Wrestling with Pigs – Why Change Leaders Get Stuck in the Mud
As change and transformation professionals you know the feeling: you’re deep in a change initiative, but progress is elusive. No matter how many workshops you run, how many stakeholder meetings you hold, or how many communications you send, the same issues keep resurfacing. The team feels exhausted, yet the problem remains – like wrestling with a pig, you end up covered in mud and no closer to a solution.
This vivid metaphor, drawn from Pete Lindsay’s Pig Wrestling, captures the frustration of grappling with persistent organizational challenges. The core message is clear: wrestling with problems using the same perspective and tactics only leads to fatigue and frustration, not progress. The mud represents the confusion, emotional drain, and sense of futility that accompanies repeated, ineffective attempts at problem-solving.
Recognising the Signs of “Pig Wrestling”
Before you can escape this cycle, it’s crucial to recognize when you’re stuck in it. Lindsay and Bawden describe several telltale signs:
You’ve tried every solution you can think of, but nothing works.
The problem feels endless – no matter what you do, it persists.
You and your team feel drained, demotivated, and stuck.
These symptoms are not just signs of a tough challenge – they’re indicators that you may be framing the problem incorrectly from the outset. When we approach issues from a limited or habitual perspective, we inadvertently block ourselves from seeing new angles or opportunities. As a result, our efforts amount to little more than wrestling a pig: exhausting, messy, and ultimately unproductive.
The Trap of the “Wrong Frame”
Every organization has its share of recurring “problems”:
There are too many change resistors.
Too many stakeholders aren’t informed about the changes.
Leaders are not supporting the change.
Stakeholders are not owning the change.
These statements are familiar to anyone leading transformation. Yet, according to Pig Wrestling, these are often not the real problems but rather symptoms of a deeper issue: a restricted or flawed framing of the challenge. When we define the problem too narrowly or accept it at face value, we limit our ability to find effective solutions.
Why We Get Stuck
The reason we get trapped in this cycle is psychological as much as organizational. Humans are wired to seek patterns and rely on past experiences. When faced with a stubborn issue, we tend to double down on what we know, trying variations of the same approaches. This creates a feedback loop: the more we struggle, the more entrenched we become in our current view, and the less likely we are to see the problem from a fresh perspective.
Pig Wrestling challenges us to step back, question our assumptions, and “clean” our thinking. Only by reframing the problem – by stripping away the mud of our biases, stories, and habitual responses – can we unlock new solutions and drive meaningful change.
Cleansing the Problem – Reframing the Four Classic Change Challenges
Transformation leaders are often confronted with recurring “problems” that seem intractable: too many change resistors, uninformed stakeholders, disengaged leaders, and lack of stakeholder ownership. According to Pete Lindsay’s Pig Wrestling, these are often not problems in themselves, but symptoms of how we’ve chosen to frame the challenge. By cleansing the problem – stripping away assumptions and viewing it from new angles – we can unlock more effective solutions.
1. “There Are Too Many Change Resistors”
The Trap: It’s easy to label widespread resistance as a people problem or a sign of cultural inertia. This framing assumes resistance is an obstacle to be overcome, rather than a signal to be understood.
Reframe the Problem: Ask: What are people really resisting? Are they lacking information, skills, or a sense of security? Is the pace or nature of change overwhelming? By reframing, resistance becomes a source of insight rather than frustration.
Recommendations:
Listen and diagnose: Use structured listening sessions and feedback tools to uncover the root causes of resistance. Often, resistance points to unmet needs or unaddressed fears.
Clarify the “why”: Ensure that the business reasons for change are clearly communicated and tailored to different groups.
Empower ownership: Shift focus from “overcoming resistance” to “enabling participation.” Involve resistors in solution design, making them co-creators rather than obstacles.
Invest in training and support: Resistance often stems from a lack of confidence or skills. Comprehensive onboarding, upskilling, and just-in-time support resources can ease anxiety and build capability.
2. “Too Many Stakeholders Aren’t Informed About the Changes”
The Trap: This framing assumes information alone is the issue, and the solution is simply to communicate more. But information overload, unclear messaging, and lack of targeted communication can all contribute to the problem.
Reframe the Problem: Ask: Are stakeholders receiving the right information, at the right time, in the right way? Is the communication two-way, allowing for feedback and clarification?
Recommendations:
Segment and tailor communication: Not all stakeholders need the same information. Map stakeholder groups and customize messages to their interests and concerns.
Engage early and often: Involve stakeholders in the planning and decision-making process from the outset, not just during rollout. Use surveys, focus groups, and regular updates to foster transparency and trust.
Enable dialogue: Move beyond broadcast communication to two-way channels – Q&A sessions, feedback loops, and forums for open discussion.
Leverage data: Track engagement with communications (open rates, feedback, participation in sessions) to identify gaps and adjust strategies in real time.
3. “Leaders Are Not Supporting the Change”
The Trap: It’s tempting to see lack of leadership support as a personal failing or lack of commitment. This framing can breed frustration and blame, rather than constructive action.
Reframe the Problem: Ask: What barriers are preventing leaders from engaging? Do they lack clarity on their role, feel excluded from planning, or have competing priorities?
Recommendations:
Clarify expectations: Define and communicate the specific actions and behaviours expected from leaders at each stage of the change process.
Provide support and resources: Equip leaders with the information, tools, and training they need to champion change. This includes regular briefings, leadership coaching, and peer support networks.
Model the change: Leaders must visibly demonstrate the mindsets and behaviours required for success. Celebrate and publicize leadership actions that align with the change vision.
Create accountability: Build change leadership into performance objectives and reward systems, ensuring leaders are recognized for their role in driving transformation.
4. “Stakeholders Are Not Owning the Change”
The Trap: This problem is often framed as a lack of motivation or engagement among stakeholders, leading to frustration and disengagement among change leaders.
Reframe the Problem: Ask: Have stakeholders been given real opportunities to shape the change? Is there a sense of shared ownership, or are they passive recipients of decisions?
Recommendations:
Co-create solutions: Involve stakeholders in designing and implementing change initiatives. Collaborative decision-making builds ownership and accountability.
Foster shared purpose: Communicate how the change aligns with stakeholders’ values and goals. Make the benefits tangible and relevant to their daily work.
Recognize and celebrate contributions: Publicly acknowledge stakeholder input and successes, reinforcing their role as partners in the change journey.
Monitor and adapt: Use data to track stakeholder engagement and adjust strategies as needed. Regular feedback and course correction keep stakeholders invested and empowered.
By cleansing and reframing these four classic problems, transformation leaders can move from wrestling with pigs – stuck and exhausted – to leading purposeful, energizing change.
The Practical Application – Problem Cleansing in Action
For transformation and change professionals, the real power of Pete Lindsay’s Pig Wrestling lies in translating the “problem cleansing” mindset into daily leadership practice. The framework is not just a metaphor; it’s a practical toolkit for breaking free from the mud of persistent challenges and unlocking new pathways to change. Here’s how to apply the principles of problem cleansing, step by step, to the four classic change problems.
Step 1: Step Back and Observe
Before diving into solutions, pause. Take a step back from the “mud” and observe the problem objectively. Ask yourself:
Is this truly the problem I need to solve, or am I reacting to symptoms?
What assumptions am I making about this situation?
