Scaled Agile Framework (SAFe) has emerged as a leading methodology to address the organisational change demands of fostering flexibility, collaboration, and continuous improvement. A cornerstone of SAFe is the principle of ‘Measure and Grow,’ which emphasizes using data and fact-based decisions to enhance change outcomes over time, including predictability. Despite its centrality, SAFe does not explicitly detail the change management components essential for its success, including its deep understanding of SAFe’s measurement model that enables the design of a tailored metrics strategy for ensuring strategic alignment. Here we outline how change management practitioners can effectively apply the ‘Measure and Grow’ principle within an Agile Release Train (ART) to lead change and improve outcomes to support the Scaled Agile environment.
What does it mean to “measure and grow” in a business context?
In a business context, “measure and grow” refers to the process of evaluating performance metrics to identify how our work drives business value and areas for improvement, aligning with strategic business goals. By analyzing data, companies can implement strategies that foster growth, enhance productivity, and improve overall outcomes. This approach ensures continuous development aligned with organizational goals.
The “Measure and Grow” Principle in Scaled Agile
What does it mean to “measure and grow” in a business context?
“Measure and grow” in a business context refers to the process of assessing performance metrics and outcomes to identify areas for improvement. By analyzing data, businesses can implement strategies that foster growth, enhance customer satisfaction, and optimize resource allocation, ultimately driving sustainable success and competitive advantage.
“Measure and Grow” is integral to SAFe, focusing on systematic measurement and continuous improvement for overall business agility within the value stream. By leveraging data and analytics, organizations can quickly respond to market changes, make informed decisions that meet the needs of our customers, identify areas needing attention, uncover improvement opportunities, and iteratively enhance meaningful change in performance. For change management professionals, this principle translates into a structured approach to evaluate the effectiveness of change initiatives, pinpoint areas for improvement, and implement necessary adjustments.
In a Scaled Agile environment, “Measure and Grow” is a core tenant or principle that applies in all types of agile environments. By continuously assessing and refining change efforts, organizations can align their initiatives with strategic objectives, mitigate risks, and ensure sustained success.
In practice, a lot of organisations have not pinpointed exactly how change management measures can make or break the outcome of the change, and in a SAFe environment, across the program, portfolio as well as enterprise.
The ‘Measure and Grow’ principle as a core part of SAFe (From Scaled Agile Framework)
To operationalize the “Measure and Grow” principle in change management, it is crucial to establish a set of metrics and assessment frameworks. Here are some broad categories of different types of change measurements that are relevant. Note that since we are talking about SAFe, it is not just at the initiative level that we are talking about metrics. More importantly, it is about establishing a system to promote change improvement across the organisation.
Change Management KPIs and OKRs
Key Performance Indicators (KPIs) and Objectives and Key Results (OKRs) are essential tools for tracking the success of change management initiatives. KPIs provide quantitative measures of performance, while OKRs align change efforts with broader organizational goals. A change management stream or function should focus on establishing KPIs or OKRs to achieve laser focus on achieving change outcomes.
Examples of Initiative-Level Change Management KPIs that may roll out to form portfolio views
Employee Engagement Levels: This KPI assesses how change impacts employee morale and engagement, providing insight into the overall acceptance and support of the change initiative.
Learning Achievement Rates: This can include tracking the percentage of employees who have completed necessary training programs, as well as achieving the target level of competence to ensure that the workforce is adequately prepared for the change.
Feedback Scores: Collecting feedback from stakeholders through surveys or feedback forms helps gauge perception and identify areas needing improvement. It is important to note that depending on the change context, stakeholders may not be happy with the content of the change. However, understanding and tracking this perception is still important.
Change Adoption Rate: This KPI measures the percentage of stakeholders who have adopted the change. High adoption rates are the ultimate goal for initiatives.
Issue Resolution Time: Measuring the time taken to resolve user-related issues related to the change highlights the efficiency of support mechanisms and the responsiveness of the change management team. This is especially important during an agile environment where there may be constant changes.
Change Readiness and Stakeholder Engagement Metrics
Evaluating change readiness and stakeholder engagement is crucial to the success of any change initiative. These metrics help assess the organization’s preparedness for change and the level of involvement and support from key stakeholders. Readiness and engagement rates can also roll up at a portfolio level to provide oversight.
Change Readiness Metrics
Readiness Assessments: Conduct surveys or interviews to gauge the organization’s preparedness for the impending change. This can include evaluating awareness, understanding, and acceptance of the change.
Resource Availability: Measure the availability of necessary resources, such as budget, personnel, and tools, to support the change initiative.
