Artificial intelligence is the buzzword of the decade. From slick demos to boardroom strategy decks, it’s tempting to believe we’re on the brink of an AI revolution in every industry. But here’s the uncomfortable truth: most enterprises will waste a lot of money on AI.
The problem isn’t the technology. Rather, it’s the misunderstanding of what it takes to make AI actually work inside a business.
Let’s start where every AI journey should: with the data.
AI is only as good as what you feed it
For all the talk of “intelligence”, AI is fundamentally dependent on the quality of your data. If your enterprise data is siloed, outdated, unstructured or poorly tagged, your AI models – no matter how sophisticated – will return vague, inaccurate or even outright wrong results.
We’ve seen companies leap into large language model (LLM) deployments without investing in the foundational work of preparing their data. The result? Hallucinations, compliance issues and sky-high cloud bills for very little value.
Before you consider a pilot, workshop or proof of concept, ask the hard question: is our data clean, structured, secure and accessible? If not, that’s your first AI project.
Understanding the alphabet soup: AI, ML, DL, gen AI
Much of the confusion around AI starts with terminology.
AI is the broad goal: machines that can reason, decide and solve problems. Within that, machine learning (ML) refers to systems that learn from data. Deep learning (DL) takes this further with neural networks that can process images, audio or text.
Generative AI (gen AI), the current star of the show, does something new: it creates. It can write text, generate code, summarise documents and even produce images. But this power comes with a caveat: what it generates is only as useful as the training and context it’s been given.
This brings us to the next trap.
LLMs are impressive but are not always the right tool
Large language models like GPT-4 are astonishing in their breadth. But they are generalists. They don’t know your business, your terminology or your regulatory environment. Worse, they’re expensive to run and hard to control.
Enter small language models (SLMs) – lighter, faster, more focused. When finetuned to your own data, SLMs can outperform LLMs in accuracy, relevance and cost. Sometimes, smaller really is better.
The same goes for virtualised LLMs (vLLMs), which use memory-efficient techniques to reduce cloud infrastructure requirements. These are crucial if you want enterprise-grade performance without enterprise-scale spending.
Grounding AI in reality with RAG and semantic search
Retrieval-augmented generation (RAG) is one of the most practical advances in gen AI. Instead of guessing answers from model memory, RAG connects the model to your actual data – policy documents, manuals, product specs – and retrieves relevant facts before generating a response.
This reduces hallucinations and improves trust, especially in regulated environments.
Semantic search plays a similar role. It doesn’t just look for keyword matches – it understands what users mean. This is essential for any internal chatbot, helpdesk or knowledge assistant: if your AI doesn’t grasp the intent behind a poorly phrased question, it won’t be useful.
Finetuning and model control: speak the language of your business
A common misconception is that gen AI works out the box. Technically, it does. But it won’t sound like you, behave like you or reflect your standards.
Finetuning is the process of training a model on your own data – your terminology, your tone, your processes. It dramatically increases the relevance of outputs and reduces the chance of off-brand, off-topic or incorrect results.
This is also where open-source models like Llama or Mistral shine. They give you full control over training, governance and deployment – without locking you into a vendor’s API or pricing model.
Inferencing: the hidden cost centre of AI
Here’s a question most leaders haven’t been asked: what happens every time someone uses your AI model?
That process – called inferencing – is where costs and latency spike. Especially at scale, the architecture that supports inferencing determines the user experience and your operating expense. Optimising for inference is critical, and too often ignored in early deployments.
AI is not a one-time project – it’s a life cycle
Deploying a model isn’t the finish line – it’s the starting gun.
To stay valuable, AI must be monitored, tested, retrained and governed. That’s where MLOps (machine learning pperations) comes in, providing the same discipline and visibility we expect from modern software development.
Observability is crucial, too. Can you track usage? Detect drift? Audit decisions? If not, your AI deployment is not enterprise ready.
Agents, not just assistants: the next leap
AI agents don’t just answer – they act. They can book meetings, trigger workflows, retrieve documents or complete tasks across systems. But like human employees, they need onboarding, access control, training and review. Treat them like colleagues, not magic.
Sometimes you’re not looking for AI, you’re looking for automation
This might be the most important insight in this whole piece.
Not every problem needs a model. Sometimes, what your business needs is workflow automation, integration or a smart rules engine.
AI is powerful, but it’s not always the right answer. If you can solve a problem with process redesign or automation, do that. It’s cheaper, faster and more sustainable.
Final thought: AI is not a tool, it’s a capability
Successful AI adoption isn’t about choosing a model. It’s about building a system, one that ingests quality data, generates insight, enables action and continuously improves.
That system must be secure, governed, efficient and aligned to business outcomes. Without those foundations, you’re not investing in AI – you’re gambling on a trend.
- The author, Deon Stroebel, is chief commercial officer and LSD Cloud Business executive at LSD Open
- Read more articles by LSD Open on TechCentral
- This promoted content was paid for by the party concerned