Have I seen the reality on the ground, or am I relying on second-hand accounts?
This initial pause is essential for gaining perspective and avoiding the trap of habitual responses.
Step 2: Remove the Frame – Challenge Your Assumptions
Every problem is surrounded by a “frame” – the stories, biases, and judgments we attach to it. To cleanse the problem, deliberately remove that frame:
What labels have I applied to people or situations (e.g., “change resistors”)?
What if my current framing is limiting my options?
How might others involved describe the problem differently?
Empathy is critical here. Seek to understand the perspectives and motivations of all stakeholders, especially those you might have labeled as obstacles.
Step 3: Gather the Facts – Separate Data from Story
With the frame removed, focus on the facts:
What is actually happening, as opposed to what I believe is happening?
What data do I have, and what data do I need?
When does the problem occur, and when does it not?
For example, if you believe “too many stakeholders aren’t informed,” review actual communication metrics, feedback, and engagement data. Are there patterns or exceptions that challenge your assumptions?
Step 4: Explore Exceptions – When Is the Problem Not a Problem?
One of the most powerful techniques in the Pig Wrestling approach is to look for exceptions:
Are there times or contexts where the problem doesn’t appear?
What’s different about those situations?
Can those conditions be replicated or scaled?
If “leaders are not supporting the change,” are there instances where certain leaders are engaged? What enables their support, and how can those enablers be extended to others?
Step 5: Define a More Solvable Problem
After gathering facts and exploring exceptions, redefine the problem in a more actionable way:
Instead of “too many change resistors,” the problem might become “we lack early feedback loops to understand concerns.”
Instead of “stakeholders are not owning the change,” it could be “our process does not provide meaningful opportunities for stakeholder input.”
A well-cleansed problem statement is clear, specific, and focused on factors you can influence.
Step 6: Experiment and Iterate
Apply new solutions based on your cleansed problem definition. Use data to monitor outcomes and remain open to further reframing if progress stalls. The Pig Wrestling framework encourages short cycles of experimentation and reflection, rather than long, exhausting battles with the same muddy challenge.
Integrating Data and Evidence
Throughout the process, data is your ally. Use it to:
Test assumptions and challenge stories.
Identify patterns, exceptions, and leverage points.
Measure the impact of interventions and adapt in real time.
For example:
Track resistance levels before and after targeted listening sessions.
Measure stakeholder engagement with different communication channels.
Analyze leadership behaviours and their correlation with team adoption rates.
Building a Culture of Problem Cleansing
Finally, embed these practices into your team:
Encourage curiosity and challenge habitual thinking.
Reward reframing and creative problem definition.
Use coaching and reflective practices to help teams step back, remove frames, and focus on facts.
Problem cleansing is not a one-off exercise but a continuous discipline. By applying these steps, you’ll move from wrestling with pigs to leading purposeful, sustainable transformation – no mud required.
“When you wrestle with a pig, you get dirty and the pig enjoys it. But when you clean your thinking, you create the change you need.”
The numbers tell a story that most change leaders already sense. IBM’s 2025 CEO study, surveying 2,000 executives globally, found that only around 25% of AI initiatives deliver expected ROI, and just 16% have scaled enterprise-wide. Investment in AI is accelerating at double-digit rates. The returns are not keeping pace. The gap is not technical. It is human. And it will not be closed by change management practices designed for a different era.
Change management in the digital age faces a challenge that goes beyond scale or speed. The tools, assumptions, and governance models that served change functions well through the ERP rollouts and restructures of the 2000s and 2010s were designed for discrete, definable transformations with identifiable endpoints. Digital transformation, AI adoption, and the automation of work do not have endpoints. They are ongoing conditions. Managing them as projects produces predictable results: partial adoption, underrealised value, and change fatigue that compounds with each successive initiative.
The organisations navigating digital transformation most effectively are not those with the biggest change budgets. They are those that have genuinely updated their change management model for the digital context, treating change capability itself as a strategic asset rather than a delivery function.
The digital transformation gap that change management must close
The scale of underperformance in digital transformation is well documented. Deloitte’s research on digital transformation value identifies three failure patterns that recur across industries: technology deployed without corresponding work redesign, adoption treated as a training problem rather than a behaviour change problem, and benefits realisation measured at go-live rather than at the point where new ways of working are actually embedded.
All three failure patterns are change management failures, not technology failures.
The IBM CEO data reinforces this. In 2026, twice as many workers across age groups say they would embrace greater AI use by their employers rather than resist it. Employee sentiment toward AI is broadly positive. The adoption gap is not about resistance. It is about the absence of the structural, managerial, and environmental conditions that convert positive sentiment into actual behaviour change. This is precisely the domain of change management. And precisely the area where traditional change management approaches are most underpowered.
What makes change management in the digital age different
Three structural characteristics distinguish digital transformation from the changes that traditional change management frameworks were built for.
There is no go-live
Classic change management models, whether ADKAR, Kotter’s 8 steps, or the Prosci methodology, are structured around a transition: a defined current state, a defined future state, and a change journey between them. Digital transformation does not conform to this structure. AI capabilities in use today are materially different from those available 18 months ago, and will be different again 18 months from now. The “future state” keeps moving.
This means that what organisations actually need to build is not a capacity to manage a specific digital change, but an adaptive organisational capability to absorb continuous digital evolution. That is a fundamentally different capability to develop and a fundamentally different change management challenge to address.
The impact is highly fragmented by role
A major ERP implementation affects large groups of employees in broadly similar ways: new system, new processes, new reporting lines. Digital transformation and AI adoption affect different roles in radically different ways. A finance analyst’s experience of AI adoption has almost nothing in common with a customer service representative’s. A supply chain planner and a legal counsel may both be in the same AI transformation programme but need entirely different support.
Generic change communications and enterprise-wide training programmes do not work well in this environment. Effective change management in the digital age requires function-level and role-level customisation at a depth that most change functions have not previously needed to operate at.
Middle management is both the opportunity and the obstacle
Gartner’s 2025 CHRO research found that 78% of CHROs agree workflows and roles will need to change to realise the value of AI investments. The people who must actually make those workflow and role changes happen are middle managers. They translate digital strategy into day-to-day practice. They also face the most immediate personal disruption from the changes they are asked to enable.
Change management approaches that treat managers primarily as a communication channel, rather than as a group with their own adoption challenge and their own need for specific support, consistently underperform. The manager layer is where digital transformation succeeds or stalls.
Data and measurement in the digital age
One of the defining features of digital transformation is the availability of adoption data. Most digital platforms generate detailed usage data. Organisations now have, or can have, precise information about which employees are using new systems and tools, how frequently, in what ways, and with what outcomes.
Traditional change management largely operated without this data. Communications were sent, training was attended, and surveys were occasionally administered. Whether behaviour had actually changed in meaningful ways was often a matter of judgement rather than evidence.
The digital age removes this ambiguity for organisations willing to use the data available. Key metrics that effective change functions track in digital transformation include:
Active usage rates by role group and function (not just platform access)
Time savings realised in specific processes, compared against baseline
Quality or output measures for AI-assisted work versus previous work
Support ticket and workaround patterns, which indicate where adoption is failing
Manager-reported team behaviour change, gathered through structured check-ins
The risk with digital adoption data is conflating access with adoption. A person who logs into a platform once a week is not the same as a person who has genuinely changed how they work. Effective measurement tracks the second thing, not the first.