Communication Effectiveness: Assess the clarity, frequency, and effectiveness of communication regarding the change to ensure stakeholders are well-informed and engaged.
Stakeholder Engagement Metrics
Engagement Scores: Use surveys or feedback forms to measure the engagement levels of stakeholders, indicating their commitment and support for the change.
Participation Rates: Track stakeholder participation in change-related activities, such as workshops, meetings, and training sessions, to gauge their involvement.
Influence and Support: Assess the influence and support of key stakeholders in driving the change, ensuring that influential figures are actively endorsing the initiative.
By monitoring these metrics, change management professionals can identify potential barriers to change and take proactive steps to enhance readiness and engagement.
Stakeholder Competency Assessment
Successful change initiatives rely on the competence and readiness of key stakeholders. Assessing stakeholder competency involves evaluating the capability of sponsors and change champions to support and drive the change.
Sponsor Readiness/Capability Assessment
Sponsor Engagement: Measure the level of engagement and commitment from sponsors, ensuring they are actively involved and supportive of the change.
Decision-Making Effectiveness: Assess the ability of sponsors to make timely and effective decisions that facilitate the change process.
Resource Allocation: Evaluate the sponsor’s ability to allocate necessary resources, such as budget and personnel, to support the change initiative.
Change Champion Capability Assessment
Training and Knowledge: Measure the knowledge and training levels of change champions to ensure they are well-equipped to support the change.
Communication Skills: Assess the ability of change champions to effectively communicate the change message and address stakeholder concerns.
Influence and Leadership: Evaluate the influence and leadership capabilities of change champions, ensuring they can effectively drive and sustain the change.
By conducting these assessments, change management professionals can ensure that key stakeholders are prepared and capable of supporting the change initiative.
Change Adoption Metrics
Change adoption metrics provide insight into how well the change has been accepted and integrated into the organization. These metrics help assess the effectiveness of the change initiative and identify areas for improvement. At a portfolio level, there may be different levels of change adoption set for different initiatives depending on priority and complexity.
Key Change Adoption Metrics
Adoption Rate: Measure the percentage of stakeholders who have adopted the change, indicating the overall acceptance and integration of the new processes or systems.
Usage Metrics: Track the usage of new tools, processes, or systems introduced by the change to ensure they are being utilized as intended.
Performance Metrics: Assess the impact of the change on key performance indicators, such as productivity, efficiency, and quality, to determine the overall success of the change initiative.
By monitoring these metrics, change management professionals can gauge the success of the change initiative and identify opportunities for further improvement. To read more about change adoption metrics check out The Comprehensive Guide to Change Management Metrics for Adoption.
Change Impact and Capacity Metrics
Understanding the impact of change and the organization’s capacity to manage it is crucial for successful change management. Change impact metrics assess the effects of the change on the organization, while capacity metrics evaluate the organization’s ability to manage and sustain the change.
Change Impact Metrics
Aggregate impacts: Aggregate impacts across initiatives to form a view of how various teams and roles are impacted by various changes.
Risk Assessments: Identify potential risks associated with the change and evaluate their impact, ensuring that mitigation strategies are in place. A particular focus should be placed on business performance during change, across initiatives.
Capacity Metrics
Resource Capacity: Assess the availability of resources, such as personnel, budget, and tools, to support the change initiative and optimize flow time, enhance flow velocity, and improve flow efficiency while monitoring Flow Load.
Change Fatigue: Measure the risk for potential fatigue within the organization and its impact on stakeholders, ensuring that change initiatives are paced and driven appropriately.
Support Structures: Evaluate the effectiveness of support structures, such as training programs, information hubs, and help desks, in facilitating the change. Support structures may also include change champion networks.
By assessing change impact and capacity, change management practitioners can ensure that the organization is well-equipped to manage and sustain the change initiative.
Change Maturity Assessment
Change maturity assessments provide a comprehensive evaluation of the organization’s capability to manage change effectively. These assessments help identify strengths and weaknesses in the organization’s change management practices and provide a roadmap for improvement.
The Change Management Institute (CMI) Change Maturity Model is a comprehensive framework that takes a holistic approach to enhancing an organization’s change management maturity. It’s divided into three core functional domains, each playing a vital role in the overall journey toward maturity:
Project Change Management
Business Change Readiness
Strategic Change Leadership.
These domains serve as the foundation for achieving higher levels of maturity within the organization.
