AI without lock-in: how to choose technological partners in the financial market

In recent years, the discussion about Artificial Intelligence in the financial market has shifted from merely technical capabilities to a strategic issue: technological dependency.

The speed at which new models, agents, and platforms emerge in the market makes it increasingly risky to build critical operations on closed, opaque technologies or those excessively dependent on a single provider. What seems today to be the most advanced solution can become obsolete — or economically unfeasible — in just a few months.

At the same time, financial institutions face an additional challenge: sensitive data, regulatory requirements, and responsibilities related to information governance. In this context, relinquishing control over data, trained models, or operational flows can represent not only a technological risk but also a business and compliance risk.

This is precisely why the search for open, interoperable AI architectures is growing, capable of evolving alongside the market. The discussion shifts from “which model to use” to “how to ensure freedom to choose the best model tomorrow, without needing to rebuild everything again.”

More than merely hiring technology, financial institutions are being called to make decisions about data sovereignty, operational flexibility, and future innovation capability. In this scenario, avoiding lock-in becomes not just a technical preference but a strategic requirement.

Why is lock-in in AI more challenging than software lock-in?

The financial market has been dealing with the risk of supplier dependency for decades. Changing an ERP, a core banking system, or an operational platform has always been a costly and complex process.

With Artificial Intelligence, however, this dependency takes on a new dimension.

While in traditional software, changing usually involves data migration, integrations, and user training, in AI much harder-to-reconstruct assets come into play: business-adjusted models, specialized prompts, agent flows, vector bases, contextual knowledge, and operational rules accumulated over time.

In practice, the institution may discover that it is not merely switching suppliers — it is losing part of the intelligence built over years of operation.

The scenario becomes even more challenging because the AI ecosystem evolves at an unprecedented speed. New models constantly emerge, prices change rapidly, and performance differences between suppliers can disappear in just a few months. Building the entire operation around a single model or platform means relinquishing the ability to leverage this market evolution.

For financial institutions, there is an additional layer of concern: regulatory requirements, audit, traceability, and data sovereignty. As AI starts participating in critical processes, the need to maintain control over where data is processed, which models are being used, and who has access to generated information also grows.

It is in this context that open architectures begin to gain traction. Rather than creating dependence on a single model or supplier, they allow the choice of the best technology for each use case and its replacement whenever a more efficient, economical, or compliant alternative arises.

What does an AI architecture without lock-in mean?

An AI architecture without lock-in is designed to allow the institution to choose, replace, or combine models, providers, and tools without needing to rebuild the entire solution with each technological or strategic change.

In practice, this means separating business logic from the AI technology used at that moment.

If today a process uses a model from OpenAI, tomorrow it may migrate to another provider, such as Anthropic, Google, Mistral, or even an open-source model hosted in its own environment, without requiring a complete rewrite of the integrations and operational flows.

This flexibility has become especially important because the AI market is undergoing an accelerated process of commoditization. What differentiates suppliers today may no longer be a differentiator in just a few months, whether due to technical advancements, cost reductions, or new regulatory requirements.

According to consulting firm Gartner, by 2027 more than 80% of companies will use multiple generative AI models in their operations, replacing the initial strategy of dependence on a single provider. This multicloud and multimodel approach is expected to become the standard in highly regulated sectors like finance.

An architecture without lock-in typically relies on several principles:

• Abstraction of the model layer, allowing provider changes without altering business processes.
• Portability of data and embeddings, avoiding lock-in to proprietary databases.
• Centralized governance, regardless of the model used.
• Observability and audit, ensuring traceability of AI decisions.
• Ability to operate private, public, or hybrid models, according to the sensitivity of the information involved.

This last point is particularly relevant for financial institutions. Not all data can circulate freely among external providers, and different processes may require different levels of security, privacy, and regulatory compliance.

It is precisely in this type of scenario that orchestration platforms gain relevance. Instead of creating a direct dependency between the institution and a specific model, they function as an intermediate layer of governance and integration, preserving technological freedom in the long run.

The proposition of pAIr by Objective follows this logic: enabling companies to use AI productively and safely while maintaining control over data, governance over flows, and the freedom to evolve their technological strategy as the market advances.

How to assess who owns the data and trained models in the partnership?

One of the most important questions in any AI project in the financial market is also one of the least discussed during contracting: after all, who owns the intelligence generated throughout the operation?

