Dominic Pereira, VP Product Management At Automation Anywhere On Scaling Agentic AI, Governance-First Automation, And System Thinking In Enterprise AI

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge speaks with Dominic Pereira, Vice President of Product Management at Automation Anywhere, about how enterprise automation is evolving with the rise of generative and agentic AI.

With over two decades of experience across Business Process Management (BPM), Governance Risk and Compliance (GRC), and intelligent automation platforms, Dominic has witnessed multiple waves of enterprise technology transformation. From early SaaS platforms that automated structured workflows to today’s AI-driven orchestration systems, his career mirrors the broader evolution of enterprise software. In this conversation, he shares how those experiences shaped his thinking around building scalable platforms and why technology must ultimately focus on measurable business outcomes rather than simply delivering new features.
 

From BPM Foundations To Enterprise Automation

Dominic’s journey began with BPM platforms, where organizations were first learning how to digitize and automate structured processes. These systems introduced enterprises to workflow automation, but they were largely deterministic and rule-based. Over time, Dominic moved into governance and risk platforms, which brought him closer to business users and operational realities.

That shift fundamentally changed how he approached product innovation. Instead of focusing purely on building technical capabilities, Dominic began prioritizing business outcomes and operational impact. This perspective continues to shape how he approaches modern automation platforms today.
 

The Evolution From Automation to Agentic AI

One of the key themes of the conversation is the shift from traditional automation to agentic AI systems.

Historically, automation relied on predefined rules and predictable workflows. These systems were powerful for repetitive tasks but struggled with processes that required judgment, context, or decision-making.

The introduction of generative AI has significantly expanded what automation can achieve. Organizations that previously automated around 30–40 percent of processes can now automate significantly more by incorporating reasoning capabilities into their systems. AI agents can interpret information, evaluate context, and execute more complex sequences of actions across enterprise platforms.

However, this new capability also introduces new complexity. Unlike deterministic systems, agentic AI systems can adapt and reason, which means their outputs may vary depending on the situation. This creates challenges around governance, accountability, and trust that enterprises must carefully address.
 

Moving From AI Experiments To Enterprise Deployment

Many organizations today are experimenting with generative AI through proof-of-concept initiatives. While these experiments often demonstrate impressive capabilities, scaling them into production systems is significantly more difficult.

Dominic highlights three foundational principles for operationalizing AI at enterprise scale:

First, organizations must shift from task-level automation to outcome-driven architecture. Rather than automating isolated activities, companies should design systems that transform entire business processes.

Second, human judgment must remain part of the system architecture. Human-in-the-loop design ensures that complex workflows still benefit from human oversight, especially when handling exceptions, approvals, or sensitive decisions.

Third, governance must be embedded from the beginning. Instead of slowing innovation, governance frameworks actually accelerate enterprise adoption by building trust and reducing risk.
 

Empowering Citizen Developers With Guardrails

Another important trend discussed in the episode is the rise of citizen development. Low-code automation platforms are enabling business users and process owners to build automation solutions without deep programming expertise.

This democratization of development allows organizations to scale innovation more quickly. However, it also introduces risks such as inconsistent design, technical debt, or security vulnerabilities.

Dominic explains that successful enterprises combine accessibility with strong guardrails. Governance frameworks, role-based access controls, reusable automation components, and observability tools help ensure that citizen developers can innovate safely while maintaining enterprise-grade standards.
 

Leadership In The Age Of AI

Beyond technology, the conversation also explores how leadership must evolve in the AI era. Dominic believes that product leaders must move beyond feature-focused thinking and adopt a system-level mindset.

Modern AI systems interact with multiple data sources, enterprise platforms, and human workflows. This means leaders must think about second-order and third-order effects when designing products. Decision-making also becomes more complex because AI systems often operate in uncertain environments where outcomes cannot always be predicted in advance.

As a result, product leaders must balance innovation with risk awareness and collaborate more closely with legal, security, and privacy teams to ensure responsible AI deployment.
 

About Dominic Pereira

Dominic Pereira is the Vice President of Product Management at Automation Anywhere, with over 20 years of experience building and scaling enterprise B2B SaaS platforms. His career spans Business Process Management, Governance Risk and Compliance, Intelligent Automation, and Generative AI–driven enterprise systems. Dominic currently leads initiatives focused on agentic process orchestration, enabling organizations to automate complex, mission-critical workflows using AI agents, APIs, and low-code platforms. Known for his governance-first and outcome-driven approach, he focuses on helping enterprises scale automation responsibly while delivering measurable business value.