Disruptive change is often framed as a technology problem. In practice, it is a people, process, and leadership challenge.
In this episode of Make or Break, Mo Berkner Boyt sat down with Dr. Alexis Fink to discuss what leaders need to do differently when navigating transformation, especially in the context of AI activation, organizational redesign, and workforce change. The conversation covered a wide range of practical topics, from data quality and workforce metrics to systems thinking and leadership behavior.
The central idea was organizations thrive when leaders align people, process, and technology in ways that create trust and clarity, resulting in better transformation outcomes. Simply pushing harder on the technology doesn’t work.
For leaders responsible for AI adoption, org design, workforce planning, or transformation strategy, this conversation offers a grounded reminder: successful change depends on what is happening deep inside the organization, not only at the top.
The real work of transformation is helping people thrive, not just survive
One of the strongest themes in the conversation was the distinction between surviving change and thriving through it.
Dr. Fink described transformation as an ongoing effort to ensure the organization is fitted to the challenges and opportunities of the present, not the past. Organizations cannot expect the same level of success using outdated structures, habits, and decision patterns when the surrounding environment has fundamentally changed.
Many organizations still approach transformation as preserving as much of the current model as possible while layering on new technology. The problems this creates are predictable. When leaders try to hold everything constant while the context is shifting, teams experience tension, confusion, and declining results.
A more effective approach is to identify what is truly core to the organization’s identity and then adapt the rest with discipline. That creates space for innovation without losing strategic coherence.
From a C2IQ perspective, this is where Human Risk Intelligence becomes essential. When leaders can measure how people are experiencing disruption, where trust is weakening, and where change capacity is uneven, they are better positioned to guide transformation with precision instead of reacting too late.
People do not resist change. They resist being changed.
A particularly useful insight from the episode was Dr. Fink’s reframing of a familiar challenge: people are not inherently opposed to change. They are opposed to change that is imposed without agency, context, or relevance.
For leaders trying to drive adoption understanding this distinction is key. People often welcome change when it aligns with their needs, aspirations, or lived reality. Organizations are no different.
If leaders want teams to engage productively with any transformation, for example new technologies, structures, or workflows, they need to involve the people closest to the work. Those individuals usually understand the actual tasks and pain points better than senior leadership does.
This is especially true in AI transformation. While executives may define the strategic intent, the best knowledge of what can be automated and what should remain human-led lives much deeper in the organization. The people doing the work often have a better understanding where risk is concentrated as well.
That is why successful change requires ongoing dialogue, not one-way communication. It also requires psychological safety, so people can surface friction early and contribute data-driven insights in order to shape the path forward.
AI activation works best when leaders focus on the right use cases
The conversation also addressed one of the biggest questions facing organizations today: what actually works in AI activation?
According to Dr. Fink, many organizations are still in the experimentation phase. Despite the intensity of public discussion, the reality inside most companies is more nuanced. Leaders are still learning where AI delivers value and where it introduces risk. They are also grappling with understanding where human judgment remains indispensable.
One of the most practical takeaways was a simple framework for identifying strong early AI use cases. The most effective use cases tend to be:
- Low risk where the consequences of errors are manageable or easily mitigated
- Low complexity where the task can be completed in a limited number of steps
- High volume where the scale of the work justifies building the system and quality controls around it
Not every workflow is a strong fit for automation. Even in organizations investing deeply in AI, there is still substantial human work involved in oversight, approval, interpretation, and judgment.
That reinforces an important truth: the goal is not simply automation. The goal is better outcomes.
Most AI initiatives are creating new opportunity, not simply reducing headcount
Another notable point from the episode was the observation that many substantive AI implementations are not primarily reducing headcount. Instead, they are creating opportunities for organizations to do work that was previously slow, too fragmented, or resource-intensive.
If leaders treat AI only as a cost-cutting mechanism, they increase fear and erode trust. If they position AI as a way to free people for more meaningful, creative, and strategic work, they open a more productive conversation about capability building, redesign, and workforce evolution.
