As large language models continue to improve, that question is becoming easier to answer. Models can summarize documents, classify information, reason through complex workflows, and automate tasks that once required human intervention. Yet for enterprises deploying AI in production, technical capability is only the beginning. The more important question is whether the system can prove it will perform reliably, consistently, and predictably in the real world. 

That distinction is one of the central themes discussed by Matt Sekac, Vice President of AI Transformation at Welocalize, during a recent episode of the Eventual Consistency podcast. Rather than treating trust as something organizations develop after deployment, Sekac argues that trust is inseparable from capability itself. “Trustworthiness is part of the capability requirement,” he explains. “It’s not bolted on after the fact. It’s part of the deliverable itself.” 

Production AI Requires More Than Good Demos 

Many AI projects begin with an impressive proof of concept. A model successfully completes a workflow, generates high-quality content, or automates a repetitive process in a controlled environment. While demonstrations often generate enthusiasm, they rarely answer the questions that matter most to enterprise leaders. Can the system produce the same results every day? Will it perform consistently across thousands of different scenarios? What happens when it encounters an unexpected edge case, and how will anyone know when something goes wrong? 

Those questions become especially important in complex business environments, where processes rarely follow a single path. During the podcast, Sekac describes how his team examined the work performed by project managers responsible for coordinating multilingual production. Rather than assuming existing documentation captured the full picture, the team observed employees performing their daily work to understand the countless variations that existed between customers, programs, and workflows. What they discovered was not a handful of repeatable processes, but hundreds of unique exceptions that had accumulated over years of serving different clients. 

As Sekac explains, “Different customers have different requirements, different customers have different expectations that we have agreed to along the way.” Those differences, combined with unique workflows inside individual customer programs, created an environment where traditional rules-based automation became increasingly difficult to maintain. 

Building Evidence Before Building Confidence 

Rather than immediately replacing existing workflows, the team adopted a deliberate validation strategy. AI agents operated in a mirrored production environment, completing the same tasks as project managers without affecting customer work. Human decisions and AI decisions were compared over an extended period, allowing the organization to collect meaningful data before expanding deployment. 

The objective was never simply to demonstrate that an AI agent could complete a task. Instead, the goal was to generate evidence that would convince every stakeholder involved, from senior executives to delivery teams and even the engineers building the system. As Sekac explains, “We needed a proof of concept. We needed to demonstrate that it could work. It was establishing trust with senior executives, with the leadership of the delivery teams, but also from us. We wanted to believe that it worked.” 

This measured approach illustrates an important lesson for enterprise AI initiatives. Organizations often think of evaluation as something that happens after development. In reality, evaluation should shape the development process itself. Success depends not only on building an intelligent system but on designing experiments that produce the evidence needed to support future decisions. 

Human Oversight Doesn’t Disappear Overnight

One misconception surrounding enterprise AI is that successful implementation immediately removes humans from the process. In practice, the opposite is often true. Early deployments frequently increase observation, validation, and measurement because organizations need to understand exactly where AI performs well and where additional refinement is required. 

Sekac describes how every action performed by the AI agents continues to be reviewed by a human. Those reviews serve multiple purposes. They verify accuracy, identify situations where prompts require improvement, uncover unexpected workflow exceptions, and generate the data needed to increase confidence over time. Rather than viewing human oversight as a limitation, his team treats it as a critical source of operational intelligence. 

As confidence grows, organizations can begin reducing manual validation for specific categories of work where performance has consistently demonstrated reliability. That gradual transition reflects how enterprise AI is typically deployed: not through a single switch from human to machine, but through continuous measurement, learning, and refinement. 

The Baseline Isn’t Perfection

Perhaps one of the most thought-provoking observations from the discussion concerns how organizations evaluate AI performance. Enterprise conversations often assume AI should achieve perfection before it can be trusted. Human processes, however, have never operated without mistakes. 

“The standard should not be perfect,” Sekac says. “If you are trying to replace a human process, then the process you’re trying to replace is already not perfect because humans aren’t perfect.” 

That perspective changes how organizations think about deployment. The appropriate benchmark is not whether AI ever makes an error, but whether it produces better outcomes than the existing process while reducing operational effort, improving consistency, or increasing speed. Measuring AI against an impossible standard risks overlooking meaningful business improvements that deliver value despite occasional mistakes. 

Evaluation Starts Before Development 

One of the strongest themes throughout the conversation is that successful AI initiatives begin by defining how success will be measured. Before building agents, organizations should understand what evidence leadership will expect, what data needs to be collected, and how progress will be evaluated over time. Without that planning, even successful implementations may struggle to demonstrate business value. 

As Sekac explains, “You really want to try to think about how the thing you’re setting out to build is actually going to have a demonstrable impact.” That means anticipating questions, identifying potential objections, and collecting the information needed to answer them before deployment begins. 

The conversation reinforces an important reality about enterprise AI. The technology itself continues to improve at an extraordinary pace, but long-term success still depends on thoughtful experimentation, rigorous evaluation, and a clear understanding of the business problems being solved. Organizations that approach AI as both a technical initiative and a business transformation are better positioned to move beyond compelling demonstrations toward production systems that consistently deliver measurable value. 

Listen to the Full Conversation 

This article highlights only a portion of the discussion between Matt Sekac and Eventual Consistency host Ross Katz. In the full episode, Sekac shares how Welocalize approached agentic AI implementation, why shadow testing became a critical part of building organizational confidence, how human validation evolves over time, and why measuring success should begin long before an AI system reaches production.