Where a catalog item delivery actually starts to break down isn’t the execution, it’s right at the intake.

A new demand shows up, saying “We need a laptop request form.” 

On the surface, it looks simple. But as soon as a developer opens the story, the gaps appear. Fields are undefined. Validation rules are missing. Assignment groups unclear. Work stops, clarifications start, and delivery stalls.

The bottleneck is almost always incomplete requirements.

Why intake breaks down, three big reasons

Level of details

Business users describe things too generally. A good analyst may enrich them, but developers still need to know — which table does that field point to? What qualifier applies? What’s the right validation logic? Without it, they’re blocked. A single missing detail, across time zones, can cost an entire day.

Scalability

Capturing requirements for one catalog item may take 30 to 60 minutes. That is manageable in isolation. But when a new business unit submits 200 items or a migration involves 500, the intake process breaks down. You cannot throw enough analysts at the problem.

Context 

Even when templates are used, they don’t solve for knowledge gaps or architectural alignment. Users leave blanks, analysts push “TBD” just to move it forward, and suddenly we’re building on ambiguity. Multiply this across distributed teams, and you lose days, sometimes weeks to questions that should’ve been answered upfront.

What good intake should ensure

For a developer to pick up a story and build without stopping, intake must provide everything that prevents ambiguity. At a minimum, requirement should capture:

• General info: the item name, a concise description, the category, the taxonomy, and the business owner. This ensures accountability and avoids confusion when similar requests appear in the backlog.
• Access: which roles, departments, or regions can see and use the item. A request available to everyone by default creates noise, while overly restrictive access frustrates users.
• Fulfillment: the primary assignment group, backup groups, SLAs, and escalation paths. Without this, tasks fall through the cracks when workflows encounter exceptions.
• Form fields: the labels, field types, defaults, mandatory status, validation logic, reference tables, and qualifiers. This is the most critical area. If even one of these details is missing, the developer has to stop mid-build.
  Workflow: the approval flow, fulfillment tasks, and exception handling. Developers need to know who approves, who fulfills, and who intervenes when something stalls.
• Metadata: dependencies, related catalog items, and notes for future maintainers. These are often overlooked, yet they matter for stability and upgrades.

Let’s take the example of a “New Laptop Request” item. Good intake would not just say “Ask for laptop type and manager approval.”

It would specify:

• Form fields for laptop model (dropdown referencing an asset table), business justification (mandatory text field), and expected start date (date field with validation logic preventing past dates).
• Access restricted to full-time employees in IT, Finance, and Sales.
• Primary assignment group as Hardware Fulfillment, with IT Support as backup.
• Workflow requires line manager approval followed by procurement.
• SLA of three business days, with escalation to IT Asset Manager if not fulfilled.
• Metadata noting that this item links to existing laptop stock workflows and pulls cost center data from HR tables.

With that level of detail, a developer can move from intake to build in one motion. There is no back-and-forth, no guesswork, and no wasted cycles.

But templates don’t cut it anymore

To improve intake, most organizations introduce templates, often spreadsheets. Templates bring order where free-text requests bring chaos. They force a baseline of structure: instead of vague notes like “We need a form with department and location,” templates prompt for specifics such as “Which table provides the location field? What qualifier applies?”

But templates are not foolproof.

The first problem is that people often do not fill them in completely.

Fields are left blank, details are skipped, and the intake looks structured but is incomplete. Developers still need to chase missing information.

The second problem is knowledge.

Business users rarely understand technical terms such as reference qualifiers or validation logic. Even analysts under delivery pressure sometimes fill in “TBD” or “same as last time” to push intake forward. On paper, the template is submitted. In reality, ambiguity remains.

Third, a spreadsheet cannot tell you if what is being asked aligns with platform best practices.

A business owner may request fields or workflows that are technically possible but architecturally unsound. Developers then have to escalate to architects or business owners, triggering another loop of clarifications.

This is where delays multiply. If teams are co-located, perhaps you resolve the issue in minutes. But with offshore delivery, or distributed teams across time zones, every missing detail stretches into hours or days.

Templates reduce ambiguity, but they do not eliminate it. They improve intake, but they do not guarantee build-ready requirements at scale.

Intake in an AI-enabled era

This is where AI changes the process.

Instead of static templates passed between roles, AI agents can consume requirements directly, identify gaps, and prompt for clarifications in plain English.

If a reference qualifier is missing, the agent asks: “Which table should this point to?” If the assignment group is unclear, it asks: “Who should own this by default?” Intake becomes interactive rather than static. Missing details are resolved on the fly, not lost across time zones.

For example, with Echelon, when you assign a catalog item story, it reviews the requirements, prompts for what is unclear, and then builds, tests, and validates the item end-to-end. What used to take days of clarification cycles now happens dynamically, with the system itself closing the gaps before development begins.

Where intake is headed

AI is already reducing the burden on analysts and business users. As AI improves at scanning environments and learning from past builds, the level of detail users need to provide will drop. Instead of spelling out every qualifier or dependency, they will describe requests in plain language. The system will infer, suggest, or pull context automatically.

It will ask clarifying questions in real time, and present a working preview. Adjustments will happen live: “Delete that field. Add another. Move this to the top.” Configuration will update on screen instantly.

Final thoughts…

Templates raised the floor. AI raises the ceiling. For platform teams, it’s now easier than ever to tighten intake and prevent backlogs, and prepare for the near future where requirements are not written down at all. They will be spoken, clarified, and built in real time.