AI has evolved and become one of the key technologies of digital transformation. Whether it is intelligent bots for customer support or code writing and content generation, LLMs have shown impressive results in multiple industries.

However, their ability to understand human language and create similar text has completely redefined approaches to business automation and decision-making.

Nevertheless, as businesses move forward from the experimental phase and start integrating AI into their operations, a new question arises: Is a huge AI model necessary to solve all the business problems?

More and more often, the answer is “No”.
Companies start understanding that certain business functions can be performed effectively by using SLMs. Compact AI models are created specifically for performing particular business tasks and providing fast, cost-effective and controlled responses with regard to business data.

A Small Language Model (SLM) is an artificial intelligence model that is built with fewer parameters and designed specifically to be used in particular use cases.

While universal AI assistants have many use cases, SLMs can be trained or fine-tuned to perform certain business functions.
Possible use cases include:

The specialisation makes it possible for enterprises to use efficient and reliable AI that suits their needs.

Nowadays, as the usage of AI technology is growing, companies are looking for AI solutions that will help them to be efficient and reliable at the same time, not too expensive, scalable and secure enough.

Deployment of sophisticated AI requires much computing power and costly infrastructure. In case your company processes many AI queries per day, operating costs might grow very quickly.

With SLMs, you can cut the necessary infrastructure for running the AI and use your budget wisely.

A significant number of enterprise applications require real-time interaction. Whether it is working with clients, fetching internal documentation or automating processes, a fast response is necessary in order to provide a smooth user experience.

Due to their lightweight nature, SLMs can process user requests with minimal latency, and thus will be useful for time-critical operations in businesses.

Privacy has become one of the major concerns in enterprise AI. The aforementioned sectors have to deal with highly sensitive information that cannot be sent outside the organisation easily.

As small language models can be applied in an internal infrastructure of a company, or even in its private cloud environment, businesses will be able to preserve their confidentiality and meet all regulations and compliance requirements.

All industries have their unique vocabulary, processes and peculiarities. AI systems require understanding of this special knowledge in order to provide relevant help.
It is easier to customise SLMs to a particular business domain, and thus, create a relevant AI solution.

Small language models have already been implemented in many industries and provided value for businesses.

Healthcare professionals have adopted SLMs to perform summarisation of patient records, clinical documentation, retrieval of medical information, and minimise the administrative burden. The adoption of such technologies allows healthcare professionals to devote more attention to patient care.

Banks and financial organisations use SLMs to automate customer interactions, classify financial documents, ease compliance, and improve efficiency without compromising security standards.

Retail companies adopt SLMs to improve product search, customer support automation, creation of product descriptions, and personalisation of the shopping experience.

Manufacturers incorporate SLMs into their maintenance systems to help technicians quickly access technical documentation and manuals to troubleshoot problems.

Educational establishments integrate SLMs into digital learning platforms to summarise educational content, assist instructors, and provide personalized learning experience to students.

There is a noticeable trend as many organisations adopt hybrid AI solutions by mixing various types of AI models according to business needs.

In this strategy, dedicated models are utilised for routine and specific tasks, while large and more sophisticated AI models are used for reasoning, content generation, and solving complex issues.

Such an approach allows optimising performance, minimising cost, and controlling sensitive data.

With the emergence of Small Language Models, data science and AI professionals can look forward to numerous possibilities in this space.
For individuals seeking to venture into this field, it would be wise to acquire knowledge in the following areas:

In the world of enterprise AI, one of the most important skills going forward will be designing AI solutions that are efficient and fit for purpose within a particular industry.

It is safe to say that Artificial Intelligence has entered a new era. One where the size of a model no longer matters. Efficiency, security, scalability and alignment with real-world business requirements will now be the criteria for determining success in AI.

SLMs present themselves as the embodiment of this trend, as they allow companies to deploy efficient AI solutions tailored to specific business use cases.
From here on, successful implementation of AI solutions for organisations will mean getting the right technology for the right problem.