Why AI Isn't Just Another Software Project

Picture this. Your team builds a financial chatbot. It passes every test your QA team throws at it. Accuracy looks solid. Stakeholders sign off. You deploy to production.

Within the first week, a customer asks a routine question about mortgage refinancing, and the chatbot confidently recommends a product your institution discontinued two years ago. Another customer gets a different answer to the exact same question asked five minutes later. A third receives advice that, while technically accurate, violates a regulatory disclosure requirement your compliance team didn't know to test for.

Nothing is "broken" in the traditional sense. The software is running exactly as designed. And that is precisely the problem.

The assumption that keeps AI stuck in pilot purgatory

Most organizations approach AI the way they approach any software initiative. Define requirements, build, test against those requirements, deploy. It is a process that has worked for decades of deterministic software development.

But AI is not deterministic software. And applying the same playbook is a core reason why, according to McKinsey, 99% of companies fail to reach AI maturity. They get stuck in what we call "pilot purgatory," a cycle of promising proofs of concept that never make it to production.

We've seen this pattern across dozens of enterprise AI initiatives. The technology works in the lab. It falls apart in the real world. Not because the models are bad, but because the governance frameworks around them were built for a fundamentally different kind of technology.

There are three critical differences between traditional software and production AI. Understanding them is the first step toward AI governance that actually works.

Difference 1: Deterministic vs. probabilistic outputs

Traditional software is predictable by design. The same input produces the same output, every time. If a calculation returns the wrong number, you find the bug, fix it, and move on. Testing is straightforward: define expected outputs, compare actual outputs, pass or fail.

AI systems, particularly those built on large language models, are probabilistic. The same input can produce different outputs on different runs. Sometimes the variation is minor, a slightly different phrasing. Sometimes it is significant, a different recommendation, a contradictory answer, a hallucinated fact.

This breaks the traditional pass/fail testing model completely. You cannot write a test that says "the output must be exactly this string" when the system is designed to generate novel responses. And you cannot rely on spot-checking a handful of outputs when the space of possible responses is essentially infinite.

What AI quality assurance actually requires is statistical evaluation across large sets of outputs, measuring consistency, accuracy, and safety over distributions rather than individual cases. We've seen organizations that adopted this approach reduce failure rates from 50% to 1.8%, a 97% improvement. The ones that kept using traditional QA methods kept getting traditional QA results: false confidence followed by production failures.

Difference 2: Single model vs. composite systems

Here is something that surprises many teams when they move from prototype to production AI. A production AI application is almost never a single model answering questions. It is a composite system: multiple models, retrieval layers, guardrails, orchestration logic, and post-processing steps, all chained together.

A customer-facing AI assistant might use one model to interpret the query, another to retrieve relevant documents, a third to generate a response, and a fourth to check that response for compliance violations. Each component introduces its own variance. Each connection between components introduces another opportunity for unexpected behavior.

Testing the individual models in isolation tells you very little about how the composite system behaves. A model that performs well on a benchmark can perform poorly when its output feeds into a downstream component that was optimized for a different model's output style.

This is why enterprise AI testing needs to evaluate the full pipeline, not just individual pieces. And it is why switching or updating a single model in the chain (something vendors do regularly, sometimes without notice) can change the behavior of your entire system.

In one case we observed, a routine model update by a provider changed the failure characteristics of a production system overnight. The organization had no systematic way to detect this. The AI Assurance layer they later implemented caught similar changes in subsequent updates within hours, comparing the new model's behavior against established baselines before anything reached production.

Difference 3: Technical QA vs. cross-functional governance

In traditional software development, quality is primarily a technical concern. Engineers write tests. QA teams run them. If the code meets its specifications, it ships.

AI governance requires something fundamentally different. Whether an AI system's output is "correct" often depends on domain knowledge that no engineer possesses. Is this medical summary clinically accurate? Does this financial recommendation comply with FCA regulations? Is this insurance claim assessment consistent with the OCC's guidance on fair lending?

These are not questions a software test can answer. They require domain experts, compliance professionals, legal teams, and risk managers, people who understand the context in which the AI's outputs will be used. Under the EU AI Act and similar emerging regulations, this cross-functional oversight is not optional. It is a legal requirement for high-risk AI systems.

Yet most organizations still treat AI quality as an engineering problem. The result is a governance gap: the people building the AI lack the domain expertise to evaluate its outputs, and the people with domain expertise lack the tools and processes to participate in AI quality assurance meaningfully.

We've seen this gap manifest in a very concrete way. When AI compliance depends on manual review by domain experts, it costs mid-sized institutions €4-7M or more annually. This is not sustainable, and it creates a bottleneck that slows model approval times to six months or longer, effectively killing any agility advantage AI was supposed to provide.

The governance gap in practice

These three differences, probabilistic outputs, composite systems, and cross-functional governance needs, compound each other. A composite system with probabilistic components requires statistical evaluation across the full pipeline, reviewed by people who understand both the technology and the domain. Most organizations are simply not set up for this.

What we've observed is a common pattern. Organizations invest heavily in model development, build impressive prototypes, and then hit a wall when they try to move to production. Not a technical wall, but a governance wall. They lack the frameworks, tools, and processes to provide the kind of evidence that regulators, risk committees, and business stakeholders need to approve production deployment.

This is the real reason behind pilot purgatory. The AI works. The organization just cannot prove it works well enough, safely enough, and consistently enough to deploy with confidence.

What production AI governance actually looks like

The organizations we've seen break through this barrier share a common approach. They treat AI governance not as a gate at the end of the development process, but as a continuous layer between model access and production applications, what we call an AI Assurance layer.

This layer provides three things:

• Systematic, statistical evaluation of AI outputs across the full composite system, not just individual models
• Tools that enable domain experts, compliance teams, and risk managers to participate in AI quality evaluation without requiring technical expertise
• Objective, auditable evidence of AI production readiness that satisfies both internal stakeholders and external regulators

The results are measurable. Organizations that implement this kind of structured AI governance have reduced model approval times from six months to three weeks. Not by cutting corners, but by replacing subjective assessments with objective evidence.

It also changes how organizations think about the three tiers of AI adoption: individual productivity tools (where governance can be lighter), citizen developer applications (where some guardrails are needed), and production systems serving customers or making decisions (where rigorous governance is essential). Each tier gets the level of AI governance appropriate to its risk profile.

Moving forward

AI is not just another software project, and treating it like one is the most predictable way to fail. The organizations making real progress with production AI are the ones that have internalized this and built governance frameworks that account for probabilistic behavior, composite systems, and cross-functional quality requirements.

This is a solvable problem. It requires new thinking, not about the AI itself, but about the processes and evidence frameworks around it.

We explore these differences and the governance frameworks that address them in detail in our whitepaper on generative AI in regulated industries. If your organization is navigating the path from AI pilot to production, it is worth a read.

Read the whitepaper →