How to use Artificial Neural Network to solve complex problems?

Artificial neural networks have moved from being a technical curiosity to establishing themselves as one of the engines of agile transformation in companies. And the numbers prove this evolution: the global market for neural network solutions — including software and services — was valued at US$ 34.05 billion in 2024, with a projection to reach US$ 385.29 billion by 2033, representing an impressive average annual growth of 31.4%, as indicated by a study from Grand View Research.

But why this leap? Because artificial neural networks are solving real problems: from accurately predicting demand to automating complex decisions based on large volumes of data. Companies that master this technology are gaining efficiency, reducing risks, and creating new business models with speed and scale.

In this article, you will understand how artificial neural networks can solve business problems, how to prepare your data infrastructure to scale solutions based on ANNs, and how to measure their real impact.

How are artificial neural networks being applied to solve business problems?

Artificial neural networks (ANNs) have transitioned from an experimental concept in computer science to a real competitive differentiator in various market areas. By simulating the functioning of the human brain, these computational structures are able to learn from data, identify complex patterns, and make decisions based on dynamic information, making them ideal for tackling business challenges in the digital age.

From a strategic perspective, the greatest value of ANNs lies in their ability to scale analytical intelligence within companies. They allow for the transformation of large volumes of data, often unstructured or fragmented, into actionable insights, automating decisions that previously required intensive human intervention or were unfeasible due to technical limitations.

Here are some examples of how this is already happening in companies: 

• More accurate demand forecasting: with ANNs, it’s possible to anticipate trends and plan inventories with greater confidence, avoiding excess or shortages.
• Real-time fraud detection: especially in the financial sector, neural networks help identify suspicious behavior quickly and accurately.
• Personalization of offers: by understanding customer behavior, it is easier to deliver the right message at the right time — increasing the chances of conversion.
• Automation of repetitive tasks: such as email sorting, document analysis, or even visual inspections in industry, reducing errors and gaining productivity.
• Data-driven decision-making: with predictive systems, leaders can act more confidently in areas such as maintenance, logistics, and even personnel management.

What criteria should guide the adoption of artificial neural networks in corporate projects?

The application of artificial neural networks (ANNs) in corporate projects can lead to significant gains in efficiency, revenue, and innovation. However, their success depends on strategic choices that go far beyond algorithm selection. It is necessary to ensure that the technology aligns with business goals, the company’s data reality, and execution capability.

Below are the main criteria that should guide this decision:

Accurate mapping of the business problem

Successful projects start with a well-defined pain point. Neural networks are particularly effective in challenges that involve:

• Large volumes of historical data;
• Complex, non-linear patterns difficult to capture with traditional rules;
• The need for real-time predictions, classifications, or automatic decisions.

Example: Predicting customer churn based on hundreds of behavioral and transactional variables.

Volume, quality, and data governance

Neural networks require large volumes of well-structured data to learn and generalize correctly. Besides the quantity, it is important to evaluate:

• Data distribution: are there biases that may affect the model?
• Updating and consistency: are the data updated frequently? Is there standardization across systems?
• Compliance: are the data compliant with LGPD and other regulations?

Practical tip: start with a data audit focused on the problem to be solved.

Capacity to scale and operate the model

Training an ANN is just the beginning. The model needs to be:

• Deployable into production, with continuous monitoring;
• Updatable as new data is generated;
• Explainable, especially in areas such as finance, healthcare, or legal, where decisions need justification.

Tools for MLOps and approaches such as XAI (Explainable AI) are essential in this context.

Technical and economic viability

Not every problem requires neural networks. It is crucial to evaluate:

• Complexity of the problem vs. expected benefit;
• Computational and development costs;
• Simpler alternatives (like regressions, decision trees, etc.) that could solve the problem with less investment.

Example: To classify emails into simple categories, an ANN might be overkill — a traditional NLP model could resolve it at a lower cost.

Maturity of the team or technology partner

Neural networks require expertise in:

• Data engineering;
• Data science;
• Scalable infrastructure (e.g., Kubernetes, GPUs, CI/CD pipelines for ML).

If the internal team is still in a maturation phase, the recommendation is to seek partners with practical experience in corporate AI projects, who bring not only technical know-how but also business vision.

Governance and organizational impact

AI models impact processes, people, and decisions. It’s essential to ensure:

• Transparency in the criteria used by the model;
• Engagement of the involved areas from the start of the project;
• Clear plans for integration with legacy systems and existing workflows.

Technology only generates value if it is embedded in the daily operations — and this requires executive sponsorship, change management, and clear communication.

