The topic of change is often inundated with literature stressing that it is about people, feeling, attitudes and behaviour. While these are important, lot of articles centred about the human-nature of change often ignore the importance of data during the change and transformation process. This is no different for the topic of employee readiness for change. People’s attitudes and behaviour need to be observed, measured and tracked during change.
Employee readiness for change is a critical factor that determines the outcome of organisational transformations. By leveraging data-driven insights, companies can proactively assess and enhance their employees’ preparedness, paving the way for smoother transitions and improved business results.
Let’s explore the concept of employee readiness for change and delve into strategies for using data to optimise readiness during transformations. We will discuss key metrics, change readiness assessments, employee engagement techniques, and real-time monitoring to help organisations navigate change effectively.
What is Employee Readiness for Change?
Employee readiness for change refers to the extent to which individuals within an organisation are prepared, willing, and capable of embracing and implementing change. It encompasses their understanding of the change, their motivation to support it, and their ability to adapt and perform effectively in the new environment.
Assessing employee readiness involves evaluating three key elements:
Organisational readiness: This aspect focuses on the company’s overall preparedness for change, including factors such as leadership commitment, resource availability, and clear objectives.
Open attitudes toward change: Gauging employees’ understanding and willingness to embrace change is crucial. Positive attitudes contribute to successful resistance management and building change readiness.
Individual readiness: On a personal level, assessing each employee’s readiness, willingness, and ability to adapt to change is essential. This involves considering their skills, knowledge, and emotional preparedness.
Note that individual readiness is only one component of the overall readiness. A lot of people only focus on this to the detriment of truly assessing the overall readiness.
By conducting a comprehensive assessment of these elements, organisations can gain valuable insights into their employees’ readiness for change. This information serves as a foundation for developing targeted strategies to enhance readiness and facilitate successful transformations.
How to Use Data to Improve Employee Readiness During Transformations
Harnessing the power of data analytics is essential for enhancing workforce preparedness during organisational transformations. By systematically gathering and interpreting relevant data, organisations can uncover potential obstacles and craft bespoke strategies to bolster readiness and ensure seamless transitions.
Determining Critical Metrics for Change Preparedness
To effectively utilize data, organisations must first establish the critical metrics that will serve as indicators of readiness. These metrics provide a foundation for assessing the current state and tracking future progress:
Engagement indices: Measure the degree to which employees are actively involved and invested in organisational activities. High engagement suggests a supportive environment for change initiatives.
Flexibility indicators: Evaluate employees’ capacity to adjust to new roles and technologies. This metric identifies those who may benefit from targeted support.
Completion rates of developmental programs: Monitor the percentage of the workforce completing essential training. This figure highlights areas where skill enhancement is necessary.
Executing a Holistic Change Preparedness Evaluation
With metrics in place, conduct a thorough evaluation of change preparedness at both organisational and individual levels. Utilize surveys, interviews, and focus groups to gather rich data. This comprehensive approach reveals resistance points and directs attention to intervention opportunities:
Cultural assessment: Analyse underlying cultural traits that influence how change is perceived and implemented. Insights into assertiveness and hierarchy can guide communication strategies.
Leadership analysis: Assess the readiness and skillset of leaders to champion change. Effective leadership is pivotal for the success of transformation efforts.
Enhancing Workforce Involvement Through Data Insights
Data-driven insights can significantly enhance employee involvement during periods of change. By examining workforce data, organisations can tailor communication and training to better resonate with their employees:
Customized messaging: Develop communication that speaks directly to the needs and concerns of various employee segments. This ensures messages are impactful and engaging.
Focused learning initiatives: Identify specific knowledge gaps and create targeted training programs. Customized learning enhances employees’ ability to adapt to change confidently.
Continuous Strategy Adaptation via Real-Time Data
Ongoing monitoring of strategy effectiveness through real-time analytics is vital. This continuous process allows organisations to refine their approaches based on evolving data patterns, maintaining high levels of readiness:
Regular data collection: Actively seek feedback from employees regarding their transition experiences. This input is crucial for identifying areas needing adjustment.
Dynamic decision-making: Leverage real-time (or least recent) data to inform strategic decisions and optimize change management initiatives, ensuring they remain aligned with organisational goals.
