Why AI-Generated Project Requests Break Down in Real Delivery

AI has become the first step in many sales conversations: polished requests, dense terminology, and professional-looking documents that seem ready for execution. But when our experts dig into the logic, the foundation crumbles. Because AI doesn't understand your business, your market, or your risk. AI-generated scopes often detach completely from delivery constraints, budget realities, and legal logic.

Drafting with AI is smart, but only to draft! Don't rely on AI output as "expert truth". This creates a False Confidence Trap. At Arounda, we value transparency, so our sales team explains how AI requests can mislead and how to use them to accelerate the initial phase without creating that false confidence.

Where AI Consistently Breaks Down in Real Projects

We analyzed 100+ project requests received over the last 6 months. Some of them were real (human-written), but 40% of them were AI-generated. 

When AI generates the scope without human oversight, our sales team notices the same structural failures repeat. They are fundamental misunderstandings of how design and development delivery work.

1. The "Yes-Man" Bias

AI models aim to be helpful and agreeable. They are programmed to provide an answer, not to refuse a bad premise.

If you ask for an "enterprise-scale ecosystem design and development for $20k in four weeks," the AI will generate a scope that makes this look possible. It will list features, timelines, and milestones that validate your request. It will not tell you that the trade-offs are impossible or that the request contradicts delivery reality.

The Result: You enter sales conversations with false confidence, only to face immediate friction when experts present the actual numbers.

2. The Missing Business Context

AI operates on patterns, not your reality. It can’t see inside your organization. It drafts requirements without knowing:

• Internal constraints
• Company politics
• Risk tolerance
• Strategy
• Market nuances
• Real priorities vs “nice to have”

And many other important points. Because you can’t tell all of these as it’s sensitive data, right?! 

The Result: AI fills these knowledge gaps with generic "best practices." It builds a solution for everyone, which means it fits no one.

3. The "Feature Salad" Effect

Writing a feature takes an AI milliseconds. Building it takes designers and engineers weeks.

AI frequently bundles conflicting requirements. It often produces “feature-complete” requests that mix enterprise expectations, startup budgets, and aggressive timelines into a single confident narrative. Each feature looks reasonable in isolation. 

The Result: Scope creep happens before the project even starts. We spend the first three meetings stripping the project back to reality rather than discussing strategy.

4. The NDA Wall (Why AI Can't See Delivery Reality)

This is the most critical blind spot. AI trains on open-source code and public marketing content. It doesn't train on delivery reality.

The most valuable data lives inside expert teams. NDAs and institutional memory protect it.

“AI suggests what is popular on the internet. It can’t warn you about the specific compliance risks or architectural bottlenecks. AI can’t see why things failed before, what caused overruns, or which shortcuts backfire later. Only a human partner with years of experience knows where the landmines are.” Roman Van, Business Development Director at Arounda

The Biggest Mistake We See: Trusting AI Over Human Expertise

Take a look at the damaging pattern that stalls projects at the final mile.

We align verbally (during the call) on scope, budget, and timeline.

↓

Our team prepares the proposal and contract based on that alignment.

↓

The client runs these documents through an AI tool for a "safety check."

↓

The AI flags standard clauses as "high risk" or "missing items."

↓

The client escalates these AI hallucinations as factual blockers.

Why is this dangerous?

AI optimizes for textual correctness, not commercial balance. It analyzes words in isolation, lacking the context of legal intent or delivery responsibility.

‍

When an AI reviews a service agreement, it often flags standard industry liability caps or intellectual property definitions as "risks" simply because they are restrictive. In the real world, these clauses protect both parties and define clear boundaries.

‍

The Critical Distinction: AI can review language, but it can’t own consequences.

The Cost of "Perfect" AI Requests

When a project starts with a hallucinated scope or an AI-redlined contract, the damage is commercial. 

1. Contract Friction (Increased Time-to-Kickoff)

If you accept AI-generated legal risks or contract edits as absolute truth, this forces legal teams to defend standard industry terms that an algorithm misidentified as "high risk."

