Como é que as empresas podem usar a inteligência artificial para crescer?

Artificial intelligence (AI) is for business today what the Internet was 25 years ago, at the dawn of the new millennium. Some — the enthusiastic and the innovative — see an opportunity in this brave new world. Others, more skeptical or cautious, are just waiting for the wave to carry them forward without much effort.
Much has been written and discussed about AI for business, and I don’t intend to simply repeat what’s already in the news every day.

This post is a personal reflection that tries to answer a question I hear frequently from my consulting clients: “How can AI help my business?”

How Can AI Help My Business?

Believe it or not, I hear this question all the time. We all have some idea of what AI is — we’ve all watched Black Mirror on Netflix, and AI is in the news almost every day — but very few businesses have a clear picture of how to incorporate it successfully into their operations. Even fewer are actively using it!

In my perspective, before we even begin thinking about AI adoption, there are two critical questions every organization must answer:

1. How mature and ready are the business processes to adopt AI?
2. What does the current data infrastructure look like?

Evaluating Business Processes Readiness for AI

Processes define how a business operates every day. You can think of processes as a set of operational rules that clearly determine how things work: a sequence of activities performed by a group of people to achieve a specific result or goal. They involve responsibilities, data, documentation, and technology.

Recruiting is a good example of a business process — so are purchasing or customer service.

To successfully integrate AI into business processes, we need to evaluate how ready those processes are. Depending on the size and complexity of the organization, this evaluation can be challenging — therefore, a structured framework is necessary.

Here are some key questions to consider when assessing the maturity of business processes:

• How repetitive is the process? Is it executed daily, hourly, or constantly? Or is it infrequent? → The more repetitive the process, the greater the potential for AI automation.
• How clearly is the process defined? Is it documented and shared with everyone? Are goals and responsibilities clearly outlined? Are employees trained on the process? Or is it subjective, with different team members executing the process in completely different ways?
• How data-intensive is the process? What type of data is involved — structured, semi-structured, or unstructured?
• Is process efficiency measured? How long does it take to execute from start to finish? How many errors or failures occur on average? How often are steps repeated?

Of course, many other questions are involved in a full assessment, but the goal is to create a framework that computes a maturity score and shows how ready the organization is to adopt intelligent automation.

Example: Procurement Process Automation

A typical procurement process might involve the following steps:

1. Request for Purchase: A department submits a request for goods or services.
2. Approval Process: A manager or procurement team reviews and approves or denies the request.
3. Supplier Selection: Procurement identifies and contacts potential suppliers.
4. Quotation Analysis: Supplier quotes are collected and compared.
5. Purchase Order (PO) Creation: A purchase order is issued to the selected supplier.
6. Delivery and Receipt: Goods or services are received and inspected.
7. Invoice Matching and Payment: The invoice is matched against the PO and delivery receipt, and then paid.

How Can AI Intelligently Automate This Process?

AI — especially Generative AI — can do a lot for intelligent automation. There are many pretrained models, like OpenAI’s GPT models, Google’s PaLM, Meta’s Llama, and Stability AI’s Stable Diffusion, that can be fine-tuned or integrated into business workflows.

For instance:

• Reading and classifying documents: AI can read purchase requests (emails, forms) and automatically classify them into categories like “IT Equipment” or “Office Supplies.”
• Intelligent Approval Workflows: Based on business rules (e.g., amount thresholds, supplier risk scores), AI can assign the approval task to the right person — or even pre-approve certain types of requests automatically.
• Invoice and PO Matching: AI can read invoices and automatically match them against corresponding purchase orders and delivery receipts, identifying discrepancies instantly.
• Duplicate and Fraud Detection: AI models can detect duplicate payments or suspicious patterns that may indicate fraud.

In short: AI doesn’t replace the human process; it augments it, making it faster, more accurate, and less expensive.

How Does the Data Infrastructure Support AI?

AI systems, especially machine learning (ML) systems, completely depend on data. Quality, quantity, and variety of data are critical to success. Without a strong data infrastructure, it’s simply not possible to develop a reliable AI or ML capability for business — just like it isn’t possible to build a modern company today depending only on scattered Excel files.

Here’s a quick self-evaluation of data infrastructure readiness for AI:

• Data Availability and Accessibility: Are data sources integrated and easily accessible to AI teams, or are they locked away in silos?
• Data Quality and Consistency: Is the data accurate, complete, consistent, and regularly updated? Or do AI teams constantly have to clean and fix it before use?
• Storage Infrastructure: Can it handle large, varied datasets (structured, semi-structured, unstructured)? Is cloud-based storage available and scalable?
• AI/ML Optimization: Is the infrastructure built to support machine learning workflows (e.g., feature stores, model versioning, experiment tracking)?

Of course, a full assessment would involve additional questions, but these cover the basics for a quick internal diagnosis. If your organization is not ready yet, the focus should be on building this foundation first — because without solid data infrastructure, there are no reliable AI systems.

What Can Businesses Do with ML Once They Are Ready?

Once the data and infrastructure foundations are in place, businesses can unlock real value by developing ML models such as:

1. Understanding customers better by creating behavioral segments (customer clusters).
2. Recommending products, services, or content to customers to boost sales.
3. Predicting customer churn to proactively retain clients.
4. Improving financial operations by forecasting sales, demand, or cash flows.
5. Optimizing operations like delivery routes, stock levels, and workforce allocation.

And for each of these functions, there are proven algorithms ready to help.

Opportunity cost – what if my business does not adapt AI?

Choosing not to adapt AI today comes with a real opportunity cost that businesses cannot afford to ignore. While some companies will use AI to improve efficiency, reduce costs, enhance customer experiences, and innovate faster, those who delay risk falling behind. Competitors who successfully integrate AI will be able to operate leaner, make better decisions with data, and react to market changes more quickly. This could mean losing customers, market share, and profitability over time — not because your products or services are worse, but because your operations and decision-making processes are slower, less precise, and more costly. In today’s fast-moving business environment, staying still is effectively moving backwards.