Most AI products are solutions looking for problems

I've noticed a pattern with AI products: most AI features get built because they're technically impressive, not because users desperately need them.

This trap is especially dangerous when you're younger or new to a domain. You see LLMs do something amazing online, think "I could build that," and dive straight into implementation without understanding the real problem you're solving.

The AI hype cycle amplifies our natural bias toward building cool stuff. But cool doesn't pay the bills. Users don't care how sophisticated your model is—they care whether it saves them time, money, or frustration.

Start with the workflow, not the model

The best AI products start with a painful manual process that takes users hours to complete. The AI is just the implementation detail that makes the automation possible.

Instead of starting with "what can LLMs do?" start with "what tedious work are people doing manually that could be automated?"

Look for workflows where people:

• Do the same repetitive task multiple times per day
• Follow a clear decision tree or process
• Spend time on work that doesn't require creativity or judgment
• Complain about how boring or time-consuming something is

The key insight: Don't build an AI tool and try to find uses for it. Identify a painful workflow and use AI to automate it.

The inexperience trap (and why customer discovery saves you)

When you're early in your career or new to an industry, you face a brutal catch-22: you don't have enough domain expertise to know what problems are worth solving, but you have just enough technical skill to build impressive demos that solve the wrong problems.

I've watched countless young developers (myself included) fall into this trap:

1. See a cool AI capability → "I could build that!"
2. Spend weeks building → Amazing technical execution
3. Launch to crickets → Nobody actually needed it
4. Blame the market → "Users don't understand how innovative this is"

Why inexperience makes it worse

When you lack domain expertise, you fill in the gaps with assumptions. You think you understand the user's workflow because you've read about it online or built a mental model from limited exposure.

The dangerous assumption cycle:

• "Obviously users want X automated"
• "Surely this workflow is painful for everyone"
• "If I find it tedious, users must hate it too"

These assumptions feel logical, but they're usually wrong in subtle ways that kill product-market fit.

Customer discovery as your competitive advantage

Here's the counterintuitive truth: being inexperienced can be an advantage if you lean into customer discovery instead of away from it.

Experienced people often think they already know the answers. When you're new, you're naturally curious and ask better questions:

• "Why do you do it this way?" (vs. assuming you understand)
• "What's the most frustrating part?" (vs. guessing based on your experience)
• "What have you tried before?" (vs. thinking your solution is obviously better)

How to talk to customers when you don't know the domain

1. Start with workflow mapping: Ask users to walk you through their current process step-by-step
2. Focus on pain points, not solutions: "What takes the most time?" not "Would you use an AI for this?"
3. Ask about failed solutions: "What tools have you tried? Why didn't they work?"
4. Quantify the problem: "How much time does this take per week? What's it costing you?"

The goal isn't to validate your AI idea—it's to understand whether there's a problem worth solving at all.

Design for graceful degradation

AI systems fail differently than traditional software. Instead of clear error messages, you get subtle wrongness that's hard to detect. The trick is designing systems that fail gracefully and give users escape hatches.

The confidence threshold approach

For critical decisions, we implemented confidence thresholds:

• High confidence (above 90%): AI acts autonomously
• Medium confidence (70-90%): AI suggests actions, user confirms
• Low confidence (below 70%): AI asks clarifying questions or hands off to human

This approach reduced user frustration while maintaining the benefits of automation.

Always provide an "undo" path

Every AI action needs a clear undo path. Users should feel safe letting the AI take action because they know they can easily reverse it if something goes wrong.

We added:

• Clear action summaries before execution
• One-click undo for any AI decision
• Detailed audit logs of what the AI changed
• Easy escalation to human support

The feedback loop is everything

Traditional software gets better through code updates. AI products get better through feedback loops. The quality of your feedback mechanism determines how quickly your AI improves.

What we measured

Quantitative metrics:

• Task completion rate by AI vs. humans
• Time to completion for common workflows
• User satisfaction scores per interaction
• Error rate and recovery time

Qualitative feedback:

• Exit interviews when users churned
• Weekly feedback sessions with power users
• Support ticket analysis for AI-related issues
• Screen recordings of AI interactions

The feedback integration system

The breakthrough came when we closed the loop between user feedback and model improvement:

1. Real-time feedback capture: Users could thumbs up/down any AI action
2. Weekly feedback review: PM and eng team reviewed all negative feedback
3. Rapid iteration cycle: High-impact improvements shipped weekly
4. A/B testing framework: We could test prompt changes on subsets of users

This type of system can dramatically improve AI performance in the first month after launch.

