A staggering 80% of AI projects fail to deliver any real value. They stall in labs, get rejected by users, or, worse, get deployed and make things catastrophically worse. We blame the tech, the data, or the talent. But the real reason is simpler and much harder to fix. We are trying to bolt a revolutionary capability onto broken, outdated beliefs about how work gets done.
The prevailing problem is that we treat AI implementation as a technical challenge when it is fundamentally a human one. We ask “what can it do?” instead of “what should we do?” The solution is to reverse the order. Before you write a single line of code, you must establish a clear and durable belief system. This article presents the three foundational Values from the AI First Principles. They provide the philosophical bedrock for any organization that wants to stop automating the past and start building a resilient future. After reading this, you will have a framework to diagnose why your AI efforts might be failing and a clear, human-centered belief system to build upon.
The Prevailing Fallacy: The Faster Horse
Most organizations approach AI like a turn-of-the-century industrialist approached the first engine. They see a powerful new motor and immediately think about how to strap it to their fastest horse-drawn carriage. They believe the goal is a faster carriage. They invest millions in optimizing a fundamentally obsolete model, never questioning the carriage itself. The result is not progress; it is a spectacular, high-speed wreck.
This is the state of AI implementation today. We take our convoluted, bureaucratic, and often nonsensical business processes and ask AI to make them “more efficient.” We automate workflows that common sense should have eliminated years ago. We build sophisticated models to predict outcomes based on data generated by a system that no one trusts. We are creating elegantly efficient ways to do the wrong things.
The negative consequences are everywhere. We see massive investments evaporate with no return. We see teams of brilliant data scientists burn out trying to solve impossible problems rooted in organizational dysfunction. And we see employees and customers grow increasingly cynical as they are forced to interact with “intelligent” systems that lack any semblance of wisdom or empathy. The core fallacy is believing that AI is a tool you add. It is not. It is a catalyst that exposes every crack in your organization’s foundation.
The Core Counter-Argument: Beliefs Before Builds
The contrarian insight is that AI safety, success, and value are not technical problems. They are organizational problems rooted in a company’s unstated and often unexamined belief system. Accountability, agency, and empathy cannot be coded into an algorithm. They must be designed into the culture that deploys it.
Before you can build a successful AI-enabled system, you must first be able to answer three simple questions:
- What do we believe about accountability?
- What do we believe about human dignity?
- What do we believe about expertise?
These questions are not abstract philosophical exercises. They are the most practical and urgent design questions an organization can ask. The AI First Principles offer three core Values that provide a clear, actionable answer to each. They force you to codify your beliefs before you codify your software.
The New Framework: A Declaration of Values
These three values are the non-negotiable foundation for building with AI. They are not a checklist; they are a constitution for a new way of working.
1. People Own Objectives
Every AI system needs a human owner who is accountable for its outcomes, good or bad. When a biased algorithm denies a deserving person a loan, the problem is not a rogue AI; it is a failure of the human who owns the objective of fair lending. The temptation in large organizations is to create “black box” systems where responsibility is so diffused that no one is to blame when things go wrong. We form committees and working groups to create the illusion of oversight, but in reality, we are building systems where failure becomes an orphan.
This value kills that excuse. It forces a simple, radical act: name the owner. This is not about finding a scapegoat. It is about empowering a single individual with the authority and responsibility to ensure a system achieves its goal safely and ethically. This person becomes the chief skeptic, the one compelled to ask “how could this go wrong?” Their neck is on the line, which focuses the mind wonderfully.
In practice, this means for every AI initiative, an executive’s name is publicly attached. That owner is accountable not for the model’s F1 score, but for the real-world impact. This shifts the entire conversation from technical minutiae to business outcomes and human consequences.
2. Individuals Come First
We must prioritize human autonomy, safety, and well-being above all else. The logic of pure optimization, when handed to a powerful AI, can become a terrifying engine for dehumanization. An AI designed to maximize call center efficiency might conclude that the best path is to turn human agents into robots, scripting their every word and penalizing any deviation. An AI built to maximize “engagement” might learn that outrage and misinformation are the most effective tools.
This value asserts that human dignity is the most important design constraint. It asks us to build systems that treat people as ends in themselves, not as means to an end. This is not a call for less effective systems; it is a contrarian bet that the most durable and profitable systems are those that earn human trust by respecting human agency. A customer who feels respected is a customer who returns. An employee who feels empowered is an employee who innovates.
To implement this, you must measure your systems against “dignity metrics.” Does the system offer a clear and easy path to appeal a decision? Is it transparent about how it works, or does it operate as an unexplainable tyrant? Does it create pressure or provide support? You are not just designing a workflow; you are designing an experience. Make it one you would be willing to be subjected to yourself.
3. Build From User Experience
The most critical design insights come from the lived, daily friction that high-level analysis always misses. The people wrestling with your broken processes every single day are the ones most qualified to design their replacements. There is a world of difference between a flowchart in a PowerPoint deck and the messy reality of a customer service agent trying to navigate three different legacy systems while a frustrated customer is on the line.
This value demands that we invert the traditional design hierarchy. Expertise is not in the corner office; it is on the factory floor, in the call center, and at the sales desk. The Japanese term for this is genchi genbutsu, or “go and see.” You cannot understand a problem by reading a report about it. You must go to the source and see it, feel it, and live it.
Practically, this means embedding designers and engineers with the people who do the work. It means a developer’s first task is not to write code, but to shadow a user for a day. It means valuing the “anecdotal” evidence of a single user’s profound frustration as a more important signal than a thousand rows of clean, aggregated data. We must have the humility to admit that the people closest to the problem are the true subject matter experts.
The Invitation
These three values—People Own Objectives, Individuals Come First, and Build From User Experience—are not suggestions. They are the intellectual and moral foundation required to wield the power of AI responsibly and effectively. They are guardrails against our own worst tendencies: to diffuse responsibility, to instrumentalize people, and to design from a place of ignorance.
Building with AI is not about having the best technology. It is about having the clearest beliefs. Before your next AI project kicks off, stop and ask your team: Do we know who owns this? Does it treat people with dignity? And are we listening to the right experts? If the answer to any of these is no, you are just building another faster horse, destined for a very expensive wreck.
This piece is based on the open source, community-driven AI First Principles. Learn more at https://aifirstprinciples.com.
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