The "AI POC Cemetery": How to Stop Playing with GenAI and Start Making Money

Summary, Core Thesis, Key Insights and Strategic Recommendations

Summary

While the hype of Generative Artificial Intelligence motivated thousands of corporate experiments, most of these initiatives stagnated in what we call the “AI POC Cemetery”. This article diagnoses the root causes of this failure, from choosing irrelevant use cases to neglecting integration, and proposes a Value Engineering methodology to ensure every line of code generates return on investment.

Core Thesis: A Proof of Concept (POC) that is not born with a clear plan for scalability, governance, and integration into the core (ERP/CRM) is merely “Innovation Theater”. To generate real value, organizations must abandon the exploratory experimentation model and adopt a Minimum Viable Product (MVP) approach focused on solving acute financial pains, not testing technological capabilities.

Key Insights:

• The “Cool Demo” Fallacy: Projects that impress on video but fail in real life (due to latency, token cost, or hallucination) are the main source of IT budget waste today.
• The Integration Abyss: Most POCs work in isolation. The project dies when it tries to connect to the company’s dirty data and legacy systems.
• Projected vs. Realized ROI: Without success metrics defined before development (e.g., 30% reduction in AHT), it is impossible to justify rollout to the board.

Strategic Recommendations:

• 1) Establish the “90 Days” rule: If the project cannot go to production (even limited) within a quarter, it is too complex to start.
• 2) Involve Security and Architecture from Day 1, killing the concept of “Shadow Innovation”.
• 3) Prioritize “boring” and high-volume processes over “creative” and low-impact ones.

Context and Business Problem

Visit any large Brazilian company today and you will find a vibrant “Innovation Lab”. The walls are covered with post-its, the team is testing the latest models from OpenAI or Anthropic, and the demonstrations are incredible.

However, go up two floors to the CFO’s office and the story changes. “We saw many demos, but where is the impact on EBITDA?”.

This scenario created the POC Cemetery. Hundreds of pilot projects that proved the technology works, but failed to prove that the business stands. Companies are suffering from a “hype hangover”. They know they need AI, but are burning cash on initiatives that never cross the finish line into production.

Market Drivers: Why Doesn’t the Math Add Up?

Our market analysis and data from the AI Landscape in Brazil point to clear barriers that turn innovation into sunk cost:

• Implementation Costs: The cost of running a POC is low, but the cost of scaling (tokens, vector infrastructure, monitoring) surprises those who didn’t do the math upfront.
• Technology Looking for a Problem: Many POCs are born because someone wants to test a new feature, not because there is a real business pain. Solutions without pain have no sustenance budget.
• The “Last Mile” Barrier: It’s easy to make AI generate text. It’s hard to make AI access legacy databases, comply with LGPD, handle internet outages, and respond in under 2 seconds. It is in this “last mile” that POCs die.

Strategic Analysis: From Experimentation to Value Engineering

To break this cycle, Zappts recommends a radical methodology shift: transitioning from POC (Proof of Concept) to value-driven MVP (Minimum Viable Product).

The Rescue Framework:

• Value Engineering (The “Why”): Before writing a prompt, define the financial equation.
  • Wrong: “Let’s create a bot for HR.”
  • Right: “We will reduce resume screening time by 40%, saving R$ 200k/month in man-hours.” If the math doesn’t work on paper, it won’t work in code.
• Production Architecture from Day 0 (The “How”): Don’t build disposable prototypes. Use robust frameworks (like the Zappts AI Agent Framework) that already include authentication, audit logs, and MCP connectors. If the pilot works, it is already the foundation of the final product.
• Governance as Enabler: Fear of hallucination paralyzes go-live. Implement Guardrails (security barriers) from the start. An AI that cannot talk about politics or competitors is an AI that Legal approves faster.

Implications for Organizations

Continuing to accumulate POCs without rollout generates two toxic side effects:

• Internal Skepticism: Business areas start seeing AI as an “IT toy”, losing the engagement needed for real adoption.
• Opportunity Debt: While you play with 10 small projects that go nowhere, your competitor chose one big project (e.g., Quote Automation), put it in production, and is already capturing margin.

Strategic Recommendations

For Innovation Leaders, CTOs, and CIOs, the order is to clean house:

• Portfolio Audit: List all your current AI initiatives. Ask: “What is the Go-Live date?”. If the answer is vague, cancel the project or redefine the scope immediately.
• Focus on “Boring and High-Volume”: AI shines where humans suffer. Invoice processing, bank reconciliation, support ticket triage. These processes have enough volume to pay for the AI investment in a few months.
• Stop “Coding”, Start Integrating: The secret is not the model (LLM), it’s the context. Invest more time cleaning your data and creating APIs (MCP) than tuning model parameters.
• Define the Success Criterion: The project is only finished when value is captured, not when code is delivered.

Conclusion

The phase of “playing with ChatGPT” ended a long time ago. The market has entered the consolidation and efficiency phase. Don’t be the company with the most innovative pilots in the sector. Be the company with the most efficient processes. The POC cemetery is full of good intentions; the market is led by those with good execution.

About the Author

Rodrigo Bornholdt is Co-founder and Chief Technology Officer at Zappts, specialized in Software Architecture and Artificial Intelligence, with solid experience in technology team leadership, complex systems development, and innovation applied to business strategies.

About Zappts

Zappts is the leading agentic transformation consultancy in Brazil, helping companies evolve from digital to agentic. With over 10 years, Zappts creates, modernizes, and evolves secure and scalable digital solutions for large organizations. Combining practical experience in software engineering, data, and artificial intelligence, it integrates technology, methodology, and processes, accelerating value delivery with efficiency, quality, and governance. Its work spans from strategy to software application and AI agent development, being a reference in Brazil on the topic of artificial intelligence agents. Click here to learn more.