Pros and Cons of Using AI in PromoStandards Development

What are the Advantages of Using AI with PromoStandards?

🔍 Navigating Inconsistent Supplier Responses

PromoStandards defines a specification, but suppliers sometimes interpret that spec differently. Some may return extra fields, omit required ones, or use non-standard structures — even when responding to the same endpoint. These inconsistencies can create major challenges when supporting multiple suppliers in a single application.

AI tools can help developers quickly scan and interpret large JSON responses to locate needed data, even when the structure varies. This makes it easier to support multiple suppliers without building overly rigid code.

🧭 Spotting Structural Differences Across Suppliers

Because PromoStandards.org provides the specification but doesn't enforce it, each supplier's implementation may be subtly or significantly different. AI can assist by comparing multiple response samples and flagging inconsistencies — such as missing fields, reordered keys, or unexpected naming variations. This is especially helpful when onboarding several suppliers and validating their data formats.

Over time, developers can even train AI workflows or custom prompts to understand what's considered "standard" and what counts as a deviation — offering a fast way to catch edge cases before they become production issues.

⚙️ Accelerating Boilerplate Code (with Oversight)

AI is useful for generating repetitive or baseline code — such as building SOAP requests, parsing endpoint responses, or setting up service wrappers in PHP. This can speed up development, reduce typing errors, and save money.

That said, this code often needs to be reviewed and adapted to fit the specific application. Each PromoStandards project may have unique requirements or architectural choices, so human oversight is critical. While AI can help lay the foundation, an experienced developer ensures everything integrates cleanly and reliably.

📚 Summarizing and Searching Documentation

PromoStandards documentation is thorough but not always easy to navigate. AI tools can summarize complex endpoint descriptions, help extract sample payloads, or quickly locate specific sections. This speeds up learning for new developers and shortens the time needed to resolve implementation questions.

While this advantage isn't unique to PromoStandards, it's still valuable given the technical depth of the documentation and the learning curve involved.

🧪 Smart Fallback for Unstructured Data

AI can help when JSON responses from suppliers don't follow the expected PromoStandards format. If the SKU-level data is inconsistent or buried, AI can be prompted with specific instructions to search the object and extract what's needed — even if the structure is irregular.

This kind of AI call can be powerful, but it's not free: prompts can get long, and AI services typically bill by token or word count. Worse, results aren't always reliable. A more cost-effective approach is to use AI as a fallback tool — saving the cleaned result to a local database so that the same product doesn't require repeated lookups.

What are the Disadvantages of Using AI with PromoStandards?

⚠️ Overconfidence in AI = Poorly Built Code

One of the biggest risks in using AI for PromoStandards development — or any programming work — is assuming the AI can do the heavy lifting without oversight. Developers who don't fully understand the PromoStandards spec may accept AI-generated code as correct, even when it's incomplete or incompatible with supplier-specific needs.

This can result in brittle or broken integrations that cost the client time and money to rewrite. AI should be treated as a coding assistant — not as a replacement for someone who understands both the spec and the business logic behind it.

🚧 Lack of Context for PromoStandards Logic

AI tools don't "know" the intent behind the PromoStandards endpoints. They can help generate SOAP requests or handle JSON responses, but they won't understand that, for example, Media endpoint data might need to be matched to configuration options, or that Inventory might need to be filtered against drop-ship locations. That logic varies from project to project and supplier to supplier — and it always requires human decision-making.

🔐 Limited Security Awareness (in Some Use Cases)

AI tools are not security experts. When used carelessly — such as pasting in real request/response payloads to debug code — there's a risk of exposing private supplier data, pricing information, or even customer details.

For most PromoStandards integrations, this risk is low since the services you're using (Inventory, Product Data, Pricing & Configuration, and Media) don't typically involve customer-sensitive data or live order submission. But in projects that extend into services like Purchase Order or Order Shipment, the stakes are higher — and AI should never be trusted with sensitive content unless you're working in a secured, local AI environment.

🤖 AI Hallucination Can Introduce Confidently Wrong Code

One of the most common issues with AI-generated content is something called "hallucination." In software development, this means the AI may generate code, function names, or request formats that sound right — but don't actually exist or work. Because the output is often written with confidence and polish, less experienced developers may not realize it's incorrect.

In the context of PromoStandards, this might look like the AI generating a field that doesn't exist in the spec, or misunderstanding how an endpoint is supposed to behave. It may even fabricate example responses or use the wrong data types. Developers should always double-check AI output against the official PromoStandards documentation.

Note: AI is also prone to errors when working with SOAP XML, particularly around namespace declarations and envelope formatting — which are critical for valid PromoStandards requests. These issues can be subtle and frustrating to debug, even for experienced developers.