Artificial Intelligence (AI) and Generative AI (GenAI) have emerged as transformative technologies in the business scenario, promising to revolutionize processes, create new business models and drive innovation. However, amid the enthusiasm, many organizations face a fundamental obstacle: the difficulty of translating the potential of these technologies into practical and tangible results. This “value mismatch” is not limited to solution providers, but affects managers, innovation teams and project leaders responsible for integrating AI into their organizational strategies.

The problem arises when costs, prices and value are not properly aligned. This disconnect results in underutilized projects, perceptions of failure, and a lack of return on investment. To overcome it, it is essential to adopt an integrated and systemic vision, which connects technological innovation, business models and strategic goals.

Costs and technological efficiency

Reducing costs has always been a crucial goal in technological projects, but in the context of AI, technological efficiency goes beyond reducing expenses: it is about managing computational resources and maximizing results through strategic choices. 

One example is the orchestration of foundational models, GenAI's Foundation Models. These models, the large language models, require intelligent use of infrastructure and optimizations, which can be achieved by implementing strategies such as combining different models for specific tasks and comparative analysis of price versus performance. Such approaches help avoid waste and ensure that technology delivers meaningful results without compromising budgets.

Still in the field of efficiency, innovative architectures play a crucial role. The adoption of practices such as “LLMOps”, which organizes the operation of GenAI models into functional blocks, allows for a more rational use of resources. 

This approach facilitates modular implementation, promoting scalability and flexibility to meet changing demands. For example, by dividing the operation into more consistent components, such as accessing corporate data or vector databases, and formatting responses and using templates, it is possible to allocate resources more intelligently, optimizing both performance and costs.

Prices and business models

Pricing is another essential element in overcoming the value mismatch. Pricing strategies that consider value perception and innovative business models are fundamental. 

An interesting example is the B2B2C (business-to-business-to-consumer) model, which can be adopted by both fintechs and solution providers. In this model, suppliers are not limited to selling directly to companies; instead, its solutions are integrated into client companies' offerings, creating a direct channel with the end consumer. This reduces barriers to entry, increases accessibility and expands the reach of solutions, while reinforcing the value perceived by customers.

By adopting this strategy, it is possible to align prices with the results delivered, strengthening the link between cost and benefit. Additionally, consumption models based on usage or performance create more flexibility and reinforce the feeling that the customer is paying in proportion to the value received.

Value, design and collaborative work

The concept of value goes beyond financial impact. It is directly linked to the ability of technological solutions to transform organizational realities and solve problems effectively. To achieve this objective, it is essential to invest in design and innovation activities, promoting the conception of solutions that connect technological capabilities to business needs. This step should be focused on deeply understanding the challenges faced by organizations and translating them into practical solutions.

At the same time, the success of these initiatives depends on collaborative work in multidisciplinary teams. The combination of technical and business skills is essential to integrate technology into organizational strategies. Professionals with AI expertise need to work side by side with experts in specific sectors to ensure that solutions meet the nuances of each market, while agile methodologies and constant feedback allow for continuous adjustments and improvements.

The path to overcoming the value mismatch

Overcoming the value mismatch requires a comprehensive and integrated approach that connects the technical and strategic aspects of AI projects. Costs must be managed intelligently, using practices such as model orchestration and optimized architectures. Pricing strategies and business models need to reflect market reality, as exemplified by the effectiveness of the B2B2C model.

Most importantly, value creation lies at the intersection of innovative design and multidisciplinary collaboration. Only solutions that are designed based on real needs, and implemented by teams with complementary perspectives, have the potential to deliver results that transform organizations.

By treating AI as a strategic tool, not just a technical resource, managers and innovation leaders can unlock its true potential. The connection between technology and strategy is what defines success – and this connection is built through conscious choices, systemic planning and collaborative execution.

The AI ​​technological revolution is not just a promise; it is already happening. It is up to each organization to shape its role in this future, transforming the mismatch in value into a lever for innovation, competitiveness and sustainable impact.