Don't anthropomorphize the AI

One of the biggest design mistakes is making AI feel too human. Users develop unrealistic expectations when the AI has a personality, name, and human-like responses.

What works better

Instead of: "Hi! I'm Sarah, your AI assistant. How can I help you today?" Try: "I can help automate your onboarding process. What would you like me to do?"

Instead of: Long conversational explanations Try: Clear, concise action summaries

Instead of: Apologizing for mistakes like a human would Try: Simply correcting the mistake and moving forward

The goal is to feel capable and reliable, not friendly and chatty.

Performance is a feature

AI products have a unique performance challenge: users expect them to be both smart and fast. A slow but accurate AI feels broken, even if it gives better results than a fast but mediocre one.

The speed vs accuracy tradeoff

Common pattern: 8-12 second response times, 85% accuracy User reaction: "It takes forever" (despite being more accurate than manual process)

Better approach: 2-3 second response times, 82% accuracy
User reaction: "This is amazing, it just works"

A small accuracy drop is often invisible to users, but speed improvements completely change their perception of the product.

Speed optimization techniques

• Streaming responses: Show partial results as they generate
• Speculative execution: Predict likely next steps and pre-compute them
• Smart caching: Cache frequent patterns at multiple levels
• Hybrid approaches: Use fast heuristics for common cases, AI for edge cases

The human-in-the-loop sweet spot

The best AI products don't replace humans—they amplify them. Finding the right balance between automation and human oversight is crucial.

What to automate

Good for AI:

• Repetitive, high-volume tasks (data entry, categorization)
• Pattern recognition at scale (anomaly detection, content moderation)
• Real-time decision making (routing, prioritization)
• First-pass analysis (summarization, initial research)

What to keep human

Keep human:

• High-stakes decisions (strategic planning, hiring)
• Creative work (design, strategy, messaging)
• Relationship building (sales, customer success)
• Edge case handling (complex troubleshooting, custom requests)

Launch strategy that actually works

Most AI products launch with a big announcement and then struggle to find product-market fit. Here's a better approach:

The private beta approach

1. Start with 5-10 power users who have the exact pain point you're solving
2. Weekly feedback sessions to understand what's working and what isn't
3. Rapid iteration based on their feedback
4. Gradually expand the beta as the product stabilizes

Set expectations early

AI products need more context-setting than traditional software:

• Clear capability documentation: What the AI can and can't do
• Expected response times: Set realistic expectations
• Confidence indicators: Show users how confident the AI is in its responses
• Escalation paths: Clear ways to get human help when needed

Lessons learned

After shipping AI products, here's what I wish I'd known from the start:

Technical lessons

1. Start simple: Basic automation that works beats sophisticated AI that's unreliable
2. Instrument everything: You need 10x more observability than traditional software
3. Plan for model drift: Performance degrades over time without active maintenance
4. Version your prompts: Treat prompt changes like code deployments

Product lessons

1. Solve real pain points: Don't add AI because it's cool, add it because it's necessary
2. Design for failure: AI will fail in unexpected ways, plan for graceful degradation
3. Optimize for perception: Fast and good enough beats slow and perfect
4. Build feedback loops: The product only gets better if you have systematic ways to improve

Business lessons

1. Price for value, not cost: AI features should be priced based on value delivered, not compute cost
2. Support is different: AI support requires specialized training and tooling
3. Compliance is complex: AI introduces new regulatory and liability considerations
4. Competition moves fast: Your AI advantage can disappear quickly as models commoditize

What's next

The AI product landscape is evolving rapidly. What worked six months ago might not work today. The teams that win will be the ones that:

• Stay close to users: Deep understanding of user workflows and pain points
• Iterate quickly: Fast feedback loops and rapid deployment cycles
• Think systems, not features: AI works best as part of integrated workflows
• Measure what matters: Focus on user outcomes, not technical metrics

The most exciting AI products aren't the ones with the most advanced models—they're the ones that seamlessly integrate AI into workflows users already care about.

Building AI products that don't suck isn't about having the best AI. It's about having the best understanding of your users' real problems and using AI as one tool among many to solve them elegantly.

The future belongs to products where AI disappears into the background, making complex tasks feel effortless. That's the standard we should all be building toward.