AI in Change & Transformation – ECLC Survey Insights

At the end of 2025, senior change and transformation leaders within the Executive Council for Leading Change completed a survey on how AI is being applied inside their functions. These are enterprise leaders responsible for activating strategy across complex organizations.

The overall picture: AI adoption in change and transformation is already happening, but it is tactical, uneven, and largely individual-driven rather than systematized. Most organizations are using AI to make existing work faster, not to fundamentally change how change is done. Skills, enablement, and clarity—not technology—are the binding constraints.

1. How AI Is Being Used Today (What’s Real, Not Aspirational)

For transformation teams, AI is primarily being used for communication-heavy and content-driven work.

Most common use cases:

Stakeholder and impact analysis:

• Communications and engagement (message drafting, tailoring, segmentation)

  → ~70% of respondents

• Training and learning delivery (content creation, adaptive learning and support)

  → ~35–40%

• Change planning and strategy

• Adoption tracking and analytics

• Knowledge capture and lessons learned

Less common / emerging:

• Stakeholder and impact analysis

• Scenario modeling and prediction

• Behavioral nudging and reinforcement

• Leader coaching and capability building

A small but meaningful group (~15%) report not using AI in change work at all yet.

• Usage is often manual, prompt-based, and ad hoc, not embedded into workflows.

Key insight:

AI is being used as a power tool for practitioners, not yet as infrastructure for transformation at scale.

2. Tools & Platforms: Fragmented and General-Purpose

Most leaders rely on horizontal AI tools rather than change-specific solutions.

Common tools include:

• Enterprise copilots (e.g., Microsoft Copilot)

• General LLMs (ChatGPT-style tools)

• Basic automation and analytics tools

Very few respondents indicated:

• Dedicated AI tools built specifically for change and transformation

• Integrated, end-to-end workflows (from planning → activation → measurement)

Key insight:

AI is being borrowed by change teams, not built for them.

3. Confidence & Comfort: Individuals Are Ahead of Organizations

100% of respondents rate themselves moderately to highly comfortable using AI personally

Most responses cluster at 3–4 out of 5, with a strong tail at 5

This contrasts sharply with organizational maturity.

Key insight:

The people are ready. The system is not.

4. Biggest Barriers (This is the Bottleneck)

Top blockers identified:

• Limited skills or training (~50%+)

• Tool access / licensing constraints

• Lack of clear use cases

• Unclear ROI

• Data quality, privacy, and governance concerns

Qualitative themes also surfaced:

• “No clear vision for how AI should be used”

• Change still misunderstood as “just comms and training”

• Support functions deprioritized vs. customer-facing roles

• No time or space to experiment safely

Key insight:

This is not a technology problem.

It’s a capability, enablement, and operating-model problem.

Without clarity on where AI fits inside the change lifecycle, adoption remains tactical.

5. Where AI Should Be Used Next (But Isn’t Yet)

Leaders see strong untapped potential in higher-leverage areas:

• Stakeholder sensing and real-time sentiment analysis

• Impact analysis and prioritization

• Scenario modeling

• Leader enablement and coaching at scale

• Continuous reinforcement post-launch

These are high-leverage transformation activities—not administrative tasks.

Key insight:

AI’s biggest value is still upstream and downstream of comms, not inside it.

6. What the Community Wants from ECLC

There is strong appetite for moving beyond experimentation.

Members expressed interest in:

• Practical use cases grounded in real transformation work

• Templates, playbooks, and real examples

• Hands-on training

• Peer case studies

• Opportunities to share lived experience

Key insight:

There is a clear appetite to move from experimentation → practice → muscle memory.

Strategic Takeaways (Board / C-Suite Level)

• AI is accelerating individual change practitioners before it transforms change functions

• The next wave is not better prompts—it’s embedded workflows

• Change is becoming a distributed capability, and AI is the only way to scale it

• Organizations that don’t operationalize AI in change will bottleneck transformation itself

• The winning model is “from expert-led change to AI-enabled everyone”

  
  

The opportunity is moving from expert-dependent change to AI-supported, system-enabled activation across the organization.