Within each of these domains, the CMI model outlines a structured path, consisting of five distinct maturity levels. These levels represent a continuum, starting at Level 1, which serves as the foundational stage, and progressing all the way to Level 5, the zenith of maturity and effectiveness. This multi-tiered approach offers organizations a clear roadmap for growth and development, ensuring that they have the tools and insights necessary to navigate the complexities of change management.
By conducting regular change maturity assessments, change management professionals can identify areas for improvement and develop targeted strategies to enhance the organization’s change management capability.
The “Measure and Grow” principle is a powerful tool for improving change outcomes in a Scaled Agile environment. By leveraging data and fact-based decision-making, change management professionals can ensure that change initiatives are effective, aligned with strategic objectives, and continuously improving. Establishing robust metrics and assessment frameworks, such as KPIs, OKRs, change readiness and stakeholder engagement metrics, stakeholder competency assessments, change adoption metrics, change impact and capacity metrics, and change maturity assessments, is essential to applying the “Measure and Grow” principle effectively.
Incorporating these metrics and assessments into change management practices enables organizations to identify areas for improvement, make informed decisions, and drive continuous improvement. By doing so, change management professionals can enhance the effectiveness of change initiatives, ensure successful adoption, and ultimately achieve better business outcomes.
Successful change management relies on having the right metrics to measure progress, gauge impact, and communicate with stakeholders. Moreover, the right metrics can drive continuous improvement and help directly achieve change outcomes. However, not all metrics are beneficial, and some can mislead or fail to meet stakeholder needs, especially when managing change projects. Let’s check out the top change management metrics to avoid and go through examples to take note.
Understanding the Disconnect: Change Managers vs. Business Stakeholders
A significant reason certain change management metrics fall short is the differing perspectives between change managers and business stakeholders. Change managers and change practitioners are trained to view metrics through the lens of change management frameworks and methodologies, focusing on detailed assessments and structured approaches as a part of the change management strategy. These include applying ratings and judgments on aspects such as impact levels indicating levels and areas of impact.
In contrast, business stakeholders prioritize business operations, strategic outcomes, and practical implications. The busy business stakeholder is often looking for practical implications from metrics that can be used to directly drive decision making, meaning “what do I do with this data to improve the ultimate business outcome”.
Of course, different stakeholders have different data needs, and you need to show the right metric to the right type of stakeholder. For example, operations-focused stakeholders expect fairly detailed metrics and internal historical data to understand what that means in terms of organisation, coordination, capacity, and performance perspectives. Senior managers may prefer higher-level data with a focus on strategic impacts, overall progress, and adoption indicators of change success rate.
This disconnect can lead to the use of metrics that do not resonate with or are misunderstood by stakeholders that disrupt change success.
Change managers may leverage metrics that are derived from the various change management documents such impact assessments, training plan or communications plan. Metrics are also often chosen for ease of use and ideally are not overly complicated to execute.
For example, impact assessments typically involve rating stakeholder groups and initiatives on a traffic light system (red, amber, green) based on their impact. While this approach is systematic, it can be problematic for several reasons:
Lack of Sufficient Stakeholder Context: Business stakeholders might not understand the practical implications of these ratings. For instance, an “impact rating per initiative” may not clearly convey what the rating means for day-to-day operations or strategic goals. For example, if an initiative has a red impact rating, stakeholders might not grasp the specific operational changes or strategic adjustments needed, in essence, “what do I do with this?”. So, incorrect usage of data could result in lack of stakeholder engagement.
Misinterpretation of Traffic Light Ratings: The red, amber, green system can be misleading. Stakeholders might interpret red as an indicator of alarm or imminent risk, while green may be seen as a sign that no action is needed. This is because stakeholders are trained to interpret traffic light ratings this way (from the various project/business updates they’ve attended). In reality, red might simply mean high impact, requiring focused attention, and green might indicate a low impact but still require monitoring. For instance, a red rating might indicate significant process changes that need careful management, not necessarily a negative outcome.
Hard to defend ratings if prompted: Business stakeholders may also want to drill into how the ratings are determined, and based on what basis. They may expect a logical data-backed reasoning of how each colour scheme is determined. If a rating is based on an overall ‘personal judgment’ this may be hard to defend infront of a group of stakeholders.
Examples of Potentially Misleading Metrics
Certain metrics, although straightforward, can be easily misinterpreted and fail to provide a realistic picture of change impacts as a part of effective change management. Often these are selected because they are easy to report on. However, easy, make not give you the outcome you are looking for.
Number of Go-Lives: Tracking the number of Go-Lives over time might seem like an effective way to represent change volume. However, the most significant impacts on people given time often occur before or after the Go-Live date. For example, the preparation and training phase before Go-Live and the adoption phase afterward are critical periods that this metric overlooks. A Go-Live date might indicate a milestone but not the challenges, progress or impacts faced during the implementation phase.
