Your AI steering committee’s 2026 checklist: Observability

Every organization wants AI to move faster and more intelligently. However, as they move from conversational assistants to autonomous agentic systems, enterprises are finding that the biggest bottleneck isn’t the technology—it’s the ability to ensure control.

To maintain velocity and control in 2026, steering committees must answer a fundamental question:

Do we have line-of-sight and control over the AI agents being deployed knowing exactly what they are, what data they touch, and what they are doing?

To help leaders navigate this complexity, we have refreshed our core framework: Grow Your Business with AI You Can Trust. This guide is a practical roadmap for structured decision-making across security and governance, now introducing a critical new pillar for 2026: Observability.

AI committee checkpoint: You cannot govern what you cannot see

As AI spreads across teams and tools, observability becomes the prerequisite for scaling. Without a centralized view, “shadow AI” and unmanaged agents may create significant risks, from security vulnerabilities to sensitive data leakage.

To achieve enterprise readiness, your AI steering committee should be able to answer four foundational questions:

• Inventory: What agents currently exist across our environment?
• Identity: Who is using these agents and for what purpose?
• Access: What systems and specific data sets do they have permission to touch?
• Outcomes: What workloads are they driving and what results are they producing?

Four capabilities for AI platform visibility

In our updated guide, we frame observability through four technical capabilities every enterprise platform should support:

1. Registry: A single source of truth to track every AI asset in the organization.
2. Agent analytics: Real-time data on performance, usage patterns, and costs.
3. Agent map: A visualization of the connections between agents, users, and data.
4. Role-specific oversight: Tailored dashboards that give IT, security, and business leaders the specific metrics they need.

The strategic impact: Accenture

Accenture saw innovation stall at the pilot stage as fragmented tools slowed their path to production. By implementing a centralized platform with built-in observability, they unified monitoring across development and deployment.

Accenture has already deployed more than 75 use cases across industries, with 16 in production, reducing AI app build time by 50%.

Executive checklist: Scaling with control

Your AI steering committee can use the refreshed guide as a checklist to support a secure foundation for AI scaling:

• Define trust: Establish clear responsible AI principles for your brand.
• Secure by design: Shift to a security-first posture across all AI operations.
• Govern the loop: Use the “Map, Measure, Manage” framework to mitigate risks.
• Achieve sustainability: Build systems with socio-economic and environmental impact in mind.
• Address digital sovereignty requirements: Understand common sovereignty scenarios and core principles to help your organization address them.
• Ensure visibility: Confirm your platform supports the 4 capabilities for agent observability.