Two recent articles by garment-decoration industry consultant Marshall Atkinson raise an important question for our industry. If AI now makes it possible for almost anyone to build an app, what does that mean for the software garment decorators already use and for the businesses considering building their own?

It is an exciting shift. A shop owner who has lived with an awkward spreadsheet, an unreliable schedule or a repetitive manual task no longer has to wait indefinitely for a software provider to solve it. With the right tools and prompts, they can begin creating something themselves.

But there is an important distinction between building a piece of software and building a dependable production system.

Start with what is actually broken

One of the strongest points in Marshall’s article about vibe coding is the need to separate a missing-tool problem from a process problem.

Those problems can look remarkably similar from the outside. A job is late. Nobody is quite sure whether the garments have arrived. A purchase order has not been sent. Production is waiting for transfers. The schedule cannot be trusted. Information is copied between systems or written on a board because the main software does not show what the team needs.

Sometimes a tool genuinely is missing. Sometimes the process has never been agreed, documented or followed consistently. Frequently, it is a mixture of both.

New software cannot fix a process nobody understands. If different people route the same type of order in different ways, adding another screen may simply digitise the inconsistency. Before asking AI to build anything, a business still needs to answer some very human questions:

Creating the app is only the beginning

Vibe coding lowers the barrier to creating useful internal tools. It does not remove the responsibility that comes with owning them.

Someone must maintain the application, test changes, manage hosting, protect customer information, monitor costs and keep it working when another service changes. The original builder can quickly become a new single point of failure. If that person leaves, becomes too busy or simply loses interest, the business still depends on what they created.

This does not mean shops should avoid building their own tools. Small, focused internal applications may create enormous value. It does mean the decision should include the long-term cost of ownership not just the excitement of producing the first working version.

AI needs evidence it can trust

Marshall’s second article focuses on speed: using AI to remove delays, connect information and help businesses make better decisions. It includes an especially useful example. An AI-assisted report showed margins that looked unusually strong. The analysis was fast and convincing but payroll data had not been included correctly, so the answer was wrong.

That example reaches far beyond financial reporting. AI can process information quickly, identify patterns and present confident recommendations. But it cannot compensate for production activity that was never captured, statuses that were not updated or timings that were recorded inconsistently.

Before AI can meaningfully understand production, the business needs reliable operational evidence: when stock arrived, when a supplier order was raised, what a task was waiting for, who completed it, how long it took, what went wrong and what happened next.

That evidence is the bridge between everyday production activity and genuine Production Intelligence.

From connected workflow to better decisions

At DecoFlow, we think about this progression through four connected stages:

First, connect the activity taking place across the production workflow. Then understand what is actually happening not what everybody assumes is happening. Use that evidence to decide what needs attention, and improve the workflow based on what the business has learned.

AI can eventually strengthen the Understand and Decide stages enormously. It could help identify jobs at risk of missing their due dates, highlight emerging bottlenecks, compare supplier performance, explain why a decoration took longer than expected or model the effect of adding another operator or machine.

But those answers only become genuinely useful when the connected evidence underneath them is dependable.

The opportunity is bigger than building another tool

Garment decorators are not short of software. Many already use an order-management platform, accounting software, online stores, supplier systems, spreadsheets, messaging tools and production boards. The gap often sits between the order being created and the work actually moving through production.

That is why the most valuable question may not be, “What app could we build with AI?” It may be, “What prevents this work from moving and what evidence would help us make a better decision?”

For some businesses, the right answer will be a small internal tool. For others, it will be a supported industry platform that removes the need to build, secure and maintain their own production system. In both cases, the starting point should be the same: understand the workflow first.