1. Identify Key Metrics for Change Readiness
Establishing a robust framework of metrics is fundamental to accurately gauge change readiness within an organisation. These metrics function as critical indicators, allowing leaders to monitor the pulse of their workforce during transformation initiatives. A well-defined set of metrics provides a structured approach to assessing readiness and identifying areas requiring attention.
Engagement Indicators
Evaluating employee engagement is crucial for understanding the workforce’s readiness for change. This involves gathering insights into how employees perceive their roles and the organisation’s objectives. A workforce that demonstrates high levels of commitment and enthusiasm tends to be more agile and supportive of change efforts. Methods such as employee sentiment analysis and engagement surveys can help capture these dynamics, offering a nuanced view of organisational health.
Flexibility Metrics
Flexibility metrics provide a window into the ease with which employees can transition to new processes and systems. This involves examining historical data on change adaptability and using tools like behavioural assessments to gauge employees’ readiness for new challenges. Understanding the flexibility of employees can guide targeted support and interventions, ensuring smoother transitions during organisational shifts.
Completion Rates of Educational Programs
Monitoring the completion rates of educational initiatives is essential to assess how prepared employees are for impending changes. This metric reflects the organisation’s dedication to equipping its workforce with the skills needed for transformation. Analysing completion data, alongside post-training assessments, can offer insights into the effectiveness of learning interventions and highlight areas for development.
Together, these metrics form a comprehensive picture of an organisation’s change readiness. By establishing a baseline for these indicators, organisations can track progress over time, adjusting strategies as necessary to enhance readiness and facilitate successful transformations.
2. Conduct a Comprehensive Change Readiness Assessment
To pave the way for a successful transformation, conducting a comprehensive change readiness assessment becomes imperative. This systematic evaluation delves into the organisation’s preparedness at both the macro and micro levels, providing insights that are critical for shaping effective change strategies. Utilizing a blend of qualitative and quantitative methods, the assessment illuminates the landscape of readiness, offering a strategic foundation for decision-making.
Strategic Evaluation Components
A multifaceted readiness assessment encompasses several strategic components, each designed to gather a holistic understanding of the organisational climate:
Cultural Insight Analysis: Delve into the organisational culture to uncover factors that may affect acceptance of change. This involves exploring existing communication styles, shared values, and prevalent behaviours that could influence the transformation journey. Gaining a clear picture of these cultural dynamics aids in crafting initiatives that resonate with the workforce’s inherent beliefs.
Leadership Capacity Evaluation: Determine the readiness and effectiveness of leadership in spearheading change efforts. Examine their ability to inspire and motivate, as well as their capacity to navigate the complexities of organisational transformation. Strong leadership commitment is essential for instilling confidence and guiding the organisation through change.
Resource Readiness Check: Evaluate the sufficiency and distribution of resources critical for supporting change initiatives. Consider the existing technological capabilities, financial support, and human resources available to drive the transformation. Addressing resource gaps early ensures that the organisation is well-prepared to meet the demands of change.
Analysing Data for Targeted Interventions
Upon gathering data through the readiness assessment, a thorough analysis is essential to uncover insights that inform strategic interventions. This analysis should focus on identifying potential resistance points and areas ripe for development:
Resistance Identification: Detect and chart areas where reluctance to change may manifest. Utilize employee feedback, trends from past projects, and current mood assessments to pinpoint these zones. Understanding these resistance factors allows for proactive measures to encourage acceptance and reduce pushback.
Opportunity Leveraging: Spot areas with high readiness levels that can be used to propel change efforts forward. Recognize organisational strengths and existing competencies that can be harnessed to support the transition. By leveraging these opportunities, organisations can accelerate progress and cultivate a culture of continuous growth.
Conducting a comprehensive change readiness assessment provides a strategic lens through which organisations can navigate the complexities of transformation. By systematically evaluating readiness and leveraging data-driven insights, organisations can craft tailored strategies that enhance employee preparedness and drive successful change outcomes.