How to prepare a company’s data infrastructure to scale initiatives with artificial neural networks?

Once the potential of artificial neural networks has been validated in initial projects, the next step is to ensure that the company has a technological foundation capable of supporting and scaling these solutions reliably. This means preparing the data and operations environment to support everything from data ingestion to continuous deployment and monitoring of models.

At this point, the focus shifts from experimentation to operationalization — where infrastructure, governance, and integration become the protagonists.

Data architecture compatible with deep learning

Modern artificial neural models require data ingestion in large volumes and variety (images, text, transactions, system logs, etc.), with temporal consistency and well-defined metadata. The architecture must support optimized pipelines to train and retrain models with massive datasets, often in different formats.

Typical requirements:

• Support for unstructured data (images, voice, logs);
• Parallelizable pipelines with version control;
• Storage that allows efficient sequential reading for GPU/TPU.

Governance that considers the life cycle of ANNs

Unlike simpler models, ANNs are sensitive to data drift, and their performance can degrade quickly without constant reevaluation. This requires ongoing governance, focusing on:

• Versioning of datasets and models;
• Monitoring post-deployment metrics (e.g., accuracy, F1, latency);
• Retraining cycles based on events or time.

Computational infrastructure adapted to the depth of the model

Deep learning networks require environments prepared to process large volumes of data with parallelism and acceleration by specific hardware (GPUs, TPUs). However, not every ANN requires the same level of resources — therefore, scaling should be done by model type and the criticality of the use case.

Effective strategies:

• Using managed environments (like AWS SageMaker, Vertex AI);
• Clusters with automatic scalability for training and inference phases;
• Adopting compact models (e.g., quantized or distilled) for production.

Integration between data and machine learning engineering

Scaling ANNs requires a solid bridge between data teams and ML/AI teams. The infrastructure needs to facilitate the efficient sharing of feature stores, reuse of transformations, and quick access to labeled and preprocessed data.

Critical point: the data lineage (traceability of input data) is essential for debugging the behavior of neural models, which can present opaque or counterintuitive decisions.

Security, explainability, and compliance focused on complex models

Neural networks, due to their complexity, increase the risks of bias, hallucinations, and decisions that are difficult to justify. This requires specific measures in the infrastructure to ensure:

• Detailed logging of inferences;
• Explainability tools (LIME, SHAP, etc.);
• Compliance with regulations like LGPD, with a focus on automated decisions.

How to validate the real impact of solutions based on artificial neural networks?

Many projects with neural networks reach production with promising technical metrics but lack a clear plan to measure their practical effect on the business. In a short time, the initiative loses traction, the model is deactivated — and the company returns to square one, unsure if it failed in technology, usage, or measurement.

Avoiding this cycle requires more than accuracy reports or performance dashboards. It is necessary to design the project with impact validation as an architectural requirement, rather than as a final step. Here are three essential fronts:

Direct connection between technical metric and financial or operational result

Before any modeling, it’s essential to define a business success metric and ensure it is sensitive to the model’s performance. Concrete examples:

• If the goal is to reduce churn, the impact metric is not just the accuracy of the classifier, but rather the actual reduction in churn rate after actions based on predictions.
• In a demand forecasting model, the real value lies in reducing stockouts or excess inventory, not in the accuracy of the forecast itself.

Best practices:

• Models should only be validated if there is a clear operational “next step”: who will use the predictions? How do they alter decision-making?
• Technical KPIs should be accompanied by business KPIs from the initial tests (e.g., reduction in cost per detected fraud, increase in conversion from personalized recommendations, etc.).

Controlled experimental validation (A/B testing or field trials)

The only reliable way to know if an ANN is generating impact is through structured and continuous testing that isolates its influence from other business variables. This can be done with:

• Classic A/B tests, where part of the users receives decisions based on the model and another part does not;
• Tests by region or channel, applying the model to geographic segments or distinct channels (stores, call center, app);
• Simulations with historical data, assessing how the model would have performed against past decisions.

Important: positive results in sandbox or historical data are merely indicative — real validation occurs in the production environment.

Long-term monitoring and continuous adjustment

The impact of neural models can vary over time due to changes in data, user behavior, or competitive environment. Therefore, it is essential to establish post-deployment monitoring mechanisms that go beyond technical performance.

Indicators to consider:

• Evolution of ROI per model (e.g., revenue generated for each percentage point of improvement);
• Cost per inference and its correlation with the benefit generated;
• Alerts for model drift — performance drop due to changes in input data.

Additionally, create regular cycles for reevaluating the model (monthly, quarterly) with participation from business stakeholders, not just technical teams.