Automation and what it means for the change management function itself
The digital age is also changing how change management work is done, not just what it is managing. Change functions are beginning to automate significant portions of the administrative and analytical work that previously consumed change practitioner time: impact assessment compilation, status reporting, communication scheduling, data aggregation across programmes.
This shift has two implications worth examining.
The first is a productivity gain. Change practitioners who are no longer spending days compiling portfolio heat maps in spreadsheets have time to do the work that requires human judgment: stakeholder conversations, resistance diagnosis, sponsor coaching, and the nuanced facilitation that data analysis cannot replace.
The second is a capability shift. The change practitioner of the digital age needs to be comfortable working with data and platforms in ways that were optional for practitioners in earlier generations. Interpreting adoption dashboards, working with automated workflow tools, and communicating findings in data-fluent ways are becoming baseline expectations rather than specialist skills.
Building a digital-age change management capability
For change leaders building or rebuilding their function’s capability for the digital context, the practical work happens in four areas.
Updating the impact methodology. Traditional impact assessment categories, such as process, role, technology, and structure, need to be extended to capture AI-specific dimensions: the degree to which a role’s core tasks are being automated or augmented, the learning curve associated with AI-enabled ways of working, and the interaction effects when multiple digital changes land simultaneously on the same employee group.
Investing in role-level differentiation. The days of enterprise-wide change communications being the primary engagement mechanism are over for major digital transformations. Effective change functions in the digital age develop function-specific change plans, with tailored messaging, use-case-specific training, and peer champion networks built around specific communities of practice rather than the whole organisation.
Building adaptive governance. Digital transformation moves faster than traditional programme governance. Change plans written at programme initiation will be outdated within months as capabilities evolve and adoption data comes in. The governance model needs to support continuous plan adaptation: regular portfolio reviews, rolling 90-day action planning, and the authority to reallocate resources based on adoption evidence rather than original project plans.
Using digital platforms for portfolio visibility. Managing the cumulative digital change burden on employee groups requires portfolio-level visibility that manual approaches cannot reliably provide. Platforms such as The Change Compass aggregate impact data across programmes, track adoption by function and role group, and enable the continuous monitoring that adaptive change governance requires. This is not a luxury for large change functions. It is the infrastructure that makes portfolio-level decision-making possible.
Where to start
For change leaders whose organisations are in the middle of active digital transformation programmes with traditional change management in place, the most useful first step is a diagnostic of the current approach against the digital age requirements.
The diagnostic questions are practical:
Are you measuring actual behaviour change or platform access?
Do you have function-specific change plans, or enterprise-wide plans applied uniformly?
How are you managing the cumulative digital change load on specific employee groups?
What is your process for adapting the change approach as adoption data comes in?
Are your managers being supported as a group with their own adoption challenge, or managed primarily as a change communication channel?
Most change functions running traditional approaches through digital programmes will find significant gaps in these areas. The gap that typically generates the fastest improvement when closed is measurement: moving from activity metrics to adoption metrics creates the feedback loop that enables everything else to improve.
Frequently asked questions
What is change management in the digital age?
Change management in the digital age refers to applying change management principles and practices to the specific challenges of digital transformation, AI adoption, and the automation of work. It extends traditional change management to address the absence of a fixed endpoint, the highly fragmented role-level impact of digital change, and the availability of adoption data that enables evidence-based course correction throughout the change journey.
Why do digital transformation programmes fail to deliver expected value?
The primary causes are change-related, not technical. Workflows are not redesigned to take advantage of new digital capabilities, middle managers are not supported as a group with their own adoption challenge, measurement focuses on system access rather than behaviour change, and change plans are not adapted as adoption evidence accumulates. IBM research found that only around 25% of AI initiatives deliver expected ROI, largely for these reasons.
How is digital transformation different from managing a standard technology change?
Digital transformation differs in three important ways: there is no defined future state because digital capabilities evolve continuously; the impact on different roles is highly fragmented, requiring function-level rather than enterprise-wide approaches; and the adoption data available through digital platforms enables a measurement-led approach that traditional change management rarely applied.
What metrics should you track in digital transformation change management?
The most informative metrics go beyond platform access to measure actual behaviour change: active usage rates by role group, time savings realised in specific processes, quality of AI-assisted output versus previous output, support ticket patterns indicating where adoption is failing, and manager-reported team behaviour change. These give a more honest picture of adoption progress than usage statistics alone.
How do you manage the cumulative digital change load on employees?
Managing cumulative load requires portfolio visibility: knowing what digital changes are landing on which employee groups at what time, and aggregating impact to identify when load is approaching the point where adoption quality begins to deteriorate. Portfolio change management platforms enable this aggregation and provide the early warning signals that allow sequencing adjustments before saturation becomes visible in adoption data.
References
IBM. CEO Study: CEOs Double Down on AI While Navigating Enterprise Hurdles (2025). https://newsroom.ibm.com/2025-05-06-ibm-study-ceos-double-down-on-ai-while-navigating-enterprise-hurdles
IBM Institute for Business Value. 5 Trends for 2026. https://www.ibm.com/downloads/documents/us-en/1443d5df79cf4c92
Deloitte Insights. Unleashing Value from Digital Transformation: Paths and Pitfalls. https://www.deloitte.com/us/en/insights/topics/digital-transformation/digital-transformation-value-roi.html
Gartner. Gartner Says CHROs’ Top Priorities for 2026 Center Around Realising AI Value and Driving Performance (October 2025). https://www.gartner.com/en/newsroom/press-releases/2025-10-02-gartner-says-chros-top-priorities-for-2026-center-around-realizing-ai-value-and-driving-performance-amid-uncertainty
AIHR. 15 Important Change Management Metrics To Track in 2026. https://www.aihr.com/blog/change-management-metrics/
The Traditional Path: Learning-Focused Change Management
For decades, the prevailing wisdom in organisational change management has been to build capability through education and training. Senior leaders and managers are sent to workshops, seminars, and e-learning modules to develop their understanding of change frameworks, stakeholder engagement, resistance management, and communication strategies. The rationale is clear: if people know more about change, they will manage change more effectively.
However, while this approach is logical and well-intentioned, its impact is often limited. The learning-focused model is inherently slow and resource-intensive. It requires significant investment in curriculum development, scheduling, and facilitation. More critically, it assumes that knowledge acquisition will naturally translate into changed behaviours and improved business results. In practice, this is rarely the case.
The Limits of Learning-First Approaches
Several challenges hinder the effectiveness of traditional capability-building:
Delayed Impact: The time lag between learning and application is significant. Leaders may attend a session on change management, but by the time they face a real change challenge, much of the content is forgotten or seems irrelevant to the context.
Low Engagement or Motivation: Not all leaders are equally motivated to become change experts. Mandatory training can breed resistance or apathy, especially if participants do not see immediate relevance to their roles.
One-Size-Fits-All: Standardised training often fails to address the unique dynamics, culture, and needs of different teams or business units.
Lack of Real-Time Feedback: Traditional approaches rarely provide leaders with ongoing feedback about their change leadership effectiveness. This makes it difficult to adjust strategies in real time or learn from mistakes as they happen.