In traditional software, the answer is usually simple: the data belongs to the client, and the provider delivers the technology. In AI, however, new layers of complexity arise.

Where are the data used for training stored? Who owns the prompts created by the operation? Can embeddings and vector bases be exported? Does the accumulated knowledge over the months remain with the institution if the contract ends?

These questions make a significant difference in the future of the operation.

Imagine, for example, a bank that uses AI to support credit analysis, customer service, or fraud investigation. After two years, the solution has already incorporated internal rules, regulatory knowledge, operational standards, and thousands of specific interactions with that business.

If all this intelligence is locked within the provider’s environment, switching becomes not just a commercial decision but practically unfeasible from an operational standpoint.

The financial market has already learned this lesson from movements like Open Banking and Open Finance, which were created to return control of their own data to customers and reduce migration barriers between financial institutions. The trend is now beginning to repeat itself in the realm of Artificial Intelligence.

Therefore, before closing any partnership, some definitions must be clearly established in the contract:

• Who owns the data used by the system?
• Do the adjusted or customized models belong to whom?
• Is it possible to export prompts, embeddings, and vector bases?
• Is there portability of the flows and agents created?
• Does the provider use the client’s data to train third-party models?
• What are the rules for the definitive deletion of information at the end of the contract?

These points are especially relevant in financial institutions, where regulatory requirements, audits, and security policies often demand complete traceability over the lifecycle of data and automated decisions. The evolving use of AI for risk analysis, fraud prevention, and decision-making has increased the need for robust governance and information sovereignty mechanisms.

In the end, perhaps the most important question is quite simple:

If I decide to change suppliers tomorrow, what stays with me and what stays with them?

The answer to this question often quickly reveals the true level of technological independence of a solution.

What questions to ask a technology partner before closing a contract?

Choosing an AI partner in the financial market is no longer just a technological decision. Today, it involves strategy, governance, regulatory compliance, and the ability to adapt to a rapidly changing market.

Therefore, before evaluating functionalities or comparing models, it is worth asking some fundamental questions:

1. Do my data remain under my ownership and control?

 The institution should be clear about where the data will be stored, who will have access to them, and whether they can be used for training third-party models.

2. Is there freedom to change models or providers in the future?

 Does the architecture allow for supplier changes, the use of multiple models, or migration to in-house solutions without rebuilding the entire operation?

3. How does governance and traceability of AI decisions function?

Especially in the financial market, it is essential to be able to audit responses, identify sources, track model versions, and demonstrate regulatory compliance.

4. Does the partner understand the peculiarities of the financial sector?

Questions related to LGPD, Bacen, CVM, audit, and information security cannot be treated as additional requirements. They are part of the solution design from the outset.

5. Does the accumulated knowledge throughout the operation remain with the institution?

Prompts, agents, flows, vector bases, and customizations should be business assets, not supplier assets.

6. Is the partner prepared to evolve with the market?

The speed of AI evolution demands flexible architectures and a continuous view of technological updates. The contract signed today will likely need to accommodate technologies that do not yet exist.

In the end, perhaps the most important question is the simplest:

Am I hiring a tool or building a long-term strategic relationship?

Because in the realm of Artificial Intelligence, the biggest risk is not choosing the wrong technology. It is choosing a partner unable to evolve alongside the business.

Financial institutions achieving the best results with AI often share a common characteristic: they treat their technology partners as extensions of their teams, combining technical expertise, regulatory vision, and a deep understanding of the business.

The trajectory of Objective with the financial market reflects this logic. Over the years, the company has built solutions for institutions operating in highly regulated environments, where scalability must walk alongside governance, security, and architectural flexibility. More recently, the company’s strategy has evolved to incorporate AI applied to development and operations while always preserving these same principles of control and technological sovereignty.

According to McKinsey, companies that scale AI with robust infrastructure and clear strategies experience profitability growth up to twice that of other organizations.

In the financial market, therefore, the discussion is no longer about whether to adopt AI or not.

The question has become: how to do this without giving up the freedom of choice that will allow continued innovation in the coming years?

If this is also a challenge in your organization, it is worth talking to the experts at Objective about how to build a scalable, governed AI strategy ready to evolve with the market — without creating unnecessary dependencies along the way.

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