AI Maturity: Challenges and Paths to generate value with artificial intelligence

In recent years, artificial intelligence has shifted from a distant promise to a strategic priority in nearly every company. However, as the topic gains traction on executive agendas, there is also a growing misconception: that adopting AI is fundamentally just a technology issue.

In practice, it’s not.

The main barrier to generating value with AI is not in the choice of tools, the model, or the infrastructure. It’s in the organization’s maturity to sustain, scale, and extract real value from this technology.

And that’s precisely where many companies get lost.

There is a common belief that “data-driven” companies are already ready to work with artificial intelligence. But being data-driven is only part of the equation—and often, a still superficial part.

Maturity in AI involves a combination of factors that go far beyond the availability of data.

It involves reliable, structured, and governed data. It involves clarity about which business problems need to be solved. It involves integration between areas, so technology and business move together. And, most importantly, it involves the ability to transform models into real decisions in day-to-day activities.

Without these elements, AI tends to become an interesting experiment but disconnected from results.

Mature companies do not use AI to test possibilities. They use AI to solve concrete problems and generate competitive advantage.

The most common mistake: starting with technology

In many conversations with executives, the initial question usually is:
“Which AI tool should we implement?”

This is the wrong question.

The right question is:
“Which business decision can be improved with AI?”

When technology comes before the problem, the outcome tends to be predictable: initiatives that do not scale, models that are not used, and investments that do not convert into value.

AI is not about technological capability. It’s about decision-making capability.

How to identify the level of AI maturity in your business

Before moving forward, it’s essential to understand what stage your organization is in. And this requires more than just perception; it necessitates a structured diagnosis.

A practical way to do this is to use approaches like the Maturity Card Game, developed by Objective.

The logic is simple but powerful: evaluate the organization’s maturity based on dimensions that directly impact its ability to generate value with technology, such as strategy, data, processes, culture, and technology.

By transforming this diagnosis into a structured and collaborative dynamic, the Maturity Card Game allows different areas to have a shared view of the current level of the company and, most importantly, about the next steps.

More than just measuring maturity, the goal is to generate alignment.

Because the greatest risk is believing that you are ready when you are not.

How to evolve in AI maturity

Evolving in AI does not mean starting big. It means starting right.

Start with a real business problem.

Choose a scenario where there is a clear pain point, available data, and measurable impact. AI needs to solve something relevant, not just demonstrate technical capability.

Structure data as a strategic asset.

Without reliable, governed, and accessible data, there is no AI that can sustain long-term value.

Integrate technology and business from the outset.

AI projects cannot be isolated in technical areas. They need to be born connected to operations and business decisions.

Think about scale from the beginning.

It’s not enough to prove it works. It’s essential to ensure that the use of AI can be incorporated into processes and scaled over time.

Develop culture, not just solutions.

Maturity in AI is directly linked to the organization’s ability to learn, adapt, and evolve continuously.

AI is not the end. It’s the result.

Perhaps the main change in perspective is this:
AI is not the starting point of transformation—it is the consequence of a mature organization.

Companies that try to skip steps tend to encounter frustration.
Companies that build a foundation (data, processes, governance, and culture) turn AI into a real competitive differential.

At the end of the day, it’s not about having AI; it’s about generating value from it.

In practice, this is already a reality in organizations that structure this journey well. One example is the case of Arezzo & Co., which, by evolving its data maturity—with the creation of a data lake and proper governance—managed to apply machine learning in a way that is connected to the business.

The result was not just increased operational efficiency but also revenue growth, greater agility in decision-making, and increased data usage across the organization. 

Where to start, then?

Start by understanding where you are.

Before thinking about models, tools, or investments, evaluate your organization’s maturity. Identify gaps, align expectations, and set priorities based on business impact.

This is the path that separates ad-hoc initiatives from consistent transformations.

If your company is evaluating how to advance in the adoption of AI with more consistency and real impact, it’s worth delving into this discussion.

At Objective, we have been supporting organizations in structuring maturity journeys in data, automation, and artificial intelligence linked to business outcomes.

If this makes sense to you, let’s share ideas with our specialists on how to evolve this journey in your reality.