Set in the heart of Miami’s Brickell district at the Hyatt Regency, the event buzzed with its usual innovation but this time a new sense of urgency gripped attendees. Three dominant themes emerged across the dynamic sessions:
Skan AI hosted the session, “From Human Agents to AI Agents: The Next Leap in Automation,” featuring Vinay Mummigatti, EVP for Skan AI, which we’re proud to say was completely full.
And the reason was clear: We cut through the AI hype and laid out what’s actually happening.
The rise of agentic AI is poised to surpass even the impressive advancements of generative AI, driving enterprises to explore how this technology can revolutionize their operations. Unlike traditional AI models that rely on explicit prompting, agentic AI operates with autonomy, making independent decisions and optimizing workflows dynamically. Gartner predicts that in just a few years, one-third of enterprise software will rely on agentic AI and these same agents will make up to 15% of daily work decisions.
But the true potential of agentic AI can only be realized through the lens of process intelligence. Without a granular understanding of operational processes, AI-driven decision-making may lead to inefficiencies rather than improvements.
Throughout the OPEX event, attendees kept coming to our team and echoing the same struggles:
The harsh reality? Enterprises are moving at hyper-speed, expecting to deliver exponential results—while stuck with broken processes and no real roadmap to fix them.
This is why Skan AI exists.
Our founders saw transformation projects fail time and again because AI and automation were treated as solutions, rather than enablers of better decision making.
Many OPEX attendees had questions about the big promises of agentic AI. AI has always promised automation, but agentic AI is about role augmentation and autonomous decision making. While Large Language Models (LLMs) have transformed knowledge work, the real leap forward lies in Large Action Models (LAMs)—AI that learns, executes, and optimizes workflows like human operators.
These models absorb vast amounts of process data, allowing them to not only perform tasks but also adapt based on experience—mirroring the way skilled workers improve efficiency over time. By embedding LAMs into agentic AI systems, businesses can achieve automation that is both highly intelligent and continuously evolving.
Here’s what we laid out for our packed audience:
Skan AI’s Process Intelligence platform bridges the gap. By observing work across applications, capturing decision points, and mapping workflows, we create a Digital Twin of Operations—essential for training LAMs.
Enterprise adoption is still in early stages. Organizations face challenges in data quality, AI orchestration, governance, and compliance, but the shift is inevitable and likely to make significant impacts in 2025.
As we shared with our OPEX audience, the key to unlocking agentic AI’s full potential lies in comprehensive process observation. By meticulously capturing every worker’s activities—whether manual or automated—organizations can create AI systems that understand context, recognize inefficiencies, and proactively recommend optimizations. This level of granular visibility ensures that AI-driven changes enhance productivity rather than introducing new constraints.
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7 Steps to Implementing Effective Process Intelligence |
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1. Define Clear Objectives |
Align AI initiatives with strategic business goals. |
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2. Secure Leadership Buy-in |
Gain executive support for data-driven decision making. |
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3. Prioritize Change Management |
Facilitate smooth adoption through stakeholder engagement. |
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4. Ensure Data Accuracy |
Clean and structured data for reliable AI analysis. |
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5. Emphasize Continuous Improvement |
Treat process intelligence as an ongoing initiative. |
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6. Align with Business Needs |
Ensure AI implementation supports overall company strategy. |
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7. Leverage Digital Accelerators |
Use automation and AI tools to enhance process intelligence efforts. |
Another huge topic at OPEX was digital twins of operations, virtual replicas of real-world processes, which play a pivotal role in refining agentic AI implementations. By continuously capturing and analyzing real-time operational data, digital twins enable organizations to simulate potential changes before deploying them in production environments. This iterative approach enhances decision making accuracy and reduces the risk of unintended consequences.
For instance, a digital twin of a manufacturing process can predict how agentic AI-driven optimizations will impact throughput, helping leaders make data-backed decisions. When combined with process intelligence, digital twins provide a comprehensive framework for continuous improvement and AI-driven automation.
The operational leaders we spoke with weren’t just looking for efficiency; they were searching for relief.
The pressure is immense. We’re now expected to fix broken workflows, increase productivity, and deliver more with less.
We actually met some real-life examples of this at OPEX:
This is the true cost of broken processes. Not just lost revenue, but a workforce stretched thin, frustrated, and unable to perform at their best.
The weight of expectations, the pressure to deliver, and the sheer exhaustion of fighting against outdated systems.
AI isn’t only about automation. It’s about empowering people to focus on work that matters.
With process intelligence, enterprises can finally see what’s been hidden, fix inefficiencies at scale, and create workplaces where employees can thrive. Because at the end of the day, it’s not just about operational success, it’s about giving people back the ability to do fulfilling, impactful work.
The most resonant moment at our OPEX session? When we mapped out a realistic, actionable timeline for AI agents in enterprise. Savvy organizations aren’t debating if AI agents are coming; they’re strategizing how fast they can transition. The social and operational impact is massive, and it starts with being properly equipped—leveraging process intelligence to bridge the gap between human-driven operations and AI-led execution.
Agentic AI is revolutionizing the enterprise landscape, but its success hinges on a robust foundation of process intelligence, digital twins, and human action modeling. By capturing real-world workflows and leveraging large action models, organizations can create AI systems that not only automate tasks but also enhance efficiency through continuous learning. As businesses navigate the future of AI-driven operations, investing in process intelligence will be the key to unlocking sustainable, intelligent automation at scale.
The conversation has shifted from AI’s potential to AI’s inevitability. The era of task-based automation is fading. The future belongs to AI agents that don’t just assist humans but think, reason, and act like them.
But the enterprises that thrive in this shift? Look for those that ensure they understand their processes, because that’s the only way AI agents can actually work.
Want to know more about how process intelligence can help you leverage agentic AI to its fullest potential? Let’s talk.