• The Case: We observe an average delay of 14–21 days in contract signing when AI is used as the primary legal reviewer.
• The Cost: This is three weeks of lost market opportunity! Instead of an already finished discovery phase and getting a design system, you are paying legal counsel to debate clauses that don’t impact actual commercial delivery.

2. Wasted Cycles (Opportunity Cost)

Time spent validating a hallucinated scope is time stolen from actual product strategy. When a lead presents an AI-generated requirements doc, our solution architects must spend days fact-checking every assumption to identify technical gaps.

• The Case: We had a situation where our experts spent 30% of previously defined hours debunking AI-generated features rather than optimizing the core architecture.
• The Cost: This dilutes the value of the discovery phase. Instead of focusing on revenue modeling or user acquisition strategy, the conversation revolves around features that shouldn't exist.

3. The "Reset" Frustration (Budget Variance)

This is the most painful moment for a client. You believe you are "ready to start" because the AI document validated your budget. Then, reality comes. We identify missing dependencies, security flaws, or third-party costs that the AI ignored.

• Cases: We consistently see a 50% to 200% variance between AI-generated budget expectations and the actual delivery costs.
• The Cost: This forces a "Project Reset." 

Decision-Makers Still Need to Think and Communicate Themselves

AI is a powerful tool for drafting, but it is dangerous for decision-making. You can delegate the typing, but you can’t delegate the responsibility.

You are the only one who understands the nuance of your business.

• You own the priorities. Only you know what is important for this specific launch.
• You own the trade-offs. Only you can decide to cut a feature to save budget.
• You own the risk acceptance. Only you will be responsible if your decision fails.

AI can’t replace strategic judgment. It has no "skin in the game." It doesn’t lose money if the project fails. It doesn’t lose sleep if the architecture doesn't scale.

And now let’s talk about how to use it smartly. 

How to Use AI Correctly: A Strategic Workflow

AI tools are excellent for turning chaotic brainstorming into readable outlines and speeding up preparation. To get a good result, you should have good preparation. 

The Golden Rule: AI is a structuring assistant, not a decision-maker. It helps you say things better; it shouldn’t decide what must be said.

1. Structure Only

Use AI to organize your thoughts, not to generate your strategy.

• Wrong: "What features does a fintech app need?" (This invites generic answers).
• Right: "Here are my business goals and user problems (you must define them and put them in your prompt). Organize them into a list of potential functional requirements or prepare a structured description for a design partner to discuss" (this structures your actual intent).

2. Ask for Questions

The most valuable output from AI is not a feature list, but a preparation guide! Instead of asking AI to write the requirements, ask it to predict the gaps.

• The Prompt: "I will speak to a design and development company. Based on my business idea (describe it with all details), what materials and what information should I prepare for our first meeting? What questions can they ask me?"
• The Value: This prepares you for the real conversation, doesn’t create false assumptions, and speeds up the process.

3. The Human Validator (Mandatory Review)

You must manually review every line of the AI output before sending it to a potential partner.

• Remove assumptions you didn’t make. If the AI adds "Blockchain integration" and you don't know why, delete it.
• Challenge "perfect" scenarios. If the AI suggests a timeline that seems optimistic, it is likely wrong.
• Own the logic. If you can’t explain a requirement to a human expert, it doesn’t belong in your RFP.

Roman Van, Business Development Director at Arounda, adds:

“It doesn't matter whether you wrote the requests yourself or with the help of AI. In the end, you are still contacting real people for services. In any case, you will receive feedback on the realism of your requests. Don't create overly high expectations about AI. It is only an assistant. In 90% of cases, if you take the text generated by AI and feed it back to the AI itself, you will be surprised by the response. Most available AI tools will not be able to fulfill the requests they generated just 5 minutes ago.”

