AI in development: how to move faster without turning your product into a stack of hacks

A few years ago, launching a digital product followed the same script: lengthy discussions, dozens of calls, months of development and endless revisions. That's changing. AI tools and methods such as vibe coding help teams ship services faster, test ideas without heavy upfront investment and avoid getting buried in routine early on.

Vibe coding isn't about replacing developers with a neural network. It's a way to accelerate product development using AI assistants, code generation and a more flexible process. Instead of weeks on basic functionality, a team can have a working prototype in a matter of days.

The question used to be: how much will development cost? Now businesses ask: how do we cut time to market and reach users sooner? That's where vibe coding becomes one of the most practical tools available.

What business problems it solves

The core value of vibe coding is speed. When a business needs to validate an idea quickly, launch a service or automate a process, traditional development is often too slow and too expensive upfront.

Take a restaurant that wants to launch its own delivery. Instead of months of development, a couple of sprints can produce a site and a simple app: complete with a menu, orders and a user account to test demand and find out whether customers will order directly or stick with aggregators.

Another common scenario is automating internal processes: spreadsheets, reports, approvals in messengers and data transfers between services. AI and templated code generation can accelerate all of this, freeing the team from routine and letting them focus on the product.

We vibe-coded our own client project tracking system and called it Signal. The dashboard shows three key indicators: project health, client pulse and team sentiment. Data is pulled automatically from meeting transcriptions and chats – both client-facing and internal. Signal helps us spot risks, communication drops and project tension before they become problems. The same service built through traditional development would have taken several months. We built it in 6 days.

Andrei Davydov CEO Depot 42

Vibe coding is also useful where things move fast: new hypotheses emerge, features change and use cases shift. The team iterates and tests ideas more quickly. For a business, that means lower risk – no need to invest in full-scale development before an idea has proved its worth.

How build time gets cut

The most visible change is faster workflows. Developers used to spend a lot of time on base components, boilerplate code and repetitive tasks. Most of it can be offloaded to AI. In one internal project, for instance, AI automatically linked related case studies on a website via tags – no manual configuration of each page required.

AI accelerates interface generation, project structure and templated functions. Instead of building from scratch, the team gets a working foundation to adapt for the product. Engineers still own decisions, architecture and AI output review – that's how businesses cut build time without sacrificing quality.

Cross-team handoffs get easier too: designers pass layouts more easily, developers assemble interfaces faster and product managers validate hypotheses sooner.

In Depot 42 projects, for example, a designer uses AI to check small visual changes directly on the page – adjusting spacing, typography and element behaviour without the usual design → develop → design cycle. Developers stay in the process, but routine iterations drop and detail work moves faster.

Where it delivers

Vibe coding works best for simple products with no complex architecture or high load: internal services, small CRMs, promo pages or design systems. These typically consist of standard components that AI can assemble quickly.

Recently, for example, we were preparing a proposal for an interactive children's centre and built a landing page prototype to make the client discussion more concrete. We had a logo, a photo archive and a brief. We loaded them into the neural network, got an initial version and then polished it to a client-ready state.

Another strong use case is the MVP. When the goal isn't to build the perfect system for the next decade but to validate an idea quickly, vibe coding helps businesses skip months of preparation and reach users sooner. This is especially useful for startups and product teams: while a hypothesis remains unproven, there's no reason to invest in complex architecture and expensive development.

Vibe coding also suits internal processes well: staff-facing services or the automation of routine tasks like parts of a project manager's workload. These tools require no complex infrastructure but cut the number of manual steps substantially.

Limitations for business

Despite the speed and convenience, vibe coding has limits. The central question is scalability. What works well for an MVP or a small service can start to break under higher load and increased product complexity.

AI handles standard tasks well but complex architecture, non-standard business logic and large systems less so. When a product acquires dozens of integrations, interdependent user roles, security requirements and high load, a strong engineering team becomes essential.

Code quality is another important factor. AI helps ship faster, but generated code still needs to be reviewed, refined and adapted to real business requirements. Without team oversight, AI can introduce vulnerabilities, unstable solutions, weak architecture and code that's hard to maintain. Over time, the project becomes a pile of workarounds that slows the product down.

Vibe coding is a tool for acceleration, not a replacement for proper development. It's excellent for fast launches but it doesn't remove the need to think about the product's future.

Where it falls short

Some situations call against vibe coding. The clearest case is large projects with high load and complex infrastructure: banking systems, large marketplaces, complex SaaS platforms or products with critical security requirements – a crypto exchange with gamification, for example. Stability, architecture and system predictability matter too much in these projects to rely on AI generation entirely.

That doesn't mean AI has no place there. It still accelerates research, hypothesis generation, design, prototyping, analytics and portions of routine code but only with strong product and engineering expertise behind it.

The key is having a team that knows where AI genuinely saves time and where it starts creating problems.

Growth is another risk. Everything looks fast and convenient at the start, but without considered architecture the system starts obstructing development. A simple delivery service gradually acquires a loyalty scheme, user roles, integrations and analytics and hastily assembled code blocks every change.

Vibe coding is also a poor fit for projects requiring deep optimisation or non-standard technologies. AI can put together the basic mechanics, but it won't design a high-load recommendation engine for a large e-commerce platform.

AI accelerates strong teams. It doesn't replace them. At Depot 42, we combine rapid AI-assisted launches with solid product and engineering work. That gets us to release in days or weeks and keeps the project on track long after.