How to balance performance and transparency when applying artificial neural networks in critical decisions?

Models based on artificial neural networks have proven to be extremely effective in tasks such as pattern detection, image classification, language analysis, and behavior prediction. But when these predictions influence critical decisions — such as granting credit, approving a diagnosis, or prioritizing service — an inevitable challenge arises: how to trust (and justify) a decision made by a model that even the engineers themselves cannot fully explain?

This is not just a technical dilemma — it is a strategic, regulatory, and reputational point. And there is no one-size-fits-all answer. Below, we highlight four complementary approaches that help find this balance:

Select the level of transparency required by the context of the decision

Not every decision demands the same degree of explanation. In low-impact or internal use decisions, the priority may be performance. In decisions with direct customer impact, regulatory involvement, or legal risk, transparency must come first.

Practical example:

• In a product recommendation via app, performance may be prioritized.
• In a credit rejection, the model needs to explain why the customer was denied — and this should be documented.

The ideal strategy is to classify use cases by risk level and explainability requirement, and apply different approaches according to the context.

Combine high-performance models with layers of explainability (XAI)

Explainable AI (XAI) tools allow for generating interpretations of complex model behavior without compromising performance. Techniques such as SHAP, LIME, and Grad-CAM can indicate which variables influenced a specific decision or which patterns were activated in an image, audio, or text.

However, it’s important to remember: XAI does not replace governance, nor does it provide absolute guarantees. The explanations are approximations and should be used as support for decision-making, not as automatic justification.

Adopt hybrid architectures to balance performance and control

A practical alternative is to combine neural networks with simpler, more explainable models in the same pipeline. This can occur in two ways:

• Hierarchical models, where the ANN filters complex cases, and the remaining are handled by rules or linear algorithms;
• Ensemble models, which use the outputs of the ANN as input for a final interpretable model responsible for the critical decision.

This approach preserves the analytical capability of ANNs while adding a layer of control more aligned with auditing and compliance requirements.

Involve legal, regulatory, and risk areas from the outset

A common mistake in ANN projects is treating compliance and explainability as “final steps.” In practice, critical decisions need to be co-designed with the areas responsible for governance within the organization.

This includes:

• Defining criteria for acceptability of opacity (when it can and cannot be accepted);
• Establishing accountability for responding to legal or customer inquiries;
• Validating that the generated explanations are understandable and useful for those impacted.

How companies are using artificial neural networks to innovate and gain a competitive edge?

Artificial neural networks have transitioned from an experimental technology to a strategic pillar in digital transformation initiatives for leading companies. And the numbers prove this shift: specifically in the deep neural network segment, the global market is estimated at US$ 42.6 billion in 2024, with projections to reach an impressive US$ 692.1 billion by 2034 — representing an average annual growth of 32.15%, according to Market Research Future.

This accelerated pace reflects the growing role of ANNs in generating efficiency, differentiation, and continuous innovation. Pioneer companies are using these technologies to reimagine processes, expand analytical capabilities, and create new business models, with a direct impact on competitiveness and market positioning.

See how organizations in different sectors are applying neural networks to transform strategy into real results.

Personalization and efficiency in retail and service

Walmart adopted ANNs to create “My Assistant”, a generative tool that helps over 50,000 employees with automatic summaries, content creation, and onboarding. Additionally, it invests in “Wallaby”, a proprietary LLM trained on internal data, and tests autonomous shopping agents to enhance customer personalization and operational efficiency. 

Delta Air Lines utilized a type of spiking neural network to attribute US$30 million in sales to Olympic sponsorship, connecting customer behavior with real marketing outcomes.

Fraud security and prevention

Mastercard employs neural networks combined with generative AI in its “Decision Intelligence” and “Safety Net” systems, analyzing transactions on a large scale to accurately identify and block fraud, also utilizing approaches like silent scoring to test new models before production deployment. 

R&D and ongoing intelligent innovation

Companies are placing neural networks at the center of their innovation processes. The use of techniques like digital twins, generative analysis, product simulations, and AI-assisted design has drastically reduced time-to-market, allowing rapid prototyping and informed decision-making based on real data.

As artificial neural networks evolve into core innovation assets, companies that structure their data, processes, and decisions around these technologies gain a difficult-to-replicate advantage. The difference between adopting AI and transforming the business with it lies in the ability to connect artificial intelligence with real impact, and that is exactly where Objective can help.

With experience in complex data and artificial intelligence projects, Objective supports large companies in the complete journey of structuring, developing, and operationalizing solutions. Contact our specialists and let’s transform data into a competitive advantage together.