Why rushing AI products fails and how to build them properly

Everyone Wants AI Magic, Yesterday

“We need an AI product to sell in four months. Make it compelling, make it profitable, and oh—figure out what problem it solves whilst you’re at it.”

Sound familiar? A recent Reddit post from a developer facing this exact scenario has struck a nerve across the tech community. It’s exposed a dangerous pattern: executives drunk on AI hype are making impossible demands that could sink both careers and companies.

As product managers, you’re likely facing similar pressure. Whether it’s “Can AI optimise our inventory?” or “What about an AI chatbot for customer service?”, the expectation is always the same: deliver transformative results in record time with minimal resources.

When AI Expectations Meet Reality

Here’s the brutal truth: building AI products under these conditions is like trying to open a five-star restaurant in six months when you’ve never seen a professional kitchen. You need the perfect recipe, quality ingredients, skilled chefs, efficient operations, and loyal customers. Instead, you’re handed a microwave and told to make magic happen.

The Reddit thread reveals what happens when this magical thinking meets reality:

• Single-person armies: One developer expected to handle product conception, development, market research, and sales strategy
• Technology-first thinking: “Build AI first, find problems later” approach that ignores basic product fundamentals
• Unrealistic timelines: Complex AI systems compressed into impossible delivery windows
• Unclear success metrics: “Make it sellable” without defining what success actually looks like

The result? Frustrated teams, failed products, and wasted resources. According to McKinsey’s State of AI report, whilst 40% of organisations plan to increase AI investment, many struggle with implementation challenges and unclear ROI.

A Practical AI Product Development Recipe

The good news? There’s a better way. Instead of chasing AI magic, follow this proven framework that treats AI as what it is: a powerful tool that needs proper product discipline.

Step 1: Start with the Problem, Not the Technology

Before touching any AI tools, identify a genuine business problem that’s costing you money or customers. In retail, this might be inventory waste from poor demand forecasting. In restaurants, it could be labour scheduling inefficiencies during peak periods.

Ask yourself: “If we solved this problem without AI, would it still be valuable?” If the answer is no, you’re building a solution looking for a problem.

Step 2: Define Success in Business Terms

Set clear, measurable outcomes before writing a single line of code. Examples:

• “Reduce inventory waste by 15% within 12 months”
• “Decrease customer service response time from 4 hours to 30 minutes”
• “Improve demand forecasting accuracy by 20%”

Notice how none of these mention AI? That’s intentional. Success should be measured in business impact, not technical sophistication.

Step 3: Build Your Minimum Viable AI (MVAI)

Start simple. Really simple. Your first AI product should solve one specific problem well, not transform your entire business overnight.

For retail: Begin with basic demand forecasting for your top 20 SKUs, not a comprehensive inventory management system.
For restaurants: Start with predicting busy periods for one location, not a full workforce optimisation platform.

Step 4: Plan for 12-18 Month Timelines

Here’s the hard truth: meaningful AI products take longer than traditional software. You need time for:

• Data collection and cleaning (3-6 months)
• Model development and testing (3-6 months)
• Integration and user training (3-6 months)
• Performance monitoring and refinement (ongoing)

This isn’t pessimism, it’s realism based on successful AI implementations.

Step 5: Manage Expectations with Data

When stakeholders push for impossible timelines, respond with concrete examples. Show them case studies from similar companies. Reference Gartner research showing that 55% of organisations haven’t successfully deployed any AI solutions despite significant investment.

Present options: “We can deliver a basic prototype in 6 months, a market-ready product in 12 months, or a comprehensive solution in 18 months. What’s your priority?”

Why This Recipe Works

This approach works because it treats AI product development as what it actually is: complex product management requiring discipline, patience, and clear thinking. By starting with genuine problems and building incrementally, you create products that deliver real value rather than impressive demos.

The developers in that Reddit thread aren’t wrong to feel overwhelmed…they’re being asked to perform miracles. But as product managers, we can create the structure and strategy that turns impossible demands into achievable goals.

Remember: your job isn’t to deliver AI magic. It’s to deliver business value using the best tools available. Sometimes that’s AI. Sometimes it’s not. The difference between a successful AI product and a expensive failure often comes down to asking the right questions before you start building.