3. Utilise Data Analytics to Foster Employee Engagement
Employing data analytics is essential to deepening employee involvement during change processes. By utilizing advanced analytical tools, organisations can uncover key drivers of motivation and engagement within their workforce. This enables the development of strategies that are not only data-informed but also tailored to enhance a culture of commitment and adaptability.
Strategic Communication Approaches
Data analytics offer organisations the ability to refine communication strategies in a way that aligns with the diverse preferences and needs of employees. By examining patterns in communication effectiveness and gathering feedback, companies can create messaging frameworks that are clear and meaningful. This strategic approach ensures that communication is not just disseminated but absorbed, fostering a sense of inclusion and understanding across the organisation.
Customised Development Pathways
Insights from analytics enable the design of development pathways that cater to the specific learning and growth needs of employees. Analysing performance metrics and capability assessments allows organisations to pinpoint where support is most needed, leading to bespoke development initiatives. These pathways not only address skill gaps but also promote a learning culture that equips employees for future challenges.
Ongoing Engagement Assessment
Real-time analytics provide a robust mechanism for continuously assessing employee engagement throughout the transformation journey. Establishing metrics that reflect engagement sentiment and participation levels helps organisations react swiftly to shifts in morale. This proactive engagement assessment ensures that initiatives remain aligned with employee expectations and organisational objectives, fostering a sustained commitment to change.
4. Monitor and Adapt Strategies Using Real-Time Data
Leveraging real-time data analytics is crucial for dynamically guiding change initiatives. This approach enables organisations to continuously evaluate the effectiveness of their strategies, ensuring they remain aligned with shifting business needs and employee expectations. By integrating adaptive feedback mechanisms, companies can refine their tactics, promoting an environment of agility and responsiveness.
Dynamic Data Acquisition
Establishing a robust system for dynamic data acquisition is essential to maintain an accurate understanding of organisational and employee dynamics. Real-time analytics platforms and dashboards provide comprehensive insights into change progress, such as engagement indices, performance metrics, and sentiment analysis. Regularly capturing this data allows organisations to proactively identify patterns and shifts that may influence the success of change initiatives.
Strategic Insights-Driven Adjustments
The insights obtained from real-time data empower organisations to make calculated adjustments to their strategies. This adaptive approach ensures that interventions remain pertinent and effective, addressing emerging challenges and capitalizing on new opportunities:
Incorporating Employee Perspectives: Integrate direct insights from employees into strategic refinements. Understanding their experiences and perceptions offers a nuanced perspective of the change process, allowing for precise enhancements.
Pattern Recognition: Use data patterns to recognize trends that may require strategic shifts. For example, a downward trend in engagement metrics could indicate the need for improved communication or support mechanisms.
Efficient Resource Deployment: Employ data insights to enhance resource deployment, ensuring that efforts are concentrated where they are most impactful. This targeted approach enhances the effectiveness of change initiatives and maximizes results.
Proactive Decision-Making
Real-time data analytics enable proactive decision-making, empowering leaders to swiftly adjust to evolving conditions. This capability is vital for sustaining momentum and ensuring that change efforts remain aligned with organisational objectives. By adopting a data-informed mindset, organisations can navigate the complexities of transformation with confidence and precision.
By harnessing the power of data analytics, organisations can proactively assess and enhance employee readiness during transformations, paving the way for smoother transitions and improved business outcomes. Embracing a data-driven approach to change management is no longer optional; it is a strategic imperative for organisations seeking to thrive in an ever-evolving landscape. If you’re ready to transform your change management processes and unlock the full potential of your workforce, chat to us to explore how we can help you leverage data and insights to navigate change with confidence and precision.
Artificial Intelligence (AI) is no longer a futuristic concept—it is here, transforming industries and reshaping how organisations operate. For change and transformation professionals, AI presents both opportunities and challenges. While it automates repetitive tasks and provides advanced insights, it also demands a shift in mindset, skillsets, and approaches to managing change.
Change and transformation professionals must now navigate a world where AI not only augments their work but also redefines their roles. Here we explore how AI is impacting the field of change management, what parts of the work will shift and evolve, and how change manager can adapt to thrive in this new era.
