AI First: trends and opportunities in the strategic use of artificial intelligence

Artificial intelligence (AI) has moved from being just a futuristic promise to becoming a transformative reality in the global business landscape. By 2025, AI adoption reached unprecedented levels, with over 90% of companies incorporating this technology into their operational and strategic processes.

This movement gave rise to the concept of “AI First”, an approach that places artificial intelligence at the center of business decisions, driving innovation, efficiency, and competitiveness. Companies that adopt this mindset are redefining operational models, creating personalized experiences for customers, and exploring new market opportunities.

Studies indicate that the global AI market is growing at a compound annual growth rate of 37.7%, signaling the increasing importance of this technology across all sectors. Additionally, over 40% of business leaders report increased productivity through AI automation, highlighting the positive impact of this technology on business operations.

In this article, we will explore the key trends and opportunities associated with the “AI First” approach, highlighting how companies can strategically position themselves to maximize the potential of artificial intelligence.

Read on to discover how your organization can lead in the era of AI.

What does an AI First approach mean?

The AI First approach represents a profound strategic shift in organizations, where artificial intelligence ceases to be just a complementary tool and becomes central to business decisions and operations. In this model, AI is integrated from the conception of products and services to optimizing internal processes, becoming fundamental for driving innovation, efficiency, and competitiveness.

Unlike traditional approaches, where AI is implemented sporadically, AI First implies a comprehensive integration of AI into the organizational structure. This includes everything from data collection and analysis for more accurate decision-making to automating repetitive tasks, freeing employees for more strategically valuable activities.

Companies that adopt AI First reevaluate their business models, processes, and organizational culture to ensure that AI is aligned with strategic objectives. This approach allows for more agile adaptation to market changes, trend anticipation, and offering personalized experiences to customers.

How does the AI First approach transform traditional business models?

Adopting an AI First approach does not simply mean incorporating artificial intelligence technologies into existing processes. In practice, this leads to a profound reconfiguration of traditional business models, creating more agile, efficient, and data-centered organizations.

From reactive models to predictive and prescriptive models

Companies cease to operate reactively—responding to problems as they arise—and begin to operate predictively and prescriptively. With AI, it’s possible to anticipate behaviors, forecast demands, detect risks, and suggest actions automatically, fundamentally changing operational dynamics.

Smarter and more personalized products and services

AI First organizations can transform traditional products into smart, data-driven solutions. From personalized recommendations to predictive maintenance of equipment, AI enables the creation of offers that are more aligned with each customer’s needs, increasing satisfaction and competitiveness.

Process automation and operational scalability

Manual, repetitive, and operational tasks are replaced by AI-based models, allowing employees to focus on strategic and high-value activities. This increases scalability, reduces costs, and improves end-to-end process efficiency.

Data-driven decision-making, no longer intuition-based

By integrating AI as the core of the strategy, decisions are no longer guided solely by experience or perception but are supported by data, analytical models, and predictive simulations. This improves accuracy, reduces risks, and generates a competitive advantage.

New revenue sources and disruptive business models

AI enables models that were previously unfeasible, such as:

• Products as a Service, based on the intelligent use of data;
• Dynamic pricing, adjusted in real-time based on behavior, market, and demand;
• Predictive sales models, optimizing inventory, production, and logistics.

Transforming the customer experience

The AI First approach allows for creating hyper-personalized journeys, intelligent automated service, detection of unspoken needs, and seamless real-time experiences, transforming the company-customer relationship.

Therefore, the transformation brought about by AI First is not only operational or technological—it changes the logic of how companies operate, making them more adaptable, intelligent, and competitive in an increasingly dynamic market.

What are the main steps for a company to become AI First?

Here are the main steps for a company to become AI First:

1. Define an AI-driven strategic vision

The first step is to align leadership around a clear vision: how AI will drive business objectives. This requires that AI is not treated as an isolated project but as part of the organization’s core strategy.

2. Invest in a data-driven culture and AI literacy

For AI to generate value, all employees—not just technical teams—need to understand the impact of data and AI applications on processes and decision-making.
➡️ Action: Training programs, workshops, and encouragement of a culture of experimentation.

3. Ensure robust and scalable data infrastructure

Quality AI depends on quality data. It is essential to have a modern infrastructure—data lakes, data warehouses, well-structured pipelines, and data governance—so that models are fed with accurate, secure, and up-to-date information.

4. Identify strategic use cases to start

The path to AI First does not begin by trying to solve everything at once. It is crucial to prioritize high-impact use cases with clear value, such as process automation, offer personalization, fraud detection, or supply chain optimization.

5. Build or acquire AI capabilities

This includes assembling an internal team of data scientists, AI engineers, and machine learning professionals, or contracting pre-trained solutions or AI as a Service (AIaaS) platforms, accelerating adoption.

6. Create governance for ethical and responsible AI

AI must be transparent, fair, and responsible. It is necessary to define ethical guidelines for the use of artificial intelligence, as well as security, privacy, and compliance policies—especially in the face of LGPD and other regulations.

7. Scale and evolve continuously

After validating initial use cases, the company should create a roadmap for expansion, scaling solutions, automating processes, and integrating AI across multiple business fronts, always with cycles of continuous improvement.

What is the role of data governance in implementing the AI First strategy?

No AI First strategy is successful without a solid foundation of data governance. Artificial intelligence relies on data—and not just any data, but on information that is reliable, organized, secure, accessible, and of high quality.

