2027
AI in your software in 2027: what changes, and how to keep control
Written by the Fiably team. Who we are.
By 2027, software with no AI feature at all will be the exception, not the rule. But adding AI just so you can say you have it is never the right starting point. This guide explains, from our own method, what is actually worth building, what it costs to run once it is live, and how to stay in control of what you had built.
What to build first
An AI feature usually takes one of three shapes: a piece added to an existing product (an assistant, search, content generation), the automation of a manual back office task (support triage, document processing, lead qualification) handed to an agent, or an internal tool that speeds up a team's own work.
The right starting point is the task already costing your team time or money, not the most impressive demo. A paid discovery exists precisely to decide which of the three shapes fits your case, and to write down the real risks before any commitment to a sprint.
What it costs to run
Once live, the real cost is not the build, it is the usage. Latency, token spend and failure rate are tracked after launch, not estimated once at the start and left alone.
The choice of model and where it runs changes that whole equation: a major provider's API trades a price per token for no infrastructure to manage, a lighter model running on your own infrastructure trades a fixed hosting cost for no usage based billing. The right choice depends on your volume, your latency constraint and your confidentiality requirement, never on whichever model shipped this month.
Fiably has no fixed price to announce in advance for this work: like the other three service lines, discovery is a fixed paid fee, every sprint then has its own fixed price set before it starts, and a monthly maintenance plan can take over once the feature is in production.
How to keep control
Our senior engineers direct the AI tooling to build the feature, but a second engineer reviews every line and tests cover it before it reaches production, exactly like on any other Fiably project. That is the line separating us from pure "vibe coding".
The prompts and the evaluation method are documented as part of delivery, not as an afterthought: you are never required to stay with Fiably to keep evolving the feature.
The code is yours from the first commit, in your own repository. And as with every sprint, you decide to continue, change direction or stop after the demo, not only at the end of a contract.
Questions
Do I need a dedicated AI team in house? Not necessarily. It can stay an added capability of your existing product team, with Fiably running discovery and the first sprints. Once the code ships, you keep everything that lets you continue on your own: documented prompts, the evaluation method, and the code itself.
Which AI model should I pick for 2027? There is no single answer. The model and tooling that fit your case best depend on your cost, latency and confidentiality requirements, and are decided case by case, never as a fixed choice made in advance.
What is the single biggest risk? Three concrete risks, each tracked from discovery onward: hallucination (the feature states something false with confidence), a cost that drifts once real usage starts, and dependency on a single vendor. The first is handled by review and tests, the second by cost tracking in production, the third by documented prompts and code that stays portable.
Is this the same as "vibe coding"? No. Our senior engineers direct the AI tooling, but every line is reviewed by a second engineer and covered by tests before it reaches production, like on any other Fiably project.
Where do we start? With a paid discovery: it decides which of the three shapes (an added feature, an automation, an internal tool) fits your case, and writes down the risk list before any commitment to a sprint.
Already have a specific feature in mind? See the AI integration offer.
Let's talk about AI in your software.