Governance as a prerequisite to scale AI in financial institutions

A possible introduction, maintaining an executive tone from someone closely following the financial sector, could go as follows:

Over the past two years, the discussion about Artificial Intelligence in financial institutions has shifted from a conversation about potential to one about scale. The market has already recognized that AI can reduce costs, accelerate processes, increase productivity, and improve the customer experience. The question has changed from “should we use AI?” to “how do we do this safely?”

This may be the main differentiator of the financial sector compared to other industries. Banks, insurance companies, brokerage firms, and payment institutions cannot simply connect any model to their corporate data and expect everything to work. There is an additional layer of responsibility that involves privacy, compliance, explainability, audit trails, access management, and regulatory adherence.

Not surprisingly, the discussion on governance has increasingly occupied space in industry forums. Experts gathered during Febraban Tech emphasized that the scalability of AI directly depends on the existence of robust mechanisms for governance, validation, and human oversight. In many financial institutions, AI models undergo risk assessments, internal controls, and privacy checks even before entering production.

There is a common perception that governance slows down innovation. In practice, experience shows exactly the opposite: the absence of governance is what prevents AI from moving beyond pilots to large-scale operations. When there are no clear criteria about which data can be used, who can access models, which decisions require human supervision, or who is responsible for results generated by AI, the natural consequence is the blocking of initiatives by risk, legal, compliance, or security teams.

Financial institutions do not just need Artificial Intelligence. They need reliable Artificial Intelligence.

Why do technology projects not lag due to technology?

There is a widespread belief in the market that projects lag because the technology is not yet mature, because the infrastructure cannot support the demand, or because the chosen solution was not suitable for the problem.

Most of the time, this is not what happens.

Corporate projects typically lag because they need to answer questions that are not technological:

• Who will be responsible for the results produced by AI?
• What data can be used by the model?
• What information is prohibited from being processed?
• What decisions will require human validation?
• How will traceability of responses be handled?
• Who will have access to the system and under what conditions?
• How will the organization demonstrate compliance in an audit or regulatory questioning?

In the financial market, these questions cease to be bureaucracy and become operational requirements.

It is relatively simple to connect a generative model to internal databases and create an assistant capable of answering questions about customers, contracts, or financial operations. The challenge arises when there is a need to explain how a particular response was produced, what data was used to generate it, and who authorized access to this information.

At this point, the discussion shifts from AI to governance.

The pilot works, results appear quickly, but the initiative struggles to advance to production because the organization still lacks mechanisms to operate that solution safely, audibly, and in compliance with regulatory requirements.

Curiously, technology tends to be the simplest part of the project.

What determines the speed of adoption is the organization’s ability to create clear rules regarding the use of data, responsibility, human oversight, security, and compliance.

In other words: AI projects rarely stall due to a lack of artificial intelligence. They typically stop due to a lack of governance to scale it.

What have I learned by seeing well-written AI policies fail in practice?

In recent months, many organizations rushed to create their AI usage policies. In a short time, extensive documents appeared, with definitions concerning responsible use, privacy, information security, and regulatory compliance.

In theory, almost all seemed sufficient.

In practice, many of them did not survive the first contact with operation.

The reason is relatively simple: governance is not a document. Governance is an organizational behavior.

It is not uncommon to find companies with policies that prohibit the sharing of sensitive data in public AI tools while, at the same time, employees use non-approved solutions to summarize contracts, analyze documents, or produce presentations containing strategic information about the company.

Similarly, there are organizations that have created extremely rigid processes for approving new use cases and ended up producing an opposite effect to what was expected: business areas began to seek alternatives outside the official channels to maintain delivery speed.

No policy can compete with productivity.

Perhaps this is one of the greatest lessons of AI adoption in companies: when governance mechanisms are perceived as obstacles, users tend to circumvent them. When seen as enablers, they become a natural part of the process.

The most successful initiatives I observed had some common characteristics:

• availability of approved corporate tools;
• clear definition of permitted and prohibited data types;
• risk classification by use case;
• human oversight proportional to the impact of the decision;
• continuous monitoring and audit trails;
• employee training on responsible use of technology.

In the financial sector, this balance becomes even more important. The goal is not to prevent the use of AI but to allow it within limits compatible with the level of responsibility that the industry demands.

Financial institutions operate in a regulated environment overseen by entities like the Central Bank and others. This means that decisions related to the use of AI must consider requirements like explainability, traceability, data protection, access segregation, and auditability right from the design of the solution—and not just after its entry into production.

It is precisely at this point that many initiatives encounter difficulties. On one side, there is pressure for speed and innovation. On the other, the legitimate need to maintain control, security, and regulatory compliance.

Experience shows that efficient governance is not one that creates barriers to technology adoption, but one that allows it to scale sustainably.

At Objective, we support financial institutions facing exactly this challenge: how to accelerate AI adoption without compromising the controls the sector requires. The answer is usually not to bureaucratize processes, but to build governance mechanisms proportional to risk, transparent for auditing, and simple enough to be incorporated into the daily operations of teams.

This combination of speed and control is not new in the financial sector. In its strategic partnership with EBANX, Objective supported the expansion of an operation that evolved from a single squad to 11 squads and 36 professionals working integrated with the business, in a context of rapid growth and high operational demand. Scaling technology without compromising quality, reliability, and operational maturity ceased to be just a technical concern and became a business necessity.

More recently, the incorporation of Eleflow enhanced Objective’s capabilities in data, machine learning, and generative AI, strengthening the offering of solutions that span strategy to execution, always with a focus on scalable and secure automation—a theme increasingly relevant for financial institutions that need to balance innovation and regulatory compliance.

Whose problem is AI governance?

One of the most common questions that arise in organizations is: who should lead AI governance?

The most honest answer might be: no one can do this alone.

For many years, technology-related issues could be handled almost exclusively by IT departments. With Artificial Intelligence, this logic no longer works.

When an AI influences a credit decision, supports a fraud prevention process, participates in document analysis, or assists in customer service, the discussion is no longer just technological. It begins to involve risk, compliance, legal, information security, auditing, and, most importantly, the business areas responsible for the results generated.

AI governance is, above all, a model of shared responsibility.

Technology can define architecture, integrations, and technical controls. Security can establish protection mechanisms and access management. Compliance and legal can evaluate regulatory adherence and risks. But only the business areas can answer whether that decision produced by AI makes sense for the customer, for the operation, and for the institution’s strategy.

In the end, the correct question may not be who owns AI governance.

The question is who needs to be at the table for it to work.

Because AI is unlikely to be the competitive differentiator for financial institutions in the coming years. Access to models is likely to democratize quickly. What will continue to differentiate organizations is the ability to transform this technology into operational advantage, safely, reliably, and in compliance with industry demands.

And this capability begins long before the first prompt. It starts with governance.

If your institution is discussing how to accelerate AI adoption without compromising security, compliance, and regulatory requirements, it’s worth speaking with those who have supported organizations in balancing innovation and control in complex and highly regulated environments.

Objective experts have been helping companies build this journey, connecting strategy, data, technology, and governance to turn AI initiatives into scalable and sustainable capabilities for the business. Get in touch with us and see how we can help!

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