Why Learning Alone Doesn’t Drive Business Results
The core issue is that learning, in isolation, does not guarantee behaviour change or business impact. Senior leaders may understand the theory of change management but struggle to apply it under pressure, in complex environments, or when faced with competing priorities. The disconnect between knowing and doing is well-documented in management literature and is particularly acute in the context of large-scale transformation.
Moreover, traditional change management often relies on intuition and anecdotal evidence to guide decisions. Leaders make assumptions about what will work, based on past experience or prevailing best practices, rather than on empirical evidence from their own organisations. As a result, change initiatives may be misaligned with actual business needs, and the true drivers of resistance or adoption remain hidden.
A New Paradigm: Data-Driven and Experiential Change Leadership
In contrast, a growing number of organisations are achieving significant change maturity by taking a fundamentally different approach. Instead of focusing primarily on education, they are embedding change capability through data and experiential leadership. This approach is not about discarding learning altogether—it is about complementing it with real-time insights, feedback loops, and hands-on experience.
Data-driven change management extends traditional methods by integrating robust processes for data visibility, analysis, interpretation and application. Leaders are equipped not just with knowledge, but with visibility into how change is progressing, where the risks and opportunities lie, and what interventions are most effective in their specific context.
The Power of Data in Change Leadership
When leaders have access to timely, relevant data about change readiness, change capacity, adoption rates, and business impact, several powerful shifts occur:
Informed Decision-Making: Leaders can move beyond gut-feel and make evidence-based decisions about where to focus their attention and resources.
Agility and Responsiveness: Real-time data allows leaders to identify emerging issues, test new strategies, and rapidly adjust course based on what is working and what is not.
Democratisation of Insight: By making change data visible at multiple layers of the organisation, leaders at all levels can take ownership of change outcomes and contribute to collective success.
Continuous Improvement: Data-driven feedback loops enable ongoing learning and adaptation, rather than one-off interventions.
Experiential Leadership: Learning by Doing
Complementing the data-driven approach is a focus on experiential leadership. Instead of passively absorbing information, leaders are actively engaged in managing real change initiatives, supported by data and feedback. They learn by doing—experimenting with different tactics, observing the results, and refining their approach in real time.
This experiential model is particularly effective because it:
Bridges the Knowing-Doing Gap: Leaders apply change management principles in the context of their actual work, making learning relevant and sticky.
Builds Confidence and Competence: Hands-on experience, supported by data, helps leaders develop the judgement and skills needed to navigate complex change.
Fosters Accountability: When leaders can see the impact of their actions (or inaction) through data, they are more likely to take responsibility for outcomes.
Case in Point: The Impact of Data and Visibility
In my own experience working with organisations on large-scale transformations, I have seen first-hand how the democratization of change data can transform outcomes. When leaders at different layers of the organisation are given visibility into change metrics—such as adoption rates, engagement levels, and business impact—they are better prepared to lead, more agile in their response, and more effective in driving results.
For example, one organisation implemented a change dashboard that provided real-time insights into change adoption, change readiness and the impact and velocity of change across business units. Leaders used this data to identify hot spots, test new engagement strategies, and track the effectiveness of their interventions. The result was a faster, smoother transition with higher levels of buy-in and measurable business benefits.
The Mechanics of Data-Driven Change Leadership
1. Establishing a Change Data Framework
The foundation of data-driven change leadership is a robust framework for collecting, analysing, and sharing change-related data. This framework should capture both quantitative and qualitative insights, providing a holistic view of how change is progressing.
Key Components:
Change Readiness Assessments: Regular pulse surveys to gauge how prepared teams are for upcoming changes.
Adoption Metrics: Tracking usage of new systems, processes, or behaviours post-implementation.
Engagement Analysis: Using surveys, focus groups, or digital tools to understand how employees feel about the change.
Business Impact and Capacity Measures: Linking change activities to key performance indicators (KPIs), such as productivity, customer satisfaction, employee experience or financial outcomes.
Practical Tip: Start small. Pilot your data framework in one business unit or for one major initiative. Refine your tools and processes before scaling across the organisation.
2. Democratising Change Data
A critical differentiator in mature change organisations is the democratisation of data. Instead of hoarding insights at the executive or project management level, make data visible and accessible to leaders and teams at every layer.
How to Achieve This:
Change Dashboards: Develop interactive dashboards that display real-time metrics relevant to each audience—executives, middle managers, and frontline supervisors.
Regular Data Reviews: Embed data discussions into leadership meetings, project stand-ups, and team huddles.
Transparent Communication: Share both successes and challenges openly, encouraging a culture of learning and continuous improvement.
Practical Tip: Don’t overwhelm people with data. Curate dashboards to show only the most actionable metrics for each audience.
3. Enabling Data-Driven Decision Making
With data in hand, leaders must be empowered—and expected—to use it in their decision-making. This requires both capability and accountability.
Steps to Embed Data-Driven Decisions:
Support on Data Literacy: Equip leaders with the ability to interpret change data and translate insights into action. Provide support as needed.
Scenario Planning: Use data to run “what-if” analyses and test the likely impact of different change strategies.
Feedback Loops: Set up mechanisms for leaders to receive feedback on the outcomes of their decisions, closing the loop between action and result.
Practical Tip: Celebrate leaders who use data effectively to drive change. Share their stories to build momentum and set new cultural norms.
The Power of Experiential Leadership
While data provides the “what” and “how much,” experiential leadership delivers the “how” and “why.” It’s about learning through action, experimentation, and reflection.
4. Embedding Change in Leaders’ Day-to-Day Work
Shift the focus from classroom learning to on-the-job application. Make change leadership a core part of every leader’s responsibilities—not a side project.
How This Looks in Practice:
Action Learning Projects: Assign leaders to sponsor or lead real change initiatives, supported by coaching and peer learning.
Shadowing and Rotations: Give leaders exposure to different parts of the business undergoing change, broadening their perspective and empathy.
Role Modelling: Senior leaders visibly demonstrate change leadership behaviours, setting the tone for the rest of the organisation.
Practical Tip: Pair less experienced change leaders with mentors who have successfully navigated transformation. Facilitate regular reflection sessions to share lessons learned.
5. Rapid Experimentation and Iteration
Encourage leaders to treat change as a series of experiments rather than a linear process. Use data to test hypotheses, learn quickly, and iterate.
Practical Steps:
Pilot Programs: Launch small-scale pilots to test new ways of working before rolling out organisation-wide.
A/B Testing: Try two different engagement or communication strategies and use data to determine which is more effective.
Retrospectives: After each change milestone, hold structured reviews to capture what worked, what didn’t, and why.
Practical Tip: Create a safe environment for experimentation. Make it clear that “failing fast” is not a failure, but a valuable source of insight.
6. Building a Feedback-Rich Culture
Change maturity flourishes in organisations where feedback is frequent, actionable, and non-punitive. Data and experiential leadership reinforce each other in this environment.
How to Foster This:
Real-Time Feedback Tools: Use digital platforms to gather and share feedback instantly.
Open Forums: Hold regular town halls or Q&A sessions where employees can voice concerns and see leaders respond transparently.
Recognition Programs: Publicly acknowledge teams and individuals who exemplify data-driven, adaptive change leadership.
Practical Tip: Encourage upward feedback. Leaders should actively seek input from their teams about what support or information they need to lead change effectively.
