Your AI Maturity Assessment Results: Disconnected, Manual Work

Congratulations on completing the AI Maturity Assessment!

Based on your responses, your organization is currently in the Disconnected, Manual Work stage.

You're in good company. Based on our assessment data, 6 in 10 organizations are starting from the same place you are.

This isn't a failure. It's a common starting point. The organizations that succeed are the ones that recognize where they are and take deliberate steps forward.

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

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

What this means for you:

The majority of organizations haven't built the foundation for AI success yet. But 26% have already moved to the next stage by focusing on convergence first. That's your path forward.

This report is designed to help you understand your current state, identify opportunities, and take actionable steps toward a more connected, efficient, and AI-enabled future.

Your organization currently operates in an environment where manual processes and disconnected systems are the norm.

Work is scattered across multiple apps and systems, and teams rely on manual processes like spreadsheets, shared drives, and email. While some employees experiment with Generative AI tools individually, there is no coordinated strategy or unified approach.

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:

• Work is scattered: Teams use disconnected apps and systems.
• Manual processes dominate: Reliance on spreadsheets, shared drives, and email.
• Individual AI experimentation: Employees use Generative AI tools (e.g., ChatGPT) on their own, without coordination.
• Manual, error-prone reporting: Tracking progress is difficult and inconsistent.
• No single source of truth: Data and metrics are not standardized.
• Collaboration is manual: Coordination depends on static, manually updated reports.

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

Remaining in this stage can create significant challenges:

• Context is lost: Effort is duplicated and visibility is limited.
• Productivity plateaus: Inefficiency increases as work is repeated across tools and teams.
• Leadership lacks visibility: It’s difficult to track performance or ROI.
• Skepticism about AI: Without clear value, employees and leaders may doubt AI’s benefits.
• Risk of falling behind: Competitors with more adaptive systems may outpace your organization.

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

Even at this early stage, you have strengths to build on. Recognizing and leveraging these strengths will help you accelerate your progress.

Strengths:

• Willingness to experiment with new tools and technologies.
• Awareness of inefficiencies and a desire to improve.
• Early adopters who can become champions for change.

By building on these strengths, you can create a culture of innovation and continuous improvement.

Opportunities for Growth

To accelerate your progress, focus on the areas that will have the greatest impact on your organization’s maturity.

Opportunities:

• Build foundational AI literacy and digital skills for all employees and managers.
• Standardize and document core manual processes.
• Consolidate tools and create a single source of truth.
• Establish executive sponsorship and clear governance for digital transformation.

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

Moving beyond this stage requires a structured, phased approach.

The following action plan is designed to help you build momentum, address foundational issues, and set the stage for long-term success.

Phase 1: Foundations and Literacy
Start by building the skills and systems you need to support future growth.

• Launch AI literacy and digital skills training for all employees and managers.
• Publish an Executive Brief outlining goals for system cleanup, AI governance, and digital transformation.
• Identify and document 3–5 core manual processes (e.g., reporting, intake, communication) for standardization.
• Create a single source of truth for task tracking and documentation.
• Establish clear guidelines for responsible AI experimentation and usage.
• Inventory all current systems and tools to uncover redundancies and shadow IT.
• Retire redundant tools and centralize communication and documentation.
• Roll out a sandbox environment for safe GenAI experimentation.
• Begin measuring time savings and engagement metrics to track progress.

By focusing on these foundational steps, you’ll create a strong base for more advanced initiatives.

Phase 2: Connect and Standardize
Once the basics are in place, work to connect your systems and standardize your processes.

• Consolidate overlapping tools into one shared platform.
• Develop and implement workflow templates for repeatable tasks.
• Define and track baseline performance metrics (efficiency, cycle time, quality).
• Certify internal workflow builders and champions to drive adoption.
• Integrate key platforms to improve data flow and reduce manual handoffs.
• Launch dashboards to visualize adoption and early process improvements.
• Promote early wins and document best practices for workflow adoption.

This phase will help you reduce friction, improve visibility, and set the stage for automation.

Phase 3: Expand to Multi-Step Automation
With standardized workflows and connected systems, you can begin to automate more complex processes.

• Identify enterprise-level workflows suitable for automation.
• Deploy multi-step automations that span multiple functions.
• Establish an automation community to share learnings and reusable assets.
• Recognize and reward automation champions across teams.
• Connect APIs and integration tools for data sharing.
• Add ROI, usage, and adoption metrics to executive dashboards.
• Begin tracking automation impact on cycle time and efficiency.

Automation will help you scale your impact and free up time for higher-value work.

Phase 4: Move Toward Agentic AI and Ambient AI
Finally, prepare your organization for the future by embedding AI more deeply into your operations.

• Launch an AI Steering Committee to define governance and scaling.
• Embed AI agents into unified systems to manage routing, escalation, and forecasting.
• Stand up an Agent Operations function for design, testing, and monitoring.
• Establish continuous improvement reviews for safety and optimization.
• Complete convergence into a single AI-native workspace.
• Share success stories and normalize human–AI collaboration.
• Institutionalize governance for ongoing safety, ethics, and performance.

By following this roadmap, you’ll be well-positioned to achieve exponential productivity gains and strategic advantage.

To ensure your progress continues, it’s important to keep moving forward and engaging your organization at every level.

What You Can Do Next:

• Share these insights with your leadership team to align on your AI maturity journey and set clear priorities.
• Engage your teams in conversations about AI adoption, challenges, and opportunities—gather feedback to inform your roadmap.
• Benchmark your progress by setting measurable goals for AI adoption, integration, 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.

By taking these steps, you’ll ensure that your organization continues 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.