Why companies struggle to maintain responsible AI practices

For organizations preparing for digital transformation, these findings point to a clear priority: leaders need not only budgets and tools but also a strong belief in AI’s economic worth. Without that belief, the technology struggles to move from short-term interest to long-term integration.

Businesses across developing economies are racing to integrate artificial intelligence into daily operations, but a new study shows that long-term success depends less on technology and more on how managers perceive its value. 

The research, titled “Driving Sustainable AI Implementation in Business: The Integrated Role of Economic Value Perception, Managerial Attitudes, and Behavioral Intentions”, was published in Sustainability and offers a unified model for understanding why some firms maintain responsible AI practices while others struggle to move beyond short-term adoption.

The study evaluates how business managers interpret AI’s economic value, how those views shape their attitudes toward the technology, and how attitudes influence real-world use. The work captures the views of 390 organizational executives across Peru and constructs a model that links perceived value, attitude, intention and sustained use in an integrated chain.

The findings make it clear that sustainable AI adoption is not simply a matter of having the right tools. It requires leaders who believe in AI’s long-term economic returns, hold positive attitudes toward its impact and form strong intentions to use it. Those combined elements shape whether a company truly integrates AI into responsible, continuous practice.

Economic value emerges as the trigger behind AI intentions

The research examines one of the most influential factors in AI adoption: the belief that the technology offers a fair balance of benefits and costs. The authors call this price–value, and the study shows that this variable is the strongest early trigger of a manager’s decision-making process. When executives believe that AI creates more value than it costs, they form stronger intentions to adopt it. When they doubt its cost–benefit logic, their willingness to embrace AI drops sharply.

The model presented in the study shows that price–value does not operate alone. Instead, it shapes attitudes by convincing managers that AI delivers meaningful returns. These attitudes then play a major role in determining behavioral intention, which ultimately predicts whether the company sustains AI practices over time.

The authors highlight that earlier work on AI in organizations usually separated financial logic from human behavior. This new study closes that gap by demonstrating that economic value and psychological readiness move together. Price–value sparks positive attitudes, and positive attitudes fuel strong intentions. These intentions, in turn, become the most decisive predictor of sustainable AI use.

The model shows that intention explains more than eighty percent of the variation in sustained AI use among managers. Price–value and attitude together explain nearly ninety percent of what shapes intention. These results show that sustainable use of AI cannot be achieved without first working on the earlier steps of belief formation and attitude building.

For organizations preparing for digital transformation, these findings point to a clear priority: leaders need not only budgets and tools but also a strong belief in AI’s economic worth. Without that belief, the technology struggles to move from short-term interest to long-term integration.

Manager attitudes become the bridge between value and action

Managerial attitudes form the emotional and cognitive evaluation that managers develop as they reflect on AI’s usefulness, its fairness, its risks and its ability to improve business processes.

The study demonstrates that attitude is a powerful bridge between economic value and intention. Even when price–value perceptions are strong, intention weakens if attitudes are negative or uncertain. Conversely, when attitudes are positive, intention strengthens, and long-term sustainable use becomes more likely.

This connection shows that sustainable AI adoption is not only about efficiency arguments. It requires emotional acceptance and a level of comfort with the changes AI brings. Managers who feel confident about AI are more inclined to integrate it responsibly. Those who feel uneasy about it hold back, even when they see economic benefits.

The study’s authors point out that attitudes are shaped by information, training, past experiences and exposure to technological benefits. This means that organizations must invest in educational programs that help managers understand AI beyond surface-level narratives. Building positive attitudes requires clarity about AI’s role, real examples of use and honest discussions about risks and limits. When those attitudes form, intentions become stronger and more stable.

The research also notes that attitudes shift over time, especially as managers observe AI in action. This dynamic means that companies must support ongoing development, not one-time training. If attitudes weaken, long-term sustainable use is at risk.

The findings reveal that the human factor is central. Technology alone cannot deliver sustainability. It must be paired with managerial acceptance that grows gradually and solidifies through experience.

Intention to use becomes the engine behind sustainable AI practice

According to the study, intention to use AI is the factor that directly drives sustainable use. When intentions are strong, companies maintain responsible AI practices, integrate the technology across workflows and continue investing in skills and systems. When intentions are weak, adoption becomes temporary, inconsistent or abandoned.

Sustainable AI use, as defined in the study, goes far beyond initial adoption. It includes maintaining the technology over time, ensuring responsible use, managing risks, updating systems and aligning AI outputs with long-term goals. The authors emphasize that sustainability requires continuity. It requires firms to see AI not as a quick tool but as a stable component of operations.

The research suggests that sustainable AI practice depends on a three-step chain: belief in value, positive attitude and strong intention. When all three are present, long-term use becomes highly likely. When any link is weak, sustainability declines.

The authors also highlight that in developing countries, where resources are often constrained, intention becomes even more critical. Managers must commit to long-term use even when financial and operational conditions are uncertain. This makes psychological readiness and economic belief even more important.

The study finds that managers with strong intentions are more proactive in seeking training, improving organizational processes and encouraging staff to adopt AI. They also become internal advocates who help build a culture of responsible and continuous use.

A unified framework for responsible AI adoption in business

The authors argue that their unified model is a significant contribution for both academics and practitioners. Prior studies focused on isolated variables, such as financial constraints, perceived usefulness or innovation readiness. This new research integrates these components into a single model that shows how they interact and evolve over time.

The work underscores that sustainable AI is both an economic and behavioral process. It is embedded in how managers think, feel and act. It requires stable internal conditions that combine rational calculation with positive emotion and strong commitment.

The study also provides valuable insights for policymakers. To encourage businesses to adopt AI responsibly, policy design must address perception, attitude and intention, not just financial incentives. Programs that push for AI adoption without strengthening managerial attitudes risk low sustainability and weak integration.

The authors note that Peru, like many developing countries, faces both high interest in AI and limited resources to support adoption. This makes the integrated model especially important for regions where managers must justify every investment. By demonstrating how economic value perception and attitudes shape long-term use, the study gives organizations in similar contexts a roadmap for building durable AI strategies.

For businesses exploring AI adoption, the study offers clear guidance:

• Managers must be convinced of AI’s long-term economic value.
• Organizations must build positive attitudes through structured training.
• Intention must be maintained over time through ongoing support.
• Responsible AI use requires continuity rather than one-time projects.
• Companies must see AI as a strategic asset, not a temporary experiment.

The results also show that sustainable AI use depends on internal alignment. If senior leadership sees AI as worthwhile but mid-level managers do not, the model breaks. If attitudes are strong but intention is not reinforced, long-term use weakens. This interdependence means companies must coordinate messaging, training and policy throughout the entire organization.