The future of AI in business: what changes when models become commodities and the differentiator becomes data and integration

For years, the race for AI in companies was a race for the best models. Whoever had access to GPT-4, Claude, or Gemini seemed to be at the forefront. The narrative was simple: the model is the differentiator. Today, that narrative is being rapidly disproven by the very dynamics of the market.

Language models are becoming a commodity. This is not a futuristic prediction — it is what is happening right now. GPT-4, which seemed untouchable two years ago, now coexists with dozens of open and closed alternatives with comparable performance. Meta's Llama 3 runs on private servers. Mistral competes with giants at a fraction of the cost. Gemini Flash delivers speed and quality for cents per million tokens. The barrier to accessing models has fallen. And when the barrier falls, the model is no longer a differentiator.

For CEOs, CTOs, and founders, this changes everything. The question shifts from "which model should I use?" to "what do I have that no competitor can easily replicate?" The answer, almost invariably, comes down to two things: proprietary data and integration capability. That is what this article is about.

The commoditization of models: understanding what is happening

In 2022, having access to the GPT-3 API was already a competitive advantage. In 2024, any startup can access cutting-edge models with a credit card and an account at OpenAI, Anthropic, or Google. The cost of inference has dropped more than 90% in two years for equivalent capabilities. This is an unprecedented technological deflation.

In Brazil, this movement is even more visible. Companies like BTG, XP, and Itaú are using the same base models. The difference between them is not in access to GPT or Claude — it lies in how each one trains, fine-tunes, and feeds these models with their own data, their own contexts, and their own system integrations.

Commoditization brings three immediate consequences for any company building a generative AI strategy:

• The cost of entry has fallen, but the cost of differentiation has risen. Deploying a chatbot with GPT-4 is within reach of any company. Building a system that truly understands each customer's history, business rules, and the sector's regulatory context is far more complex.
• The advantage of "being the first to use AI" is disappearing. What matters now is depth, not superficial pioneering.
• The game has shifted from technological to strategic. The decision about AI has moved out of the CTO's hands and onto the agenda of the CEO and the board.

Why proprietary data is the new oil — but with one condition

You have heard that "data is the new oil." It is a worn-out analogy, but one that takes on new meaning in the context of generative AI. The difference is that crude oil has value on its own. Raw data does not. Data needs to be clean, structured, contextualized, and accessible for a language model to use it effectively.

Consider the case of a mid-sized Brazilian insurance company. It has 15 years of claims history, fraud patterns, customer profiles, and vehicle telematics data. This data exists in silos: a legacy core insurance system, an operations spreadsheet, an outdated CRM, and reports in PDF format. The world's most advanced language model will not perform miracles with that. The theoretical competitive advantage has become noise in practice.

Companies that are reaping real results from generative AI in Brazil have already understood this. Livelo, for example, does not compete with its partners solely through models — it competes through a deep understanding of the redemption behavior and engagement of millions of users, data that no competitor possesses. MaxMilhas built a differentiator on top of pricing data and flight purchase behavior that took years to accumulate.

The one condition for data to become a real competitive differentiator is: governance. Data that cannot be used — due to quality issues, privacy concerns, regulation, or technical silos — is a fictitious asset. The data strategy must resolve three layers before any AI initiative: cataloging (what we have and where it is), quality (what is clean and reliable), and controlled access (who can use it, how, and with what protections).

System integration: where most companies get stuck

If proprietary data is the fuel, integration is the engine. And this is precisely where most Brazilian companies get stuck when trying to scale AI beyond pilot projects.

The reality of the technology landscape in large Brazilian companies is complex: legacy systems from the 1990s, heavily customized ERPs, poorly documented APIs, integrations via flat files or FTP that "have always worked this way." When you place a language model on top of this architecture without resolving the integration layer, the result is a sleek chatbot that cannot do anything useful — or worse, one that delivers responses disconnected from the company's operational reality.

The corporate AI architecture that works in production does not start with the model. It starts with questions like: which systems need to be consulted in real time for the AI to respond accurately? What is the acceptable latency? How do you ensure that a response generated by the AI is auditable and traceable? How do you handle sensitive data without exposing it to the external model?

