Why Every Business Doesn’t Need an LLM
Artificial intelligence has reshaped customer engagement, efficiency, and innovation through the development of expensive AI systems, specifically Large Language Models (LLMs). It has been at the centre of the conversation because of its ability to process and generate natural language with remarkable fluency. Despite their capabilities, LLMs do not always suit every business or enterprise. Many companies overestimate AI requirements, which is why it’s crucial to understand why every business doesn’t need an LLM. Hence, many organizations may find SLMs more tailored to their goals, as deploying LLMs without strategic consideration can be impractical and sometimes unnecessary.
The Illusion of Scale
Responding to diverse queries, generating human-like texts, and performing reasoning tasks make LLMs versatile. They train on massive datasets, which enables them to handle tasks ranging from drafting marketing content to powering chatbots and analyzing documents. However, their great size brings inevitable trade-offs: high computational demands, potential overkill for specific tasks, and increased infrastructure costs. (1) The financial impact of adopting requires a clear understanding of the problem scope and available resources. Prioritizing strategy ensures that AI adoption aligns with business goals rather than chasing trends. Without this focus, organizations risk investing in solutions that are either too broad to be effective or too narrow to scale sustainably.
For large-scale implementation, licensing fees can accumulate rapidly, resulting in significant financial strain. For small and mid-sized enterprises, these costs can outweigh the benefits. Organizations can avoid the “bigger is better” misconception by scaling down and achieving the same outcomes more efficiently. For small and mid-sized businesses, these costs can easily outweigh the benefits. For example, a mid-sized SaaS company with a few hundred employees might pay per-user or usage-based fees to add an LLM to its customer support system. As more support tickets, internal questions, and automated workflows rely on the model, the monthly cost can jump from a few hundred dollars to tens of thousands of dollars, without a corresponding increase in revenue or customer satisfaction.
Solving Problems That Don’t Exist
One common pitfall in adopting AI is embracing it for novelty over necessity. This results in what can be termed “solution-first thinking“: a scenario in which the tool dictates the strategy rather than vice versa. Consider content creation: while large language models (LLMs) can produce polished copy, their output often lacks the specific tone a brand requires. As a result, the content can become generic and devoid of domain expertise. In contrast, small language models (SLMs) fine-tuned on the company’s content library and insights can provide more relevant, on-brand results.
On the other hand, entering invalid details, whether as a technique or an integrity issue, is where the term “hallucination” in artificial intelligence first originated. Unreliable or incomplete responses hinder adoption in many disciplines, leading to problems such as the generation of false facts in media reports or judicial precedents, and even endangering human lives in medical disciplines like diagnostic imaging. Over-engineering can create confusion, diminish clarity, and increase costs, often while addressing the wrong issue. (2)
Strategy Before Scale
“What business problems are we solving?” The decision to adopt an AI model should start with whether the problem is broad or narrow and which language models are required. Teams should assess data fragmentation, contextual relevance, and the dynamic nature of data before implementing solutions or jumping on the LLM bandwagon. Moreover, while these general-purpose language models have good intentions, they also pose a risk of privacy violations, as they may retrieve confidential data via linguistic embeddings. (3) Considering why every business doesn’t need an LLM encourages a smarter, scalable approach to integrating AI. Strategies, such as collaborative learning and differentiated confidentiality, can mitigate privacy concerns during development, but models may still unintentionally embed confidential information in their parameters. In essence, smaller models yield proven accuracy and ensure sophistication, but in relevance. (4)
Leaders who recognize this will stop treating LLMs as a default upgrade and start treating them as one option among many, sometimes the right one, often not. They will ask sharper questions: What specific friction are we removing? What would success look like without AI? Where does a smaller, more controllable system serve us better? In doing so, they shift the narrative from “How do we get an LLM?” to “How do we build the leanest, safest, most effective intelligence for our context?”
In that shift lies a quiet but profound realization: the future of AI in business will not be won by those who wield the largest models, but by those who are most honest about their problems, most precise in their use of technology, and most resistant to the hype. The organizations that thrive will be those that understand that real intelligence, human or artificial, is not measured by parameters but by purpose.