An exclusive BIX Tech study that decodes the maturity and practical application of AI agents in companies. Deep analyses and expert insights to guide the future of your technological operations.
How organizations are actually deploying autonomous agents in Brazil and in the international market. A strategic overview conducted by BIX Tech specialists, turning market data into essential insights for decision-makers.
of respondents
of respondents
of respondents
among those in production
of those in production
integration, talent, governance
Those in advanced stages have dedicated teams or responsible owners, review agents frequently and report measurable results. Adoption is not happening casually.
A 0.7-point gap (priority 4.1 vs. ROI 3.4) persists even after agents enter production.
Nearly half of the selected organizations (49%) have no defined budget for the next 12 months, even among those that rate the topic as a high strategic priority.
The challenge shifts from lack of clarity and talent at the start, to ROI uncertainty and integration at the pilot stage, reaching complex issues of governance, data quality and decision delegation in production.
The true driver of AI Agents in the selected ecosystem is operational expansion. Expanding current team capacity and creating new delivery fronts lead the objectives at 31% each, while direct headcount reduction attracts only 14% of mentions from the leaders consulted.
Who is behind this research
Founded in 2014 in Florianópolis, BIX Tech is a data and technology consultancy that operates end-to-end across the analytics journey. Major corporations trust BIX to lead their digital transformation, from billion-dollar giants to high-growth businesses.
BIX conducts this research from a position of privileged observation: over a decade accompanying organizations from the first practical experiments to production, partly as a consultancy that implements, partly as an organization that also adopts internally.
Offices in Florianópolis (Brazil) and Miami (United States). Serving clients across the Americas and Europe.
Learn about BIX →Data architecture, pipelines, governance, modeling and visualization for decision support.
Development of platforms, custom systems and business-applied AI solutions.
Deployment of autonomous agents from pilot to production, with governance, integration and measurable results.
Hybrid research combining structured online survey with strategic qualitative interviews. The goal is to produce exclusive data with analytical depth from a selected ecosystem of organizations with real involvement in technology and operations decisions.
Online form, BIX Tech base and BIX Tech referral partners
March to May 2026
Professionals with involvement in technology or operations decisions within organizations
Structured survey combined with strategic qualitative interviews
Technology and Software, followed by Industry and Financial Services. Organizations of up to 50 employees and mid-size (201–1,000) predominate.
* 35 qualified leaders consulted, with direct involvement in technology or operations decisions within their organizations.
To assess the real technological impact on operations, it is essential to differentiate concepts that the market often confuses, such as traditional automation, copilots and AI Agents. While automation executes predefined rules and copilots act as assistants dependent on human commands at every step, AI Agents receive an objective, plan and execute actions in systems autonomously.
Executes predefined rules without adaptation. If/then logic. No reasoning.
Workflow that follows the same path every time a trigger fires.
Respond to prompts, but depend on user initiative at every step.
Chatbot that answers HR questions when the employee asks.
Receives an objective, plans, executes actions in the real world and adjusts the plan as it encounters obstacles.
System that reads calendars, identifies conflicts, proposes solutions and reschedules meetings autonomously.
The minimum criterion for calling something an 'agent' is the presence of three elements: perception, reasoning, action.
The majority of respondents in the pilot stage are between levels 2 and 3. Few cases reach level 4. Level 5 is cited as an aspiration, not an operational reality.
The AI agent adoption landscape in Brazil and adjacent markets shows an ecosystem still taking shape, with clear signs of where real commitment is manifesting.
BIX Tech conducts this research from a position of privileged observation: over a decade accompanying organizations from the first practical experiments to production. Major corporations trust BIX to lead their digital transformation, from billion-dollar giants to high-growth businesses, partly as a consultancy that implements, partly as an organization that also adopts internally.
The goal is not to map intentions, but to understand where real commitment manifests, and where only discourse still exists.
of those in production. Real operational commitment, not casual use.
Correlates with low observed ROI.
Under BIX analytical curation, we observe that reference organizations in the Technology sector lead the practical shift to production. In Financial Services and Telecommunications, selected players focus efforts on pilot projects aimed at operations and compliance, while the industrial segment adopts more measured steps focused on engineering and data intelligence.
Our specialists' analysis indicates that the strategic market segment is clearly divided between qualified organizations that advance with structure (31% already in production) and those still planning their steps. Company size does not determine maturity, as lean companies also operate successfully, but real evolution happens when leadership combines clarity of objectives with a dedicated budget.
The use cases identified in the research can be organized along two axes: implementation complexity and expected business impact. This framework helps prioritize where to start and where to focus efforts as maturity advances.
Our market experience shows that first implementations prioritize internal areas such as Operations, Engineering, IT, Data and Analytics. The leaders consulted direct AI Agents toward well-defined processes where risks are perfectly manageable, leaving expansion to external channels (such as sales and customer service) for a later stage, after consolidating solid governance.
The true driver of AI Agents in the selected ecosystem is operational expansion. Expanding current team capacity and creating new delivery fronts lead the objectives at 31% each, while direct headcount reduction attracts only 14% of mentions from the leaders consulted.
in production
The convergence of these conditions guarantees a sustainable and growing return.
Support (FAQ, triage), report summarization, email classification, internal onboarding (HR), content generation.
Multi-agent orchestration, sales agent (SDR), financial risk monitoring, engineering: code review, continuous competitive analysis.
Document formatting, automatic scheduling, internal semantic search.
Regulatory automations without governance, legacy integrations without data quality.
In the early stages of study or pilot, observed returns tend to fluctuate due to expectations not yet confronted with practical reality. Our market experience indicates that qualified organizations that declare high strategic priority but do not define a dedicated budget face the highest frustration rates. In contrast, the presence of active executive sponsorship and a named project owner guarantees significantly more consistent results.