Number of Activities Implemented: Similar to Go-Lives, this metric focuses on quantity rather than quality. Simply counting the number of activities does not account for their effectiveness or the actual change they drive within the organisation. For example, reporting that 50 training sessions were conducted does not reveal whether employees found them helpful or if they led to improved performance.
Number of impacts or stakeholders impacted: Again, using a numerical way to indicate progress can be very misleading, or unmeaningful. This is because it may be ‘interesting’ but with no real action for your stakeholder to take in order to somehow lead to a better overall change outcome. If metrics do not result in some kind of action, then over time it will not shape your change(s) toward the targeted outcomes. Or worse, your stakeholders may lose interest and lose confidence in the strategic impact of these metrics.
Another common way to report change metrics is to use the number of impacts or number of stakeholders impacted by the organizational change. This can be in terms of the following:
Number of divisions impacted
Number of stakeholder groups impacted
Number of employees impacted
Number of initiatives per division/stakeholder
Metrics That May Be Too Operational
Metrics that are overly operational can fail to capture meaningful progress or adoption. Perhaps if the metric are for reporting within the Change Management team that may be OK. However, when you are showing metrics to stakeholders, a different set of expectations should be cast.
If you are presenting metrics to senior managers, you need to ensure that they hit the mark for that audience group. If the group is more interested in strategic impact, and higher level progress outcomes, you need to tailor accordingly.
Examples of metrics that may be too operational include:
Number of Communications Sent: This metric measures activity but not effectiveness. Sending numerous emails or messages does not guarantee that the message is received, understood, or acted upon by stakeholders. For instance, stakeholders might receive 100 emails, but if the content is unclear, the communication effort is wasted. Or worse, the emails may not even have been read.
Number of Training Sessions Attended: This one is a classic. While training is crucial, the number of sessions attended does not necessarily reflect the attendees’ understanding, engagement, or the practical application of the training. For example, employees might attend training but not apply the new skills if the training is not relevant to their roles for various reasons.
Number of workshops/meetings: Another way of articulating the change management progress in terms of activities is the number of workshops or meetings conducted with stakeholders including focus groups to indicate employee engagement. Again, this may be good to track within the change management team. However, presenting this metric to stakeholders may not be appropriate as it may not meet their needs nor indicate change management success.
Number of changes: This may be a common way to report on changes planned, but it doesn’t really inform the extent of the change. One change can be significantly impactful whilst another does not have major stakeholder impacts and are more system impacts. Listing number of changes may be deceiving or misleading. This kind of data may not get you the level of acceptance targeted.
The way metrics are presented is just as important as the metrics themselves. Poor visualization can lead to misinterpretation, confusion, and misguided decisions. Here are some common pitfalls to avoid:
Ineffective Use of Pie Charts
Pie charts can be misleading when used to show data points that are not significantly different. For example, using a pie chart to represent the percentage of divisions impacted by a change might not effectively communicate the nuances of the impact if the differences between the divisions are minimal. A pie chart showing 45%, 30%, and 25% might not convey the critical differences in impact levels among divisions.
Misleading Traffic Light Ratings
Using red, amber, and green to indicate high, medium, and low impacts can send the wrong message. Stakeholders might associate these colours with good and bad outcomes rather than understanding the actual levels of impact. Stakeholder may be used to interpreting these in the context of their usual project or business updates where red indicated alarm and ‘bad’. This can lead to unnecessary alarm or complacency. For instance, a green rating might suggest no need for action, while in reality, it might require ongoing monitoring.
Overuse of Colours
Using too many colours in charts and graphs can overwhelm stakeholders, making it difficult to discern the key message. Using colours in data visualisation can be two-edged sword. Colour can effectively point your stakeholders are the area where you want them to focus on. But, too many colours can lose your audience. A cluttered visual can obscure the critical data points and lead to misinterpretation. For example, a graph with ten different colours can confuse stakeholders about which data points are most important.
Data visualisation tools are also important. A lot of people use Power BI which works for a foundational level of charts. For tailored charts, specifically designed to to influence stakeholders to clearly see certain angles of risks and opportunities leverage tools such as Change Compass.
Practical Takeaways for Senior Change Managers
To ensure that change management metrics are effective and take into account best practices practices, consider the following practical takeaways:
Align Metrics with Key Stakeholder Perspectives
Understand Stakeholder Priorities: Engage with stakeholders to understand their business goals, priorities and concerns. Tailor your metrics to address these aspects directly. For example, if stakeholders are concerned about operational efficiency, focus on metrics that reflect improvements in this area.
