Your AI Maturity Assessment Results: Disconnected, Manual Work

Thank you for completing the AI Maturity Assessment. Based on your responses, your organization is currently in the Disconnected, Manual Work stage.

This is a common starting point for many teams embarking on their AI journey. Recognizing where you are today is the first step toward meaningful transformation.

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.

You are currently operating in an environment where manual processes and disconnected systems are the norm. This stage is defined by fragmented workflows, limited automation, and a lack of unified systems.

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

Key Characteristics:

• Work is scattered across disconnected apps and systems.
• Teams rely on manual processes like spreadsheets, shared drives, and email.
• Employees experiment with GenAI tools (e.g., ChatGPT) individually, but there is no coordinated strategy.
• Reporting is manual and error-prone, making it difficult to track progress.
• No single source of truth or standardized metrics exists.
• Collaboration depends on manual coordination and static reports.

Recognizing these patterns is the first step in building a roadmap for improvement. By addressing these issues, you can unlock greater efficiency, visibility, and value from your work.

Your placement in the “Disconnected, Manual Work” quadrant of the AI Transformation Matrix means that your organization is using basic GenAI tools and point solutions, but both AI maturity and context maturity are low.

Manual work dominates, and there is little integration or automation to support your workflows.

Impacts of This Stage:

• Context is lost and effort is often duplicated.
• Visibility is limited, leading to inefficiency.
• Leadership lacks a clear picture of performance or productivity.
• Skepticism about AI’s value persists.
• Your organization risks falling behind more adaptive competitors.

Understanding these impacts helps you prioritize the changes that will have the greatest effect on your organization’s productivity and growth.

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.

Addressing these opportunities will help you unlock new levels of efficiency and value.

Before you can move forward, it’s important to recognize the symptoms of this stage in your daily work.

These signs are common among organizations at this level of maturity and serve as clear indicators of where to focus your efforts.

Common Symptoms:

• Employees use AI tools individually, not as a coordinated effort.
• Work is spread across many apps and disconnected systems.
• Teams use separate suites; handoffs are manual and error-prone.
• Reporting is inconsistent and often requires duplicated effort.
• ROI visibility is lacking and there are no standardized metrics.
• Collaboration depends on static, manually updated reports.

By identifying these symptoms, you can better understand the root causes of inefficiency and begin to address them systematically.

Remaining in the Disconnected, Manual Work stage can have significant consequences for your organization.

It’s important to be aware of these risks so you can build urgency and alignment around the need for change.

Risks If Stalled:

• Operational inefficiency increases, along with tool costs and data inconsistency.
• Context is lost and effort is duplicated, limiting visibility and efficiency.
• Leadership struggles to see productivity or ROI, which can stall investment.
• AI exploration remains tactical and disconnected from business value.
• Your organization risks falling behind more adaptive competitors.

Understanding these risks can help you make the case for investment in digital transformation and AI enablement.

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.

You don’t have to wait to make improvements. There are several actions you can take right away to build momentum and demonstrate early value.

Quick Wins:

• Inventory all current systems and tools to uncover redundancies and shadow IT.
• Identify and document core manual processes for standardization.
• Launch AI literacy and digital skills training for your teams.
• Establish clear guidelines for responsible AI experimentation and usage.

These steps will lay the groundwork for bigger changes ahead and help you build support across your organization.

As you gain momentum, focus on a few strategic initiatives that will have a lasting impact.

Strategic Moves:

• Consolidate overlapping tools into a shared platform.
• Create a single source of truth for task tracking and documentation.
• Develop workflow templates for repeatable tasks.
• Launch dashboards to visualize adoption and early process improvements.

These moves will help you build a more connected, efficient, and data-driven organization.

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.