Is Your AI Product Worth It?
Most teams focus on AI features while ignoring the economics that will make or break them
I was recently helped retrospect a “successful” AI chatbot implementation as a friendly consultant. The team was celebrating—70% engagement rate! Users were interacting with the bot in droves.
I asked two questions:
- “Are those interactions actually solving customer problems?” Silence.
- “And what’s your monthly AI cost running?” More silence.
It turns out nobody had checked if engaged users were satisfied users. The bot was great at conversation, terrible at resolution. Adding insult to injury, their pilot budget of $200/month had ballooned to $20,000 in their first month. Nobody had modeled whether the cost exceeded the value of what they were replacing—in this case, tier-1 support ticket deflection.
They’d optimized for the wrong metric entirely. This isn’t unusual.
Most AI projects start with the wrong question: “What cool AI features can we build?”
The right question is always: “What customer problems are we trying to solve?”
Only then ask: “Can AI provide an optimization path for us?”
Most problems you want to solve with AI can be solved with other tools and technologies. AI provides a different path, usually optimized for speed, scale, or personalization. But optimization only matters if you’re solving the right problem and the economics work.
The Hidden Economics of AI Implementation
I learned this reality the hard way. Early in one of my startup ventures, we built a recommendation engine that looked brilliant in testing. Production costs blindsided us (back in the day of paying by SQL server transaction in shared racks). We’d optimized for accuracy without modeling real-world query patterns, like how many times the user might refresh the screen. That expensive lesson shaped how I approach AI economics now.
I’ve written before about the quiet power of invisible AI. But even when you know what to build, most teams drastically underestimate the hidden costs of AI implementation.
The challenge is that most teams aren’t used to accounting for usage this way–it’s new and a bit hard to estimate for net new projects. According to Gartner research, 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, with cost overruns being a primary factor.
Understanding Token Pricing
Before diving into costs, here’s the reality. Most AI services (OpenAI, Anthropic, Azure) charge per token, essentially chunks of text your system sends and receives. A typical conversation might use 1,000 to 5,000 tokens. When you’re handling thousands of users daily, these add up fast.
The Cost Reality Gap
The RAG Explosion: A knowledge base chatbot was pulling 50,000+ tokens per query because nobody optimized retrieval. The team assumed “more context equals better answers.” Their monthly bill suggested otherwise.
Conversation Memory Overflow: A customer service bot was maintaining full conversation history for “better context.” Each interaction snowballed, with later questions costing 10x more than initial ones due to context window expansion.
The Retry Death Spiral: When AI calls failed, systems retried with full context. Network hiccups became budget disasters as expensive queries fired multiple times.
Going back to our earlier example, that 10x cost multiplier for their first month became a quick, and painful reality.
The 10x Cost Multiplier
Why: Real users have longer conversations, expect comprehensive answers, and trigger complex retrieval patterns you probably didn't test in pilots.
What “AI-Ready” Actually Means
According to the AI Infrastructure Alliance’s 2024 survey, 96% of companies plan to expand their AI compute capacity and investment. However, Gartner research shows that 63% of organizations either do not have or are unsure if they have the right data management practices for AI. AI-ready means you can model and control costs, not just implement features.
Token Budget Architecture
Before building any AI feature, establish token budgets per user interaction.
Maximum token spend = (Value created per interaction × Success rate × Target margin)
Example: If deflecting a support ticket saves $15 (value created), and your bot successfully resolves 60% of interactions (success rate), your maximum token cost is roughly $9 (maximum token spend) per conversation to break even. Build in margin from there.
Keep in mind that while we’re focusing on token budget, understand your full end to end costs. Don’t forget about:
- API fees (input and output)
- Compute resources
- Data storage for RAGs, training data, logs, etc.
and … of course, the cost of your team to build and maintain the system!
Context Management Strategy
Unlimited context doesn’t equal unlimited value. Design conversation flows that maintain just enough context for quality responses without burning tokens unnecessarily.
Example: A financial advisor chatbot needs context about portfolio positions, not the entire conversation history including small talk.
Retrieval Optimization
RAG systems multiply token usage. Healthcare analytics teams reduced their time-to-insight by 78% through careful retrieval design that allowed them to swap out models and data sources without disrupting downstream applications. Retrieve precisely what’s needed, not everything that might be relevant.
The Infrastructure-First Approach
Most teams build AI features and then worry about costs. Successful implementations work backward from economic constraints.
Start with the unit economics:
- What value does each successful interaction create?
- What’s the maximum token cost per interaction that makes sense?
- How does conversation depth affect token usage in your specific use case?
- What’s your retrieval token budget per query?
- How do you handle failed requests without costs spiraling?
Then design within those constraints:
- Context window management strategies
- Fallback and degradation patterns
- Usage monitoring and alerting
- Automatic cost circuit breakers
Starting Without Perfect Data
If your team can’t answer the cost questions yet, here’s your incremental path.
Week 1: Instrument one AI feature to track token usage per interaction. Start with production logs. You probably have more data than you think.
Week 2: Calculate cost per interaction for that single feature. Compare to the value it creates (ticket deflection, conversion lift, time saved).
Week 3: Present findings to stakeholders with simple ROI: “Feature X costs $Y per use, creates $Z in value, giving us $W margin per interaction.”
You don’t need comprehensive analysis across all features. One solid example gives you the conversation starter with your team.
AI Readiness Assessment
Can't model costs OR value
Can model costs OR value
Can model both but no controls
Model costs, value, and have controls
Most teams fall into "Getting There." The path forward is systematic instrumentation, not perfect planning.
Making the Economics Work
The teams getting AI economics and scaling right aren’t necessarily the most technically sophisticated, but they’re treating AI costs as a first-class product constraint.
- Budget-driven design: Start with cost targets and design backwards to features that fit those constraints.
- Usage pattern modeling: Understand how real users will interact with your system, not how your demo users do.
- Graceful degradation: Design systems that maintain value while reducing costs as usage scales.
Your AI rollout isn’t wrong because you chose the wrong model or framework. It’s wrong if you’re optimizing for engagement metrics instead of sustainable value creation.
Ask your team this Monday morning: “What’s the real cost per successful customer interaction, and how does that compare to the value we’re creating?”
If they can’t answer both parts of that question confidently, you’re not ready for production scale.
Next in this series: How API-first design patterns can help control AI integration costs without sacrificing functionality.