This does not remove risk. It does, however, create a more honest foundation for change. For transformation leaders, the implication is clear: AI strategy should include workforce strategy from the beginning.
Before AI scales, get the data house in order
One of the most actionable parts of the conversation centered on data readiness.
Dr. Fink made the point plainly: before organizations can expect strong results from AI, they need to get their arms around the data they already have or lack. She advised gaining an understanding of the quality, structure, and relevance of the data they are feeding into their systems. Many organizations have large amounts of data, but that does not mean they have usable data.
In practice, data may be:
- Sparse or incomplete
- Outdated
- Trapped in disconnected systems
- Labeled inconsistently across functions
- Structured around transactions rather than the underlying reasons those transactions occurred
The episode offered vivid examples of data fields with the same name but different meanings, as well as fields that were dramatically inaccurate because no one had revisited how the information was captured over time.
The lesson: do not confuse data volume with data quality.
What should a data audit include?
If your organization is preparing for AI activation or broader transformation, start by asking:
- What data source are we relying on?
- When was this data collected or last updated?
- Does this data reflect current reality?
- Are definitions consistent across systems and teams?
- Can we connect the data we need across financial, people, and operational systems?
This kind of audit may not feel high-profile, but it is foundational. From a Human Risk Intelligence standpoint, this is especially important because weak data creates blind spots. If the underlying data is flawed, leaders will struggle to identify where burnout risk or adoption barriers are actually emerging.
Use a microscope and a telescope when looking at people metrics
One of the most valuable parts of the discussion focused on workforce metrics, especially attrition.
Dr. Fink emphasized that the usefulness of any people metric is highly contextual. Metrics only become valuable when leaders understand why they matter and what decision they inform.
That is why a “microscope and telescope” approach is so effective. Leaders need the telescope to understand the broader strategic environment. They also need the microscope to see what is happening inside specific teams, levels, or functions.
A metric such as attrition can be highly informative when examined at a granular level. It may reveal pockets of strain, management issues, role design problems, or localized barriers to thriving. But the same metric can become misleading when used too broadly or too casually.
Which people metric should leaders track during change?
A useful answer from the conversation was this: track metrics that reveal meaningful differences inside the system.
For example, attrition patterns across teams or levels can help leaders identify where problems are concentrated. That allows for better intervention than looking only at companywide averages.
The point is not that one metric is always correct. The point is that leaders need to ground metrics in strategy, context, and the level of analysis that actually reflect how people experience change.
When attrition becomes the wrong metric
The conversation also offered an important caution: attrition is often overused.
It is easy to count and visible. But it is also influenced by many variables, including labor market conditions, management quality, business performance, role design, and broader organizational shifts.
That means it can become a poor measure of whether a specific initiative is working. For example, using companywide attrition to evaluate a new onboarding program may tell leaders very little. There is too much dilution between the intervention and the outcome.
A more rigorous approach is to ask what the metric is actually supposed to reveal and whether behavior-based evidence may offer better insights. Before tracking anything, ask:
- Why does this matter?
- How will we know?
- What decision will this metric improve?
- Are we measuring behavior, perception, or both?
That line of questioning helps organizations move away from surface-level reporting and toward more actionable intelligence.
Systems thinking is not optional in complex change
When Mo asked about tools and frameworks, Dr. Fink pointed to systems thinking less as a tool and more as a philosophy as transformation challenges are not merely complicated. They are complex.
A complicated problem has a right answer that can be calculated. A complex problem requires leaders to make informed judgments inside a system of moving parts, feedback loops, and human behavior.
Culture, employee experience, AI adoption, and organizational redesign all fall into the complex category. That is why systems thinking is so valuable. It pushes leaders to look beyond isolated symptoms and ask how different parts of the organization interact.
For organizations dealing with transformation fatigue, this is a critical shift. It helps leaders stop treating people challenges as disconnected issues and start seeing them as part of a larger system that includes incentives, workflows, management practices, trust, and technology design.