The Impact of AI on Change Management
AI is revolutionizing change management by automating processes, providing predictive analytics, and enabling personalization at scale. It allows organisations to identify resistance early, tailor interventions for specific stakeholders, and measure the effectiveness of change initiatives in real time. However, these advancements also mean that the traditional ways of working are evolving rapidly.
For change professionals, this transformation requires a deeper understanding of how to integrate AI into their processes while maintaining a human-centered approach. Beyond the usual AI use for pictures and communications, let’s break down the key areas where AI is making an impact:
1. Automation of Repetitive Tasks
One of the most immediate benefits of AI in change management is its ability to automate repetitive and time-consuming tasks. For example:
– Stakeholder Analysis: AI tools can analyse large datasets to identify key stakeholders, map their influence networks, and predict their responses to change.
– Communication: Generative AI can draft personalized emails, newsletters, or FAQs tailored to different stakeholder groups.
– Reporting: Automated dashboards powered by AI can provide real-time updates on adoption rates, engagement levels, and other key metrics.
This automation frees up time for change professionals to focus on higher-value activities such as strategy development and stakeholder engagement.
2. Data-Driven Insights
AI enables access to advanced data analytics that were previously unavailable or too complex to process manually. Predictive analytics tools can forecast employee resistance, identify potential risks, and recommend targeted interventions before problems escalate. For example:
– Sentiment analysis tools can assess employee feedback from surveys or social media platforms to gauge morale and identify concerns.
– Behavioural analytics can track how employees are interacting with new tools or processes, providing insights into adoption patterns.
However, it is worth noting that the more data collected, including historical data, the richer the AI insights will be as it will generate more accurate observations and recommendations.
These insights allow change professionals to move from reactive approaches to proactive strategies based on real-time data.
3. Personalisation at Scale
AI empowers organisations to deliver highly personalised experiences for employees during times of change. Instead of one-size-fits-all approaches, AI tools can segment stakeholders based on their preferences, behaviours, or roles and tailor communication or training accordingly. For instance:
– Adaptive learning platforms can create customised training modules for employees based on their skill gaps.
– Chatbots powered by natural language processing (NLP) can answer individual questions about new systems or processes in real time. With the ease of designing and implementing chatbots nowadays, designing a chatbot for implementing a change initiative is absolutely feasible.
Personalisation improves engagement and reduces resistance by addressing the unique needs of each individual or group.
What Will Decrease in the Work of Change manager?
While AI enhances many aspects of change management, it also reduces the need for certain traditional tasks:
1. Routine Communication
AI tools like chatbots or automated email systems can handle routine communication tasks such as sending updates or answering frequently asked questions (FAQs). This reduces the time spent on drafting generic messages or managing basic inquiries.
2. Manual Stakeholder Analysis
In the past, stakeholder analysis often involved manual mapping exercises based on interviews or surveys. With AI-driven tools that analyse organisational networks and sentiment data, this process becomes faster and more accurate.
3. Administrative Reporting
Manual reporting on metrics like adoption rates or training completion will decrease as AI-powered dashboards provide real-time analytics. Change managers will no longer need to spend hours compiling reports; instead, they can focus on interpreting the data and making strategic decisions.
What Will Increase in the Work of Change manager?
While some tasks decrease with AI integration, others become more critical:
1. Strategic Oversight
With AI handling operational tasks, change manager will need to focus more on strategic oversight. This includes ensuring that AI tools align with organisational goals and values while driving meaningful outcomes.
For example:
– Interpreting data insights provided by AI tools to refine strategies. With the range and volume of insights generated, the change professional needs to be focused on what parts add value and where the attention should be placed
– Ensuring that predictive analytics align with broader business objectives. AI generated data will need to be evaluated together with other sources of data. There may be data points that are not captured by AI, thereby impacting the predictive recommendations.
– Balancing short-term efficiency gains with long-term cultural shifts. The use of AI must align with the appetite of the organisation and what the people are capable of adopting. The change professional needs to careful assess the extent of the shifts required and adjust the AI usage and resulting business impacts accordingly. Is the organisation actual ready for the operating model changes inflicted by AI? Work efficiency aside, what will the organisation do with excess people capacity? And will it be ready to implement various business efficiency changes resulting from AI? This is a core question that leaders need to answer.