Use Business Language: Frame your metrics in a way that resonates with business stakeholders. Avoid change management jargon and reference, and ensure that the implications of the metrics are clear and actionable. For example, instead of using technical terms, explain how the metrics impact business outcomes. Think in terms of business activities, milestones, busy periods, and capacity challenges.
Focus on Meaningful Metrics
Measure Outcomes, Not Just Activities: Change leaders should prioritize metrics that reflect the outcomes and impacts of change indicate level of knowledge, rather than just the activities performed as a part of change management KPIs. For example, instead of counting the total number of employees attending change management training sessions, measure the improvement in employee performance or knowledge retention post-training.
Example: Instead of reporting that 100 employees attended training sessions, report that 85% of attendees showed improved performance in their roles after training, or that certain level of competencies were gained. Note that quantifiable metrics have more impact on the audience.
Track Engagement and Adoption: Monitor metrics that indicate the level of engagement and adoption among stakeholders or their perception of the change. This could include surveys, feedback forms, or direct measures of behaviour change and the overall success rate of the change.
Example: Use post-training surveys to measure employee confidence in applying new skills or managerial rating of application of learnt skills rather than employee satisfaction of the training sessions using satisfaction scores. Track the percentage of employees who actively use new tools or processes introduced during the change.
Example: Instead of reporting that 100 employees attended training sessions, report that 85% of attendees showed improved performance in their roles after training, or that certain level of competencies were gained.
Example: Use post-training surveys to measure employee confidence in applying new skills or managerial rating of application of learnt skills. Track the percentage of employees who actively use new tools or processes introduced during the change.
Improve Metric Visualization
Simplify Visuals: Use clear, simple visuals that highlight the key messages. Avoid clutter and ensure that the most important data points stand out.
Example: Use bar charts or line graphs to show trends over time rather than pie charts that can be harder to interpret.
Contextualize Data: Provide context for the data to help stakeholders understand the significance. For example, instead of just showing the number of Go-Lives, explain what each Go-Live entails and its expected impact on operations. Or better, focus on showing the varying levels of impact on different stakeholders across time within the initiative.
Example: Accompany a Go-Live count with a visual showing the varying impact level of various implementation activities of the changes.
Example: Use bar charts or line graphs to show trends over time rather than pie charts that can be harder to interpret.
Example: Accompany a Go-Live count with a visual showing the varying impact level of various implementation activities of the changes.
Narrative Approach: Combine metrics with a narrative that explains the story behind the numbers as a part of the change management process. This can help stakeholders understand the broader context and implications.
Example: Instead of presenting raw data, provide a summary that explains key trends, successes, and areas needing attention.
Educate your stakeholders: Depending on stakeholder needs you may need to take them on a phased approach to gradually educate them on change management metrics and how you ultimately want them to drive the outcomes.
Example: You may start the education process to focus on more simplistic and easy-to-understand measures, and as your stakeholders are more change-mature, move to drill into more detailed metrics that explain the ‘why’ and ‘how’ to drive outcome success.
Continuously improvement: Provide regular updates on key metrics and adjust them based on feedback from stakeholders. Continuous communication ensures that everyone remains aligned and informed.
Example: Hold monthly review meetings with stakeholders to discuss the latest metrics, address concerns, and adjust strategies as needed.
Example: Instead of presenting raw data, provide a summary that explains key trends, successes, and areas needing attention.
Example: You may start the education process to focus on more simplistic and easy-to-understand measures, and as your stakeholders are more change-mature, move to drill into more detailed metrics that explain the ‘why’ and ‘how’ to drive outcome success.
Example: Hold monthly review meetings with stakeholders to discuss the latest metrics, address concerns, and adjust strategies as needed.
Examples of Effective Metrics
Employee Adoption and Engagement
Percentage of Employees Adopting New Process/System: This metric measures the rate at which employees are using new processes or systems introduced during the change. High adoption rates indicate successful integration.
Implementation: Use software usage analytics or surveys to track tool adoption rates.
Visualization: A graph showing adoption rates over time.
Employee Feedback Scores: Collect feedback on change initiatives through surveys or stakeholder ratings to measure sentiment/feedback and identify areas for improvement.
Implementation: Conduct regular surveys asking employees about their experience with the change process. Do note that depending on the change you may expect negative feedback due to the nature of the change itself (vs the way it was implemented).
