Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon

The skepticism surrounding AI has shifted dramatically in the last year. We have moved from questioning whether the technology is real to a frantic race to integrate it. Yet, despite the enthusiasm, execution remains messy. The uncomfortable truth is that most AI products fail to gain traction or are shut down due to reliability issues. Why? Because teams are attempting to build AI products using the same playbooks designed for traditional, deterministic software.

To understand how to fix this broken lifecycle, we look to insights from Aishwarya Ratan and Kiriti Bottom. With experience spanning OpenAI, Google, Amazon, and Microsoft, and having overseen more than 50 AI product deployments, they have identified the specific structural changes teams must make to succeed. The secret lies not in better prompting or faster models, but in fundamentally rethinking the relationship between human control and artificial agency.

Key Takeaways

• AI is non-deterministic by nature: Unlike traditional software where an input leads to a predictable action, AI interfaces are fluid. You cannot predict exactly how a user will phrase an intent, nor can you guarantee how the LLM will respond.
• Respect the Agency-Control Trade-off: There is a direct inverse relationship between AI autonomy and human control. Successful products start with low agency and high control, gradually shifting the balance only after trust is earned.
• Adopt the CCCD Framework: The "Continuous Calibration, Continuous Development" lifecycle replaces standard CI/CD. It focuses on iterating through versions of agency (e.g., suggestion vs. action) based on behavioral data.
• Evals are not a silver bullet: Evaluation datasets only catch known errors. You must pair them with rigorous production monitoring to catch "unknown unknowns" and emerging behaviors.
• Pain is the new moat: There are no shortcuts. The companies winning in AI are those willing to slog through the messy process of manual data review and workflow calibration.

The Two Fundamental Differences of AI Product Building

To build successful AI products, you must first acknowledge that the underlying material has changed. We are no longer working with rigid logic trees; we are working with probabilistic engines. This shifts the development paradigm in two critical ways.

1. Non-Determinism at Both Ends

In traditional software—like a booking engine—the user journey is mapped. A user selects dates, clicks "search," and the system executes a specific query. The input is constrained, and the output is predictable. AI products disrupt this reliability on both sides of the equation.

On the input side, the interface is natural language. Users can express the same intention in thousands of different ways, many of which you cannot anticipate during the design phase. On the output side, the "processing" layer is a black box. LLMs are sensitive to phrasing and can produce variable outputs even with identical inputs.

Most people tend to ignore the non-determinism. You don't know how the user might behave with your product and you also don't know how the LLM might respond to that.

This creates a chaotic environment where you are trying to achieve a deterministic business outcome (e.g., "refund this ticket") using a non-deterministic technology.

2. The Agency-Control Trade-off

The second major shift is the relationship between the system's power and the user's oversight. In the rush to build "autonomous agents," many teams forget that giving an AI system agency (the ability to make decisions) inherently requires the human to relinquish control.

If you hand over high agency to a system that hasn't earned it through reliability, you risk eroding user trust immediately. A helpful mental model is to view agency not as a binary switch, but as a dial that should only be turned up when you have calibrated the system's behavior.

Every time you hand over decision-making capabilities to agentic systems, you're kind of relinquishing some amount of control on your end.

The CCCD Framework: A Roadmap for Reliability

Because of the unpredictability of AI, you cannot simply write code, pass unit tests, and deploy. You need a lifecycle designed for behavior calibration. Ratan and Bottom advocate for a framework called Continuous Calibration, Continuous Development (CCCD).

This framework encourages building in steps, increasing the AI's agency only as you validate its behavior against real-world data.

Step 1: The Co-Pilot Phase (High Control, Low Agency)

Do not start with an agent that takes actions. Start with an agent that provides suggestions. For a customer support product, V1 should not resolve tickets. V1 might simply classify and route tickets to the right human department.

Even simple routing reveals hidden complexities. Enterprise data is often messy—taxonomies might be outdated or contradictory (e.g., categories for "Shoes" and "Mens Shoes" existing at the same hierarchy level). By starting with routing, you expose these data infrastructure issues without risking customer interactions.

Step 2: The Drafter Phase (Medium Control, Medium Agency)

Once routing is accurate, move to V2: The Drafter. The AI generates a suggested response or action plan, but a human must review and approve it. This is the most critical phase for data gathering.

Every time a human accepts, edits, or rejects an AI draft, you are generating a high-quality, annotated dataset. This "implicit logging" allows you to perform error analysis at scale. You aren't just guessing if the model is good; you are measuring how often humans find the output useful enough to use unchanged.

Step 3: The Agent Phase (Low Control, High Agency)

Only when the edit rate in the Drafter phase drops below a certain threshold do you graduate to V3: The Autonomous Agent. At this stage, the AI takes actions (like issuing a refund or deploying code) without human intervention.

This progression forces you to solve the business problem rather than getting distracted by the complexity of the solution. It ensures that when you finally deploy an autonomous agent, it is built on a foundation of calibrated trust.

In all this advancements of the AI, one easy slippery slope is to keep thinking about complexities of the solution and forget the problem that you're trying to solve.

Beyond Evals: The Need for Production Monitoring

A common debate in the AI engineering community is "Evals vs. Vibes." Ratan and Bottom argue that this is a false dichotomy. You cannot rely on evaluation datasets alone because evals only test for the problems you have already imagined.

The Trap of "Static" Evals

Evaluation datasets (Evals) are essentially regression tests. They ensure your prompt changes haven't broken known capabilities. However, users are unpredictable. They will use your product in ways you never anticipated.

For example, an underwriting tool designed to look up specific policy clauses might eventually be used by underwriters to "read the whole document and tell me what to do." If you only evaluated the model on specific lookups, you will miss the hallucinations generated when the model attempts high-level synthesis.

Vibes and Monitoring

Production monitoring captures the "unknown unknowns." This involves tracking implicit signals:

• Did the user copy the code snippet?
• Did they hit the "regenerate" button?
• Did they switch the tool off entirely?

At OpenAI's Codex team, they utilize a balanced approach. They use evals to prevent regression on core tasks, but rely heavily on "vibes"—qualitative feedback and usage patterns—to understand if the product feels right to the developer.

Leadership and The "Pain Moat"

Successful AI adoption isn't just a technical challenge; it is a leadership challenge. The leaders who succeed are those who are willing to rebuild their intuition from scratch.

Hands-on Leadership

Intuitions built over the last decade of SaaS management often do not apply to AI. Leaders like the CEO of Rackspace are setting aside dedicated time—4:00 AM to 6:00 AM blocks—solely to catch up on AI developments and interact with the models. You cannot steer an AI strategy if you haven't felt the non-determinism of the models yourself.

Pain is the New Moat

Finally, there is a dangerous allure to "one-click agents" and marketing promises of instant integration. Real value comes from the unglamorous work. The "moat" for modern AI companies is the willingness to endure the pain of cleaning messy data, calibrating workflows, and manually reviewing traces.

Successful companies right now building in any new area... They are going through the pain of learning this, implementing this and understanding what works and what doesn't work. Pain is the new moat.

Conclusion

The path to a successful AI product is not paved with complex multi-agent architectures or massive unchecked autonomy. It is built through a disciplined, step-by-step process of calibration. By respecting the trade-off between agency and control, and by valuing the messy feedback loop of production data, teams can move past the hype and build products that actually work.

The era of "busy work" is ending. The next few years will reward those who obsess over the problem rather than the tool, and who have the persistence to build reliability into systems that are inherently probabilistic.