2. Ethical Governance
As organisations increasingly rely on AI for decision-making, ethical oversight becomes a core responsibility for change manager. Whilst this may not be considered as the ‘core job’ for change managers, it is important to incorporate this as a key part of monitoring of employee feedback and adoption management. They must ensure that:
– AI systems are free from biases that could harm employees or stakeholders. If biases are found, that there is action plans to address these
– Data privacy is maintained while using analytics tools. This will affect which tool is chosen and mode is utilised.
– Transparency is upheld in how decisions are influenced by AI. For example, does the AI recommendation reference data points specifically to support transparent tracing.
Building trust in AI systems among employees will be a critical part of this role.
3. Human-Centered Leadership
Despite its capabilities, AI cannot replace human empathy or emotional intelligence—qualities essential for navigating complex organisational changes. Change manager must:
– Act as empathetic leaders who address fears about job displacement or role changes due to automation.
– Foster trust in both leadership and technology by maintaining open lines of communication.
– Focus on building resilient teams that embrace adaptability and continuous learning.
Mindset Shifts Required for Change manager
To succeed in an AI-driven environment, change manager must adopt new mindsets:
1. From Control to Collaboration: Embrace collaboration with AI as a partner rather than viewing it as a tool to control outcomes.
2. From Static Expertise to Lifelong Learning: Continuously update skills related to data literacy, digital transformation strategies, and emerging technologies.
3. From Reactive Risk Management to Proactive Adaptation: Use predictive insights from AI tools to anticipate challenges rather than reacting after they occur.
4. From Fear of Displacement to Trust in Co-Creation: Recognize that AI enhances human capabilities rather than replacing them entirely.
These mindset shifts will enable change manager to lead effectively in an era where technology plays an increasingly central role in organisational transformation.
Immediate Use Cases for Change managers to Leverage AI
As AI continues to transform the workplace, change managers must adopt practical strategies that integrate AI into their workflows while maintaining a human-centered approach. Below are actionable steps to help change professionals thrive in the AI-driven future.
1. Use AI to Enhance Stakeholder Engagement
AI provides powerful tools to analyze and engage stakeholders more effectively. Change manager can leverage these capabilities to build stronger relationships and drive alignment across the organisation.
Actionable Steps:
– Leverage Sentiment Analysis Tools: Use AI-powered sentiment analysis to gauge stakeholder attitudes and concerns from surveys, emails, or social media. This allows you to identify resistance early and address it proactively.
– Develop Personalized Communication Plans: Use AI tools to segment stakeholders based on their roles, preferences, or behaviours. Tailor communication strategies for each group, ensuring messages resonate with their specific needs.
– Deploy Chatbots for Real-Time Support: Implement AI chatbots to provide stakeholders with instant access to information about change initiatives. This reduces the burden on change teams while improving responsiveness.
Example in Practice:
A global organisation undergoing a digital transformation may use AI sentiment analysis to monitor employee feedback during the rollout of a new system. By identifying teams with low engagement scores, the change team can intervene early with targeted workshops and one-on-one coaching sessions.
2. Integrate Predictive Analytics into Change Planning
Predictive analytics is one of the most transformative aspects of AI for change management. It allows change manager to anticipate challenges, forecast outcomes, and refine strategies based on data-driven insights.
Actionable Steps:
– Identify Potential Resistance Hotspots: Use predictive models to analyse historical data and identify departments or teams likely to resist upcoming changes.
– Forecast Adoption Rates: Leverage analytics tools to predict how quickly employees will adopt new processes or technologies. Adjust timelines and training plans accordingly.
– Optimise Resource Allocation: Use AI insights to determine where resources (e.g., training budgets or change champions) will have the greatest impact.
Example in Practice:
A financial services firm used predictive analytics during a merger to identify which regions were most likely to experience resistance based on past organisational changes. This allowed the team to deploy additional resources in those areas, reducing delays and improving overall adoption rates.
3. Focus on Building Trust in AI
As AI becomes more integrated into organisational processes, trust becomes a critical factor for success. Employees and stakeholders may feel uncertain about how decisions are being made or fear that their roles will be replaced by automation.