In the projects I follow closely, I see a consistent pattern in successful implementations. They almost always involve three critical technical elements:

• RAG (Retrieval-Augmented Generation) with reliable data sources: instead of relying solely on the model's knowledge, the company connects the model to its own knowledge bases, internal documents, policies, and operational data.
• Robust orchestration layer: tools like LangChain, LlamaIndex, or custom solutions that coordinate when and how the model accesses each system, with fallbacks and error handling.
• Observability and auditing: logs of every interaction, hallucination monitoring, response quality metrics — especially critical in regulated sectors such as financial services and healthcare.

Brazilian financial sector companies, such as Bradesco and Safra, face an additional challenge: the Central Bank's regulations require traceability and explainability in decisions that affect customers. This means that integration is not just a technical matter — it must be designed with compliance built in from the start.

The real competitive differentiator: the combination no one can easily copy

The commoditization of models reveals an uncomfortable truth: most companies are competing on the wrong layer. They are spending energy choosing between GPT-4o and Claude 3.5 Sonnet when they should be investing in building assets that competitors cannot replicate in six months.

Sustainable competitive advantage in AI is always a combination of three factors:

• Unique data accumulated over time — transaction history, customer behavior, process data, institutional knowledge
• Deep integration with critical systems — AI that truly operates within business processes, not alongside them
• Continuous feedback loop — mechanisms for the system to learn from real usage and improve over time

B3, for example, has market data and investor behavior data that are absolutely unique. No AI startup will have access to that volume and quality of data about the Brazilian capital markets. The differentiator is not in the model B3 uses — it is in what it can do with data that only it possesses.

The company that will win the AI race is not necessarily the one with the most advanced model. It is the one with the most relevant data, the deepest integration, and the organizational culture to turn that into a continuous operational advantage.

The role of architecture: building to scale, not to impress

One of the most common pitfalls I see in Brazilian companies is building the AI architecture for the demo rather than for production. The pilot impresses the board, becomes an internal case study, and when the time comes to scale, the problems emerge: runaway inference costs, unacceptable latency, difficulty maintaining multiple models, inability to audit decisions.

A corporate AI architecture designed for production must address from the outset:

• Multi-model strategy: using different models for different tasks. Small, fast models for triage and classification, larger models only when complexity demands it. This can reduce inference costs by 60–70% without any perceived loss of quality.
• Data mesh or data fabric: a data architecture that allows each business domain to be responsible for its own data while maintaining interoperability. Without this, scaling AI creates new silos instead of breaking down the old ones.
• Security by design: especially in AWS or Azure cloud environments, ensuring that sensitive data does not leave the company's environment, with private VPCs, dedicated endpoints, and encryption in transit and at rest.
• MLOps and LLMOps: processes and tools for versioning models, monitoring performance degradation, deploying updates safely, and tracking the impact of changes.

In the context of the Brazilian market, AWS cloud infrastructure has been the dominant standard among large corporations, partly due to the maturity of AI services like Bedrock and SageMaker, and partly due to the local presence with a region in São Paulo. The architecture decision, however, must be driven by the business problem, not by the availability of services.

What leaders need to do now

For CEOs, CTOs, and founders reading this article, the practical message is straightforward: the window to build sustainable competitive advantage in AI is open now, but it will not stay open forever.

The companies that pull ahead over the next 18 months will not be the ones that chose the best model — they will be the ones that did the hard work of organizing their data, modernizing their integrations, and building the organizational culture to operate with AI in production.

Some concrete actions that leaders should be addressing today:

• Proprietary data audit: mapping which data the company has that is genuinely unique and assessing the current state of quality and accessibility of that data.
• Integration strategy review: identifying which legacy systems need to be modernized or exposed via API so that AI can access relevant information in real time.
• Defining use cases by impact and feasibility: not trying to do everything at once. Prioritizing 2–3 use cases where the combination of proprietary data and deep integration creates real advantage.
• Investment in people and processes: technology is the smallest obstacle. The greatest challenge is having people capable of operating, evolving, and governing AI systems in production.

The data strategy and integration capability are not technical matters for the CTO to resolve alone. They are strategic decisions that determine whether the company will create real value with AI or accumulate pilots that never scale.

The language model you use today will be replaced by something better in six months. The data you have about your customers, your processes, and your market — that is something no one will hand you. The question every leader needs to answer honestly is: what am I building that will last?

If you are revisiting your AI strategy and want an independent perspective on where your company stands and what to prioritize, get in touch at abraao.tech. I have helped companies like BTG, B3, XP, and Inter make technology decisions that created real value — not just pilot projects that impress in presentations, but architectures that work in production and scale with the business.