Tools and Technologies to Enable Data-Driven, Experiential Change
Modern change leaders have access to a growing suite of tools that make data-driven, experiential leadership scalable and sustainable:
People Analytics Platforms: Digital tools can automate sentiment analysis, engagement tracking, and pulse surveys.
Change Management Software: Platforms such as The Change Compass to provide structured frameworks for tracking change progress and impact.
Collaboration and Communication Tools: Microsoft Teams, Slack, and Yammer facilitate real-time data sharing and collaborative problem-solving.
Business Intelligence (BI) Tools: Power BI, Tableau, or Google Data Studio can visualise change metrics and make insights accessible to all. Alternatively, use the tailor-designed visuals with The Change Compass.
Practical Tip: Choose tools that integrate seamlessly with your existing systems and workflows. Prioritise user experience to drive adoption.
Overcoming Common Barriers
Transitioning to a data-driven, experiential change model is not without challenges. Common barriers include:
Data Overload: Too much data can paralyse decision-making. Focus on a handful of high-impact metrics.
Cultural Resistance: Some leaders may be uncomfortable with transparency or experimentation. Address this through role modelling and incentives.
Skill Gaps: Not all leaders are naturally data-savvy. Invest in targeted upskilling and peer support.
Practical Tip: Start with “coalitions of the willing”—leaders and teams who are eager to try new approaches. Use their successes to build momentum and expand adoption.
The Role of the Change Function
In this new paradigm, the role of the central change function shifts from being the “owners” of change to enablers, advisors and coaches. Their responsibilities include:
Designing and maintaining the change data framework
Curating and sharing best practices in data-driven, experiential leadership
Facilitating cross-functional learning and collaboration
Providing coaching and support to leaders at all levels
Practical Tip: Position the change function as a centre of excellence, not perceived as an ‘unnecessary cost centre’. Empower business leaders to take ownership of change outcomes.
Real-World Case Studies: Data and Experience in Action
1. Turning Around Transformation with Data-Driven Communication
A recent case study from ChangeFirst illustrates how a struggling business transformation was revitalised using data analytics. The organisation implemented a communication assessment which provided concrete, real-time data about the effectiveness of their change communications. By analysing this data, leaders identified gaps and altered their communication strategy accordingly. The result: more targeted engagement, improved buy-in, and a successful turnaround of the transformation effort. This case underscores the value of arming leaders with actionable insights, enabling them to make evidence-based decisions and quickly adjust tactics to drive better outcomes.
2. HMRC: Digital Transformation in the Public Sector
Her Majesty’s Revenue and Customs (HMRC) in the UK faced outdated systems and processes that hampered efficiency and customer experience. Their transformation journey was anchored in leadership development, employee engagement, and technology integration. By leveraging digital tools and data, HMRC modernised its operations, resulting in measurable improvements in service delivery and employee satisfaction. This case demonstrates how combining data-driven strategies with experiential leadership—such as empowering employees to test new digital solutions—can deliver sustainable change in even the most complex environments.
3. Adobe: Continuous Feedback and Data-Driven HR Transformation
Adobe’s shift from traditional software sales to a cloud-based model required a complete overhaul of HR practices. The company adopted a data-centric approach to employee engagement, using continuous feedback mechanisms and analytics to inform decision-making. This enabled leaders to rapidly identify issues, experiment with new strategies, and iterate based on real-world results. The transformation led to increased employee retention and a culture of ongoing growth and adaptability.
4. Dashboard-Driven Change at Scale
Organisations that centralise change data and make it accessible through dashboards empower leaders at all levels. This approach mirrors how other business functions—like sales and finance—operate, and it enables leaders to make informed decisions about change capacity, project prioritisation, and resource allocation. The transparency and visibility provided by dashboards foster greater engagement and accountability, making it easier for leaders to see what’s working, what isn’t, and how to course-correct as a team.
5. Process-Centric Change Management through Analytics
A case study presented at the Intelligent Automation Summit highlighted how a hybrid change management and data analytics professional used KPIs and data storytelling to align initiatives with organisational goals. By translating analytics into actionable KPIs, the organisation improved process efficiency, accelerated project delivery, and ensured that change initiatives were tightly integrated with business objectives. This approach demonstrates the power of combining analytics, process management, and people-centric leadership to drive meaningful transformation.
Key Lessons from Data-Driven, Experiential Change Initiatives
Data Democratization Accelerates Change: When change data is accessible to leaders and teams at all levels, it fosters ownership, agility, and faster decision-making.
Continuous Feedback Loops Drive Improvement: Real-time data and feedback mechanisms help leaders test, learn, and iterate, closing the gap between planning and execution.
Integration with Business Strategy is Essential: Data-driven change must be tightly aligned with organisational goals and KPIs to ensure relevance and impact.
Leadership Engagement is Easier with Data: Leaders are more likely to engage with change initiatives when they have clear, actionable insights at their fingertips, mirroring their experience in other business domains.
Qualitative and Quantitative Data Both Matter: Combining hard metrics with employee sentiment and qualitative feedback provides a holistic view of change readiness and impact.
Actionable Recommendations for Senior Change Professionals
1. Build a Centralised Change Data Platform
Aggregate change data from multiple sources (surveys, adoption metrics, business KPIs) into a single, accessible platform.
Use dashboards to visualise key metrics for different leadership layers, ensuring information is relevant and actionable.
2. Make Data a Leadership Habit
Embed data review into regular leadership routines—project stand-ups, executive meetings, and team huddles.
Train leaders in data literacy, focusing on interpreting insights and translating them into action.
3. Foster Experimentation and Rapid Iteration
Encourage leaders to treat change as a series of experiments, using data to test hypotheses and iterate quickly.
Create safe spaces for “failing fast” and learning from real-world outcomes, not just theory.
4. Democratise Data and Feedback
Ensure that change data is not siloed at the top; make it available to middle management and frontline leaders.
Use real-time feedback tools to capture and act on employee sentiment and engagement throughout the change journey.
5. Align Change Metrics with Strategic Objectives
Link change metrics directly to business outcomes—such as customer satisfaction, productivity, and financial performance—to demonstrate value and relevance.
Regularly review and refine metrics to ensure they reflect evolving organisational priorities.
6. Integrate Data-Driven and Traditional Change Practices
Don’t abandon the people side of change; use data to complement intuition, experience, and stakeholder engagement.
Balance quantitative insights with qualitative understanding to address both operational and cultural aspects of transformation.
7. Position the Change Function as an Enabler
Shift from being the “owners” of change to coaches and enablers, supporting business leaders in using data and experiential learning to drive outcomes.
Curate best practices, provide coaching, and facilitate cross-functional learning to sustain momentum.
The Future of Change Maturity
Organisations that reach significant change maturity do so by making a decisive shift: from slow, learning-centric capability building to a dynamic, data-driven, and experiential model. By democratising data, embedding feedback loops, and empowering leaders to learn by doing, these organisations achieve faster, more sustainable transformation and deliver measurable business results.
Change and transformation professionals who champion this approach will not only accelerate their organisation’s change maturity but also position themselves as strategic partners in shaping the future of business. The imperative is clear: harness the power of data and experience—not just knowledge—to lead change that matters.
