How Small, Specialized Language Models Can Outperform the AI Giants
By Ari Widlansky, Managing Director & U.S. Chief Operating Officer, Esker
You wouldn’t hire a generalist for a job that requires a specialist. So why are many businesses relying on broad, general-purpose large language models (LLMs) for tasks that demand greater precision?
From Open AI’s ChatGPT-4 to Amazon’s recent investment in Anthropic, LLMs have taken the AI world by storm. And while these models offer impressive breadth and a myriad of applications, they often lack the precision and nuance required for specialized business applications — where small language models (SLMs) are proving a worthy competitor.
As companies look to refine their AI investments in the year ahead, IT decision-makers have a chance to look beyond the LLM hype. SLMs prioritize specificity over scale, enabling businesses to boost accuracy and efficiency through targeted, domain-specific AI solutions.
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The limitations of large language models
The GenAI market has exploded since ChatGPT’s launch in November 2022, with the IDC forecasting global AI spending will reach $632 billion by 2028.
Yet, despite the ongoing swell in AI investments, the technology’s tangible effects can still be hard to define. Nearly half (41%) of organizations have struggled to measure the exact impact of their GenAI efforts, and Gartner predicts that 30% of GenAI projects will be abandoned by the end of 2025, partially due to unclear business value.
LLMs, like those powering ChatGPT, often contribute to this problem.
Trained on massive datasets collected from diverse sources across the internet, LLMs typically leverage millions or billions of parameters to support a broad range of tasks. This versatility has its strengths, but also well-documented limitations, namely the risk of producing inaccurate and misleading outputs known as “hallucinations.” Additionally, LLMs are resource-intensive to onboard, train and fine-tune for specific use cases, which can quickly drive up implementation and management costs as use cases scale.
Comparatively, SLMs are built for specialization. Unlike their larger counterparts, SLMs are trained on highly curated datasets to develop narrower, domain-specific expertise. Since their scope is intentionally limited from the start, businesses gain more relevant and predictable responses while significantly reducing operational and infrastructure overhead.
LLMs are certainly powerful generalists, but many businesses now recognize the need for greater focus in their AI use, which SLMs are poised to deliver.
Think small: 3 steps to leverage SLMs for greater business efficiency
For functions that demand fast, relevant outputs, SLMs are a precise and cost-effective alternative to LLMs. In particular, teams burdened by repetitive, high-effort tasks — like customer service — benefit from AI models designed to maximize efficiency without sacrificing accuracy.
Moving forward, CIOs and CTOs can leverage the following three steps to determine strategic applications for SLMs aligned with organizational priorities:
Define goals and use cases for early SLM adoption.
It’s critical to identify and define areas where SLMs can provide measurable value from the outset. Cross-functional collaboration helps surface team-level pain points, particularly workflows that may benefit from automation but require more contextual understanding and specificity than LLMs can provide.
For example, conversations with your department heads could reveal that customer service teams struggle with error-prone tasks, such as managing high volumes of repetitive inquiries or pulling data from incoming orders. In this scenario, an LLM might generate inconsistent outputs or require extensive training to perform effectively, negating potential efficiency gains.
On the other hand, an SLM trained to extract key order data can streamline this specific process, while also offering greater transparency and explainability to avoid the “black box” issue commonly associated with LLMs.
Launch a pilot program to test and adapt.
Once you’ve identified potential SLM use cases, the next step is to launch a pilot program. As part of this process, consider your level of in-house expertise to deploy and manage the SLM, whether it’s a pre-trained model or a custom-built solution.
Some organizations may choose to train their own model for highly specialized tasks or adapt an existing tool for faster deployment. But if you’re working with a pre-trained SLM that aligns with a specific function — like your customer service department — start by applying the model to a single workflow. Throughout the pilot phase, collect feedback from employees and monitor performance against predefined metrics, such as accuracy, speed and consistency.
For instance, a pilot might reveal that the SLM significantly reduces order processing times and boosts team morale. These proof points offer concrete validation of the SLMs usefulness and reinforce ROI.
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As you evaluate off-the-shelf options, assess how the model’s training data matches your operational challenges. An SLM trained on order-specific language will more accurately identify, route and extract order details, which means faster processing and more tailored recommendations for your customer service representatives.
Review success metrics and scale strategically.
If an SLM pilot demonstrates value for a particular workflow, consider its application in other contexts. For example, an SLM that optimizes order processing for customer service could also be applied to procurement or accounts payable to streamline invoice processing.
Additionally, remember that integrating SLMs doesn’t mean abandoning LLMs altogether. Many organizations benefit from a hybrid approach, deploying SLMs for precision use cases while employing LLMs for more complex organizational interactions.
To position your AI initiatives for long-term success, form a cross-functional AI oversight team with representatives from IT, operations and department leadership. This team can help monitor SLM performance, identify areas for improvement and align both SLM and LLM AI deployments with broader organizational goals.
Small models, big impact
AI, and in particular GenAI, is now table stakes for businesses across industries. But the opportunity for innovation shouldn’t stop at LLMs.
Organizations that can look beyond LLMs to embrace the potential of SLMs stand to unlock more precise, targeted support for key business functions, and a competitive differentiator in crowded markets.
It may feel counterintuitive at first, but if you’re looking to build an agile, future-focused AI strategy that drives lasting value, it’s worth thinking small.
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