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There was a time when talking about Artificial Intelligence in a business context sounded like something reserved for tech giants or well-funded startups. Today, that perception has changed.
AI tools are now available to companies of any size, at progressively lower costs and with a far more accessible learning curve than in the past. You don’t need a team of data scientists to get started. Nor do you need to overhaul your entire operation at once.
This article is here to answer the question many managers ask: where do I begin?
What can AI actually do for a business?
Before talking about implementation, it’s worth understanding what AI actually does in a company’s day-to-day operations.
AI is not synonymous with robots replacing people. In a corporate context, it works as an intelligence layer applied to processes: it analyzes data, identifies patterns, automates repetitive tasks, and surfaces useful information for decision-making.
Some concrete examples by area:
- Customer service: chatbots and virtual assistants that handle common queries around the clock, freeing up the human team for more complex cases.
- Marketing: campaign personalization, audience behavior analysis, content generation, and automated message testing.
- Finance: transaction anomaly detection, cash flow forecasting, automatic expense categorization.
- Operations: demand forecasting, inventory optimization, identification of bottlenecks in internal processes.
What do all these applications have in common? They all generate measurable returns, whether in time saved, costs reduced, or revenue generated. And none of them eliminate the role of people: they change what people need to do, not whether people are needed.
Why is now the right time to start?
Adoption costs have dropped significantly. Tools have become more accessible. And the data shows an acceleration that’s hard to ignore: according to the IBGE (Pintec Semestral 2024), AI adoption in Brazilian industries jumped from 16.9% in 2022 to 41.9% in 2024, a 25 percentage point increase in two years. AI was the fastest-growing advanced technology in that period.
On the global stage, McKinsey (The State of AI in early 2024) reports that 72% of organizations already use AI in at least one business function. And an IBM survey with over 2,400 IT decision-makers, including Brazil, found that 78% of Brazilian companies planned to increase their AI investments by the end of 2025, with 48% already seeing a positive return on those investments.
The takeaway is straightforward: companies that haven’t started exploring AI are falling behind competitively. Those already using it are operating more efficiently, making faster decisions, and serving customers better.
How to identify where AI can work in your business
There’s no single answer. The right starting point depends on each company’s context, and that’s precisely why so many get it wrong: they choose a tool before understanding the problem.
A simple method for finding the best entry points is to map three categories of processes:
- Repetitive, low-strategic-value tasks: natural candidates for automation. If someone on the team performs the same action dozens of times a week, that’s a red flag.
- Bottlenecks that rely on manual data analysis: reports that take hours, decisions waiting on someone to consolidate spreadsheets, these are the points where AI generates fast returns.
- High-volume, low-complexity interactions: such as frequently asked customer questions or standardized internal requests.
Questions that help with this diagnosis: Where does the team spend the most time on tasks that require no real judgment? Which processes depend on information that already exists in your systems, but that nobody processes in time? Where does demand volume consistently outpace your capacity to respond?
💡 NextAge does this mapping with you, identifying real AI integration opportunities focused on ROI, before any investment decision is made.
The most common AI solutions businesses use today
To make it easier to decide where to start, here’s a look at the main solution categories available today.
- AI agents and virtual assistants are systems that execute tasks autonomously or interact with users in natural language. They go well beyond simple chatbots: they can query databases, trigger other systems, and complete entire workflows without human intervention. The direct impact shows up in customer service, internal support, and operational workflow automation.
- AI applied to data analysis and decision-making transforms large volumes of information into actionable insights. Predictive dashboards, automated alerts, market trend identification, all of this reduces the time between data and decision, which is where a lot of money is lost in most companies.
- Process automation with AI (RPA + AI) combines traditional task automation with the intelligence to handle variations. It’s ideal for finance, HR, and logistics processes.
- Content generation and personalization is perhaps the most visible application right now. AI that drafts content, adapts messages by segment, and suggests campaign variations.
What to consider before implementing
Implementing AI without preparation is a recipe for frustration, not because of the technology itself, but because of contextual factors that directly affect your return.
- Digital maturity. AI needs data to work. If your processes still rely on outdated spreadsheets or systems that don’t talk to each other, the first step might be getting that foundation in order before moving to more sophisticated solutions.
- Integration with existing systems. The solution needs to work with your ERP, CRM, and support platform. An implementation that creates more silos than it solves doesn’t generate value — it generates cost.
- Team: hire, outsource, or staff augmentation? This is one of the most important decisions. Building an in-house AI team from scratch is expensive and time-consuming. Most companies that see fast results opt for specialized partners, either for specific projects or for bringing in ready-to-go professionals. It’s no coincidence that a survey of nearly 4,300 global executives found that 99% of leaders outsource essential IT services to reduce operational risk.
- Tracking from day one. A concerning figure: according to a TOTVS study, only 7% of Brazilian companies calculate the ROI of their AI initiatives. That means 93% are investing without knowing whether it’s paying off. Defining metrics before you start, time saved, cost per service interaction, conversion rate, is what separates a successful implementation from a project that gets shelved.
Start with a pilot project
There’s no single correct sequence for implementing AI in a business. The pace depends on context and digital maturity. That said, the approach that consistently delivers results follows a progressive logic.
- Diagnosis: map the processes with the highest return potential, assess existing infrastructure, and define success metrics. This stage determines whether it makes sense to move forward, and in which direction.
- Pilot: a limited-scope implementation with a defined timeline and a clear objective. The pilot validates the hypothesis, generates learning from real usage, and produces concrete results to present internally.
- Iteration: based on pilot data, refine the solution before scaling. This is where course corrections happen, and they make all the difference in the next phase.
- Scale: apply the validated solution to other areas or replicate the model across new processes. This is the phase where ROI compounds.
Conclusion
Artificial Intelligence is not a bet on the future. It’s already a competitive advantage in the present, and those still waiting for the right moment to start are delaying ROI.
Getting started requires an honest diagnosis of the business, clarity on what you want to measure, and a partner who understands both technology and operations.
If you want to understand how AI can be applied specifically to your business, with a focus on ROI and without wasting time on solutions that don’t fit your context, talk to NextAge.
