Why Not Just Use Off-the-Shelf LLMs for Enterprise Intelligence?

Hardly a week goes by without a new foundation model, AI agent, or research assistant promising to transform the way businesses work.  

Models from Google, OpenAI, Deepseek, Mistral, et al. have made a certain type of artificial intelligence more accessible than ever, and for many tasks they are genuinely impressive. They can summarize documents, answer questions, write reports, generate ideas, and, all in all, help people work faster.  

Given this context, it's a fair question for customers and prospects to ask: 

If LLMs I have free access to are so good, why can’t they be used for enterprise intelligence? 

The short answer is that the problems enterprise intelligence solves are very different to what general-purpose chatbots can deal with. Enterprise clients don't want to see plausible answers. They need real insights based on vast amounts of real-world data about their brands.  

That requires a combination of AI, machine learning, statistics, governance, and domain expertise that goes well beyond what an off-the-shelf LLM can provide.  

Sounding right isn’t being right

Ask an LLM about a topic you don’t know about and it will sound very smart. Have a similar chat about your pet niche interest and you might quickly notice some serious errors creeping in. LLMs are getting better at being factual, but they often make mistakes. And part of this is simply their design – they are language models, so will never be as good at stats as a statistical model.  

Large language models are designed to generate likely next words based on patterns they learned during training. Often, they are extremely useful, but they can also hallucinate facts, overlook important context, or produce inconsistent answers to the same question. 

And even in their "deep research" mode, they tend to read a handful of non-randomly sampled posts, not the billions of articles and posts within a database like Cision’s. 

For casual research, that may be acceptable. But a global brand deciding how to respond to a reputational issue, launch a product, or allocate millions of dollars in marketing budget, has a totally different set of requirements. 

Often generative AI will form part of the picture, but in other cases, more specialized approaches are required. 

Different problems, different models

A common misconception is that every AI problem should be solved by a large language model. In practice, different tasks require different technologies. 

Let’s take the example of estimating Reach for social media and online news. This is fundamentally a statistical problem, not a language one. Asking a language model to estimate reach would be like using a calculator to edit a photograph, or a typewriter to do your calculus. It's just the wrong tool for the job. 

Statistical models are specifically designed to understand relationships between variables and make quantitative predictions, so they remain the appropriate solution.  

Some tasks are best handled by statistical models. Some by specialized classification models. Some by generative AI. Increasingly, the best results come from combining the results of various models and synthesizing them. That means using LLMs to read all the results from the other models and distil them into something our customers can understand easily – that’s a language task, for a language model. 

Scale changes everything

Enterprise intelligence operates at enormous scale. And customers rightfully expect insights in near real time, particularly for use cases like crisis monitoring, reputation management, and competitive intelligence. 

This creates requirements that many general-purpose LLMs struggle to meet. A sentiment model used for crisis detection must be fast, consistent, and predictable. If the same piece of content receives different sentiment classifications on different days, benchmarking becomes difficult. If predictions aren’t fast enough, crises may be detected too late, giving customers less time to react.  

Trust requires transparency

As AI becomes more widely adopted, organizations increasingly need to understand how systems behave, what their limitations are, and how risks are managed. 

Explainability, auditability, monitoring, and bias testing must be built into AI development processes. Maintaining documentation, governance reviews, and model-level evaluation frameworks are vital to ensure systems remain reliable and accountable. 

That level of transparency is often difficult to achieve when relying entirely on third-party black-box systems. For this reason, models should be used which can explain how answers were produced. 

LLMs have their own (important!) place

None of this means we should reject large language models. Far from it. In fact, Cision has been using LLMs since before they were cool. Our AI Search feature powered by a self-hosted GPT model was released back in 2020, years before the release of ChatGPT started a global conversation about AI. In other words, this isn’t our first rodeo. Cision has been building and using advanced models for a very long time. 

We continue to invest heavily in agentic AI, generative AI, and highly custom models, because we believe these add genuine value. The key is grounding these models in high-quality data, real life workflows, and rigorous evaluation. 

AI agents should be orchestrators that can help users move faster through complex research tasks. They can summarize, explain, and connect information. But the underlying insight should still be supported by robust data, specialized models, and sound methodology. 

In other words, the future is probably not "LLMs versus traditional AI." It’ll be based on a variety of models, given the varied tasks enterprise clients need to do. 

The real answer is always more complicated

So, when someone asks, "Why not just use ChatGPT?" the answer is always going to depend on what specific use case, tool, or model we’re talking about.  

Enterprise consumer and media intelligence is about more than generating text. It's about accuracy, scale, transparency, governance, consistency, and helping people make better decisions.  

Generative AI is an important part of that future, but it works best when combined with purpose-built models, decades of domain expertise, and access to the world's largest collections of consumer and media data.