How Governance and Data Readiness Power the Next AI Revolution
177The first day of the AI & Big Data Expo and Intelligent Automation Conference showcased a clear focus on how organizations can prepare for the rise of agentic AI systems. While many talked about AI acting as a digital co-worker, the real emphasis was on building the infrastructure to support these advanced tools. The conversation centered on moving from simple automation to intelligent, reasoning systems that can plan and execute tasks independently.
From Automation to Agentic AI
Attendees heard how enterprises are transitioning from traditional robotic process automation (RPA) to more sophisticated, agentic systems. These new tools are capable of reasoning, planning, and acting across workflows, rather than just following set scripts. Amal Makwana from Citi explained how these systems operate throughout business processes, making them more flexible and powerful than earlier automation tools.
Experts like Scott Ivell and Ire Adewolu from DeepL pointed out that this shift helps close the “automation gap,” enabling AI to serve as a true digital partner. Instead of merely automating tasks, agentic AI reduces the delay between an organization’s intent and its execution, unlocking more value. However, Brian Halpin from SS&C Blue Prism noted that organizations need a solid base of standard automation before deploying these more advanced systems. This transition requires strong governance frameworks to manage unpredictable outcomes and complex decision-making.
The Critical Role of Data Quality and Governance
One of the biggest hurdles identified was ensuring high-quality, trusted data. Andreas Krause from SAP emphasized that AI, especially generative AI, depends heavily on accurate, connected enterprise data. Without it, AI models can produce unreliable or hallucinated outputs. Meni Meller from Gigaspaces discussed how retrieval-augmented generation (eRAG) combined with semantic layers can help address these issues. This approach allows AI systems to fetch real-time, factual data from enterprise sources, reducing errors and hallucinations.
Storage and analysis are also key challenges. A panel featuring representatives from organizations like Equifax, British Gas, and Centrica highlighted the importance of cloud-native, real-time analytics. For these companies, being able to analyze data quickly and at scale provides a competitive edge. They stressed that the success of agentic AI hinges on both the quality of data and the ability to analyze it instantaneously.
Safety and Physical Environment Integration
The conversation extended beyond software to physical safety concerns. As AI systems are integrated into factories, offices, and public spaces, safety protocols become vital. Edith-Clare Hall from ARIA and Matthew Howard from IEEE RAS discussed how embodied AI—robots that interact with humans—must be designed with safety in mind.
Perla Maiolino from the Oxford Robotics Institute shared her research into sensors and electronic skin that give robots a sense of self-awareness and environmental understanding. Technologies like Time-of-Flight sensors help robots detect obstacles and avoid accidents. These advancements are crucial for industries like manufacturing and logistics, where safety is a top priority. Proper safety measures and perception systems ensure that AI-powered robots can work alongside humans without risks.
Overall, the event highlighted that the future of AI depends heavily on governance, high-quality data, and safety innovations. Building these foundations now will enable enterprises to unlock the full potential of agentic AI while managing risks effectively.
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