Ask a senior leader whether they have adequate sponsorship for each of their change programmes, and most will say yes. Ask them how much cumulative change load their front-line teams are carrying across the full portfolio right now, and very few can answer. This gap, between confidence at the programme level and blindness at the portfolio level, is one of the most consistent and consequential failure patterns in enterprise transformation.
Change portfolio literacy is the ability to read, interpret, and act on a portfolio-level view of organisational change: what is changing, for whom, at what pace, and with what cumulative effect on the people being asked to absorb it all. In most organisations, this literacy is concentrated in change functions, if it exists at all. Senior leaders, the people with the authority to make the sequencing, resourcing, and prioritisation decisions that actually determine portfolio outcomes, typically lack it.
Closing this gap does not require turning executives into change managers. It requires giving them the information and the language to ask different questions of their change portfolios, and to act on the answers.
Why executives default to programme-level thinking
The governance structures that senior leaders use to oversee change are almost universally designed around individual programmes. Investment committees evaluate programmes. Executive sponsors are assigned to programmes. Status reporting comes from programmes. RAG dashboards present programme-level health. The system trains leaders to ask programme-level questions: Is this initiative on track? Is the business case holding? Are the milestones being met?
These are legitimate questions. The problem is that they are the wrong level of analysis for understanding whether organisational change is actually being managed well.
Prosci’s 12th edition Best Practices in Change Management study found that 52% of executive sponsors do not have an adequate understanding of their role in change. More revealing is what they are not being asked to do. Sponsor briefings cover individual initiative progress. They rarely cover cumulative load, portfolio interaction effects, or how a specific programme’s timeline is affecting the absorption capacity of the teams it targets.
This is a literacy problem, not an engagement problem. Most senior leaders are genuinely committed to sponsoring their change programmes. They are simply not equipped to see, or therefore to manage, the portfolio-level dynamics that determine whether the aggregate of those programmes succeeds.
What change portfolio literacy looks like in practice
A change-literate senior leader can engage meaningfully with four categories of information that portfolio-illiterate leaders typically cannot.
Cumulative impact by employee group
The most important thing a senior leader needs to understand about their change portfolio is not what each programme is doing, but how much aggregate change is landing on specific employee groups and when. A front-line operations team handling a systems migration, a restructure, and two new process changes simultaneously is in a materially different position from a team handling one of those changes in isolation. The risks to adoption, productivity, and retention are different. The support investment required is different.
Change-literate executives understand this. They can read a cumulative impact view by business unit or role group, recognise when load is elevated, and ask the right questions about whether the current portfolio plan is creating avoidable saturation risk.
Adoption evidence, not delivery evidence
Delivery reporting, milestones hit, go-lives completed, budgets on track, tells leaders that work is being done. It does not tell them whether change is actually occurring. A programme can be on time, on budget, and fully compliant with its governance requirements, while adoption in the target group is running at 40% of plan.
Change-literate executives insist on seeing adoption data alongside delivery data. They understand that a portfolio where every programme is green from a delivery perspective can simultaneously be in serious trouble from a change perspective, if adoption is consistently underperforming across multiple initiatives.
Change load relative to absorptive capacity
Every employee group has a finite capacity to absorb change over a given period. That capacity is shaped by prior change history, current baseline workload, the quality of management support, and the degree to which prior changes have genuinely embedded. When demand exceeds capacity, adoption quality degrades across the board.
Change-literate executives can engage with the concept of absorptive capacity and understand when their portfolio plan is structurally likely to exceed it for specific groups. This understanding changes how they approach sequencing decisions. Instead of defaulting to the programme that has the most political momentum or the most urgent business driver, they can weigh the organisational cost of proceeding on the current timeline against the cost of adjustment.
Portfolio governance authority
Effective change portfolio management requires a governance body that can make cross-programme decisions: delay a go-live, consolidate two programmes with overlapping target groups, redirect resource from a low-priority initiative to a high-saturation-risk group. Individual programme sponsors cannot make these decisions, because each has a rational incentive to advocate for their programme’s priority.
Gartner’s research indicates that by 2026, 30% of organisations will have invested in the talent and tools needed for strategic portfolio management. Change-literate senior leaders understand that this portfolio governance body needs to exist, what authority it requires, and why it cannot be replaced by bilateral conversations between programme sponsors.
The language executives need to understand
Building change portfolio literacy is partly a matter of vocabulary. Executives who can use these terms precisely are better equipped to ask useful questions of their change functions.
Change load refers to the aggregate demand that active and planned change initiatives place on a specific employee group over a defined period. High load is not inherently bad. Load that exceeds absorptive capacity is the problem.
Change saturation is the condition that occurs when cumulative load has depleted an employee group’s capacity to engage with change meaningfully. Saturated groups show characteristic patterns: disproportionate resistance to new initiatives, declining engagement scores, elevated support demand after go-live, and adoption curves that plateau well below target.
Change collision occurs when two or more initiatives demand significant behavioural change from the same group simultaneously, without coordination of timing or support. Collision reduces adoption outcomes for both initiatives and is almost entirely preventable with adequate portfolio visibility.
Absorptive capacity is a group’s ability to take on and embed new changes given their current and recent change history. It is not a fixed attribute. It is shaped by management quality, support availability, and the embedding status of prior changes.
Portfolio sequencing is the deliberate ordering and timing of change initiatives across the portfolio to minimise collision, respect absorptive capacity, and prioritise strategically important changes when load is high.
Building change portfolio literacy in your senior team
The most effective approach to building executive change portfolio literacy is showing, not telling. Most senior leaders do not become change-literate through briefings or methodology overviews. They become change-literate through repeated exposure to portfolio-level data and the decision-making conversations it enables.
The practical steps that change functions have found most effective include:
Starting with a portfolio view presentation. The first exposure to a cumulative impact map, showing load by business unit across the next two quarters, typically generates immediate questions from executives who have never seen change represented this way. The visual is more effective than any explanation. Use it to introduce vocabulary and invite questions rather than present conclusions.
Integrating portfolio data into existing governance forums. The most sustainable path to change portfolio literacy is connecting it to forums that already have authority: transformation steering committees, executive leadership team meetings, and business unit leadership reviews. A dedicated change forum that sits outside the existing governance structure will struggle to influence sequencing and resourcing decisions.
Framing in the language executives use. Change functions that speak the language of adoption rates, impact dimensions, and change saturation scores when executives are thinking in terms of revenue risk, talent retention, and business case delivery lose the room. The translation layer is the change leader’s job: “this programme’s go-live creates a 12-week window where our customer operations team carries a load equivalent to three major initiatives, based on what we know about their prior absorption rate.”
Making sponsor coaching a regular practice.Prosci’s research consistently finds that active and visible executive sponsorship increases change success rates by up to six times. But sponsorship quality depends on sponsor understanding. Regular, structured coaching conversations with programme sponsors, covering not just their individual programme but the portfolio context their programme sits within, is one of the highest-return investments a change function can make.
What good looks like: the change-literate leadership team
In organisations where change portfolio literacy is genuinely embedded at the senior level, the conversations in governance forums are qualitatively different. Rather than programme-by-programme status reviews, leadership teams engage with portfolio-level questions:
Which employee groups are carrying the highest cumulative load over the next quarter, and is the planned timeline for the new system programme going to push them into saturation risk?