Visualization: Bar/Line charts comparing feedback scores across different departments or time periods. Bar/Line charts are the standard go-to for data visualisation. They are easy to understand and interpret.
Implementation: Use software usage analytics or surveys to track tool adoption rates.
Visualization: A graph showing adoption rates over time.
Implementation: Conduct regular surveys asking employees about their experience with the change implementation process. Do note that depending on the change you may expect negative feedback due to the nature of the change itself (vs the way it was implemented).
Visualization: Bar/Line charts comparing feedback scores across different departments or time periods. Bar/Line charts are the standard go-to for data visualisation. They are easy to understand and interpret.
Impact on Business Outcomes
Improvement in Key Performance Indicators (KPIs): Track changes in KPIs that are directly impacted by the change initiatives, such as productivity, customer satisfaction, customer experience, improvement in process inconsistencies or financial performance.
Implementation: Identify relevant KPIs and measure their performance before and after change initiatives.
Visualization: Use line/bar graphs to show trends in KPI performance over time.
Operational Efficiency Metrics: Measure improvements in operational processes, such as reduced cycle times, error rates, or cost savings.
Implementation: Track specific operational metrics relevant to the change initiatives.
Visualization: Bar charts or heatmaps showing improvements in efficiency metrics across different operational areas.
Implementation: Identify relevant KPIs and measure their performance before and after change initiatives.
Visualization: Use line/bar graphs to show trends in KPI performance over time.
Implementation: Track specific operational metrics relevant to the change initiatives.
Visualization: Bar charts or heatmaps showing improvements in efficiency metrics across different operational areas.
Change management effectiveness requires metrics that not only measure progress but also resonate with business stakeholders and accurately reflect the impact of change initiatives. They should provide valuable insights. Avoiding common pitfalls such as relying on easily misinterpreted or overly operational metrics is crucial. By aligning metrics with stakeholder perspectives, focusing on meaningful outcomes, improving visualization, and communicating effectively, senior change and transformation professionals can ensure that their metrics truly support the success of their change initiatives.
The top change management metrics to avoid are those that fail to provide clear, actionable insights to business stakeholders. By understanding and addressing the disconnect between change managers and business stakeholders, and by prioritizing metrics that truly reflect the impact and progress of change, you can drive more effective and successful change management efforts by influencing your stakeholders in your organisation.
As a next step, Chat with us if you would like to discuss more about leveraging AI and technology to generate high-impact change management metrics and data for your stakeholders, both at project and portfolio levels, using data visualisation tools.
Change adoption metrics are the usage, proficiency, sustainment and outcome indicators that show whether people are working differently after a change, not merely that the change was delivered to them.
The average organisation uses only 54% of the software licences it pays for, according to Zylo’s 2026 SaaS Management Index, which is the adoption problem in one number: Access is not use. The same gap runs through change programmes. A project can report full training attendance, every account provisioned and a green status, while the new process is used for the easy cases and bypassed for the hard ones.
This article is for change, HR and transformation leaders who already know they should measure adoption and need to decide what, exactly, to put on the page. It argues that credible adoption evidence rests on four families of metric (usage, proficiency, sustainment and outcome), that each family fails in a predictable way when read alone, and that the skill is in reading them together. By the end you will be able to choose a short, defensible metric set for a given initiative and interpret it without overclaiming. If you need the method for collecting this data, see our guide to how to measure change adoption; for the wider framework, start with the ultimate guide to measuring change.
An adoption metric is any measure of what people do after the change that can be tied to a specific behaviour the change requires, and the strongest sets combine usage, proficiency, sustainment and outcome measures.
Prosci’s research gives practitioners a useful starting taxonomy. It describes speed of adoption, ultimate utilisation and proficiency as the core measures once people have reached the ability stage: How quickly people begin to use the change, how many are using it, and how well they apply it. We add two more families, because a programme that stops at those three cannot answer the questions executives actually ask later.
Usage: Are the intended people using the change, how often, and how fully? (Speed of adoption and ultimate utilisation.)
Proficiency and behaviour: Are they doing it correctly, in the way the benefit depends on?
Sustainment: Does use hold after launch support is withdrawn, or does work drift back?
Outcome: Is the business result the change was funded for visibly moving?
Each family answers a different question, and each has a blind spot. Usage can be high while proficiency is poor. Proficiency can be strong in the pilot team and absent elsewhere. Sustainment is invisible if you stop measuring at go-live. Outcomes move for many reasons other than adoption. The rest of this article takes each family in turn and then shows how to read them together.
Usage metrics: Reach, frequency and depth
Usage metrics tell you whether the intended population is using the change, how often and how deeply, and the most common error is treating login counts as evidence of any of these.