What makes or breaks change success? Alignment.
Late in the conversation, Dr. Fink gave a succinct answer to a big question: what makes or breaks success in disruptive change?
The answer was not technology. It was leadership support and alignment across people, process, and technology.
She was clear that symbolic support doesn’t cut it. It must be real support.
That means leaders remove blockers, make decisions when teams are stuck, allocate resources where needed, and create the conditions for progress. It also means involving the people doing the work so that transformation reflects operational reality, not just strategic intent.
What does it mean for leaders to remove blockers?
The episode described several practical examples of blocker removal:
- Resolving conflict between teams when peers cannot reach a decision
- Standing up missing systems or data sources required for progress
- Allocating budget or focused staffing to accelerate a priority initiative
In each case, the principle is the same: when teams are stuck, leadership has to do more than encourage progress. Leaders need to actively create it.
A practical framework: Start, stop, continue
One especially useful framework from the conversation was the simple discipline of start, stop, continue.
During periods of transformation, leaders should identify:
- What must continue because it is essential to identity or performance
- What must stop because it no longer serves the organization
- What must start because future success depends on it
This framework helps leaders move beyond vague enthusiasm for change and into concrete organizational design choices. It also surfaces second-order questions that matter deeply in execution:
- How should teams be organized?
- What skills should we recruit for?
- How should incentives or pay structures evolve?
- Which habits and legacy practices are now misaligned with strategy?
Asking these question and designing around the answers are an integral part of the work of transformation.
Key takeaways for leaders navigating AI and organizational change
If you are leading transformation in your organization, this conversation points to several practical priorities:
- Treat transformation as a human system, not only a technology rollout.
- Involve the people closest to the work early and often.
- Use AI where risk is manageable, complexity is low, and volume is high.
- Audit your data before expecting AI to deliver reliable value.
- Use people metrics contextually rather than relying on broad averages.
- Apply systems thinking to understand how people, process, and technology interact.
- Focus leaders on removing blockers, not only communicating priorities.
- Build trust through clarity, agency, and psychological safety.
For C2IQ, these themes reinforce a core belief: organizations need measurable, data-driven insights into human capability, readiness, and risk if they want transformation to succeed. Technology can accelerate change, but only people can absorb it, adapt to it, and sustain it.
Frequently asked questions
What is human-centered change management?
Human-centered change management is an approach to transformation that focuses on how people experience, interpret, and adapt to change. It emphasizes trust, agency, psychological safety, and practical support, not just process compliance.
What makes AI adoption successful in organizations?
AI adoption succeeds when leaders align people, process, and technology. Strong use cases, clean data, active leadership support, and involvement from the people doing the work all improve adoption outcomes.
Why is data quality so important for AI transformation?
Data quality matters because AI systems depend on the accuracy, consistency, and relevance of the information they use. If the underlying data is outdated, fragmented, or mislabeled, the outputs will be less reliable and less useful.
What are the best early use cases for AI at work?
The strongest early AI use cases are typically low risk, low complexity, and high volume. These are workflows where organizations can build reliable processes and oversight without exposing the business to unnecessary risk.
Should leaders use attrition as a core change metric?
Attrition can be useful, but only in context. It is more informative when analyzed at a team or segment level than when used as a broad companywide score for evaluating a specific program.
What does it mean to align people, process, and technology?
Alignment means designing transformation so that workflows, systems, leadership behaviors, and employee realities all support the same goal. If one part of the system changes without the others, friction and failure become more likely.
How can leaders reduce friction during disruptive change?
Leaders can reduce friction by involving employees early, clarifying what will change and what will remain stable, removing operational blockers, and using data-driven insights to identify where support is most needed.
Final thought
The most useful message from this conversation may be the simplest one: transformation succeeds when leaders stop treating people as a variable to manage after the fact.
People are the system through which change happens.
When leaders pair strategy with trust, technology with readiness, and ambition with actionable data intelligence, they create the conditions for organizations to shift from enduring disruption to thriving through it.