The first wave of enterprise LLM deployments is complete. Companies that moved aggressively in 2023-2024 have now had time to see what works. The lessons are instructive.

I’ve talked to technology leaders at 25 companies that deployed LLMs in production over the past 18 months. The patterns of success and failure are consistent enough to be useful for organizations planning their own deployments.

The Successful Patterns

Let me start with what worked.

Internal productivity tools first. The most successful deployments were internal-facing - helping employees work more effectively. Code assistants, document summarization, research support, internal knowledge search.

Why these work better: the stakes are lower. An internal tool that’s wrong 10% of the time is annoying but manageable. A customer-facing tool that’s wrong 10% of the time is a crisis.

One professional services firm deployed an internal research assistant that could search and summarize their knowledge base. Usage grew steadily, and employee feedback was positive. When I asked what made it successful, the CTO said: “We set expectations low. We told people it was a draft generator, not an answer generator. They adapted their workflow around that.”

Narrow, specific applications. The successful deployments solved specific problems well, rather than trying to be general-purpose AI assistants.

A logistics company built an LLM-powered system that reads shipping documents and extracts structured data. That’s it - a narrow, well-defined task. It works reliably because the domain is constrained and the success criteria are clear.

Heavy investment in evaluation. Teams that built rigorous evaluation systems before deployment had better outcomes. They could measure whether the system was working, catch regressions, and improve systematically.

One e-commerce company built a test suite of 3,000 labeled examples for their customer service LLM. Every model update, configuration change, or prompt modification got tested against this suite. “It sounds like overkill,” the engineering lead told me. “It’s not. We caught so many issues before they hit production.”

The Failure Patterns

The failures were also consistent.

Overambitious scope. Projects that tried to build “general AI assistants” for customer service, or “intelligent automation” that could handle any workflow, mostly failed.

The technology isn’t there yet for open-ended autonomy. Projects that assumed it was ended up in pilot purgatory - never quite working well enough to scale.

Underestimating hallucination risk. Many organizations were surprised by how often LLMs produce plausible-sounding nonsense. They deployed systems assuming accuracy rates similar to traditional software, then scrambled when errors emerged.

A financial advisory firm deployed a client-facing Q&A system and had to roll it back within weeks. The system was generating plausible but incorrect advice about regulations. “We didn’t have a good process for validation,” the CTO admitted. “We assumed it would just work.”

Ignoring organizational change. Deploying an LLM tool without changing processes to accommodate it leads to low adoption. People need to understand how the tool fits into their work, what it’s good at, and what it’s not good at.

Cost surprises. LLM inference is expensive at scale. Organizations that didn’t model costs carefully found themselves with tools that were too expensive to operate. Some successful pilots became too costly to scale.

Technical Lessons

Beyond organizational patterns, some technical lessons emerged:

RAG is table stakes but not magic. Retrieval-Augmented Generation - connecting LLMs to your data - is necessary for most enterprise applications. But it’s not sufficient. The quality of your retrieval, the quality of your prompts, and the quality of your data all matter enormously.

Prompt engineering is real engineering. Organizations that treated prompt design as an afterthought got inconsistent results. The teams with dedicated prompt engineering effort - systematic testing, version control, iterative improvement - got much better outcomes.

Fine-tuning is usually unnecessary. Most enterprise applications work fine with general-purpose models and good prompting. Fine-tuning adds complexity and cost, and the benefits are often marginal. The exception is highly specialized domains where terminology and patterns differ significantly from general language.

Guard rails are essential. Systems to detect and handle failures - hallucination detection, confidence scoring, graceful degradation - are as important as the core LLM capability. Build these from day one, not as an afterthought.

Deployment Recommendations

Based on these lessons, here’s how I’d approach enterprise LLM deployment now:

Start internal. Build tools for your own employees before exposing LLMs to customers. Learn from lower-stakes deployments.

Pick narrow, measurable use cases. Avoid vague projects like “AI-powered customer experience.” Choose specific applications where success is clear - document extraction, code review, summarization of specific document types.

Budget for evaluation and monitoring. Assume you’ll spend as much on testing, evaluation, and monitoring infrastructure as on the core LLM integration. This isn’t overhead - it’s essential.

Plan for ongoing iteration. LLM deployments aren’t projects that complete. They’re products that need continuous improvement. Budget for ongoing prompt refinement, model updates, and evaluation suite expansion.

Model costs carefully. Token consumption scales with usage. Make sure you understand the unit economics before committing to scale.

Prepare users. Set appropriate expectations. Train users on how to work effectively with LLM tools. Build feedback mechanisms so they can report issues.

Working with Partners

Many organizations lack the expertise for LLM deployment and need external help.

What to look for in a partner:

• Production experience. Have they actually deployed LLMs in production environments, not just built demos? Ask for references and details.
• Evaluation methodology. How do they measure success? What testing frameworks do they use?
• Pragmatism about limitations. Partners who promise too much are dangerous. Look for honest acknowledgment of what LLMs can’t do.
• Security and compliance understanding. If your industry has specific requirements, make sure the partner understands them.

For organizations in Australia looking for this kind of expertise, firms like AI consultants Melbourne specialize in practical enterprise AI deployment - though any partner should be evaluated against the criteria above.

What’s Coming

The technology keeps improving. GPT-5 and competing models will be more capable. Costs continue to decline. Tooling for deployment, evaluation, and monitoring is maturing.

This means the lessons from early deployments will need updating. What’s hard now will become easier. New capabilities will create new applications.

But the fundamental patterns - starting narrow, measuring carefully, managing expectations, investing in evaluation - will remain relevant. They’re not specific to current model limitations; they’re how you successfully adopt any powerful but imperfect technology.

The organizations that learned these lessons early will be best positioned to capture value as the technology continues to mature.