The State of AI Maturity 2025

Nearly every organization is investing in AI, yet most are not seeing transformative results. They find themselves running experiments, dabbling in automation, but failing to scale or integrate AI into the fabric of daily work.

Our survey of 316 professionals reveals that true AI Transformation requires more than just adopting AI features—it demands Convergence at the technical, operational, and cultural level.

Organizations investing in AI face 6 critical challenges to AI Transformation:

• 47% don't measure AI's impact
• 54% operate with scattered systems
• 49% rarely or never share context across teams and tools
• 33% of organizations report active resistance to change
• 53% have no governance or only informal guidelines for AI use
• ...and only 12% report achieving full AI integration

This study shows that the real barrier isn't technology; it's a lack of Convergence.

• True transformation is rare because most organizations lack the unified context that AI needs for compounding benefits.
• Scattered tools, siloed data, and fragmented workflows create an environment where AI can only deliver marginal improvements rather than exponential value.
• Work Sprawl costs organizations $2.5 trillion annually in lost productivity. Without Convergence, AI investments compound this problem rather than solve it.

This report examines the current state of AI adoption, exposes the convergence gap holding organizations back, and provides a practical framework for moving from fragmented systems to Ambient AI—where intelligence becomes invisible, always-on, and seamlessly integrated into every aspect of work.

The Surface-Level Reality

Our survey reveals that AI adoption remains shallow across most organizations:

• 38% don't use AI at all in their daily work
• 35% use AI for basic tasks like text generation or summaries
• Only 12% say AI is fully integrated and context-aware in their organization
• Just 10% report AI acting as an agent—managing and optimizing tasks autonomously

Despite the hype, most organizations remain at the starting line. AI is present but not pervasive. The majority of use cases focus on simple automations rather than strategic transformation.

The Agentic Future: A Small But Growing Frontier

A small cohort is pushing the boundaries of what's possible:

• 10% report AI acting as an agent, managing and optimizing work
• 9% say their AI anticipates and resolves issues autonomously

These organizations aren't just automating tasks—they're building adaptive, self-improving systems. This represents the future of work, but it remains out of reach for most because they lack the unified context required for agentic AI to function effectively.

The Data Reveals a Critical Gap

• 47% don't measure AI's impact at all
• Only 10% use data-driven, outcome-based metrics
• 23% check results occasionally
• 13% have no formal measurement process

Without measurement, AI remains a "nice to have" rather than a business driver. Organizations that measure rigorously can prove value, secure buy-in, and scale successful initiatives. Those that don't measure remain stuck in pilot mode, unable to justify further investment.

No measurement → No proof of value → No buy-in → No scale

Work Sprawl Creates Context Blindness

Fragmentation emerged as the most significant barrier to AI maturity:

• 54% report scattered or mostly scattered systems
• 49% rarely or never share context across teams and tools
• Only 14% say context is unified and easy to access company-wide

The Consequences Are Severe:

• Lost context and duplicated work
• Knowledge workers spend 2.5 hours daily searching for information
• Teams toggle between apps 1,200 times per day, losing 4 hours weekly to context switching
• 21% of time spent recreating information that already exists
• AI that is only as smart as the narrow slice of data it can access

How Fragmentation Kills AI Value

When work is scattered across disconnected tools, AI operates blind.

It can only see fragments of the full picture—a task list here, a document there, a conversation somewhere else. This fragmentation prevents AI from:

• Understanding full project context
• Connecting related work across teams
• Learning from organizational patterns
• Proactively anticipating needs
• Automating complex, multi-step workflows

Resistance to Change Remains Widespread

• 33% of organizations report active resistance to change
• Only 19% are quick to adopt and scale new AI tools
• 32% say their organization tends to resist change
• 17% are slow to adopt new technologies

Even the best technology will fail without a culture that embraces experimentation, learning, and adaptation. Organizations that successfully transform AI capabilities invest heavily in change management, executive sponsorship, and continuous enablement.

AI Governance: Most Organizations Have Little or None at All

• 53% have no governance or only informal guidelines for AI use
• Only 15% have a strong, enforced framework for governance and risk management

The Risks Are Real:

• Unregulated AI adoption can lead to compliance issues, ethical lapses, and reputational damage
• Shadow AI deployments create security vulnerabilities
• Inconsistent practices undermine trust and adoption

High-maturity organizations build trust through clear policies, transparent practices, and proactive governance—making AI safe to scale across the organization.

ClickUp's AI Transformation Matrix provides a framework for understanding organizational readiness across two critical dimensions: AI Maturity and Context Maturity.

Context Maturity (X-Axis): The Foundation for AI Success

Organizations progress through three stages:

Point Solutions (Low Context Maturity)

• Teams use many separate, specialized tools for different tasks
• Information is scattered across project management, chat, documentation, and other apps
• Employees spend significant time switching between apps to get work done
• Survey finding: 54% of organizations operate with scattered or mostly scattered systems

Bundled Solutions (Medium Context Maturity)

• Organizations have reduced some app sprawl by adopting platforms with multiple capabilities
• While there is less context-switching, data and processes may still be siloed within different modules
• True integration across all work remains limited
• Survey finding: 49% rarely or never share context across teams and tools

Converged Solutions (High Context Maturity)

• All critical work, communication, and knowledge are unified within a single, fully integrated workspace
• Teams collaborate, automate, and access information in one place
• Complete context enables seamless workflows and sets the foundation for advanced AI
• Survey finding: Only 14% have unified, accessible context company-wide