Data governance acts as the foundation that supports AI, ensuring that data aligns not only with the needs of the models but also with the legal, ethical, and strategic requirements of the business.

Main functions of data governance in the AI First journey:

1. Ensure data quality

AI models are only as good as the data that feeds them. Governance ensures that data is accurate, consistent, up-to-date, and error-free, reducing biases and increasing the effectiveness of AI solutions.

2. Ensure security, privacy, and compliance

In an environment where AI deals with large volumes of sensitive data (clients, employees, markets), governance protects this data, ensures compliance with regulations such as LGPD, GDPR, and others, and establishes access, use, and sharing rules.

3. Facilitate data access and availability

AI needs accessible, organized, and structured data. Governance establishes policies and standards that ensure the right data is available to the right teams at the right time, securely.

4. Reduce operational and ethical risks

Poorly governed data can lead to distorted results in AI models, as well as security risks, fines, and damage to reputation. Governance mitigates these risks by defining clear data management, control, and auditing processes.

5. Increase efficiency and scalability of AI

By ensuring that data is organized and ready for use, governance reduces rework, improves productivity of data and AI teams, and enables projects to scale with greater agility and consistency.

6. Sustain continuous intelligence and model evolution

Governance ensures that data is continuously updated and that data pipelines are maintained efficiently, allowing AI models to evolve and adapt to changes in business and market conditions.

In summary, without governance, there is no AI First. Data governance is not an optional support—it is a strategic pillar responsible for transforming raw data into reliable assets capable of feeding intelligent models and generating real value for the business.

What metrics are effective for evaluating the success of an AI First strategy?

Measuring the success of an AI First strategy goes far beyond simply assessing whether the technology was implemented. The true indicator of success lies in how much AI generates real value, impacts the business, and creates competitive advantage.

Thus, it is essential to combine metrics of technological performance, operational efficiency, and impact on the company’s strategic objectives.

Main metrics to evaluate an AI First strategy:

1. Alignment with business objectives

• How much does AI impact strategic KPIs?
  ➡️ Examples:
• Revenue growth;
• Churn reduction;
• Improvement in customer satisfaction (NPS);
• Reduction in operational costs.

2. AI adoption rate in processes

• Percentage of processes, products, or services that utilize AI.
  ➡️ A high adoption rate indicates alignment of AI with the company culture and operation.

3. Time-to-Value

• Time between the development/implementation of an AI solution and the realization of value or impact on results.
  ➡️ The faster, the better the return on investment.

4. Operational efficiency generated by AI

• Reduction in operational hours;
• Increased productivity of teams;
• Processes automated or optimized with AI.

5. Performance of AI models (Technical metrics)

➡️ Depending on the application, monitor:

• Accuracy, precision, recall, and F1-Score for predictive models;
• Latency, response time, and up-time for models in production;
• Cost per inference in generative AI or large-scale models.

6. ROI of AI projects

• Direct evaluation of the financial return generated by AI projects relative to development, infrastructure, data, and maintenance costs.
  ➡️ Simplified formula:

7. Level of AI maturity within the organization

• Qualitative and quantitative assessment of culture, processes, governance, data, and capacity to scale AI.
  ➡️ This can be measured by AI maturity frameworks, such as models from Gartner, McKinsey, or even a proprietary framework.

Trends and the future of the AI First approach

Adopting an AI First strategy is no longer a matter of competitive differentiation but rather of survival in today’s digital market. Here are the main trends:

1. Generative AI as an innovation driver

The explosion of Generative AI—such as large language models (LLMs) and content generation—creates a new frontier, where companies can automate everything from product development to communication, marketing, and customer service.

2. Increasingly autonomous and contextualized artificial intelligence

AI systems will operate with a higher degree of autonomy, understanding more complex contexts, interacting naturally with humans, and dynamically adapting to changes in business.

3. Explainable, ethical, and responsible AI

Transparency in AI models will be mandatory. Explainability, bias mitigation, ethical governance, and compliance will be priorities, especially with new regulations, such as the European AI Act and local legislations.

4. Deep integration with ESG and sustainability

AI will increasingly be used to support sustainability initiatives, energy efficiency, waste reduction, and environmental compliance—aligning technology with companies’ ESG commitments.

5. Intelligent automation of the entire value chain

From backoffice to customer service, from supply chain to customer experience, AI will become the backbone of intelligent automation, optimizing processes end-to-end.

6. Fusion of AI, IoT, and Edge Computing

The convergence of AI, connected devices, and edge computing will enable intelligent decisions to be made in real-time, close to operations, increasing agility and efficiency.

7. Data-centric AI: focus on data quality, not just models

The future of AI First is directly linked to data quality. Companies are migrating from a purely model-focused approach to a data-centric approach, understanding that well-governed data is the true competitive differentiator.

The future belongs to companies that place artificial intelligence at the center of strategy, culture, and operations. Adopting an AI First mindset is no longer a choice—it is a necessity for those who want to grow, innovate, and remain competitive.

If your organization wants to understand how to turn this future into reality, talk to our specialists now. Together, we can build a secure, scalable, and results-oriented AI strategy—placing your company at the forefront of the artificial intelligence era.

Description: Understand how the AI First strategy transforms businesses, generates value with AI, and discover the main benefits, challenges, and trends of this approach.