Are our adoption rates across the portfolio consistent with our transformation ambitions, or are we systematically leaving value on the table by treating change management as a delivery function?
What would we need to do differently in the next six months to build absorptive capacity in our most change-impacted groups, rather than continuing to deploy at the current pace?
These are the questions that change-literate leaders ask. They are also the questions that drive the resourcing, sequencing, and investment decisions that determine whether an enterprise transformation programme delivers its intended value.
Developing the digital infrastructure to support these conversations, through portfolio platforms that aggregate impact data, track adoption across programmes, and generate the portfolio views that executive conversations require, is a practical prerequisite. Tools such as The Change Compass are built specifically for this purpose: providing the portfolio visibility that makes change portfolio literacy actionable rather than aspirational.
Where to start
Building change portfolio literacy in a senior team takes time, but the first step is quick. Prepare a single portfolio view: all active and planned change initiatives, mapped against the employee groups they affect, with a simple cumulative load indicator for the next 90 days.
Present it at a senior forum where decisions about transformation investment and sequencing are made. Do not frame it as a change management presentation. Frame it as a risk and capacity picture for the organisation’s transformation programme. The questions it generates will do more to build change portfolio literacy in 20 minutes than any amount of methodology briefing.
From there, the task is to make this view a regular feature of the governance conversation, not a one-off analysis. Literacy builds through repeated engagement with data and the decisions it informs.
Frequently asked questions
What is change portfolio literacy?
Change portfolio literacy is the ability of senior leaders to read and act on a portfolio-level view of organisational change: understanding cumulative change load by employee group, interpreting adoption evidence across multiple programmes, recognising change collision and saturation risk, and making portfolio-level sequencing and resourcing decisions that reflect these dynamics.
Why do senior leaders struggle with change portfolio management?
The governance structures most organisations use for managing change are designed around individual programmes, not portfolios. Status reporting, sponsorship briefings, and investment decisions all happen at the programme level. This structure trains senior leaders to ask programme-level questions and leaves them without the visibility to engage with portfolio-level dynamics, even when they are the primary driver of adoption outcomes.
How is executive sponsorship different from change portfolio literacy?
Executive sponsorship is the active, visible support a senior leader provides to a specific change initiative. Change portfolio literacy operates above this level. It is the ability to understand the collective effect of all change initiatives across the portfolio, and to make cross-programme decisions that optimise overall adoption outcomes rather than individual programme outcomes. Both are necessary for effective enterprise change management.
What data does a change portfolio view need?
At minimum: a list of all active and planned change initiatives, the employee groups affected by each, the intensity and duration of impact, and the current adoption or readiness status. Aggregated across programmes, this data produces the cumulative load view by employee group that is the foundation of portfolio-level decision-making.
How do you develop change portfolio literacy in a senior team?
The most effective approach is repeated exposure to portfolio-level data in governance forums where decisions are made. Starting with a single portfolio view presentation, integrating change data into existing leadership forums, and making sponsor coaching a regular practice are the three interventions that change functions consistently find most effective for building executive change literacy over time.
References
Prosci. Best Practices in Change Management, 12th Edition, Executive Summary. https://empower.prosci.com/best-practices-change-management-executive-summary
Prosci. 5 Strategic Decisions for Building Organizational Change Capability in 2026. https://www.prosci.com/blog/5-strategic-decisions-for-building-organizational-change-capability
Gartner. Top Trends for Program and Portfolio Management Leaders for 2025. https://www.gartner.com/en/documents/6533602
Smartsheet. 2025 Project and Portfolio Management Priorities Report. https://www.smartsheet.com/content-center/inside-smartsheet/research/2025-ppm-priorities-report-key-takeaways
Managing multiple changes simultaneously is not an edge case in enterprise transformation. It is the norm. Most large organisations are running ten, twenty, or more concurrent change initiatives at any point in time. The assumptions that change practitioners rely on to manage this complexity have largely been inherited from single-initiative change management and applied wholesale to the portfolio context. Many of them are wrong.
This matters because wrong assumptions about managing multiple changes lead to specific, predictable, and expensive failures: adoption rates that fall short of targets, employee fatigue that accumulates into resistance, and programme sequencing decisions that look reasonable in isolation but create unnecessary risk in aggregate. Gartner’s research on change adoption found that only 32% of business leaders report achieving healthy change adoption by employees. The gap between change investment and change outcomes is real and persistent.
Working through seven assumptions that are widespread in change management practice, and what the evidence actually shows, offers a clearer picture of where portfolio-level management typically breaks down.
Assumption 1: If each programme is managed well, the portfolio will be managed well
This is the foundational assumption of most enterprise change management: that quality at the programme level aggregates into quality at the portfolio level. It is comforting because it is consistent with how resourcing models work: staff each programme with capable change managers, and the organisation’s change burden is handled.
The evidence suggests otherwise. A programme can have excellent communication, well-designed training, rigorous stakeholder engagement, and still fail to achieve target adoption if it lands in a quarter when the relevant employee group is simultaneously absorbing two other significant changes. The failure is not programme-level. It is portfolio-level. And it is invisible to a resourcing model that assigns one change manager per programme.
The assumption treats change capacity as infinite. Smartsheet’s 2025 Project and Portfolio Management Priorities Report found that 92% of PPM professionals struggle to adapt to workplace changes, and 71% say constant workplace shifts make it difficult to stay productive. Employee capacity to absorb change is finite and varies by group and by history. Portfolio management of change requires treating it as such.
Assumption 2: Change saturation is visible
Most change managers who have worked in large organisations have seen change saturation: the glazed look when a new initiative is announced, the rising resistance that seems disproportionate to the scale of the change, the help desk calls that stay high long after go-live. The assumption is that saturation is detectable when it occurs, and that practitioners will notice it in time to respond.
The problem is that saturation often builds slowly, through the accumulation of changes none of which individually seems overwhelming. By the time the symptoms are visible, the capacity depletion has already occurred and the immediate change is already in trouble.
Managing multiple changes effectively requires measuring cumulative load before saturation becomes visible. This means tracking what is landing on specific employee groups across the full portfolio, quantifying the aggregate impact, and identifying when load is approaching or exceeding historical absorption capacity. This cannot be done by observing individual programmes in isolation. It requires portfolio-level data.
Assumption 3: Communications from different programmes can be managed separately
In organisations running multiple concurrent programmes, each programme typically has its own communications plan, its own channels, and its own messaging cadence. The assumption is that employees can contextualise each communication separately and engage with it on its own terms.
In practice, employees receive communications from multiple change initiatives, often in the same week or the same day. The communications compete for attention. Employees develop filters, often unconsciously, that route change communications directly to low-priority status. The most sophisticated change communication strategy for any individual programme has to work within this noise environment.
Effective management of multiple changes requires cross-programme communication coordination: understanding what employees in specific groups are receiving from all programmes simultaneously, and designing communications that acknowledge the full change context rather than pretending each change exists in isolation. An employee who has received three change communications this week does not need a fourth that opens with “we are excited to announce.” They need a communication that is specific, brief, and gives them exactly what they need to act.
Assumption 4: Training is the primary adoption lever
The allocation of change budget in most programmes is disproportionately weighted toward training design and delivery. This reflects an implicit assumption that knowledge is the primary barrier to adoption: if employees understand the new system or process, they will use it.
