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Description
As the market advances in AI, a new and troublesome challenge gains notoriety behind the corporate scenes: how to go beyond the hype?
Transcript
Generative artificial intelligence has unleashed one of the greatest disruptions in the history of global technology. Unlike past revolutions, it is advancing horizontally and simultaneously across the planet, placing companies from all markets on the same footing. However, as the market progresses, a new and troublesome challenge is gaining notoriety behind the corporate scenes: how to go beyond the hype?
In an exclusive chat between Bruno Lauterjung, editor of TI Inside, and Romulo Cioffi, Chief AI and Innovation Officer at Squadra, the path for CIOs to overcome the “gold rush” for agents, control their budgets, and ensure innovation with active governance was discussed.
Many companies that have embraced generative AI without proper planning are facing serious budget issues. Romulo cites real market scenarios where expenses have alarmingly gotten out of control:
“We’ve noticed many companies that have become very detached from their established budgets. We saw, for example, a mobility company that spent in just four months the Generative AI tokens planned for an entire year.”
This explosion of expenses happens because the market has plunged into this “gold rush” for agents, focused on creating isolated proofs of concept (POCs) and departmental silos. Without structured governance, AI ends up being seen as an end in itself, generating surprising invoices without real value reaching the business strategy.
To reverse this equation, leadership needs to undergo a profound change in culture and responsibilities. Large language models (LLMs) are rapidly becoming commodities, and the competitive edge has shifted.
In this new scenario, the role of the technology leader gains a new strategic definition:
The “I” now stands for Intelligence;
The “O” now stands for Orchestration.
The role of the CIO and corporate leadership is to design a holistic vision of the organization. It is necessary to understand exactly where each intelligent agent fits in, its level of operational autonomy, and how its organic cooperation will occur with human squads and the systemic platforms the company already has.
For the Squadra team, code curation and problem mapping follow a living methodology called integral design. Former developers, previously focused on writing sequential algorithms in traditional languages, change their roles and act in deep problem modeling.
The method's premise is based on a holistic vision focused on the explicit and implicit needs of the operation. According to Cioffi:
“Pain is the source and raw material of innovation. If we capture the pain correctly and everyone in the organization sees it with the same purpose, innovation is born naturally. The role of Integral Design is to map the present state and project the future with clarity, transforming what was once pain into comfort for the business.”
By structuring the problem in a common language shared by the entire organization, providing context to large language models (LLMs) becomes precise. With objectives properly set by the human factor, AI ceases to be a generator of superfluous expenses and assumes its true place: that of an accelerator of highly profitable strategic infrastructures.
If at the peak of cloud computing the FinOps methodology became indispensable for controlling infrastructure, the era of generative AI demands the maturation of AIOps.
AIOps consists of a rigorous method for real-time cost traceability. Through dedicated tools, companies can map the operational consumption of projects bit by bit by:
Identifying how many tokens are being generated per model;
Knowing exactly which employee or human agent triggered the request;
Linking the cost of the AI agent and human hours directly to each specific client's project.
This level of surgical control is what prevents scaling errors and allows for the justification of return on investment (ROI) before invoices become unsustainable.
The proof that it is possible to balance disruptive innovation, speed, and governance was evident in a partnership between Squadra and a major player in Brazil's logistics sector, with the mission of modernizing a complex and highly sensitive system.
The client's legacy software dictated the entire rail transport logistics for each line segment and the corresponding cargo volume on each train. Starting from a complete lack of knowledge of the original system, Squadra used intelligent agents from its AI platform to conduct the entire discovery phase, rewrite, and deploy the fully modernized code in just three months.
The project not only promoted a disruption in a critical process but also made the application fully operational in a clean, documented, and measurable way.
Is your company ready to move beyond isolated POCs and govern AI with a focus on real value? Talk to our team of specialists.
00:00 - 00:26 - Introduction
00:26 - 02:56 - How to justify the cost of generative AI?
02:57 - 05:06 - What has been the biggest difficulty for CIOs today in applying generative AI to justify ROI?
05:07 - 07:15 - How to manage hybrid squads, intelligent agents, and humans?
07:16 - 10:25 - How to apply governance without hindering innovation?
10:26 - 12:19 - What is the importance of having a leader responsible for AI within companies? And what are their functions?
12:20 - 14:08 - How to choose a partner technology consultancy to accelerate the GenAI process?
14:09 - 16:31 - How to absorb all knowledge and leverage it within an AI-focused culture?
16:32 - 20:46 - How to understand what is hype and what is not? How to keep up with this entire innovation cycle?
20:47 - 22:26 - How does the generative AI governance process work? How to justify ROI?
22:27 - 23:52 - When a CIO hires a technology consultancy, what is the main pain point usually raised?
23:53 - 26:13 - How to ensure innovation and security at the same time?
26:14 - 29:24 - How has Squadra been innovating in-house?
29:25 - 31:45 - What message remains for technology leaders?
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