Conference Description
Key Takeaways
- Executive-level summit addressing enterprise AI scaling, data governance and digital regulation challenges
- Focus on agentic AI, autonomous decision-making systems and trusted data architectures
- Designed for CDOs, Heads of Data, AI leaders and senior technology executives from large organisations
- Covers operational challenges including unstructured data growth, risk management and compliance frameworks
- Features participation from Strategy (formerly MicroStrategy), Syncari, HPE, Boomi, SAP and BlueCat Networks
Introduction
The IDC AI & Data Summit Chicago brings together senior technology and business executives to address the practical challenges of scaling artificial intelligence and establishing robust data governance frameworks within enterprise environments. Hosted at Convene 311 West Monroe in Chicago, the summit targets decision-makers responsible for AI and data strategy, offering a programme of keynotes, panel discussions and collaborative sessions led by IDC analysts and industry practitioners.
The timing reflects a critical inflection point for enterprise AI adoption. Organisations that moved quickly to pilot AI initiatives now face the more complex task of operationalising these systems at scale whilst navigating an increasingly stringent regulatory landscape. The summit addresses this transition directly, focusing on the architectural, governance and organisational changes required to move from experimentation to production-grade AI deployment.
About This Event
IDC, the global technology research and advisory firm, positions this summit as a strategic gathering for executives who must balance innovation velocity with risk management and compliance requirements. The in-person format emphasises peer-to-peer exchange and direct engagement with analysts who track enterprise technology adoption patterns across industries.
The programme structure combines thought leadership content with practical frameworks. Rather than focusing solely on emerging capabilities, sessions address the operational realities of building AI systems that can withstand regulatory scrutiny, maintain data quality at scale and deliver measurable business outcomes. This approach reflects the maturing enterprise AI market, where proof-of-concept success no longer satisfies boards and stakeholders demanding quantifiable returns.
Agentic AI and Autonomous Decision-Making
A central theme of the summit is the emergence of agentic AI—systems capable of autonomous decision-making and action without continuous human oversight. This represents a significant evolution from earlier AI implementations that primarily augmented human decision-making rather than replacing discrete workflow components entirely.
The shift toward agentic architectures introduces new considerations for enterprise technology leaders. Autonomous systems require different governance frameworks than their supervised counterparts, with particular attention to exception handling, audit trails and intervention protocols. Organisations must establish clear boundaries for autonomous action whilst preserving the efficiency gains that make these systems valuable.
The summit examines how enterprises can prepare their infrastructure and organisational processes for agentic AI workflows. This includes technical considerations around system integration and monitoring, as well as broader questions about accountability, transparency and the evolving relationship between human workers and autonomous systems.
Data Quality, Governance and Trusted Architectures
Effective AI deployment depends fundamentally on data quality—a challenge that intensifies as organisations attempt to scale beyond initial pilots. The summit addresses the architectural and procedural requirements for building what IDC terms “trusted data architectures,” systems designed to ensure data reliability, lineage tracking and appropriate access controls across the enterprise.
Master data management and data integration remain persistent challenges for large organisations, particularly those operating across multiple business units, geographies or legacy system environments. The proliferation of cloud-native analytics platforms has expanded analytical capabilities but also increased the complexity of maintaining consistent data definitions and quality standards across distributed environments.
Unstructured data growth presents additional complications. Documents, images, communications and other non-tabular data sources contain valuable information for AI systems but resist the governance frameworks developed for structured databases. The summit explores approaches to incorporating unstructured data into enterprise AI initiatives whilst maintaining appropriate oversight and quality assurance.
Navigating Digital Regulation and Risk Management
The regulatory environment for AI continues to evolve rapidly, with new frameworks emerging across jurisdictions that impose specific requirements on AI system development, deployment and monitoring. For multinational enterprises, compliance complexity multiplies as different regulatory regimes establish varying standards for transparency, fairness and accountability.
The summit addresses risk management in AI from both compliance and operational perspectives. Beyond meeting regulatory requirements, organisations must manage reputational risks associated with AI system failures, biased outputs or unexpected behaviours. Building resilient AI initiatives requires anticipating failure modes and establishing response protocols before problems emerge in production environments.
Responsible AI operationalisation—deploying systems that are ethical, transparent and aligned with organisational values—has moved from aspirational goal to business imperative. Stakeholders including customers, employees, regulators and investors increasingly expect organisations to demonstrate thoughtful AI governance rather than simply pursuing capability expansion.
Technology Ecosystem and Vendor Participation
The summit features participation from technology providers spanning the enterprise AI and data management landscape. Strategy (formerly MicroStrategy) brings expertise in business intelligence and analytics platforms. Syncari and Boomi address data integration and synchronisation challenges that underpin effective AI deployment. HPE contributes infrastructure perspective, whilst SAP represents the enterprise application environment where AI capabilities must ultimately integrate. BlueCat Networks addresses the network infrastructure layer that supports distributed AI workloads.
This vendor mix reflects the reality that enterprise AI success depends on coordinating capabilities across multiple technology domains. Isolated AI implementations rarely deliver sustained business value; effective deployments require integration with existing enterprise systems, data sources and operational workflows.
Who Should Attend
The summit targets senior executives with direct responsibility for AI and data strategy decisions. Chief Data Officers and Heads of Data will find relevant content on governance frameworks and architectural approaches. AI and innovation leaders can engage with peers facing similar scaling challenges. Technology executives responsible for infrastructure and platform decisions will benefit from discussions of deployment requirements and integration considerations.
The executive focus means content assumes familiarity with enterprise technology environments and strategic decision-making contexts. Sessions address organisational and business considerations alongside technical topics, reflecting the cross-functional nature of successful AI initiatives.
The Path from Strategy to Operational Impact
The central challenge the summit addresses—bridging the gap between AI strategy and deployed, value-generating systems—reflects where many enterprises currently find themselves. Initial enthusiasm for AI capabilities has given way to harder questions about sustainable implementation, measurable returns and responsible scaling.
Organisations that successfully navigate this transition will establish AI and data capabilities as genuine competitive advantages rather than experimental initiatives. Those that struggle with governance, quality or operational challenges risk falling behind as AI-enabled competitors capture efficiency gains and market opportunities. The IDC AI & Data Summit Chicago aims to provide the insights and frameworks that help executives position their organisations in the former category.