There is a strange gap between the software businesses can buy and the software they actually wish they had.
Most companies have no trouble finding an app for a common task. There are products for accounting, customer management, scheduling, communication, recruitment, and dozens of other everyday jobs. The trouble starts when a business has a process that does not fit neatly into any of those categories.
That is when people start improvising.
A team might use a form to collect information, a spreadsheet to organise it, email to discuss it, and another platform to keep track of what happens next. None of these tools is necessarily bad. They simply were not designed to work together around that particular process.
For years, building something custom was the obvious answer but rarely the practical one. The amount of development work involved could make a relatively small problem look like a very expensive project.
AI is starting to make that calculation more interesting.
The awkward middle ground between software and spreadsheets
There are plenty of businesses operating in this middle ground.
They have outgrown spreadsheets but don’t need, or can’t justify, a huge enterprise system. They have specific workflows but aren’t large enough to commission a completely bespoke platform. So employees end up doing things manually because that is easier than changing the entire system.
Consider a training company that runs hundreds of sessions a year. It may need to keep track of trainers, venues, registrations, attendance, and payments, but none of the software it has tried quite handles the process from beginning to end.
Or take a small property business managing dozens of buildings. A generic task manager can record maintenance requests, but perhaps it doesn’t connect them properly with tenants, contractors, invoices, and property records.
These aren’t problems that need another generic feature.
They need software that understands the particular process.
This is where AI changes the equation
The interesting development isn’t simply that AI can write code. Developers have had tools that generate code for some time.
What is different now is that AI can participate in much more of the process of turning an idea into an application.
A person can explain what the application needs to do, describe the different users, outline the workflow, and then make adjustments as the project develops. The system can take those instructions and generate substantial parts of the application instead of requiring every component to be built manually from the beginning.
That makes the early stages of custom software much easier to explore.
A business can find out what a solution might look like before making a major commitment to it.
There is still plenty for people to figure out
The technology can build software, but it cannot decide what software a business actually needs.
That part remains surprisingly difficult.
A manager might ask for a dashboard because the current reporting process is frustrating, when the real problem is that three departments are recording the same information differently. A founder might request ten features for an app when five would be enough to test the idea properly.
AI can help turn those requirements into something usable, but someone still needs to ask the uncomfortable questions about what should be built in the first place.
This is why the more interesting use of AI development tools may not be replacing developers. It may be giving businesses a cheaper way to experiment before they decide how much development work a project really requires.
Emergent is part of this new category
Emergent is one example of a platform taking this approach. It allows users to describe an application in natural language and generates the frontend, backend, database, APIs, and business logic needed for the project.
The applications can then be modified through further instructions, which means the initial version doesn’t have to be treated as a finished product. Users can add features, change workflows, and connect external services as the project develops.
It supports web and mobile applications as well as projects such as SaaS products, customer portals, internal tools, dashboards, and AI-powered applications. GitHub integration is also available for teams that want to bring generated projects into an existing development workflow.
The important distinction is that the platform isn’t limited to creating a visual mock-up. The aim is to produce working software that can actually be tested and developed further.
Pricing depends on how far you want to take the idea
Emergent has a free plan that provides 10 monthly credits, access to its core platform, web and mobile app development, one-click LLM integration, and its latest AI models.
The Standard plan costs $20 per month or $204 annually. It increases the allowance to 100 monthly credits and adds private project hosting, GitHub integration, task forking, and the ability to purchase additional credits.
The Pro plan costs $200 per month or $2,004 annually. It is aimed at more demanding projects and includes 750 monthly credits, a one million-token context window, high-performance computing, custom AI agents, advanced reasoning capabilities, and priority customer support.
The more interesting question isn’t whether AI can build an app
It can. The harder question is whether businesses will become more willing to build software that would previously have been considered too niche, too expensive, or simply too much trouble.
That could be where AI development has its biggest effect.
Not every company needs a custom application. In plenty of cases, buying existing software will remain the sensible choice. But there is a large group of businesses stuck between “this tool isn’t quite right” and “building our own would be ridiculous.”
For them, the barrier is starting to move.
And that could eventually mean fewer spreadsheets held together by formulas, fewer awkward workarounds, and more software designed around the way a business actually operates.
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