Gallup’s May 2026 indicator shows how misleading access data can be. Nearly half of US employees (47%) say their organisation has integrated AI tools, yet only 30% use them a few times a week or more and 15% use them daily. Implementation, in Gallup’s words, does not guarantee use. Any programme that reported adoption from provisioning or first-login data would have overstated it by roughly half.
Split usage into three measures, because each exposes a different failure:
Reach: The share of the intended population who have used the new process or tool at all, by role and by team. This is your speed-of-adoption view when plotted weekly from go-live.
Frequency: How often the people who use it do so, compared with how often the work requires it. A tool needed daily and used monthly is not adopted.
Depth: The share of use that happens in the core workflow, measured on the two or three features or steps the benefit depends on, not on every feature.
The depth measure matters most. For a system implementation it might be the percentage of orders created in the new system rather than raised by spreadsheet, or the share of customer records completed to the required standard at first save. For a process change it is the proportion of cases that follow the new sequence end to end. Choose the critical few and ignore the long tail of features.
Read usage by segment, not by average
An enterprise average hides the teams that matter. Report usage by role, location and manager, then look for the lowest quartile. A 78% overall rate that conceals one division at 35% describes a rollout that has succeeded in some places and not started in others, and the response to each is different.
Proficiency and behaviour metrics: Are people doing it right?
Proficiency metrics show whether people perform the new way correctly and consistently, and they are the family most often missing from adoption reports because they require more than system data.
Proficiency is measured through observable behaviours (sometimes called micro-behaviours): The small, specific actions that show the change is embedded. Useful examples:
Error and rework rate: The share of transactions returned, corrected or escalated because the new process was not followed.
Time to complete a key task against a pre-change baseline, once the learning curve has passed.
Workaround rate: The proportion of work still routed through the legacy spreadsheet, email or manual step.
Assessed competency: A short practical check or manager sign-off, rather than a quiz on course content.
Manager coaching frequency: How often team leaders observe and reinforce the new behaviour. Gallup finds that employees whose managers actively support their team’s AI use are 1.7 times as likely to use it a few times a week or more, and only 36% of employees in AI-integrating organisations strongly agree their manager does.
Behaviour data is cheaper to collect than it sounds if you decide in advance which three or four behaviours the benefit depends on. For a deeper treatment of how behaviours form and hold, see our guide to behaviour change in the workplace.
Sustainment and outcome metrics: Does it last, and does it pay?
Sustainment metrics test whether adoption survives after launch support ends, and outcome metrics test whether the benefit the change was funded for has actually arrived; reporting neither leaves the programme unable to prove its value.
Most of the value in a change is realised, or lost, after the programme team has moved on. BCG’s 2025 analysis of transformation concluded that only about a quarter of transformations successfully create value, which is a reminder that delivery and realisation are different events. Go-live is the start of the measurement window, not the end.
Sustainment indicators
Re-measure the same usage and proficiency indicators at fixed intervals after support ends (30, 90 and 180 days is a workable pattern) and add one specific measure:
Reversion rate: The share of work, by team, that has returned to the old process or an unapproved workaround.
Stability of use: Whether each team’s usage is flat or rising without fresh intervention. A decline that begins when champions or project staff withdraw points to a reinforcement gap, not a design flaw.
Support demand: The trend in help requests per user. A fall followed by a rise often signals a second wave of problems as edge cases appear.
Outcome indicators
Outcome metrics connect adoption to the business case: Cycle time, cost to serve, error cost, compliance incidents, customer satisfaction, or revenue per seller, depending on the change. Their discipline is attribution. Where possible compare adopting teams with lagging ones, or a team before and after it reached a stable usage level, rather than reading a portfolio-wide trend that moves for many reasons. Where you cannot isolate the effect, say so in the report. Our guide on the problem with change management metrics covers the most common ways outcome claims overreach.
Choosing metrics by change type
The right adoption metrics depend on what the change asks people to do, so start from the target behaviour and pick the two or three metrics in each family that evidence it.
A system implementation turns on depth of use in the core workflow. A compliance change turns on observed behaviour and incident trends, because logins say nothing about whether the rule is followed. A restructure has few clean usage measures, so engagement, role clarity and collaboration patterns carry more weight, though these are better described as readiness and outcome indicators than adoption in the strict sense.
