Artificial Intelligence in Business: How to Turn Technology into Business Results

Implementing artificial intelligence in business requires more than keeping up with new tools, models, and applications. To generate business results, organizations need to identify opportunities, assess return on investment, establish proper governance, and integrate technology into existing processes.

For technology leaders and executives, the challenge is not simply keeping pace with AI’s evolution. It is understanding how to apply it strategically, delivering measurable impact without falling into the hype trap.

This was the starting point of the third episode of Tech for Decision Makers (in Portuguese), Zallpy’s podcast. In this conversation, Renan Deves, CTO at Zallpy, welcomed Luiz Zaka, CEO and Founder of The Drops, and Felipe Gonzales, Head of Operations at Zallpy, to discuss a question increasingly present in organizations: how can businesses navigate AI FOMO without making decisions driven solely by market pressure?

The answer lies in strategy, governance, engineering, and, above all, business context.

What Is AI FOMO and How Does It Affect Businesses?

The pace at which artificial intelligence evolves creates a constant sense of urgency. New models, platforms, AI agents, and promises of transformation emerge continuously, putting pressure on companies to adopt technologies quickly.

The problem is that not every new development needs to become a strategic priority.

AI-related decisions involve budgets, security, architecture, people, processes, and operational impact. Therefore, an AI strategy should not be driven by the fear of falling behind, but by the ability to monitor trends, test hypotheses, measure results, and make informed decisions.

The point is not to keep up with everything. It’s knowing how to separate signal from noise.

A company does not need to adopt every new tool that enters the market. It needs to identify which technologies can solve relevant problems and deliver measurable impact.

How to Identify Artificial Intelligence Opportunities in Business

Successful AI initiatives start with a business problem, not with technology.

AI adoption should begin by identifying business-critical challenges and concrete opportunities to create value, rather than by choosing tools first.

Before starting a project, consider the following questions:

• What business problem are we trying to solve?
• Where is there a concrete opportunity for improvement?
• How will success be measured?
• Which systems and data are involved?
• What risks need to be considered?
• Should the solution be purchased, built, or integrated?
• How can a pilot project evolve into production?

This shift in perspective reduces initiatives without clear direction and increases the likelihood of delivering real results.

Instead of chasing trends, organizations can direct their efforts toward areas where artificial intelligence can measurably improve a decision, process, operation, product, or experience.

The first step, therefore, is to connect every initiative to a business objective and define how its impact will be evaluated.

How to Implement AI in Business Through Pilot Projects

Experimentation is part of an effective artificial intelligence adoption strategy. However, experimenting does not mean testing tools randomly.

Every pilot project should be tied to a clear business hypothesis. For example: Can we reduce the time required to perform a task without compromising its quality?

With a well-defined hypothesis, companies can establish a baseline, measure actual gains, understand user behavior, assess risks, and calculate the initiative’s potential return.

A structured approach to AI implementation can follow four steps:

1. Identify the opportunity: Select a relevant problem and define the expected outcome.
2. Establish a baseline: Document how the process currently works, considering time, cost, quality, and productivity, depending on the use case.
3. Run and evaluate the pilot: Test the solution within a controlled scope and compare the results against the initial baseline.
4. Make the scaling decision: Evaluate results, risks, costs, and the conditions required to integrate the solution into operations.

This approach allows AI implementation to evolve in a controlled manner. The goal is not to accumulate pilot projects, but to identify which initiatives deserve investment, scaling, and integration into business processes.

How to Calculate the ROI of Artificial Intelligence

The discussion around artificial intelligence inevitably leads to an important business question: What will the return on investment be?

Many solutions seem affordable at first but can become significantly more expensive as the number of users, token consumption, processing requirements, or integration complexity increases.

That’s why evaluating AI ROI requires a broader perspective than simply looking at licensing costs.

Some important questions include:

• How does the vendor charge for the solution?
• Do costs increase per user or based on usage?
• Are there additional integration and maintenance costs?
• Is there technological dependency on a specific vendor?
• What operational gains are expected?
• How will success be measured?

Beyond direct costs, it is important to consider how the solution affects operations. Depending on the use case, the evaluation may include reduced time spent on tasks, less rework, increased productivity, or improved delivery quality.

These metrics should be defined according to the problem the initiative aims to solve. Whenever possible, compare results against an established baseline and account for implementation, operational, and maintenance costs.

Talking about ROI does not mean reducing every AI initiative to a financial spreadsheet. It means establishing a clear connection between investment, operational change, and expected outcomes.

Without this connection, organizations risk adopting technology without knowing exactly what value it is generating.

AI Governance: How to Mitigate Risks and Manage Shadow AI

As artificial intelligence tools become more accessible, concerns about Shadow AI are growing: the use of AI solutions by employees without the company’s knowledge or formal approval.

This creates a scenario in which technology is already being used, but the organization may not know exactly where, by whom, for what purposes, or with which data.

The problem is not necessarily the use of AI itself, but the lack of visibility and clear guidelines governing its use.

An AI governance strategy should define:

• Which tools are approved for use.
• Which data can be shared with each solution.
• Which applications present greater risks.
• Who is responsible for each use case.
• How solution performance and behavior will be monitored.
• Which criteria determine whether a pilot can move into production.

These guidelines help establish boundaries for technology use without preventing experimentation.

