Rodrigo Paiva, VP of U.S. Sales at Pipefy, discusses AI orchestration, AI agents, low-code platforms, governance, security, and how enterprises can achieve measurable ROI from AI-driven automation.
Rodrigo Paiva, your career spans over two decades across global technology leaders. How have these experiences shaped your perspective on AI orchestration and its role in modern enterprises?
My career has been a journey through the “geologic layers” of the technology landscape—from the foundational rigidity of Mainframes and Client-Server architectures during my time at Oracle and Microsoft, to the strategic insights of Gartner, and finally into the frontiers of Big Data and AI with H2O.ai and Pipefy. This 25-year trajectory has taught me that innovation requires the flexibility to adapt to each seismic shift. Historically, enterprises always had to make difficult trade-offs: choosing between the extreme reliability of mainframes versus the cost-efficiency of client-servers, or the safety of on-premise systems versus the rapid benefits of the cloud.
For the first time in history, AI orchestration eliminates these trade-offs. It acts as the “connective tissue” that allows legacy enterprise power to operate at the speed of generative intelligence, turning fragmented systems into a unified, high-performance engine. Today, clients no longer have to choose between productivity and safety; they can capture all the benefits AI brings—massive ROI, rapid time-to-value, and high accuracy—while staying completely secure with the right IT safeguards, governance, and human-in-the-loop checkpoints.
AI orchestration is gaining attention across industries. How do you define it in practical business terms for enterprise leaders evaluating its relevance today?
For enterprise leaders, AI orchestration is best understood not as just another automation tool, but as a centralized control plane that unifies human workers, legacy systems, and AI agents into a governed workflow. In business terms, that orchestration layer sits on top of your rigid foundational systems—like ERPs—allowing the business to innovate and deploy AI rapidly without destabilizing the core architecture.
Many organizations still operate in siloed systems. What challenges do enterprises face when attempting to orchestrate AI across disconnected processes and platforms?
Deploying AI within these siloed departments without centralized IT oversight introduces the risk of Shadow AI and Ghost AI. You can have Marketing data in Excel, Sales data in a CRM and Finance data in an ERP, if these systems do not communicate, it results in a lack of visibility that forces leadership to fly blind and make critical business decisions based on incomplete data. Today, IT leaders no longer just fear data leaks, their nightmare is unsupervised AI agents executing unintended or harmful actions on behalf of the company.
From your experience at Pipefy, what distinguishes effective AI orchestration strategies from those that fail to deliver meaningful outcomes?
We see a lot of failing strategies that remain focused on piloting basic generative AI solely to improve internal employee productivity, never advancing to tangible ROI. Strategies stall when organizations aggressively deploy technology without simultaneously investing in the necessary skills, operating models, and training required for human-AI collaboration.
How are enterprise expectations evolving when it comes to measurable ROI from AI-driven process orchestration?
We’re seeing a shift in strategic approach for enterprise AI that has moved from simply adopting generative AI for employee productivity to aggressively increasing their funding to extend and scale agentic AI, demanding that these solutions deliver tangible financial impact. The expectation now is AI results in days, not months. Organizations are seeking platforms like Pipefy that can deploy enterprise-grade, complex use cases in two weeks —a big contrast to the 4 to 6 months period that we see traditional vendors requiring.
Low-code platforms are often positioned as enablers of faster transformation. How do they intersect with AI orchestration in driving scalability and operational efficiency?
They’re driving scalability and operational efficiency by solving the tension between the business speed for development and IT control over tools and solutions. Low-code platforms and AI orchestration represent a profound shift in enterprise architecture, merging into what is being called as the Business Orchestration and Automation Technologies (BOAT). Low-code is providing the speed and accessibility, while AI orchestration provides the intelligence, integration, and security framework.
Security and governance remain top concerns. How should organizations approach risk management while implementing AI orchestration at scale?
The implementation of AI orchestration at scale demands that organizations treat security as an embedded aspect of the framework. The threat landscape has now evolved from data leakage to unsupervised AI agents executing unintended, harmful actions on behalf of the company. It is important to separate governance from execution, to establish railguards and safe zones for AI and to guarantee human-in-the-loop checkpoints.
What role do AI agents play in reshaping workflow automation, and how should enterprises think about integrating them into existing operations?
AI agents are shifting workflow automation from rigid, rule-based task execution to autonomous softwares capable of perceiving environments, making decisions, and achieving complex goals. Today, enterprise IT no longer just worries about building automation, but about safely managing the continuous change across human workforce, legacy systems and autonomous agents.
For organizations at an early stage of adoption, what foundational steps are essential to ensure a successful AI orchestration journey?
Going straight into deploying autonomous tools without the right foundation is not the right path for organizations at an early adoption stage. Without the proper steps, it can lead to architectural chaos and security risks. Effective orchestration is less about the model and more about the infrastructure that surrounds it. IT leaders must establish the proper governance, architecture, and human strategies first, like centralizing your model access, prioritizing data quality and evaluating the performance objectively whenever you change a prompt or model.
Looking ahead, how do you see AI orchestration evolving over the next few years, and what should enterprise leaders prioritize to stay competitive?
The evolution of AI orchestration is moving rapidly from an era of fragmented experimentation into a moment that will fundamentally restructure the competitive dynamics for the enterprises. We see that the market is already undergoing a correction where hype is giving way to consolidation of tools and solutions that can guarantee enterprise-grade scaling. By 2030, 60% of organizations will adopt AI agent orchestration platforms like Pipefy, and these platforms will increasingly support organizations to adopt, scale and manage multi-behavior AI agents across existing disconnected systems.
A quote or advice from the author
Don’t choose between AI productivity’s benefits over IT safety or vice versa. Now you can have both.
To achieve maximum ROI without compromising security, enterprises must move beyond fragmented AI adoption and embrace AI orchestration as their central control plane.
This architecture allows you to capture the massive productivity gains of generative AI—slashing time-to-value from months to weeks—while simultaneously eliminating the risks of “ghost AI” or unsupervised autonomous agents.
Orchestration ensures that you never have to choose between speed and safety; it provides the “connective tissue” that embeds IT-approved safeguards and human-in-the-loop checkpoints directly into every automated workflow. By unifying your human workforce, legacy systems, and AI models under one governed framework, you gain the ability to scale intelligence across the enterprise with the total confidence that every action remains transparent, compliant, and under human control.
Rodrigo Paiva
VP of U.S. Sales, Pipefy
Rodrigo Paiva is Vice President of U.S. Sales at Pipefy, a global leader in AI-driven business process orchestration, where he leads go-to-market strategy and revenue growth initiatives for AI-driven solutions. He brings more than 25 years of global sales leadership experience across the United States, Latin America, and international markets, with deep expertise in B2B sales management. Prior to joining Pipefy, Paiva held senior sales leadership roles at leading technology organizations, including Microsoft, Oracle, LTMindtree, H2O.ai, and Gartner.