Metric
System implementation
Compliance change
Restructure
Reach and active-use rate
High priority
Moderate
Low
Feature or workflow depth
High priority
Low
Low
Process compliance rate
Moderate
High priority
Moderate
Incident or error rate
Moderate
High priority
Low
Manager observation or coaching
Moderate
High priority
High priority
Assessed proficiency
Moderate
High priority
Moderate
Engagement or clarity pulse
Low
Moderate
High priority
Reversion rate
High priority
Moderate
Moderate
Productivity vs pre-change baseline
High priority
Moderate
High priority
How many metrics is enough?
Aim for a core set of 8 to 15 metrics per initiative, grouped under the four families, with only the handful that answer the sponsor’s question reported upward. Include a metric only if you can name the decision it would change and you can collect it reliably at the cadence you need. Mix system data with at least some observation or survey data, because system logs cannot tell you why a team is lagging.
How to read the numbers together
Adoption is established when usage, proficiency and sustainment point the same way, and every divergence between them is a diagnosis waiting to be made.
Read the families as a set and look for the patterns below.
What you see
Likely meaning
Where to look
High reach, low depth
People log in but bypass the core workflow
Process design, training on the critical steps
High usage, low proficiency
Use without the intended behaviour
Coaching, job aids, manager reinforcement
Strong pilot, weak elsewhere
Local conditions, not a general result
Team-level segmentation, champion coverage
Good at go-live, decline at 90 days
Reinforcement withdrawn too early
Sustainment plan, manager accountability
Good adoption, no outcome movement
Wrong behaviour targeted, or benefit case flawed
Revisit the benefit logic
Leading and lagging indicators
Treat reach, frequency and early proficiency as leading indicators: They move first and can still be acted on. Treat reversion, incident rates and outcome measures as lagging: They confirm or contradict the story weeks later. Report both, and set thresholds in advance (for example, 70% reach in the target population by week six) so that a number triggers a decision rather than a discussion about whether it is good.
Make the data visible where decisions happen
A metric set earns its keep when managers and sponsors see it regularly. Spreadsheets and BI tools work for a single programme, but they struggle once an organisation runs many changes at once and needs a consistent view by team. Digital change platforms such as Change Compass bring adoption and impact data for multiple initiatives into one view, so leaders can see where adoption is lagging by team without rebuilding the report each month. For the layout of such a view, see our guide to designing a change adoption dashboard.
Where to start next week
Pick one live initiative and write down the two or three behaviours its benefit depends on. For each, name one usage metric, one proficiency metric and a sustainment check at 90 days, then agree with the sponsor what level would count as adopted. That small set will tell you more than a dashboard of twenty indicators, because every number on it is tied to a behaviour and a decision. Retire any existing metric that nobody acts on, and add the outcome measure only once you can say how you will separate the effect of adoption from everything else that moved. Revisit the set after the first reporting cycle: If two metrics always move together, keep the cheaper one. The aim is not a complete picture of the organisation but a trustworthy answer to one question, whether people are working differently and whether it is lasting.
Frequently asked questions
What are the most important change adoption metrics?
The most useful set covers four things: Usage (reach, frequency and depth of use), proficiency (whether people perform the new way correctly), sustainment (whether use holds after launch support ends) and outcome (whether the intended benefit is showing up). Prosci frames the first three as speed of adoption, ultimate utilisation and proficiency. No single metric is enough, because each can look healthy while another is failing.
What is the difference between adoption, utilisation and proficiency?
Adoption is the point at which people start using the change, utilisation is how many of the intended people keep using it and how fully, and proficiency is how well they perform with it. A team can show 95% utilisation and low proficiency if people use the new system but work around its intended process. Measure all three separately.
How many adoption metrics should we track for one initiative?
Most initiatives are well served by a core set of 8 to 15 metrics, grouped under usage, proficiency, sustainment and outcome, with only a handful reported to executives. More than that dilutes attention and turns data collection into a burden. Choose metrics tied to the specific behaviour the change needs, and drop any you cannot act on.
Are system logins a good measure of adoption?
Logins show access, not adoption. Gallup’s May 2026 data shows 47% of US employees say their organisation has integrated AI tools, yet only 30% use them weekly or more. Track feature use in the core workflow, the share of work processed the new way, and proficiency, and treat logins only as a first signal.
How do you know whether adoption has been sustained?
Check the same usage and proficiency measures at 30, 90 and 180 days after launch support ends, and watch the reversion rate: The share of work that returns to the old process or a workaround. Sustained adoption shows as stable or rising use in each team without extra intervention. A drop after support ends usually points to a manager reinforcement or process design gap.
This guide focuses on tracking adoption, what people are actually doing with the change. Three closely related guides on this site each cover a distinct angle, pick the one that matches what you need now:
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/
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
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