07 Oct 2026 6 min read
Artificial intelligence is no longer an initiative restricted to the areas of technology. Today, it influences processes, decisions, operating models, and corporate strategies, becoming an increasingly present agenda on the agenda of senior leadership and boards of directors.
According to Grant Thornton's CFO Survey Q2 2026, about 67% of finance leaders intend to increase investments in artificial intelligence and digital transformation in the next 12 months, even in a scenario of lower economic optimism. The data reinforces that the debate is no longer centered on the decision to invest or not in AI, but on the ability to transform these investments into measurable results for the business.
At the same time, the discussion about AI is undergoing an important change. companiesng to Maikon Silva, IT Risk partner at Grant Thornton: "The discussion is no longer whether the company should invest in artificial intelligence. The question now is how to ensure that these investments produce measurable results."
In many companies, the adoption of artificial intelligence began in a decentralized way. Teams started to use tools to produce content, automate tasks, organize information, and speed up processes. This phase was important to stimulate experimentation.
However, the maturation of AI requires a change in approach. Before asking where artificial intelligence can be used, organizations need to answer a more strategic question. The big question is not where to invest in artificial intelligence, but where artificial intelligence generates results in the business.
More than implementing technology, it is necessary to understand how it contributes to concrete indicators, such as:
If none of these factors change, it is worth questioning what value is being scaled. Value creation happens when technology is connected to clear strategic objectives and relevant business problems.
Another recurring challenge is to start projects based on technology and not on business problems. Successful initiatives usually start by identifying a concrete business need, such as increased costs, loss of customers, decreased margin, or operational inefficiencies. From this diagnosis, technology starts to act to solve a real problem and generate measurable impact.
Although many organizations focus investments on initiatives aimed at productivity, experts warn that the greatest potential for generating value is in applications connected to the strategic core of companies.
Artificial intelligence governance is not a brake. It is a lever for organizations to move forward with confidence, manage risk, and be able to demonstrate value to the business.
In this sense, governance and value creation are now directly related. According to the AI Impact 2026 survey, organizations with successful AI initiatives invest up to four times more in fundamentals such as data, governance, and change management than organizations with less AI maturity, demonstrating that these elements are an essential part of the infrastructure needed to capture sustainable results.
Far from representing a barrier to innovation, governance creates the necessary conditions for organizations to move forward with more security, confidence, and the ability to scale.
The data shows that the challenge is not only in the adoption of technology, but in the ability to generate trust and return for the business. Currently, only 39% of executives believe that investments in AI will produce a positive financial impact, while 46% point to governance or compliance barriers among the main causes of poor performance of initiatives.
The need to strengthen supervisory mechanisms also appears when the topic is assurance. In a scenario of rapid technological evolution, the ability to measure results, supervise risks, and demonstrate confidence becomes a competitive advantage for organizations that want to transform investments into sustainable value.
Artificial intelligence should not be treated only as a technological agenda. Its implications involve strategy, reputation, risks, compliance, organizational culture, and business sustainability.
As organizations seek to capture value with technology, it becomes necessary to establish clear mechanisms for oversight, accountability, and monitoring of results. In this context, trust is not only the result of implementing controls. It depends on the ability to demonstrate that AI initiatives are aligned with business strategy, are continuously monitored, and have clearly defined stakeholders.
Therefore, leaders and councils need to formulate fundamental questions:
Boards do not need to master all the technical aspects of artificial intelligence. However, they need to be prepared to ask the right questions about value creation, risks, oversight, transparency, and business impacts.
In addition, building a responsible culture is essential. Policies that only restrict the use of technology, without providing adequate direction, can encourage parallel initiatives and reduce visibility on the risks that actually exist.
While artificial intelligence can process large volumes of data, identifying patterns, and supporting complex decisions, the responsibility for the results remains human.
"There will always be a person in charge. Regardless of the degree of autonomy of technology, human supervision remains essential," points out Maikon. Models can generate incorrect answers, reproduce biases, or produce content that requires specialized validation. Therefore, supervision, professional judgment, and accountability remain indispensable components of any AI strategy. The greater the potential impact of the decision, the higher the level of monitoring and validation must be.
The responsible adoption of technology also depends on the training of people. More than knowing the tools, professionals and leaders need to understand their limits, risks, and responsibilities. Artificial intelligence can expand the analysis capacity of organizations, but it does not replace human discernment in decision-making.
AI governance is not a project with a beginning, middle, and end. Technologies evolve, regulations change, and new risks constantly emerge. For this reason, mature organizations need to maintain continuous processes for monitoring, risk assessment, team training, policy updates, and results tracking. Far beyond controlling tools, governing AI means monitoring the entire application lifecycle.
This journey also involves the quality of information. As artificial intelligence increasingly relies on data to generate results, AI governance and data governance become inseparable topics. Transparency about the origin of information, access criteria, security, and traceability of results are fundamental elements to strengthen trust and sustain value creation.
Turning potential into sustainable value
Artificial intelligence will continue to transform markets, processes, and business models. The competitive advantage of organizations will not only be in the ability to invest in technology, but in the ability to convert it into concrete, sustainable value aligned with business objectives.
The companies that achieve the best results will be those capable of integrating strategy, governance, data, people, and risk management into a consistent and value-oriented approach. Governance plays an essential role in this process, creating the necessary conditions for innovation to advance with trust, security, transparency, and accountability.
The challenge is not to eliminate the risks inherent in innovation, but to understand, monitor, and manage them effectively. In a scenario of rapid technological evolution, this ability to balance opportunities and risks will be decisive in transforming the potential of artificial intelligence into competitive advantage and concrete results for the business.
This content brings together reflections on the main challenges related to governance, value creation, risk management, and responsible adoption of artificial intelligence. To explore these topics in more depth, check out the full recording of the AI Under Governance event.
Grant Thornton supports organizations in building artificial intelligence governance models aligned with business strategy, risk management, and best market practices.
Our professionals assist companies in assessing maturity, structuring governance programs, managing technological risks, data governance, regulatory compliance, and responsible adoption of artificial intelligence.