Subvolts take Deploying AI agents in business requires more than technology; it demands a focus on outcomes and integration. Apr 15, 2026 - Developers
- Understand the challenges and strategies for deploying AI agents in complex enterprise environments.
- Business
- Learn why focusing on business outcomes and integrating AI with existing systems is crucial for successful AI adoption.
This interview provides a practical, grounded perspective on enterprise AI deployment challenges and solutions from an industry leader.
#333 Adi Kuruganti: Why Your AI Pilot Is Failing and What It Takes to Reach Production
Audio streams from Craig S. Smith. Your place is remembered on this device.
From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Craig Smith interviews Adi Kuruganti on Eye on AI about deploying agentic AI in enterprise production. Kuruganti explains why combining deterministic and agentic systems is key, and how companies focusing on business outcomes, not just technology, succeed. The discussion covers orchestrating agents, governance, compliance, and improving agent performance with Automation Anywhere's Process Reasoning Engine.
This episode explains why smart computer programs, called AI agents, are hard for businesses to use in real work. It shows how combining these agents with older computer systems helps them work better and achieve business goals, not just solve tech problems.
This episode offers a grounded view of enterprise AI deployment challenges. It's for business leaders and IT professionals struggling with AI pilots that don't reach production. Kuruganti's insights on combining AI types and focusing on business outcomes resonate with current discussions around making AI practical and valuable, a goal shared by frontier labs like Google Gemini and OpenAI as they push for real-world applications.
- HostCraig S. Smith
- GuestAdi KurugantiChief AI and Development Officer, Automation Anywhere
- Episode Title: #333 Adi Kuruganti: Why Your AI Pilot Is Failing and What It Takes to Reach Production
- Show: Eye on AI
- Guest: Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere
- Length: 58 minutes
- Process Reasoning Engine Improvement: Continuously improving agent performance using metadata from over 400 million running processes.
Most enterprises are excited about agentic AI. But very few are actually deploying it in production.
In this episode of Eye on AI, Craig Smith sits down with Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, to break down why agentic AI is so hard to get right in the enterprise and what it actually takes to move from a promising pilot to a mission-critical deployment.
Adi explains why the future of enterprise automation is not agentic AI alone, but the combination of deterministic and agentic systems working together, and why companies that treat AI as a technology problem instead of a business outcomes problem are setting themselves up to fail.
They dig into how Automation Anywhere is orchestrating agents across legacy systems, healthcare platforms, and financial services workflows, why governance and compliance are the first questions every enterprise asks, and how their Process Reasoning Engine is continuously improving agent performance using metadata from over 400 million running processes.
The conversation also covers the real timeline to a fully autonomous enterprise, why the POC to production gap is the biggest failure point in enterprise AI today, and what companies that wait too long risk losing to competitors who started the journey earlier.
If you want to understand where enterprise AI actually stands today and what it takes to deploy it responsibly at scale, this episode gives you a clear and grounded perspective.
Subscribe for more conversations with the people building the future of AI and emerging technology.
Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI
(00:00) Why Enterprises Are Struggling With Agentic AI
(02:39) What Automation Anywhere Does and the APA Category Explained
(08:01) Deterministic vs Agentic AI: Why You Need Both
(10:59) How Human in the Loop Works in Enterprise AI
(17:16) The Mozart Orchestrator and Process Reasoning Engine
(23:50) How AI Is Upgrading and Replacing Classic RPA
(27:31) How Automation Anywhere Works With Enterprise Customers
(31:53) The Biggest Challenges of Scaling Agentic AI
(41:10) The OpenAI Partnership and What It Means
(47:06) Training Staff and Building AI Literacy at Scale
(51:39) Staying Close to Customers as the Technology Shifts
(53:17) Is the Autonomous Enterprise Actually Coming
- agentic AI
- Adi Kuruganti
- AI systems that can act independently to achieve goals.
- deterministic systems
- Adi Kuruganti
- Computer systems that follow exact, predictable steps.
- POC
- Adi Kuruganti
- Proof of Concept, a small test to show an idea works.
55 min42 min51 min34 minUkraine's Secret Weapon: The Points System Winning the Drone War | Andrii Hrytseniuk
WithAndrii Hrytseniuk
Questions people ask
- Why do most AI agent pilots fail to reach production?
- Companies often treat AI as a technology problem instead of a business outcomes problem, and fail to integrate deterministic and agentic systems effectively.
- How can companies improve agent performance?
- Automation Anywhere uses its Process Reasoning Engine, which improves agent performance by analyzing metadata from over 400 million running processes.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.