025 will be the year that agentic AI jumps from demo-ware directly into the hype cycle, says Ciaran Dynes, the chief product officer at data integration provider Matillion, who also predicts that data governance will be back in vogue.
"Time will tell if the agents can be controlled and not go off into a never-ending cycle of handoffs between themselves, but the early signs are very positive that agentic AI is about to kick off," Dynes says. "It’s been a tough couple of years for data governance projects, however next year will see the reemergence of investment in data management and data governance practices. Data as product is the methodology of the day. But that’s a good thing for data teams."
The Semantic Layer: A Key Enabler for LLM Adoption
Ariel Katz, the CEO of embedded analytics provider Sisense, isn’t one to argue semantics. But he does make a strong argument for the semantic layer becoming a key enabler for LLM adoption in the enterprise in 2025.
"In 2025, the semantic layer will become the crucial enabler for LLMs in enterprises, acting as a bridge between internal data and LLMs to deliver precise, contextually relevant insights," Katz says. "By unifying enterprise data with global knowledge, this integration will revolutionize decision-making and productivity, making GenAI indispensable. Companies that embrace this convergence will dominate in innovation and customer experience, leaving competitors behind."
GenAI and Search: A Match Made in Heaven
Search has been called the original AI use case, particularly with the neural search techniques that go beyond brute-force keyword matching. In 2025, the integration of GenAI and search will reach new levels, predicts Keri Rich, the vice president of product management and search provider Lucidworks.
"I expect to see different generative AI-powered experiences woven seamlessly into the entire commerce search and discovery experience next year, including saying goodbye to the search bar," Rich says. "We’ve already seen plans from companies like Amazon to use GenAI to expedite customer research and simplify product comparisons. Retailers first need to understand which parts of their shoppers’ journeys require the most attention and then design their experiences accordingly."
GenAI to Operate with Greater Independence and Precision
Most businesses have kept their GenAI projects on a short leash, lest the LLMs run amok and embarrass the company with erroneous output. But in 2025, GenAI will operate with greater independence and precision, particularly as advancements in symbolic reasoning and methods to combat hallucinations take hold, predicts Kelly Littlepage, CEO and co-founder of OneChronos, which provides search optimization for trading markets.
"In financial markets, AI adoption will progress incrementally across different functions. Back and middle office operations will see continued productivity gains through AI-driven human-in-the-loop (HITL) automation," Littlepage says. "Trading desks are already leveraging generative AI to process semi-structured data more efficiently, though current capabilities are all HITL and stop short of completing trades or acting autonomously. As AI develops more sophisticated reasoning and explainability, it will gradually move into more complex front-office tasks and play increasingly central roles in trading workflows."
GenAI to Update Itself
GenAI tech is advancing at an incredible rate, but humans typically are still required to update foundation models. In 2025, the models will begin to update themselves, predicts Mike Bachman, the head of architecture and AI strategy for data integration and automation firm Boomi.
"Memory improvements and techniques associated with retrieving and merging greater levels of context pave the way for LLMs to adaptively learn, improve their answers, and update their own ‘world model,’ once deployed," Bachman says. "Expanded context windows, quantization techniques, and agent-based information retrieval will allow a model to intrinsically update itself without retraining. As enterprises invest more resources in data quality up front, LLMs with better memory systems will be able to update their foundational training with novel information based on more context-dependent, filtered, and accurate information — and it’s only a matter of time before they’re so well-informed that they’re able to write, debug, and improve themselves."
Quantum Computing and AI: The Future of Computing
Quantum computing (QC) has made a lot of progress in recent years, to the point where companies are close to finding real-world applications for QC. In 2025, the combination of QC and AI will start to look intriguing for business, says Enrique Lizaso Olmos, the CEO and co-founder of Multiverse Computing.
"In 2025, Quantum Computing will further solidify its position as a transformative technology with real-world applications. Also, the synergy between quantum computing and artificial intelligence (AI) will become increasingly evident. Quantum technology is emerging as a critical tool for enhancing AI’s efficiency, while AI plays a key role in integrating quantum solutions into practical applications. This reciprocal relationship has enabled both technologies to address their respective challenges more effectively."
Conclusion
As we look to 2025, it’s clear that GenAI will continue to shape the future of business and technology. With advancements in small language models, agentic AI, the semantic layer, and the integration of GenAI and search, the possibilities are endless. But as we move forward, it’s crucial to prioritize AI literacy, data governance, and the responsible development and deployment of GenAI technologies.
Frequently Asked Questions
Q: What is GenAI?
A: GenAI refers to the next generation of artificial intelligence, characterized by its ability to generate human-like language, perform complex tasks, and learn from data.
Q: What are the benefits of using GenAI?
A: GenAI can improve productivity, enhance decision-making, and provide new insights and opportunities for businesses and individuals.
Q: What are the challenges of using GenAI?
A: GenAI can be difficult to implement and require significant resources, and there are concerns about its potential impact on jobs and society.
Q: What is the future of GenAI?
A: The future of GenAI is bright, with advancements in small language models, agentic AI, the semantic layer, and the integration of GenAI and search set to shape the future of business and technology.