Knowledge is necessary but not sufficient. The research on adoption failure consistently finds that employees who have completed training and understand the new way of working often do not adopt it. The barriers are motivational, structural, and environmental, not informational. They include:
Performance frameworks that still measure old behaviours
Line managers who are themselves uncertain about the change and cannot credibly reinforce it
Peer norms that make the old way of working the default
Practical friction in the new process that makes old habits easier
When managing multiple changes, this assumption is compounded because training resources are frequently the binding constraint. Programmes compete for training developer time, LMS bandwidth, and employee training hours. If training is over-weighted as an adoption lever, the resource allocation is wrong in two ways: too much investment in content development, and not enough in manager enablement, environment redesign, and performance alignment.
Assumption 5: Resistance means the change is wrong
When a change encounters significant resistance, the instinctive response is to investigate what is wrong with the change: Is the design flawed? Is the business case unclear? Are sponsors not visible enough? These are legitimate questions. But in a portfolio context, resistance is frequently not a signal about the specific change. It is a signal about cumulative load.
A team that has been through three restructures and two major system implementations in 18 months may resist a relatively modest change with intensity that is disproportionate to the change’s actual impact on their work. The resistance is real and needs to be addressed, but diagnosing it as a problem specific to the current programme leads to misguided responses: more communication, more engagement sessions, more executive visibility. What the team may actually need is a genuine pause in change load, or meaningful acknowledgement of the cumulative burden they have been carrying.
This distinction matters for how change managers advise programme sponsors. When resistance patterns look inconsistent with the scale of the change, the right question is: what is the change history for this group, and what is the current portfolio load they are carrying?
Assumption 6: The sponsor of each programme is the right governance mechanism
In single-programme change management, executive sponsorship is consistently identified as one of the strongest predictors of change success. The programme sponsor provides visibility, resources, decision-making authority, and legitimacy for the change effort.
In a portfolio context, individual programme sponsorship is necessary but not sufficient. Each programme has a sponsor who is rationally motivated to advocate for their programme’s priority. The result is a governance dynamic where each sponsor argues for their programme to go first, receive the most resource, and face the fewest constraints on timeline. Without a portfolio governance mechanism that can make cross-programme trade-offs, these competing claims default to whoever has the most political capital. This is not portfolio management; it is portfolio politics.
Effective management of multiple changes requires a governance structure that sits above the individual programme sponsor level and has the authority to make sequencing and resource allocation decisions that may disadvantage individual programmes in service of better portfolio outcomes. This structure is often a change portfolio board or a change steering committee with cross-programme scope.
Assumption 7: Progress reporting from multiple programmes gives a complete picture
Most organisations aggregate progress reporting from individual programmes into a portfolio status report: traffic lights, milestone tracking, issue logs. This gives a picture of delivery status. What it does not give is a picture of adoption status across the portfolio, cumulative change load by employee group, or the interaction effects between programmes.
A portfolio where every programme is green from a delivery perspective can still be in serious trouble from a change management perspective, if multiple programmes are delivering simultaneously to the same groups, if adoption rates across programmes are uniformly low, or if change fatigue signals are accumulating in the engagement data.
The Change Compass is designed specifically to provide the portfolio-level view that standard project reporting cannot: cumulative impact by business unit and role group, adoption trend lines across multiple initiatives, and early warning signals when load or adoption patterns indicate portfolio risk. The shift from delivery reporting to adoption intelligence is the most significant operational change in how effective change portfolio management differs from traditional programme reporting.
What managing multiple changes well actually looks like
Effective management of multiple changes is defined less by any single practice and more by a shift in orientation: from programme-centric to portfolio-centric. It asks different questions.
Not “is this programme on track?” but “what is the cumulative change load on the groups this programme targets, and how does this programme’s go-live affect their absorption capacity?”
Not “why is this group resistant?” but “what is the change history and current portfolio load for this group, and is the resistance a programme signal or a portfolio signal?”
Not “how do we communicate this change effectively?” but “how does our communication for this programme fit into the total communications these employees are receiving from all sources this month?”
These questions require portfolio visibility. They cannot be answered with programme-level data. And the answers they generate drive meaningfully better decisions about sequencing, timing, resourcing, and intervention design.
Building that portfolio visibility, through consistent impact methodology, aggregated data across programmes, and regular portfolio governance, is the single most valuable investment that enterprise change functions can make in improving their outcomes from managing multiple changes.
Frequently asked questions
Why is managing multiple changes harder than managing individual changes?
Managing multiple simultaneous changes introduces portfolio-level problems that do not exist at the programme level: change collision (multiple demands landing simultaneously on the same groups), change saturation (cumulative load depleting absorption capacity over time), and cross-programme communication noise. Each of these requires portfolio-level management, not just better single-programme execution.
What is change collision?
Change collision occurs when two or more initiatives simultaneously require significant behavioural or process changes from the same employee group, without coordination of timing or support. The demands compete for attention, reinforce each other’s resistance, and result in lower adoption for both initiatives than would have been achieved if they had been sequenced or staggered.
How do you measure the change load on an employee group?
Change load is measured by aggregating the impact assessments from all active initiatives affecting a specific employee group. This requires a consistent impact taxonomy across programmes so that impact severity can be summed and compared meaningfully. High-load groups are those where the cumulative impact score exceeds historical absorption benchmarks for similar periods of change.
What is the right governance structure for managing multiple changes?
Effective governance requires a cross-programme body, typically a change portfolio board or steering committee, with authority to make sequencing and resource allocation decisions across the portfolio. Individual programme sponsors should sit below this level for portfolio decisions. The portfolio body needs consistent data on cumulative load, adoption status, and portfolio risks to make informed decisions.
How should I prioritise changes in a portfolio?
Prioritisation should be based on three factors: strategic importance (which changes are most critical to the organisation’s strategy), adoption readiness (which employee groups have the capacity and readiness to absorb which changes at this time), and interaction effects (which sequencing minimises collision between high-impact initiatives). Data from a portfolio management platform enables all three factors to be assessed systematically rather than through negotiation alone.
What tools help with managing multiple changes?
Portfolio change management platforms such as The Change Compass aggregate impact data across programmes, visualise cumulative load by business unit and role group, and enable the portfolio governance conversations that managing multiple changes well requires. Without this kind of tooling, portfolio management at scale defaults to manual aggregation and informal coordination, neither of which is reliable at the complexity levels most large organisations face.
References
Gartner. Gartner HR Research Finds Just 32% of Business Leaders Report Achieving Healthy Change Adoption by Employees (2025). https://www.gartner.com/en/newsroom/press-releases/2025-07-08-gartner-hr-research-finds-just-32-percent-of-business-leaders-report-achieving-healthy-change-adoption-by-employees
Smartsheet. 2025 Project and Portfolio Management Priorities Report: Teams Are Fatigued, and Executives Need to Pay Attention. https://www.smartsheet.com/content-center/inside-smartsheet/research/2025-ppm-priorities-report-key-takeaways
WTW. Future-Proofing Work: Key Drivers and Strategies for Work Transformation (2024). https://www.wtwco.com/en-us/insights/2024/09/future-proofing-work-key-drivers-and-strategies-for-work-transformation
Prosci. The Correlation Between Change Management and Project Success. https://www.prosci.com/blog/the-correlation-between-change-management-and-project-success
