Small language models (SLMs)
Let’s talk about small language models (SLMs). These aren’t just smaller versions of the large language models (LLMs) dominating headlines, they’re a smarter way to do business. SLMs focus on delivering targeted, efficient AI solutions for specific tasks, industries, and domains. Imagine a tool that uses less energy, integrates more easily into your existing systems, and costs less, all while producing highly accurate results. That’s the promise of SLMs.
Why do they matter? LLMs like GPT-4 are incredibly powerful, but they’re also resource-hungry. Training these models can consume the same amount of energy as powering a small city. SLMs, on the other hand, are designed for sustainability. Gartner defines them as models with fewer than 10 billion parameters, still sophisticated, but optimized for specific needs.
“SLMs are faster, cheaper, and, frankly, more practical for most organizations.”
The stats are hard to ignore. A Harris Poll revealed that 75% of IT decision-makers believe SLMs outperform LLMs in terms of speed, cost, and return on investment. Industries that rely on precision, like healthcare, legal, and finance, are already adopting these models. Forrester even predicts a 60% increase in SLM adoption in 2025. If your business is looking to balance innovation with efficiency, SLMs are the way forward.
Predictive AI
Generative AI might be the buzzword, but let’s not forget predictive AI. It’s best for forecasting demand, optimizing supply chains, and predicting maintenance needs. This is about solving real-world problems reliably and efficiently.
The key difference? Predictive AI analyzes historical data to anticipate future outcomes. Generative AI creates something entirely new, like text or images. While generative AI is great for creative tasks, predictive AI is better for scenarios where precision and reliability are key.
Jayesh Chaurasia of Forrester predicts that over 50% of enterprise AI use cases will swing back to predictive AI by 2025. It’s a refocus on what works. Predictive AI applications, like demand forecasting or supply chain optimization, deliver measurable ROI and reduce risk. Combine this with generative AI, and you unlock new possibilities: predictions that guide creation, making your AI investments work even harder.
Generative AI
In 2024, many businesses dabbled in generative AI pilots, only to face steep costs and unclear results. Now, the game is changing. In 2025, companies will move from experimentation to implementation.
Generative AI creates new content, whether it’s text, images, or videos. It’s powered by deep learning methods like Generative Adversarial Networks (GANs), which allow AI to generate outputs that feel almost human. But the real power lies in its ability to drive value. Imagine automating content creation, simplifying workflows, or improving customer interactions at scale.
The numbers tell a compelling story. Forrester predicts that by 2025, 750 million apps will incorporate large language models. The market is set to grow from $1.59 billion in 2023 to a staggering $259.8 billion by 2030. Enterprises need to improve collaboration between IT and business teams, prioritize high-quality data, and anchor their AI efforts to clear objectives.
“In short, the next phase of generative AI is going to be driven by results. Focus on what matters, and the rest will follow.”
Agentic AI
Agentic AI is the shiny new toy everyone’s talking about. These domain-specific AI systems are designed to perform complex, autonomous tasks by leveraging multiple models and advanced techniques. It sounds amazing, right? But here’s the reality: while the promise is there, the execution still has a long way to go.
Why the delay? Agentic AI relies on components like Retrieval-Augmented Generation (RAG), which combines pre-trained models with real-time data retrieval. It’s a powerful concept, but aligning these moving parts to deliver consistent results is a major hurdle. Gartner says this technology is at least two years away from living up to its full potential.
It’s not a technical challenge. A Capital One survey revealed that 70% of technologists spend hours fixing data issues, which limits their ability to develop advanced AI solutions. As a result, Forrester predicts 75% of enterprises attempting to build their own AI agents in 2025 will fail, opting for third-party solutions instead. The takeaway? For now, focus on building a solid data foundation and partnering with trusted vendors while the technology matures.
Multimodal AI
Imagine AI systems that don’t just process text but integrate images, videos, and audio to create a richer understanding of the world. That’s multimodal AI. It’s huge for industries that rely on diverse data streams, from healthcare and financial services to autonomous vehicles.
The concept is simple: instead of training an AI model to handle just one type of input, multimodal learning combines multiple data types. This allows the AI to identify patterns and correlations that a single-mode system might miss. For example, in healthcare, combining patient history with medical imaging can lead to more accurate diagnoses. In autonomous driving, integrating LiDAR, GPS, and camera inputs improves navigation and safety.
The potential is enormous. Organizations will need high-quality, diverse data and the ability to integrate it into cohesive models. Forrester highlights multimodal AI as a key area of growth in 2025, with applications already expanding in sectors like medical diagnostics and autonomous vehicles. If your company isn’t exploring this yet, now’s the time.
AI success in 2025
Here’s the truth about AI success: it’s not just about technology; it’s about leadership, data quality, and talent development. Without these foundations, even the most advanced AI systems will fall short.
Let’s start with data. AI is only as good as the information you feed it. Yet, 70% of technologists report spending hours daily resolving data issues, according to Capital One. This is why enterprises need Chief Data Officers (CDOs) embedded within their IT teams to lead AI initiatives and ensure data quality. Forrester predicts 30% of CIOs will integrate CDOs into their teams by 2025, a trend that’s already gaining traction.
Leadership is another key piece. Strong collaboration between technical and business stakeholders is key to aligning AI initiatives with strategic goals. It’s no longer enough for CDOs to act as liaisons; they need to be key decision-makers driving change and delivering ROI.
Finally, talent development is non-negotiable. The gap between enterprise AI needs and workforce skills is growing. Upskilling employees and attracting top talent will be the difference between falling behind and leading the pack. In short, if you want to win with AI, focus on the basics: people, processes, and data.
Sustainability concerns
AI’s environmental impact is a growing concern, and the numbers are staggering. Training an LLM like GPT-3 consumed 500 metric tons of carbon, the equivalent of powering a small town. As businesses aim to reduce their carbon footprints, small language models (SLMs) are becoming an attractive alternative.
SLMs are not just smaller; they’re smarter in terms of resource use. They deliver precision and efficiency without the massive energy costs of LLMs. Emmanuel Walckenaer, CEO of Yseop, highlights their value in highly regulated industries like healthcare and finance, where waste from overbuilt models can be avoided. Gartner also emphasizes their role in improving task specialization while reducing costs and risks.
SLMs allow companies to achieve their AI goals while aligning with broader environmental, social, and governance (ESG) objectives. Smaller models are better for the planet and they’re good for business.
Key takeaways
- Small language models (SLMs): Businesses are increasingly adopting SLMs for their cost-efficiency, faster deployment, and industry-specific accuracy. Leaders should explore SLMs to optimize AI investments and reduce energy costs without sacrificing performance.
- Predictive AI’s resurgence: Over 50% of AI use cases in 2025 will focus on predictive applications like demand forecasting and supply chain optimization. Decision-makers should integrate predictive AI for proven reliability and measurable ROI.
- Generative AI scaling: Companies will shift from experimental pilots to production deployments, emphasizing measurable outcomes and collaboration between IT and business teams. Focus on aligning AI strategies with core business goals to ensure success.
- Leadership and talent: Upskilling and embedding Chief Data Officers in IT teams will be critical for closing the gap between AI aspirations and execution. Prioritize building strong data ecosystems and cultivating AI-ready talent.
- Smarter AI models: Sustainability concerns around LLMs are driving interest in SLMs, which offer reduced computational costs and environmental impact. Leaders should prioritize energy-efficient models to meet ESG goals and reduce operational waste.
