In a landscape dominated by the sheer scale of large language models, our guest today, Chloe Maraina, a Business Intelligence expert with a passion for data science, urges us to look at their smaller, more agile counterparts. She argues that for many enterprises, the key to unlocking real, measurable ROI from AI doesn’t lie in having the biggest model, but the smartest, most focused one. Chloe is here to cut through the hype and provide a blueprint for how small language models are becoming the unsung heroes of automation in corporate IT and HR.
Today, we’ll explore how these nimble AI systems are moving beyond theory to deliver tangible cost savings and productivity boosts. We’ll delve into the practical steps for tailoring SLMs to a company’s unique DNA using its own internal data. Chloe will also shed light on the crucial decision-making process for CIOs: when to deploy a fast, efficient SLM versus a more powerful but slower LLM. Finally, we’ll discuss how a balanced, hybrid architecture can create resilient AI systems and look ahead at how SLMs might just be the key to avoiding the high failure rates predicted for agentic AI projects.
The article frames SLMs as an “ROI savior” for automating routine IT and HR tasks. Beyond freeing up staff time, how does this translate into measurable cost savings and productivity gains? Could you walk us through a real-world example of an agent autonomously resolving an employee’s request?
It’s a fantastic question because it gets right to the heart of the matter. The “savior” aspect isn’t just about deflecting tickets; it’s about fundamentally changing the cost and productivity equation. The savings are multi-layered. First, there’s the direct reduction in operational costs from using models that require significantly less energy, memory, and compute power, which is a huge benefit when you’re running on cloud platforms. Second, you get this incredible productivity lift. Imagine an employee who needs proof of employment for a mortgage application. The old way involved finding the right form, emailing HR, waiting, and maybe following up. Now, they can simply send a Teams message: “I need proof of employment.” The AI agent, personalized to their profile, instantly generates the correct document and delivers it. That employee just saved 30 minutes of distraction and the HR professional didn’t have to touch it, allowing them to focus on strategic work like talent development instead of administrative tasks. It’s that immediate, frictionless experience multiplied across the entire organization that creates a massive, measurable return.
You highlight fine-tuning SLMs with internal data like support tickets to increase relevance. How crucial is this step for success, and what are the first three practical steps a company should take to prepare its data for fine-tuning an SLM for a specific purpose like employee onboarding?
Fine-tuning isn’t just crucial; it’s the difference between a novel toy and a mission-critical business tool. An off-the-shelf model doesn’t know your company’s specific policies, system names, or internal jargon. Grounding the model in your own data is what makes the responses accurate and truly helpful. For a company starting this journey for employee onboarding, the first three steps are foundational. First, you must clearly define the scope. Instead of boiling the ocean, focus on the top 10-15 most common onboarding questions. This narrows your data requirements significantly. Second, you need to aggregate the right data sources. This means pulling not just IT and HR support tickets related to new hires, but also internal Slack channel conversations, and relevant sections of your employee handbook. This is where the real nuance of your organization lives. Finally, the third step is to curate and structure that data. You’ll need to anonymize sensitive information, clean up irrelevant chatter, and format the data into a conversational, question-and-answer format that the model can learn from effectively.
The text contrasts fast SLMs with “overly reasoned” LLMs for agentic AI. Can you describe a specific IT or HR workflow where an SLM’s lower latency is the clear winner? How should a CIO decide when the task is simple enough for an SLM versus needing an LLM?
A perfect example where an SLM’s speed is the undisputed champion is a VPN connection failure. An employee is on a deadline and suddenly gets locked out. They send a message: “I can’t connect to my VPN.” A well-trained SLM can immediately recognize the intent, call the necessary API to check the connection status, and trigger a reset sequence—all within a second or two. The problem is solved before the user’s frustration even sets in. An LLM, in contrast, might engage in what the text calls “overly reasoned handling.” It might try to ask diagnostic questions like “Have you tried restarting your computer?” or “What error message are you seeing?” While well-intentioned, this dialogue introduces delay and friction into a simple, high-frequency problem. The CIO’s decision calculus should be based on complexity and consequence. If the task is a direct action—like routing a ticket, calling an API, or fetching a specific piece of knowledge—the SLM’s speed and efficiency win. If the task requires multi-step, complex reasoning, like diagnosing a novel system-wide outage with multiple dependencies, that’s when you need the firepower of an LLM.
A hybrid architecture is proposed, where SLMs handle most interactions and escalate to LLMs. What observability metrics are key to deciding when to escalate? And how do you ensure this handoff is seamless for the user, so they don’t feel like the system is failing?
This hybrid approach is incredibly powerful, and the key to making it work is intelligent escalation based on solid observability. You’re not just looking at one metric. A primary one is the SLM’s confidence score; if the model’s certainty about its own generated response or action falls below a predefined threshold, that’s an immediate trigger for escalation. Another key metric is task completion rate. If the agent fails to resolve the issue after one or two attempts, it shouldn’t keep trying and frustrating the user. You also want to monitor user sentiment in the conversation. A sudden shift to negative language is a clear sign the SLM is not meeting the user’s needs. The handoff has to feel like a natural part of the service. You avoid the feeling of failure by framing it positively. Instead of the agent saying “I don’t understand,” it should say, “This is a complex issue. Let me connect you with a specialist,” before seamlessly passing the entire conversation history to the LLM or a human agent. The user feels supported and elevated, not abandoned by a broken bot.
Gartner predicts over 40% of agentic AI projects will fail by 2027. Considering this, what is your forecast for the role SLMs will play in making these AI initiatives more successful and resilient against failure in the coming years?
My forecast is that SLMs will become the bedrock of successful agentic AI implementation, precisely because they directly counteract the reasons for that high failure rate. Those projects are predicted to fail due to immense complexity and a long, uncertain path to ROI. Companies are spending ungodly amounts of money on massive models for tasks that don’t require that level of firepower. SLMs offer a far more practical, focused, and cost-effective path to value. You can deploy an SLM for a specific, high-impact use case like IT ticket automation, prove its value in months, not years, and build from there. This approach delivers measurable results quickly, securing executive buy-in and momentum. By starting small and smart, organizations can build resilient, scalable AI architectures that deliver on their promise, making SLMs not just a tool, but a core strategy for ensuring their AI investments succeed rather than becoming another statistic.
