What It Takes to Turn an AI Pilot Into Lasting Impact

Posted November 6, 2025November 6, 2025 in Alumni, Data Science, Uncategorized, Voices of Rutgers-Camden

Reading Time: 3 minutes

Artificial Intelligence (AI) pilots get launched every day, but very few make it past the novelty stage. That tension is where this reflection begins. Data Science MS alum Eshaa Gogia, now a Data Engineer at Florida Blue in the Miami and Fort Lauderdale area, lays out why so many promising ideas never become scalable change. This piece appears in Voices of Rutgers–Camden, created to highlight the perspectives of faculty, staff, and alumni whose work pushes their fields forward. Eshaa offers clarity in a moment crowded with hype, and it’s worth reading closely.

From Pilots to Performance by Eshaa Gogia

It’s easy to launch an AI pilot. It’s much harder to turn that pilot into a transformation. According to recent commentary, many organizations are still stuck in “pilot fatigue” — fascinating experiments that never convert into enterprise-scale impact📈

One of the biggest myths? That scaling AI is just “more computing and more data.”
🔗The Missing Link?
Alignment. Period.

Launching an AI pilot is easy. Turning that pilot into something real requires alignment, adoption, and a connection to business value
Eshaa Gogia

This raises three critical questions:
1. Are the people who need to use the AI part of the design and decision-making?
2. Does the AI solution fit into existing workflows, or is it an island?
3. Does the initiative tie back to a measurable business outcome, and is there executive sponsorship?

A piece from Grant Thornton (US) shows that pilots often fail when they are too narrowly scoped, isolated in silos, or missing clarity on adoption and change management.

So how do you optimize these processes without skipping the core elements of scalable AI systems?

Anchor your AI strategy to business value, not technology for its own sake.

Ecosystem shift?
Yes – define jobs, train people, adapt culture. Not to forget treating infrastructure, data governance and compliance as foundational and viewing them as “enablers”.

Here’s to pivoting from “pilot” to “performance”✅

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Mastering Data: The Data Science Program

The Master of Science in Data Science at Rutgers University–Camden is an interdisciplinary program that equips students with skills in data analysis, machine learning, and statistical modeling. The curriculum integrates tools from mathematics, computer science, public policy, and prevention science, emphasizing practical applications across various industries. Core courses include Foundations of Data Science, Statistical Methods for Data Science, Data Visualization, and Applied Data Mining and Machine Learning. Students also engage in a thesis or capstone project, applying their knowledge to real-world problems.