With 95% of AI projects failing to scale, the problem isn’t the tech—it’s the lack of controls. To tame AI chaos, enterprises must adopt governance from day one.
The artificial intelligence revolution has created an uncomfortable paradox: while organizations rush to deploy AI, 95% of proof-of-concept (POC) projects never progress to production. The culprit isn’t the technology itself; it is the absence of proper controls from day one.
After three decades of building enterprise systems, I’ve watched this pattern repeat itself in AI deployments across industries. Organizations hand out public LLM licenses to 50, 100, or 500 employees without asking fundamental questions: What data are they sending? Do we have clear visibility into their activities? And can we trace what happens when things go wrong?
Consider the rigor of traditional assembly line manufacturing and its quality control. At each station, the expected outcome is known. When the finished product reaches QA and a fault is found, the process allows us to trace back exactly where the breakdown occurred.
With AI agents, the work often goes into a black box and comes out the other side. If you cannot inspect what happened at each step, you cannot identify where things went wrong or ensure consistent quality. We need to bring that same manufacturing rigor to AI systems.
The Wild West Scenario
The ease of access to powerful AI tools has created what I call the “Wild West” scenario. An employee can build an impressive AI agent on their laptop over a weekend. It works beautifully in demos. Then comes the hard part: How do you integrate that agent into an enterprise environment where data governance, security protocols, and compliance requirements are actually enforced?
This isn’t just a technical challenge; it’s an existential business risk. The threat surface in computing has expanded dramatically over the decades, from isolated mainframes to networked systems, and from internet connectivity to cloud infrastructure. AI represents another exponential leap. With agents communicating with other agents, APIs connecting to critical servers, and employees using AI tools across thousands of enterprise applications, the CISO’s job of protecting the organization becomes nearly impossible without proper AI architecture and controls. The solution isn’t to slow AI adoption. It’s to implement governance from the start, not as an afterthought.
This means managing AI systems with the same structured controls and continuous monitoring we apply to critical technology systems today. We need the ability to inspect what we don’t expect. When agents start doing things you didn’t intend—and they will—you need controls that allow you to trace back through the decision chain to understand precisely what went wrong.
The Small Language Model Advantage
One of the most powerful AI architectural decisions organizations can make is moving toward private-hosted Small Language Models (SLMs) for specific business tasks. This approach offers multiple advantages:
- Containment: When you run models in your own environment, sensitive business data never leaves your secure enclave.
- Consistency: With a private model, you should get the same results every time for the same query. This matters more than most people realize. I’ve asked identical questions to public LLMs over time and received different answers. Was the model retrained? Was malicious data injected? Did the model arbitrarily decide my first answer wasn’t what I wanted? In a business context, this kind of drift is unacceptable.
- Accuracy: A focused SLM trained on specific, domain-aware data often outperforms a general-purpose large model for specialized tasks. You don’t need a sledgehammer when a scalpel will do.
- Control: You maintain visibility into model updates, data flows, and inference environments, ensuring compliance with regulatory requirements.
This isn’t about closed versus open-source models; you can use open-source models like Llama in private-hosted architectures. It’s about architecting systems where you control the data boundaries.
The Salesforce Example
Consider using AI to help salespeople draft emails and research prospects. If they’re using public AI models, they are keeping enriched customer data in their own enclaves. When they leave the company, that valuable intelligence walks out the door—the very problem CRMs were designed to solve a decade ago.
With proper AI controls, you can provide salespeople with AI tools that integrate seamlessly with your CRM, ensuring that communications and insights remain within the organization. The individual gets productivity gains, and the business maintains institutional knowledge.
The Path Forward
The organizations that will truly succeed with AI aren’t necessarily the ones moving fastest; they are the ones building proper systems that enable rapid, controlled innovation. They are treating AI governance as a non-negotiable foundation, not a constraint.
This means having tools that can monitor and trace everything agents do. It means breaking down complex tasks into manageable, inspectable components. It means understanding that when you give broad access to AI tools without structure, you’re not accelerating innovation—you’re accumulating technical debt and business risk.
Ninety-five percent of POCs that fail to reach production don’t fail because the AI doesn’t work. They fail because organizations haven’t thought through the proper controls required to deploy AI at enterprise scale. The good news? We know how to build robust, scalable systems. We just need to apply those same principles to AI before we find ourselves managing an uncontrollable sprawl of agents operating without oversight. The Wild West phase of AI adoption needs to come to an end, and the era of controls-first must begin.
