Past the hype cycle
Two years ago, most conversations about generative AI in the enterprise were speculative. Today, we're deploying production systems for clients that measurably reduce cost, accelerate workflows, and unlock products that weren't feasible before. The gap between hype and real value has narrowed considerably — but only for teams that scope their use cases carefully.
Where LLMs are creating real value right now
The highest-ROI applications we've built recently fall into a few categories: internal knowledge assistants that cut support ticket resolution time by summarizing documentation and prior tickets; document processing pipelines that extract structured data from unstructured contracts, invoices, and forms; and customer-facing copilots that guide users through complex product workflows in natural language.
The unglamorous part: data and evaluation
Every successful AI deployment we've shipped spent more engineering time on data pipelines and evaluation harnesses than on prompt engineering. Teams that skip building a rigorous evaluation set before launch tend to discover quality problems in production instead of in testing — a much more expensive place to find them.
Where we tell clients to be cautious
Not every workflow needs a large language model. Deterministic business logic, financial calculations, and anything requiring perfect accuracy on every run are usually better served by traditional software, sometimes paired with AI for the parts that genuinely benefit from natural language understanding.
What this means for your roadmap
The businesses seeing the best results treat generative AI as a new capability to apply surgically to specific, well-defined problems — not a strategy in itself. Start with a workflow where the cost of an occasional imperfect answer is low, measure rigorously, and expand from there.
