We have normalized a dangerous lie in the adoption of corporate Artificial Intelligence: believing that if the answer is good, the user won’t mind waiting.
The operational reality is quite different. In a production environment, friction destroys adoption. If your internal sales assistant or analytics tool takes 8 seconds to respond, your team will stop using it and go back to searching the manual PDF or the usual Excel sheet. It’s not a lack of patience; it’s that a slow tool breaks the flow state of daily work.
This is where infrastructure becomes more critical than the language model itself. The main challenge we face when taking technology to production is not just data quality or model creativity but Latency.
For those not in technical operations, latency is simply the time that elapses from when the user clicks send to when they receive the first sign of life from the system. In traditional software, this is measured in milliseconds and is almost imperceptible. In Generative AI, due to the immense amount of compute needed to predict each word, if not optimized, it can be measured in painful seconds.
How do we solve this with engineering so that AI feels instant and professional? Here are five key strategies to reduce friction.
Change the Metric: From Total Time to TTFT (Time To First Token)
The number one mistake is measuring how long the AI takes to finish writing. That is irrelevant to the user experience. We must obsess over TTFT (Time To First Token): how long it takes for the first word to appear on the screen.
Psychologically, there is an abyss between waiting 5 seconds staring at a blank screen (which generates anxiety and a sense of error) and seeing text start to flow at 0.8 seconds. The user doesn’t mind if the full answer takes several seconds to generate, as long as the interaction begins immediately. Optimizing the architecture to reduce TTFT is the engineering hack that most impacts perceived quality.
Streaming: The Illusion of Speed and Life
You’ve surely noticed how leading tools write word by word instead of dropping a block of text all at once. That is not an aesthetic effect; it is a data transmission technique called Streaming.
In traditional integrations (REST APIs), the server processes the entire request and sends the full response at the end. In AI, this is deadly. With streaming, we configure the connection (often using protocols like Server-Sent Events or WebSockets) to send fragments of text as they are generated on the server.
This transforms a passive, frustrating wait into active reading. The user starts consuming information while the system is still thinking of the end of the sentence. Implementing this in legacy corporate systems is complex but it makes the difference between a tool that feels broken and one that feels alive.
Semantic Caching: The Fastest Answer Is the One Not Calculated
Another layer of invisible engineering is preventing the AI from working unnecessarily. If a user asks, what is the travel expense policy? and the AI generates a perfect answer, the next time another employee asks, how do I submit my travel per diems? we shouldn’t consult the large model (LLM) again, which is expensive and slow.
Here enters Semantic Caching. Unlike a traditional cache that looks for exact text matches, the semantic cache uses vectors to understand that both questions mean the same thing. The system detects the similarity and delivers the stored answer instantly (in milliseconds).
This has a double impact: it reduces latency to zero for frequently asked questions and drastically cuts the cloud consumption bill.
Model Routing: Don’t Use a Sledgehammer to Crack a Nut
Not all questions require a Genius type model like GPT-4 or Claude 3 Opus. If a user just says Hello or asks to extract a date from a text, using the largest and smartest model is inefficient and slow.
A mature architecture uses a model routing system. A small, ultra-fast, and cheap model classifies the user’s intent.
Is it a complex reasoning question? Send to the large model (slower, higher quality).
Is it a simple or repetitive task? Send to a lightweight model (instant, lower cost).
This orchestration reduces the system’s average latency without sacrificing intelligence when it is truly needed.
Optimistic UI and Loading States
Finally, when engineering reaches its physical limit, UX (User Experience) Psychology steps in. If we know a complex query will take 10 seconds because it requires reading 50 documents, the interface cannot freeze.
We must implement Optimistic UI or detailed status indicators (e.g., Searching database…, Analysing documents…, Drafting response…). Informing the user what is happening reduces uncertainty and makes the wait time feel shorter and justified.
The magic of generative AI is not just in what it says, but in how and when it delivers it. In the business world, a slow tool is a dead tool. Our responsibility is to ensure the infrastructure is as robust and agile as the model it hosts, guaranteeing that technology is not an obstacle, but an accelerator.
Does your AI project work well in demos but feel slow, heavy, or unstable in day-to-day business operations? At Intech Heritage, we design the invisible architecture that makes your systems fluid, scalable, and profitable.