The Invisible Payload: The Bottom of the Iceberg of AI Project Success

CONVERSATIONAL AI

“We Trained the Model, So We’re Done.”
This is often the first expectation many organizations have when it comes to AI projects. Like pressing a magic button: the AI solution is deployed, efficiency increases, costs drop, and everyone is happy.

But things don’t work that way in the real world. Putting AI systems into production is not just about training a model. The real challenge lies in integrating that model into the existing structure of the organization, making it reliable and continuously operational, and generating sustainable value from it.

At CBOT, we’ve deployed dozens of large-scale enterprise AI projects into live environments over the past decade. One of the most important lessons we’ve learned is this: the magic isn’t in the code — it’s in the process. In this article, we aim to make visible the often-invisible work that drives success in AI projects.

1. Expectations: The Shiny Tip of the Iceberg

AI conversations are often full of promise. Some of the statements we frequently hear at executive meetings include:

  • “If the model is ready, the ROI is ready.”

  • “We already have the data, the system will work right away.”

  • “Plug & play — let’s start!”

These statements reflect a worldview in which AI is seen not as a technological solution, but as a magic wand. The focus is solely on the coding aspect. However, AI is not just a technical tool — it’s a driver of organizational transformation. And that transformation can only be achieved by addressing the hidden part of the iceberg.

2. Reality: The Hidden Effort Beneath the Surface

At CBOT, we begin every AI project by asking the following questions:

  • Have we correctly defined the real problem?

  • What data are we working with, and is it clean enough?

  • Can we integrate with existing systems?

  • Who will use the output of this model, and who owns it?

Each of these questions requires going beyond the technical solution to change processes, teams, habits, and culture.

Below, we’ve listed the often-overlooked but critical “invisible tasks” that form the foundation of successful AI production deployments:

a. Framing the Problem Correctly
A poorly defined business problem — no matter how well-modeled — leads to wrong solutions. A shared definition of the problem by business units and technical teams at an early stage sets the backbone of the project.

b. Aligning Teams
Tech teams, business units, security, data governance… everyone has different goals. AI projects require harmonizing these differences through a coherent plan. This demands solid project management and a strong governance model.

c. Integrability
A perfectly working model is not enough. If it can’t be integrated into existing systems or decision-making processes, it won’t generate results. CBOT’s success hinges critically on our technical integration capabilities.

d. Data Quality and Continuity
AI follows the “garbage in, garbage out” rule. Model success depends on data quality and its ongoing availability. Often, 80% of project time is spent on cleaning and preparing data.

e. Security, Privacy, Accountability
Data is a sensitive resource. Especially in sectors like finance, healthcare, and government, what the model doesn’t do is just as important as what it does. Full compliance with security and privacy policies is non-negotiable.

f. Sustainability: Model Drift and ROI Tracking
AI models degrade over time. “Model drift” causes a loss in accuracy as new data comes in. That’s why it’s essential to monitor models, retrain them regularly, and continuously measure ROI.

3. A Roadmap for Organizations: What We’ve Learned at CBOT

At CBOT, we operate on a few core principles that help organizations overcome these challenges:

  • Start with the Process, End with the Code: Understand the problem first, then build the model.

  • Include All Stakeholders Early: Get everyone around the table from the beginning.

  • Deploy Gradually: Start with a Minimum Viable Model (MVM) and scale to full production step-by-step.

  • Build Around the Model: Data infrastructure, API integrations, governance policies — these supporting elements are just as important as the model itself.

  • Track ROI Continuously: The project should not only function — it must deliver value.

AI projects succeed not just through technical competence but with a clear strategy, robust governance, and an organizational culture that embraces change. The iceberg visual is a metaphor for this reality: the visible ease at the top is made possible by the hidden effort below.

At CBOT, we see AI not just as a technology, but as a strategic part of an organization’s transformation journey. What brings success is the systematic preparation, sustainability, and organizational alignment surrounding the model.

To put it simply:
In AI projects, the magic is not in the code — it’s in the process.