Putting It All Together: Pitfalls and When to Get Help

Part 7 of 7 in the AI Change Framework Series

We've covered all five stages of the Adaptive AI Change Framework. Now let's address the elephant in the room: what goes wrong, and when should you bring in outside expertise?

Framework Recap

The Adaptive AI Change Framework provides a five-stage, end-to-end method to lead AI change with rigor and humanity:

1. Foundation—Build alignment and shared purpose
2. Assessment—Create a readiness baseline
3. Design—Turn insights into pilots and governance
4. Implementation—Launch with learning loops
5. Sustainment—Embed and renew quarterly

Each stage has mastery gates. The framework loops continuously—sustainment feeds back into assessment, not into “done.”

Common Pitfalls

1. Tool-First Thinking

The trap: “We need ChatGPT/Claude/[latest AI]!” before anyone asks “Why?”

The fix: Complete Stage 1 (Foundation) before evaluating any tools. Your AI Purpose Statement filters tool decisions.

2. Skipping Assessment

The trap: Assuming the organization is ready because leadership is excited.

The fix: Run the readiness survey. Cross-functional differences often surprise people—Marketing may feel ready while Ops does not.

3. Pilot Purgatory

The trap: Experiments run indefinitely without clear success criteria or scale decisions.

The fix: Define success metrics upfront. Use midpoint reviews with explicit continue/pivot/stop decisions.

4. Change Fatigue

The trap: People feel AI is happening to them, not with them.

The fix: Involve front-line voices in Foundation and Design. Celebrate small wins publicly. Make learning loops visible.

5. Governance Gaps

The trap: Moving fast without establishing decision rights, risk frameworks, or ethical review.

The fix: Build governance into Stage 3 (Design) before launching pilots. Refresh quarterly during Sustainment.

Warning Signs You Need Help

Watch for these signals:

• Silent resistance—people nod in meetings but don't adopt in practice
• Fragmented priorities—different teams pursuing different AI visions
• Stalled pilots—experiments that never conclude or scale
• Leadership disconnect—executives asking “where's the ROI?” without context
• Cultural friction—fear, cynicism, or active pushback from teams
• Repeated false starts—multiple initiatives that fail to gain traction

When to Bring in Expertise

Consider external support when:

• You lack internal change management or organizational development experience
• Cultural resistance is stronger than internal credibility can address
• You need objective facilitation for difficult conversations
• Scale or complexity exceeds your team's bandwidth
• Stakes are high and failure is costly
• You want to accelerate what would otherwise take quarters of learning

“The right consultant doesn't do the work for you—they build your organization's capability to do the work themselves.”

Making It Stick

The framework succeeds when:

• AI practices are institutionalized into normal operations
• The organization updates itself through quarterly renewal cycles
• People at all levels can articulate purpose and participate in improvement
• Learning is visible, celebrated, and feeds forward into new initiatives
• Governance evolves with technology and organizational needs

Final thought: AI transformation is not about the technology. It's about how humans and organizations adapt to new possibilities. The framework provides structure; your leadership provides the will.

Read the Full Series

• Part 1: Why Most AI Initiatives Fail
• Part 2: Building Alignment Before Building AI
• Part 3: Diagnosing AI Readiness
• Part 4: From Insights to Pilots
• Part 5: Launching AI with Learning Loops
• Part 6: Embedding AI into Operations
• Part 7: Pitfalls and When to Get Help (You are here)