The goal is not to restrict innovation, but to ensure initiatives are carried out safely, responsibly, and in alignment with the organization’s needs.

Why Integration Is Essential to Creating Value with AI

The value of artificial intelligence rarely lies in the tool itself. It emerges when technology connects with the organization’s business context.

A solution may produce impressive results in a generic environment and still fail to generate an impact within a company’s actual operational reality.

For AI to effectively contribute to business results, enterprise projects typically require:

• Integration with existing systems.
• Controlled access to relevant data.
• Business domain knowledge.
• Process automation.
• Business rule implementation.
• Continuous performance monitoring.

This integration allows technology to be applied to real activities and processes while accounting for the organization’s specific requirements.

As highlighted in the Tech for Decision Makers episode: the magic happens in the integration.

It is through this connection between technology, data, processes, and context that AI can move beyond isolated experimentation and begin generating sustainable value.

The Role of Software Engineering in AI Implementation

There is a perception that artificial intelligence makes software engineering less important. In practice, incorporating AI into critical business processes makes engineering even more relevant.

Testing a technology is relatively simple. Turning it into a reliable, secure, and scalable operational capability is a much greater challenge.

That’s why enterprise AI projects need to consider aspects such as:

• Architecture: Defining how the solution connects with existing systems and components.
• Integration: Ensuring communication between models, applications, APIs, and data sources.
• Observability: Monitoring solution behavior and identifying failures.
• Security: Protecting data, access, and sensitive information.
• Quality: Evaluating response consistency and application performance.
• Governance: Establishing responsibilities and control criteria.
• Continuous improvement: Monitoring results and adapting the solution to changing business needs.

The discussion around AI does not replace engineering. It reinforces the need to combine technical expertise, architectural vision, and an understanding of business processes to turn experiments into reliable solutions.

Buy, Build, or Partner: How to Choose an AI Strategy

Adopting artificial intelligence typically leads to a practical decision: purchase an off-the-shelf solution, develop one internally, or work with specialized partners.

There is no universal answer. The best approach depends on each organization’s needs and capabilities.

Key decision-making criteria include:

• Problem complexity.
• Expected competitive differentiation.
• Data availability and quality.
• Security requirements.
• Required implementation speed.
• Total cost of ownership.
• Internal technical capabilities.
• Future scalability and maintenance requirements.

An off-the-shelf solution may be sufficient when the goal is to accelerate adoption and the product meets the company’s requirements.

In other scenarios, organizations may need to develop specific capabilities or integrate different technologies to address their operational needs. Partnering with specialists may also be an option when a company needs to complement its internal expertise or accelerate execution.

The decision should consider not only adoption speed, but also the solution’s long-term sustainability, integration with the technology environment, and expected return.

Most importantly, technology strategy must serve business objectives, not the other way around.

Artificial Intelligence Trends Businesses Should Be Paying Attention To

AI’s evolution requires business leaders to stay informed about relevant developments, but that doesn’t mean turning every new development into an investment initiative.

Leadership’s role is to distinguish trends that may affect the company’s strategy from those that can simply be monitored without immediate adoption.

Some topics deserve particular attention:

Multimodal Models

The ability to work simultaneously with text, images, audio, and video expands the possibilities for applying artificial intelligence across different business contexts.

The challenge is identifying which of these capabilities address real operational needs.

Model Strategy

Not every problem requires the largest model available. Choosing between larger, smaller, locally deployed, or hosted models depends on the operational context, technical requirements, costs, and required performance levels.

Infrastructure Costs

Data centers, GPUs, computing consumption, and processing capacity are relevant factors in AI strategy, especially as applications scale.

That’s why infrastructure costs need to be part of the financial evaluation of AI initiatives.

Governance and Shadow AI

Establishing clear rules for usage, security, accountability, and data sharing is essential to managing technology adoption within organizations.

Unauthorized tool usage must also be on leadership’s radar, with visibility mechanisms and guidelines that balance control and innovation.

Integration with Systems and Processes

Integration remains one of the key factors in turning model capabilities into business results.

Connecting AI to an organization’s systems, data, and business knowledge enables the development of applications that are better aligned with operational needs.

Given these trends, the priority should not be to adopt everything first. It should be to evaluate what makes sense for the business, considering opportunities, risks, costs, and execution capabilities.

How to Turn Artificial Intelligence into Business Results

The speed of AI’s evolution can create the impression that every company needs to do everything at once. But strategy is not about keeping up with every trend.

Strategy is about building a consistent process to identify opportunities, test hypotheses, measure results, and allocate resources to initiatives that deliver real impact.

Organizations should prioritize business-critical problems and identify where AI can create value and competitive advantage, directing resources toward the initiatives with the greatest potential impact.

This shift in mindset is what distinguishes experimental initiatives from programs that actually generate business value.

Achieving this requires connecting strategy, data, technology, processes, people, governance, and engineering. Companies that combine strategic vision with execution capabilities are better positioned to turn technology into a sustainable competitive advantage.

AI FOMO is unlikely to disappear anytime soon. New models, agents, platforms, and applications will continue to emerge at an accelerated pace.

Competitive advantage will not come from being the first to adopt everything, but from understanding the context, evaluating opportunities against clear criteria, implementing appropriate governance, and scaling initiatives that demonstrate real value.

AI maturity is not measured by the number of tools an organization uses. It is measured by its ability to turn technology into business results.