The main tension in the data is between declared strategic priority and effectively observed ROI. The gap does not disappear as maturity is gained; it changes its cause.
| Stage | Avg priority | Avg ROI | Gap | Reading |
|---|---|---|---|---|
| In production | 4.1 | 3.4 | +0.7 | Gap shifts to governance and data |
| Studying | 3.8 | 2.5 | +1.3 | Expectations still disconnected |
| Pilot / PoC | 3.1 | 3.0 | +0.1 | Moment of greatest alignment |
| Not using | 2.3 | 2.5 | -0.2 | No practical reference |
In the early stage, the gap is related to unrealistic expectations about speed. In production, it shifts to governance, data quality and difficulty in delegating decisions.
Several respondents mark "strategic priority" and "no defined budget" on the same line. A classic sign of organizational hype.
Expressive initial gain for simple tasks. As complexity increases, governance and human review consume part of the gain, stabilizing ROI.
BIX analysis indicates that challenges change as maturity advances, and treating scenarios linearly is a strategic mistake. The 17% three-way tie at the top between system integration, lack of talent and governance reflects exactly this dynamic in the selected ecosystem. This balance signals that the concerns of the leaders consulted are diversifying as the market gains operational maturity.
System integration System integration with existing systems is concentrated among those in production or pilot. The obstacle is both technical (legacy systems rarely expose clean APIs) and organizational (integration becomes the task no one wants to take on).
Lack of technical talent Lack of technical talent is an entry barrier: the problem is not a shortage of engineers in general, but of professionals who combine LLM understanding, system integration and business vision.
Security, governance and data quality appear at all stages. Delaying data care only postpones the cost.
Cultural resistance is rarely recognized directly because it usually manifests as another problem.
Based on the patterns observed in the sample, organizations can be positioned at five distinct maturity levels. These levels do not describe only the technological stage, but the combination of four dimensions: governance, data quality, team structure and agent autonomy. An organization's real level is the lowest point across its four dimensions, not the average.
Exploration without commitment. The organization recognizes the relevance but has not yet committed resources or named a responsible owner.
Isolated pilots in controlled contexts, without integration with critical processes. There is technical learning, but no formal owner.
Agents connected to real systems, with a named owner and periodic review. First level at which the organization generates measurable value.
Multiple coordinated agents, with formal governance, performance metrics and a dedicated team. Usage begins to influence operational decisions.
Agents integrated into strategy, with critical decisions mediated by agents. Center of excellence and institutionalized AI governance.
The few organizations approaching this level report that the differentiator is not technological.
It is cultural: treating agents as decision infrastructure, not automation tools.
The concentration at levels 2 and 4 reflects a polarized market: on one side, organizations still exploring without formal commitment; on the other, those that have already decided and are in production. Level 3, Integrator, represents the most difficult transition, when the pilot needs to become real operations. This is where most projects stall.
The transition from level 2 to level 3 requires a named owner, integration with real systems and KPI definition before going live. Assuming the pilot became production just because part of the team is using it is the most common mistake in this transition.
The five organizational maturity levels mirror the five agent autonomy levels. It is not possible to operate level 4 agents with level 1 governance. Advancing in one dimension without the others tends to generate fragile projects.
To enable objective comparisons between organizations, each level has binary criteria. The presence or absence of each criterion objectively defines the minimum level reached.
| Criteria | Observer | Explorer | Integrator | Orchestrator | Str. Operator |
|---|---|---|---|---|---|
| Dedicated budget | — | — | — | ✓ | ✓ |
| Named formal owner | — | — | ✓ | ✓ | ✓ |
| Agents access critical systems | — | — | ✓ | ✓ | ✓ |
| KPIs and performance metrics | — | — | ✓ | ✓ | ✓ |
| Formal AI governance policy | — | — | — | ✓ | ✓ |
| Agents in continuous production | — | — | ✓ | ✓ | ✓ |
| AI influences operational decisions | — | — | partial | ✓ | ✓ |
| AI influences strategic decisions | — | — | — | partial | ✓ |
Financial Services leads with an average level of 3.3. The Tech sector does not lead, suggesting that the advantage in technology access does not automatically translate into operational maturity with agents.
The most expressive difference in the sample. It is not just a matter of resources, but of organizational signaling about real priority.
Choose a concrete, small operational problem to serve as the first pilot. The selection criterion is: process with available data, identifiable owner and measurable impact within 90 days.
Starting with the most ambitious problem. Large projects at level 1 tend to run into integration, talent and ROI challenges simultaneously.
Name a formal owner for the agent before leaving the pilot. This owner does not need to be a technical expert, but must have authority to make decisions about the agent.
Assuming the pilot "became production" just because some people are using it. Real production requires SLA, monitoring and error escalation processes.
Create a minimum documentation standard for each agent in production: what is the objective, which systems it accesses, what decisions it can make, when to escalate to a human.
Trying to create a center of excellence before having an established standard. Structure without process tends to generate bureaucracy without results.
Invest in observability: tools and processes that enable output auditing, decision tracking and proactive identification of performance degradation.
The transition to level 5 involves explicitly connecting agent outputs to strategic decisions with clear human oversight.
The data is clear: the market is accelerating, not waiting. Organizations that have formalized commitment are expanding scope, increasing agent autonomy and building competitive advantage. Those still postponing are not just standing still, they are falling behind. The next 12 to 24 months will be decisive in defining who leads and who tries to catch up.
of respondents intend to increase, with 11% intending a significant increase
includes organizations that declare high strategic priority
Retraction is not the risk. Indefinition is.
The BIX Maturity Framework was built from the patterns of this research to serve as a self-assessment tool and market reference. Talk to BIX specialists.