Your AI Maturity Assessment Results: Siloed Automation

Congratulations on completing the AI Maturity Assessment!

Based on your responses, your organization is currently in the Siloed Automation stage.

You're in rare territory. Based on our assessment data, only 9% of organizations have invested as heavily in AI as you have.

But here's the challenge: your AI is operating without the unified context it needs to deliver full value. You've built AI maturity without first building context maturity. That's why results may feel inconsistent.

Here's how assessment takers are distributed across the AI Transformation Matrix:

• Disconnected, Manual Work: 60.7%
• Unified, Manual Work: 26.2%
• Siloed Automation: 9.8% ← You are here
• Ambient AI: 3.3%

This report will help you understand your current state, highlight key opportunities, and guide you with actionable steps to move toward unified, accountable, and scalable AI operations.

Automation is present in your organization, but it’s confined to isolated apps, departments, or teams.

Multiple groups independently deploy agentic solutions, which can lead to duplication, inconsistent performance, and unmanaged risk.

AI workflows are often standardized within departments, but remain reactive and experimentation is limited. There is no unified governance or monitoring, and outcomes can vary widely. Leadership recognizes the potential value but is increasingly concerned about compliance, security, and reputational risks.

Understanding these characteristics is crucial, as it highlights both the challenges you face and the opportunities ahead.

At this stage, you may notice the following patterns:

• Experimental AI agents are deployed by multiple teams, often without oversight.
• Overlapping automations: Different groups build similar solutions, leading to redundancy.
• Standardized but reactive workflows: AI is used to respond to needs, not anticipate them.
• Minimal governance: Safety controls and oversight are limited or nonexistent.
• Low organizational trust in AI: Opaque results and risk exposure undermine confidence.
• Limited experimentation: Innovation is confined to small teams or pilots.
• Inconsistent outcomes: User experiences and data reliability vary across teams.

Recognizing these characteristics helps you pinpoint where to focus your next efforts.

Remaining in this stage can create significant challenges:

• Increased reputational, compliance, and operational risks as automation scales without oversight.
• Unregulated agents can expose sensitive data or produce errors at scale.
• Innovation continues but without accountability or shared learnings.
• Diminishing returns from AI as automation fatigue sets in.
• Leadership risks seeing AI as chaotic rather than transformative.

Awareness of these impacts and risks can help build urgency and alignment for your next phase of transformation.

You’ve already achieved important milestones that will help accelerate your progress:

Strengths:

• Widespread experimentation with AI agents and automation.
• Standardized workflows within departments.
• Leadership recognition of AI’s potential value.

Growth Opportunities:

• Establish enterprise standards for AI agent design, deployment, and oversight.
• Build a central repository for agent use cases, prompts, and workflows.
• Consolidate agent pilots onto a unified platform for monitoring and control.
• Launch dashboards that display agent performance, risk indicators, and ROI.
• Build awareness of agent operations through cross-functional learning sessions.
• Appoint executive-level sponsors responsible for agent governance and risk mitigation.
• Integrate disparate agent systems through shared APIs or data lakes.

Building on your strengths and targeting these opportunities will help you unlock new levels of efficiency, visibility, and value.

To move beyond this stage, you’ll need to consolidate, govern, and integrate your automation and AI agent efforts. The following phased action plan will help you build momentum and unlock measurable value.

Phase 1: Consolidate and Control

Lay the groundwork for scalable, safe automation by centralizing oversight and reducing redundancy:

• Mandate consolidation of agents and automations into a single monitored environment.
• Inventory all agent pilots and identify redundant or overlapping agents.
• Deploy an agent registry and observability framework for version tracking.
• Assign “Agent Stewards” responsible for local compliance and documentation.
• Shift perception of governance from restriction to protection.
• Begin reporting on agent coverage, redundancy, and compliance adherence.
• Centralize visibility and reduce risk through structured oversight.

Phase 2: Govern and Standardize

Establish clear standards, policies, and monitoring to ensure safe, reliable AI operations:

• Define and enforce AI agent lifecycle management (creation, testing, deployment, deprecation).
• Introduce standardized escalation, review, and rollback procedures.
• Implement monitoring tools for ethical compliance, data privacy, and performance anomalies.
• Train AI and compliance leads to manage audit cycles and policy enforcement.
• Promote transparency in AI decision-making and incident reporting.
• Measure risk reduction, uptime, and adherence to governance policies.

Phase 3: Optimize and Integrate for Scale

Connect and orchestrate your agentic solutions for enterprise-wide efficiency and impact:

• Transition from departmental agents to enterprise-grade orchestration.
• Connect agent functions across domains (finance, HR, supply chain, etc.).
• Create an enterprise orchestration layer that synchronizes agent activity.
• Build a small Agent Operations Team to monitor, tune, and optimize performance.
• Encourage a data-driven innovation model based on telemetry insights.
• Begin quarterly reviews of ROI, agent performance, and governance outcomes.
• Integrate AI agents into unified workflows to amplify enterprise efficiency.

Phase 4: Expand to Agentic and Ambient AI

Prepare for the future by embedding AI agents and reinforcing a culture of trust and transparency:

• Align AI initiatives with strategic objectives and integrate predictive analytics.
• Enable agents to manage workflows proactively with human oversight for exceptions.
• Deploy telemetry-driven continuous optimization loops.
• Transition the Agent Operations Team into a permanent AI Center of Excellence.
• Institutionalize AI transparency through open dashboards and published trust metrics.
• Evolve from compliance-driven governance to trust-driven accountability.
• Achieve a fully integrated, resilient, and transparent AI ecosystem.

By following this roadmap, you’ll be ready to unlock exponential gains and future-proof your organization.

Sustained progress requires ongoing engagement and clear priorities. To keep your momentum:

• Share these insights with your leadership team to align on your AI maturity journey and set clear priorities.
• Engage your teams in conversations about agent consolidation, governance, and risk management—gather feedback to inform your roadmap.
• Benchmark your progress by setting measurable goals for agent coverage, compliance, and impact.
• Stay informed on AI trends and best practices by subscribing to thought leadership resources and participating in industry forums.
• When you’re ready for hands-on support, consider connecting with experts or partners who can help you accelerate your transformation.

Taking these steps will help your organization continue to evolve and thrive in an AI-driven world.

This assessment is designed to spark meaningful conversations and guide your next steps on the path to AI maturity. When you’re ready to go deeper, our team is here to help.