Your AI Maturity Assessment Results: Unified, Manual Work

Congratulations on completing the AI Maturity Assessment!

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

You're ahead of the majority. Based on our assessment data, only 1 in 4 organizations have built the foundation you have.

While 61% of organizations are still struggling with disconnected tools and scattered workflows, you've already unified your work. That's the hardest part. Now you're positioned to accelerate.

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

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

This report is designed to help you understand your present state, highlight key opportunities, and guide you with actionable steps to move toward a more integrated, automated, and AI-driven future.

Your teams and processes are now centralized under a primary platform. This has made collaboration easier and improved data consistency.

However, most workflows remain manual, and AI is used only for simple, isolated tasks. Some departments have started automating, but these efforts are disconnected and lack standardized measurement or ownership.

Understanding this landscape is the first step toward meaningful progress.

At this stage, you may notice the following patterns:

• Centralized operations: Teams work within a unified platform, but processes are still manual.
• Isolated automation: AI and automation are limited to specific departments or workflows.
• Local success, global gaps: Automation works well within teams but breaks down across departments.
• Inconsistent measurement: ROI tracking is anecdotal or varies by team.
• Lack of ownership: No standardized workflow templates or clear process owners.
• Limited leadership visibility: Leaders have minimal insight into automation benefits or efficiency metrics.

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

While you’ve made progress, remaining in this stage can create challenges:

• Productivity plateaus: Gains are fragmented and hard to scale.
• Duplicated efforts: Tools and processes are repeated across teams, missing opportunities for efficiency.
• Scattered wins: Leadership sees isolated successes but struggles to drive systemic value.
• Visibility gaps: Without unified data, it’s difficult to justify further AI investment.
• Skepticism: Employees may question AI’s value if benefits remain limited to individual tasks.

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:

• Centralized operations that improve collaboration and data consistency.
• Leadership recognition of improved coordination and documentation.
• Early automation pilots and a willingness to experiment with AI workflows.

Growth Opportunities:

• Standardize and document workflows across departments.
• Build and share workflow templates that blend manual and AI-enabled steps.
• Establish an internal AI Champions Network to promote workflow adoption.
• Nominate executive sponsors for workflow automation and AI adoption.
• Provide workflow design training to operational leaders.

Building on your strengths and targeting these opportunities will help you reach the next level of AI maturity.

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

Phase 1: Strengthen Foundations

Lay the groundwork for scalable automation by documenting, standardizing, and aligning your workflows.

• Document and standardize key workflows across departments.
• Build a shared library of templates that incorporate both manual and AI-enabled steps.
• Define data entry standards and process documentation for consistency.
• Align all work under the unified platform and connect data across tools.
• Issue an Executive Brief outlining standardization goals, AI adoption expectations, and governance requirements.
• Provide workflow design training to operational leaders.
• Foster collaboration and celebrate early wins.
• Capture baseline productivity and engagement metrics.

By strengthening your foundations, you’ll make it easier to scale automation and measure its impact.

Phase 2: Launch AI Workflow Pilots

Begin piloting AI automation in high-impact workflows and share learnings across teams.

• Identify 2–3 lighthouse workflows to pilot AI automation.
• Expand automations to handle routing, reporting, and approval steps.
• Implement integrated automation triggers between platform modules.
• Train pilot teams to maintain and optimize AI workflows.
• Share pilot results and promote cross-department learning.
• Begin measuring automation adoption and cycle time improvements.
• Use insights from dashboards to guide prioritization of AI use cases.

Piloting and sharing early wins will help build confidence and momentum for broader adoption.

Phase 3: Scale and Govern

Replicate successful pilots, expand integrations, and establish lightweight governance.

• Create a lightweight governance framework for AI safety, reliability, and version control.
• Replicate successful pilots across departments with standard templates.
• Expand integrations to external systems and data sources.
• Establish a centralized “Automation Operations” or “Agent Operations” function.
• Build a cross-functional community of practice for workflow innovation.
• Add ROI and performance metrics to the enterprise dashboard.
• Conduct quarterly reporting on AI-enabled efficiency gains.

Scaling and governing your efforts will ensure consistency, safety, and measurable results.

Phase 4: Transition to Agentic AI and Ambient AI

Prepare for the future by embedding AI agents and reinforcing a growth mindset.

• Develop a roadmap for agent adoption aligned to business objectives.
• Deploy AI agents for signal detection, triage, and escalation handling.
• Enable unified agentic systems within the platform for proactive execution.
• Upskill the AI Champions Network to design and manage agentic workflows.
• Reinforce a growth mindset around AI as a co-pilot for human work.
• Conduct continuous reviews for AI performance, ethics, and outcomes.
• Publicly recognize departments demonstrating measurable automation ROI.

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 workflow standardization, automation, and AI adoption—gather feedback to inform your roadmap.
• Benchmark your progress by setting measurable goals for workflow 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.

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.