AI Maturity (Y-Axis): The Evolution of Intelligence

Organizations progress through three stages:

Basic GenAI Tools (Low AI Maturity)

• Organizations use general-purpose AI tools for isolated tasks
• Examples: text generation, content summarization, simple question answering
• These tools help individual productivity but aren't connected to core workflows
• Survey finding: 35% use AI for basic tasks; 38% don't use AI at all

Workflow Automations (Medium AI Maturity)

• AI is integrated into everyday operations to automate repetitive tasks
• Examples: automated reminders, smart task assignments, AI-driven data entry
• These automations save time but typically operate within defined boundaries and require human oversight
• Organizations at this stage are moving beyond individual productivity to team-level efficiency

Agentic AI (High AI Maturity)

• AI acts as an intelligent agent that understands context, makes decisions, and executes complex processes
• Agentic AI proactively manages workflows, coordinates tasks, and continuously learns from behavior
• Enables teams to focus on high-value work while AI handles orchestration and optimization
• Survey finding: Only 10% report AI acting as an agent; 9% say AI anticipates and resolves issues autonomously

According to MIT research, 95% of AI projects fail. Our survey data suggests these failures cluster in the bottom-left, top-left, and bottom-right quadrants. Ambient AI—the top-right quadrant—is where AI projects succeed because both convergence and AI maturity are high.

1. Honestly Assess Your Current Position

Use the AI Transformation Matrix to benchmark where your organization stands today:

• Evaluate your Context Maturity: Are you operating with point solutions, bundled solutions, or a truly converged workspace?
• Evaluate your AI Maturity: Are you using basic GenAI tools, workflow automations, or agentic AI?
• Identify your quadrant and understand the specific barriers holding you back

Use objective metrics and peer comparisons rather than aspirational self-assessment. Organizations often overestimate their AI maturity while underestimating the impact of fragmentation.

2. Make Measurement Mandatory

Make outcome-based metrics a requirement for every AI initiative:

• Track adoption rates across teams and departments
• Measure time saved, tasks automated, and manual work eliminated
• Calculate productivity recapture and business impact
• Establish clear ROI frameworks that connect AI investments to business outcomes

The 47% of organizations that don't measure AI impact can't prove value, secure buy-in, or scale successful initiatives. Measurement breaks the vicious cycle and enables data-driven decisions about where to invest next.

3. Prioritize Convergence Before Adding More AI

• Focus on eliminating work sprawl before layering in additional AI capabilities:
• Audit your current tooling landscape and identify redundancies
• Consolidate critical work, knowledge, and collaboration into a unified platform
• Break down information silos to give AI full organizational context
• Standardize workflows and processes to ensure consistency

Adding more AI tools to a fragmented environment compounds the problem rather than solving it. Convergence creates the foundation that allows AI to deliver exponential value.

4. Build a Culture of Experimentation and Continuous Learning

Technology alone doesn't transform organizations—culture does:

• Encourage teams to try new AI features, fail fast, and share learnings
• Reward innovation and continuous improvement
• Provide comprehensive enablement and training to build confidence
• Celebrate early wins to build momentum and overcome resistance

Executive sponsorship is non-negotiable. Leaders must set the vision, model the behaviors they expect, and hold the organization accountable for progress.

5. Establish Clear Governance and Risk Management

Build trust through transparency and proactive governance:

• Create and enforce ethical guidelines for AI use
• Establish security protocols and compliance frameworks
• Define clear roles and responsibilities for AI oversight
• Regularly audit AI usage and address risks proactively

The 53% of organizations with no formal AI governance are exposed to compliance issues, ethical lapses, and security vulnerabilities. High-maturity organizations make AI safe to scale through clear policies and transparent practices.

6. Focus on the Journey to Ambient AI

Use the AI Transformation Matrix as your roadmap:

• If you're in Disconnected Manual Work: Start with convergence. Unify your workspace before investing heavily in AI features
• If you're in Unified Manual Work: You have the foundation. Now introduce workflow automations and basic AI capabilities to eliminate routine work
• If you're in Siloed Automation: Your AI is powerful but limited by fragmentation. Prioritize convergence to unlock the full value of your AI investments
• If you're approaching Ambient AI: Focus on scaling agentic capabilities, continuous optimization, and fostering a culture of AI-augmented work

Move systematically toward the top-right quadrant where convergence and AI maturity compound to create Ambient AI—proactive, context-rich intelligence that operates invisibly in the background.

The winners in the next era of work will not be those who simply adopt more AI tools. They will be the organizations that unify their workspace, measure rigorously, and create the conditions for AI to amplify human potential.

Our survey data is unambiguous: fragmentation is the primary barrier to AI success.

• The 54% of organizations operating with scattered systems cannot unlock the value of AI investments because their tools lack the context needed for intelligent automation.
• The 47% that don't measure AI impact remain stuck in pilot mode, unable to prove value or scale successful initiatives.

The path forward requires convergence first, AI second. Organizations must:

• Eliminate Work Sprawl by unifying tasks, docs, chat, and knowledge
• Give AI complete organizational context
• Measure outcomes rigorously to prove value and guide investment
• Build a culture that embraces experimentation and continuous learning
• Establish clear governance to make AI safe to scale

The technology is ready. The workforce is eager. The business case is proven—with Forrester documenting

The only question: Will your organization break free from fragmentation and lead the AI transformation, or remain stuck in the bottom-left quadrant where 95% of AI projects fail?

The data is clear. The framework is proven. The tools exist. The time to converge is now. Are you ready?