Uploaded August 2026 | Updated September 2026, 1 week ago
Data alone isn't enough to build truly intelligent AI. As businesses move toward agentic AI and autonomous workflows, knowledge, context, and relationships are becoming just as important.
Traci Gusher, EY Americas AI and Data Leader, explores why organizations need to look beyond data to improve AI decision-making, reimagine business processes, and build more intelligent agentic workflows. The discussion examines how businesses can connect data with contextual knowledge to make better decisions as we enter the age of the agentic enterprise.
Topics covered:
What is agentic AI?
Why AI needs context, not just data
How knowledge improves AI decision-making
The role of relationships and business processes in AI
How organizations can prepare for autonomous workflows
Part of an ongoing MIT Technology Review series, sponsored by @EY_US, exploring the technologies, strategies, and challenges shaping the future of AI.
Frequently asked questions:
What is agentic AI?
Agentic AI refers to AI systems that can take actions, make decisions, and execute workflows with increasing autonomy.
Why isn't data enough for AI?
Data provides information, but AI also needs context, relationships, and business knowledge to make accurate decisions.
What is enterprise knowledge in AI?
Enterprise knowledge includes organizational processes, business rules, expertise, and relationships that help AI understand how work gets done.
How does context improve AI?
Context helps AI interpret information correctly, reducing errors and improving decision quality.
Ready to move beyond AI pilots? Explore EY.ai Value Blueprints and learn how leading organizations are embedding AI into core business processes to unlock growth, innovation and enterprise-wide transformation.
#AgenticAI #AI #EnterpriseAI
Data alone isn't enough to build truly intelligent AI. As businesses move toward agentic AI and autonomous workflows, knowledge, context, and relationships are becoming just as important.
Traci Gusher, EY Americas AI and Data Leader, explores why organizations need to look beyond data to improve AI decision-making, reimagine business processes, and build more intelligent agentic workflows. The discussion examines how businesses can connect data with contextual knowledge to make better decisions as we enter the age of the agentic enterprise.
Topics covered:
What is agentic AI?
Why AI needs context, not just data
How knowledge improves AI decision-making
The role of relationships and business processes in AI
How organizations can prepare for autonomous workflows
Part of an ongoing MIT Technology Review series, sponsored by @EY_US, exploring the technologies, strategies, and challenges shaping the future of AI.
Frequently asked questions:
What is agentic AI?
Agentic AI refers to AI systems that can take actions, make decisions, and execute workflows with increasing autonomy.
Why isn't data enough for AI?
Data provides information, but AI also needs context, relationships, and business knowledge to make accurate decisions.
What is enterprise knowledge in AI?
Enterprise knowledge includes organizational processes, business rules, expertise, and relationships that help AI understand how work gets done.
How does context improve AI?
Context helps AI interpret information correctly, reducing errors and improving decision quality.
Ready to move beyond AI pilots? Explore EY.ai Value Blueprints and learn how leading organizations are embedding AI into core business processes to unlock growth, innovation and enterprise-wide transformation.
#AgenticAI #AI #EnterpriseAI
