Large Language Models (LLMs) are having a moment. They’re hot topics around boardroom tables, branches of product roadmaps, and part of just about every vendor pitch deck. But behind the buzzword is a concept that’s both simpler and more nuanced than it first appears. For tech and business leaders, understanding what an LLM is (and just as importantly, what it is not) is key to making smart, strategic decisions.

This week we dive into LLM’s from the perspective of a software development company who fields questions about with our advisory client’s and uses them to achieve our client’s custom software goals.

 So, What is an LLM?

At its core, an LLM is a type of artificial intelligence model trained on massive amounts of text data to understand and generate human-like language. Models like ChatGPT, Claude Sonnet, and others learn patterns in language including grammar, context, and relationships between words by processing billions (or trillions) of data points.

They don’t ‘think’ or ‘know’ things in the human sense. Instead, they predict what word (or token) should come next based on probabilities learned during training. This distinction matters more than most people realize.

LLMs are built on transformer architectures, first introduced in a 2017 Google research paper, ‘Attention Is All You Need.’ While today’s tools feel new, the underlying ideas have been evolving for decades through natural language processing (NLP), machine learning, and computational linguistics.

Before the Hype: LLMs Weren’t Always Cool

Long before ChatGPT made headlines, early versions of language models were already quietly powering parts of our digital lives. Think autocomplete in email, search engine query suggestions, or basic chatbots.

Companies were using NLP techniques for:

• Customer support automation
• Document classification
• Sentiment analysis
• Fraud detection

What changed wasn’t the existence of the technology, it was the scale. The combination of cloud computing, massive datasets, and improved architectures turned niche tools into more broadly useful systems. This law of neural scaling, seemingly happened overnight.

In other words: LLMs didn’t suddenly appear. They grew up.

What LLMs Are Not

This is where things get interesting, and where many organizations get tripped up.

LLMs are not:

• Sources of truth – They can generate incorrect or outdated information (‘hallucinations’).
• Reasoning engines – While they simulate reasoning, they are fundamentally pattern predictors.
• Plug-and-play business solutions – They require integration, oversight, and governance and general corporate AI Literacy.
• Replacements for human expertise – Especially in regulated or high-risk environments. Assuming there are can lead to long term staffing burdens.

Understanding these limitations and challenges are critical to the decision making process. According to McKinsey’s 2025 State of AI report, while AI adoption is increasing, only a minority of organizations are successfully scaling it across the enterprise; often due to unrealistic expectations and lack of alignment between tools and business processes.

Why LLMs Matter Now

Despite their limitations, LLMs are incredibly powerful when used correctly. They excel at:

• Summarizing complex documents
• Generating code and technical documentation
• Automating repetitive communication tasks
• Extracting insights from unstructured data

For software development teams, this can be a game changer.

GitHub reports that developers using an AI coding assistant completes tasks up to 55% faster in controlled studies. That doesn’t mean fewer developers, it means more productive ones, and the opportunity to focus on higher-value work.

LLMs can help:

• Accelerate legacy system modernization
• Generate boilerplate code and test cases
• Improve documentation quality
• Assist with debugging and code reviews

For organizations dealing with aging infrastructure, that’s a compelling value proposition.

LLM Risks & Concerns

Of course, no conversation about LLMs is complete without addressing the risks.

1. Accuracy & Hallucinations

LLMs can confidently produce incorrect information. This is particularly risky in industries like insurance, healthcare, or finance. Read more about the potential cost for businesses here.

• Data Privacy & Security

Using public LLMs without proper controls can expose sensitive data. Enterprises must consider private deployments or controlled environments.

• Bias & Compliance

Because LLMs are trained on large datasets, they can reflect societal biases. This can create regulatory, reputational  and staff retention risks.

• Over-Reliance

There’s a temptation to treat LLMs as decision-makers rather than assistants. That’s where things go sideways.

Recently, there have been several notable examples of legal cases where lawyers submitted AI-generated filings containing fabricated citations or AI hallucinated cases. The severity of these implications highlight how avoidable repercussion are and the importance of human oversight.

LLMs in the Real World: What’s Actually Working

Beyond the headlines, there are meaningful, practical applications emerging across industries:

• Insurance: Automating claims summaries, improving underwriting insights, enhancing customer service.
• Construction: Analyzing project documentation, improving reporting workflows, reducing administrative burden and improving jobsite safety.
• Manufacturing: Supporting maintenance logs, generating technical documentation, improving knowledge transfer. Check out Rockwell Automation’s State of Smart Manufacturing for more details.
• Financial Services: Assisting with compliance documentation, risk analysis and fraud detection/prevention.

According to McKinsey, gen AI could add between $2.6 trillion and $4.4 trillion annually to the global economy, with significant impact in knowledge-heavy industries.

Why Third-Party Expertise Matters

Here’s the reality: most organizations don’t struggle with access to LLMs, they struggle with implementation.

This is where working with a third-party software partner becomes advantageous.

A strong partner can:

• Integrate LLMs into existing systems (rather than layering them on top)
• Ensure proper data governance and security controls
• Customize models for industry-specific use cases
• Align AI capabilities with real business outcomes

For companies running legacy systems, this is especially important. Retrofitting AI into outdated architecture without a clear strategy can create more problems than it solves.

Industries that benefit most from this collaborative to unlock efficiency gains without introducing unnecessary risk approach are:

• Insurance and financial services (complex workflows, regulatory requirements)
• Industrial manufacturing (data-rich environments with operational dependencies)
• Construction and infrastructure (fragmented systems and documentation-heavy processes)

Final Thoughts: From Buzzword to Business Tool

LLMs are not magic. They are not a silver bullet. But they are also not hype without substance.

They are tools, powerful ones, that can transform how organizations handle information, build software, and operate at scale.

The organizations that win won’t be the ones that adopt LLMs the fastest. They’ll be the ones that understand them the best, and work with trusted partners to create their competitive advantage.

In a time where technology moves quickly but business risk moves slowly, understanding how technology augments real business needs where we excel at STEP, reach out if you are looking to take advantage of power of LLMs.