The Best Practice Workflow

To save time without sacrificing quality, follow this sequence:

We understand that “time is money” and you want to speed up the preparation, but don’t forget about the consequences. 

Following the workflow we’ve offered, you get the speed of AI preparation combined with the safety of human expertise.

And now we promised the prompt, here it is ↓

A practical AI prompt we recommend to our clients

This prompt will save your time without damaging alignment. It helps structure a request for design and/or development services without letting AI invent scope, guarantees, or delivery logic.

Copy and paste this entire block into your AI tool and insert your information in [your info]:

‍

[START OF PROMPT]

You are an assistant helping me structure a project request for design and/or development. Your role is to organize my thinking, not to invent requirements, estimates, guarantees, or solutions. 

Important rules:

• Do not assume enterprise-level features, unlimited budget, or aggressive timelines unless I explicitly confirm them.
• Do not merge features into a “complete solution” on your own.
• If information is missing, ask clarifying questions instead of filling gaps.
• Clearly label any assumptions you make as assumptions.

My Context (enter your information by yourself):

• Project Type: [e.g., Healthcare Mobile App, HRTech Web Platform, FinTech Redesign]
• Target Audience: [e.g., Enterprise Finance Managers, Gen Z Consumers]
• Current Stage: [e.g., Outdated platform, Prototype ready, Legacy code exists]
• Rough Budget Range: [Insert Range]
• Target Launch Date: [Insert Date]
• Geographic Scope: [Global / Regional (APAC / EMEA / Americas) / Country-specific]
• Market/Industry Context: [Current trends, regulatory environment, competitive landscape]
• Success Criteria (high-level): [Top 3–5 business outcomes you want to achieve]
• Stakeholders (primary & secondary): [roles, decision rights]
• Non-Goals / Exclusions: [What is out of scope or explicitly not wanted]
• Priority Features if you know: [Must-have / Should-have / Nice-to-have]
• Known Constraints: [Budget, tech stack, regulatory, vendor obligations]
• My Raw Notes & Goals: [Paste your messy notes, voice memos, stakeholder interviews, desired outcomes, or feature ideas here]

Your Task: Analyze my notes and generate a structured Request using the following format. Do not invent features I did not mention; only structure what I provided.

Help me structure a draft request that includes:

• Project type
• Business context and objectives
• Current state and main pain points
• Target users and stakeholders
• Scope of work and prioritized needs (must-have vs optional)
• Constraints (budget range, timeline expectations, internal resources, tech stack, regulatory, vendor obligations)
• Known risks or uncertainties
• Open questions that require expert validation
• Areas where trade-offs are expected

Constraint: Keep the tone professional, objective, and concise. Avoid marketing buzzwords.

[END OF PROMPT]

‍

Then, review the draft, flag, and delete or rewrite:

• Unclear or conflicting requirements
• Hidden assumptions
• Areas that typically require technical or product discovery

Our tips:

• Don’t provide delivery timelines, pricing, or legal commitments.
• Don’t present the output as final or “ready to execute.”
• The result should be a discussion-ready document for a human delivery team.

An important note on legal documents

“There is no safe prompt for validating contracts, scopes of work, or legal risk. AI doesn’t understand legal intent, delivery responsibility, or commercial liability. It can’t evaluate consequences, only text.  AI feedback on legal documents often increases risk instead of reducing it. Legal review is a task for qualified human legal experts.”  Kristina Bohdanova, Partnership Manager at Arounda

Final note: Why We Still Rely On People And Always Will 

Real delivery expertise comes from experience, failures, and judgment built over time. That knowledge is contextual, often confidential, and rarely visible in public sources.

AI learns from what is accessible.
Great delivery depends on what is not.

How do we work at Arounda? 

We act as a strategic filter. We take raw input and apply market reality, strategic insights, design logic, engineering constraints, and delivery nuances. That is how scope becomes executable instead of theoretical.

We hope this article was helpful.
And if you have a project to discuss, send us your vision (you’ve already got a prompt), and let’s build the scope.