Amirtha Saminathan is a data and analytics leader specializing in scalable platforms, data governance, and AI-driven decision-making.
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Somewhere in your organization right now, there is an AI tool that sits technically live and completely untouched.
There is no debrief, no lessons-learned email and no honest conversation. One week, it is the company's most-discussed initiative. Then someone stops updating the dashboard, the budget shifts and, eventually, everyone moves on as if the whole thing never happened.
I have sat in those rooms. What unsettles me is not the failure itself but the silence around it.
MIT researchers found that established companies adopting AI frequently experienced "declines in the use of structured management practices," which "accounted for nearly one-third of their productivity losses." In 2025, organizations scrapped, on average, 46% of AI proofs-of-concept before production, not from technical breakdown but because of unclear ownership and unstructured data. Here, the problem lies in the organization, not in the model.
Most enterprises start their AI journey with targeted pilots. A small team is asked to solve a specific problem, such as fraud detection, predictive maintenance, customer analytics or intelligent automation. Speed becomes the priority. Teams work with curated datasets, simplified infrastructure and very few system dependencies. In that environment, success is common. Models perform well, prototypes show value and stakeholders begin to imagine what scaling could look like.
But that early success can be misleading. Pilots are built to reduce complexity. Production environments bring that complexity back. When organizations try to scale these solutions, they run into the realities of enterprise systems. Data is fragmented, legacy platforms are hard to integrate and regulatory requirements add pressure. When placed into real workflows, systems that functioned well in isolation start to encounter difficulties.
AI does not usually fail in production. More often, the organization is not ready for it.
I saw this firsthand during a transformation program where we built a predictive model to help operations teams anticipate system performance issues. During testing, the model performed well using historical data and delivered strong accuracy. Once we moved toward real deployment, new challenges appeared.
The model depended on data from multiple systems, each with different formats and refresh cycles. Some systems were updated every few minutes, while others were refreshed only once a day. These factors made it difficult for the model to reflect the current state of the environment.
At that point, the issue was not the algorithm. It was everything around it. This stage is where many AI initiatives slow down or stop. The models are often good enough. The surrounding systems are not.
Across enterprise programs, I have seen three common issues appear when teams try to move beyond the pilot stage.
Many pilots start without clear ownership. That works early on, but it becomes a problem at scale. Questions start to come up. Who owns the model? Who monitors performance over time? How are decisions reviewed or explained? Without clear answers, organizations become cautious. That hesitation often delays or blocks wider deployment.
AI systems rely on consistent and reliable data. In pilots, data is usually prepared and cleaned in advance. In production, it is often inconsistent and spread across different systems that do not always work well together. When data pipelines are unstable, model performance degrades quickly. What looked strong in testing becomes unreliable in practice.
Even strong AI systems can fail if they do not fit into how people actually work. If the outputs are hard to understand, arrive too late or do not connect clearly to decisions, teams will not use them. People fall back on familiar processes, and the AI system becomes something that exists but is rarely used. Scaling AI means making it part of everyday workflows, not just deploying it.
Moving from pilot to production is not just a technical step. It is an organizational shift. From my experience, a few practices make a meaningful difference.
It is tempting to move fast early on, but shortcuts create problems later. If data, workflows and integrations are not aligned up front, teams end up redoing the work. Building with real conditions in mind makes the shift to production smoother.
This also means planning for scale early, including how the system will handle higher data volumes, edge cases and failures. Thinking through monitoring and maintenance at this stage avoids surprises later.
Teams often focus on improving accuracy, but unreliable data is what breaks systems. Different sources, delays and inconsistencies make outputs hard to trust. Strong data foundations matter more than small gains in performance. Clear data ownership, validation checks and consistent pipelines help ensure stability. Without these, even well-performing models degrade quickly once exposed to real-world variability.
If the system does not fit into how decisions are made, it will not get used. Bringing in business, operations and compliance teams early ensures the output is practical and trusted. It also helps clarify expectations around how decisions will be made and what level of explainability is needed. This alignment reduces resistance and increases the chances of adoption.
Accuracy alone is not enough. The focus should be on whether the system saves time, improves decisions or reduces effort. That is what makes it worth scaling. Defining these outcomes early helps teams stay focused and makes it easier to evaluate success over time.
Right now, in many teams, AI never really moves past early trials. It shows promise, but it does not hold up once it hits real systems. The model is rarely the issue. It usually comes down to unclear ownership, messy data and workflows that do not support it. The teams getting value are the ones making it work day-to-day and tying it to real outcomes.
At that point, the question is simple: Can your organization actually run it?
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