Executive Summary: ISG Provider Lens® Generative AI Services - Brazil 2026
11 Nov 2026
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What sets GenAI apart is its ability to turn intelligence into value, with scale, governance and adoption
This ISG Provider Lens® study evaluates providers capable of supporting Brazilian enterprises throughout their generative AI (GenAI) adoption journey. The evaluation ranges from strategy and consulting services to development and implementation, examining how providers help clients identify opportunities, establish governance models and transform use cases into secure solutions integrated with organizational processes.
Market Context
According to data from the International Labor Organization, Brazil ranks only 94th among the world’s most productive countries, out of 184 nations. GenAI can play a central role in boosting productivity and contributing to economic growth. According to a recent study by Reglab (Center for Strategy and Regulation), AI could add nearly R$1 trillion to the Brazilian economy, boosting GDP by 2.77 percent by 2030. The largest portion of the impact on GDP— 65 percent —would come from increased worker productivity.
Boosting workplace productivity
Data released by ChatGPT shows that in June 2026, 35 percent of accounts classified as individual accounts on ChatGPT in Brazil were work-related, compared with the global average of 30 percent. Among the requests, 53 percent involved performing a task or producing an outcome, such as drafting proposals, analyzing information, writing code or developing marketing materials.
Infrastructure becomes a strategic factor
The expansion of the AI market depends on the availability of computing power, connectivity, cloud environments and reliable data. The development of models adapted to the Portuguese language and the national context is also gaining importance to broaden the applicability and accuracy of solutions.
Governance as a strategic priority
GenAI introduces new categories of risk; data breaches, concerns about model reliability, loss of control over intellectual property, unclear liability and contractual obligations that do not reflect AI-based delivery models are just some of the emerging risks that concern the Brazilian market. Furthermore, the consolidation of regulatory guidelines is expected to influence the development and responsible adoption of AI in the coming years.
Enterprise Priorities
With the advancement of generative and agentbased AI, Brazilian companies are focusing their efforts on value creation, technological evolution, cost control, team training and the integration of AI into business processes.
ROI-driven GenAI strategy and value realization
After a phase of experimentation, clients are less willing to fund PoC projects and are now demanding returns on their AI investments. Metrics related to access and availability, such as the number of deployments and pilot projects, are being replaced by results-oriented metrics that demonstrate contributions to business KPIs. ISG’s 2026 AI Value survey reveals the key results expected by executives: 59 percent believe AI will increase team productivity, 50 percent expect to reduce costs and 59 percent expect to increase revenue.
Data requirements
Realizing value with AI still faces some challenges, chief among them being datarelated issues. For 37 percent of executives, the quality, availability and accessibility of data are among the most common obstacles. Without a consistent data foundation, AI tends to generate only marginal gains and remain limited, failing to scale to production as desired.
Accelerating technological modernization
Advances in AI have led more companies to pursue technological modernization, including cloud migration, GPU-enabled infrastructure and centralized data platforms, such as data lakes, data warehouses and vector databases, to support LLMs and RAG. There is also a trend toward consolidating or decommissioning legacy SaaS solutions and monolithic systems, incorporating AI as a new layer on top of CRMs, ERPs and analytics tools, rather than completely replacing core systems.
Cost control and efficiency with LLMOps
Companies are prioritizing cost control as GenAI solutions move into production and increase token consumption and demand for infrastructure. To achieve greater predictability, they require LLMOps practices that enable them to monitor usage, performance and quality, set limits and optimize model selection. The goal is to scale applications efficiently, balancing cost, performance and value generated.
The human factor is crucial for the adoption of agentic AI
The difficulty of providing large-scale training, resistance from leadership and skill disparities among employees slow the expansion of these initiatives. Overcoming these barriers requires ongoing programs tailored to different professional and profiles, as well as active leadership involvement, clear communication about the technology’s impacts and mechanisms that encourage its use.
Provider Dynamics
Consultative approach
Brazilian providers seek to go beyond technical capabilities to guide their clients in achieving a return on their AI investments. This requires combining industry knowledge, business acumen and consulting skills to understand business needs, prioritize use cases, evaluate costs and benefits, and define performance metrics.
Simplifying AI adoption
Leading providers must be able to support clients in assessing their existing architecture and defining a phased modernization roadmap for AI adoption. This includes integrating data and systems, streamlining applications and establishing controlled testing environments, while incorporating security and governance from the outset.
Use of reusable components
The development of accelerators and proprietary assets, such as APIs for enterprise systems, agent libraries and governance components, enables vendors to reuse capabilities across different projects, reducing development and integration effort. These resources incorporate knowledge and experience into solutions that can be tailored to each client’s needs, helping shorten deployment timelines and increase the predictability of deliveries.
Data preparation and governance for AI
Providers capable of integrating, organizing and governing data help clients overcome the fragmentation of information across departments and systems, a barrier to AI adoption. This involves modernizing architectures, connecting platforms and improving data quality, thereby creating a foundation that enables AI models and agents to access reliable, contextualized information with the necessary controls.
Managing the human dimension of AI transformation
The best-prepared vendors are integrating change management into their AI offerings, supporting clients in preparing leadership, training teams and reviewing roles and responsibilities. This support should include clear communication about the technology’s impact on work, listening to employees’ concerns and involving business units in the redesign of work activities. Addressing fears about job replacement, loss of autonomy and new skill requirements is an essential part of this process.
To this end, providers can combine technical expertise with skills in people development and organizational transformation. These partners structure training programs by role, guide collaboration between people and AI agents and help define supervisory and decisionmaking responsibilities. Support must go beyond simply providing tools; it must assess adoption, user confidence and challenges in day-to-day use to fine-tune implementation and sustain new ways of working.
Process knowledge for applying AI to business
Providers need to understand their clients’ processes to identify where AI can generate value and what changes are necessary to incorporate it into operations. This requires knowledge of workflows, business rules, exceptions and dependencies between departments and systems, taking into account the specific characteristics of each industry. This understanding allows for an assessment of which activities can be automated, which benefit from AI support and which require human judgment.
Overview
In the coming months, the Brazilian market is expected to move toward the industrialization of GenAI and agent-based AI. Companies will tend to replace isolated initiatives with platforms and models that enable the continuous development, integration, monitoring and improvement of AI solutions. As a result, demand is expected to increase for LLMOps, governance, security, observability and the lifecycle management of models and agents.
The focus will also shift from individual productivity gains to the transformation of business processes. AI agents will begin to perform multi-step activities, interact with corporate systems, consult different data sources and support decision-making. Initially, adoption is expected to focus on repetitive tasks with measurable results, while maintaining human oversight for critical decisions or those subject to regulatory requirements.
However, adoption is not expected to occur uniformly. Larger, more heavily regulated companies will tend to implement specialized agents in controlled environments, while midsize organizations may accelerate their use of pre-configured solutions and managed services offered by cloud providers and partners. For both, value will no longer be measured solely by the capabilities of the models but will instead be demonstrated through KPIs such as cost reduction, execution time, quality, revenue growth and improvements in the customer and EX.
Providers must set themselves apart through a combination of process expertise, data engineering, integration, governance and change management. In addition to implementing models, they will need to redesign workflows, prepare teams for new forms of collaboration between people and agents, and operate these environments securely and within budget.
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