In the past 18 months, few technologies have captured boardroom attention like Large Language Models (LLMs). They’re the engines behind tools like ChatGPT, Claude, and Gemini – AI systems capable of generating text, answering complex questions, and even writing code.
We felt at MWS that it would be insightful to actually go into detail on what an LLM is before we consider how it might be used in an enterprise scenario.
So what is an LLM?
At its core, an LLM is a type of artificial intelligence trained on vast amounts of text. It doesn’t “understand” language in the human sense, but it recognises patterns, learning how words, phrases, and concepts tend to connect.
That pattern recognition enables LLMs to:
- Generate human-like text
- Summarise long documents
- Translate between languages
- Write and debug code
- Extract insights from unstructured data
How Enterprises Are Using Them Today
C-level leaders often hear about LLMs in the context of flashy demos, but the real enterprise use cases are already here:
- Customer experience: AI assistants handling support tickets, live chat, and FAQs at scale.
- Productivity: Automated drafting of reports, emails, and presentations, freeing teams to focus on strategy.
- Software development: Code completion, debugging suggestions, and documentation generation.
- Knowledge management: Internal search that retrieves insights from thousands of company documents instantly.
- Data analysis: Extracting patterns or anomalies from huge volumes of unstructured logs, contracts, or feedback.
Training and Creating an LLM
There are two main paths:
1) Pre-trained + fine-tuned
Most enterprises don’t train models from scratch (it’s costly and requires billions of data points). Instead, they take an existing model – like GPT or LLaMA – and fine-tune it with domain-specific data.
2) From scratch
Reserved for the largest organisations, training an LLM requires enormous compute power, specialised talent, and massive datasets. While rare, it gives complete control over model behaviour and data governance.
In both approaches, data quality matters more than quantity. Feeding an LLM carefully curated, accurate, and relevant information is what transforms it from a generic chatbot into a trusted enterprise tool. Of course, this can be facilitated with experts, or engineers, that are capable of deciding which data would contribute to a streamlined LLM that benefits the enterprise in achieving it’s business objective.
The Challenges
- Data privacy: Sensitive information can’t be casually fed into public models.
- Bias and accuracy: LLMs generate plausible-sounding text – even when it’s wrong.
- Cost: Running large models can be expensive without careful optimisation.
- Change management: Staff need training not just to use AI, but to trust and validate it.
Why It Matters
LLMs won’t replace teams, but they will reshape how teams work. The greatest value comes when companies integrate LLMs into secure, well-designed workflows that pair machine efficiency with human oversight.
At Mobile Wave Solutions, we believe this is the real story: not LLMs as a magic button, but as a new layer of infrastructure – as fundamental to the next decade of digital products as cloud computing was to the last.