Actionable Steps:
– Be Transparent About AI’s Role: Clearly communicate how AI is being used in decision-making processes and emphasize that it is a tool to support—not replace—human judgment.
– Address Ethical Concerns: Ensure that AI systems are free from bias and comply with data privacy regulations. Regularly audit AI tools for fairness and accuracy.
– Foster Open Dialogue: Create forums where employees can ask questions about AI implementations, share concerns, and provide feedback.
Example in Practice:
A healthcare organisation introduced AI-powered scheduling software but faced resistance from staff who feared losing control over their work schedules. By hosting workshops that explained how the system worked and allowing employees to provide input into its configuration, the organisation built trust and improved adoption rates.
4. Lead with Emotional Intelligence
While AI automates many tasks, it cannot replace the human touch required for effective leadership during times of change. Change managers must double down on emotional intelligence (EI) to complement AI’s capabilities. It may not be that employee emotional reactions and nuances are fully captured by AI, so care need to be taken in this regard.
Actionable Steps:
– Empathize with Employee Concerns: Actively listen to employees’ fears about job displacement or role changes caused by automation.
– Foster a Growth Mindset: Encourage teams to see AI as an opportunity for personal and professional development rather than a threat.
Example in Practice:
During an automation initiative at a manufacturing company, senior leaders held town halls where they acknowledged employees’ concerns about job security but emphasized opportunities for upskilling. This approach helped reduce anxiety and fostered a more positive attitude toward the changes.
5. Redefine Training Strategies
AI is transforming how organisations approach employee training during times of change. Traditional one-size-fits-all training programs are being replaced by adaptive learning platforms that deliver personalized content based on individual needs.
Actionable Steps:
– Implement Adaptive Learning Platforms: Use AI-powered tools that assess employees’ existing skills and create customized learning paths.
– Focus on Digital Literacy: Ensure employees understand how to use new AI tools effectively as part of their daily workflows.
– Provide Continuous Learning Opportunities: Move beyond one-time training sessions by offering ongoing development programs that evolve with organisational needs.
Example in Practice:
A retail company introduced an adaptive learning platform during its e-commerce transformation. Employees received tailored training modules based on their roles and skill gaps, resulting in faster adoption of new systems and improved performance metrics.
6. Balance Efficiency with Culture implications
AI brings remarkable efficiency gains, but change managers must ensure that these do not come at the expense of organisational culture. Careful analysis should be done to understand potential impacts of AI on the cultural and behavioural norms of the organisation before proceeding.
Actionable Steps:
– Prioritize Culture Over Speed: While AI can accelerate processes, take time to ensure that cultural alignment is not overlooked during rapid transformations. What behaviours need to be there to support the adoption and implementation and how are these reinforced?
– Balancing cultural norms and behaviours: Are there particular rituals and behaviours that are critical to the culture of the organisation that AI should not try and replace? Are there practices that should remain despite AI gains in efficiency due to cultural goals?
– Measure Success Holistically: Go beyond efficiency metrics by assessing employee engagement, morale, and overall satisfaction during changes.
Example in Practice:
A tech company undergoing rapid scaling used AI tools for project management but ensured that team leaders continued holding regular one-on-one meetings with employees. This balance preserved trust and engagement during a period of significant growth.
The Evolving Role of Change managers
As organisations embrace AI, the role of change manager is shifting from operational execution to strategic leadership. Key areas of focus include:
1. Strategic Visioning: Aligning AI-driven initiatives with long-term organisational goals.
2. Ethical Oversight: Ensuring responsible use of AI while maintaining transparency and trust.
3. Proactive Adaptation: Using predictive insights from AI tools to stay ahead of challenges.
4. Human-Centered Leadership: Balancing technological advancements with empathy and emotional intelligence.
Change manager who embrace these shifts will not only remain relevant but also play a pivotal role in shaping the future of work.
The proliferation of AI is transforming every facet of change management—from automating routine tasks to enabling data-driven decision-making and personalized engagement strategies. For change manager, this evolution presents an opportunity to elevate their roles by focusing on strategic oversight, ethical governance, trust-building, and human-centered leadership.
By adopting practical strategies such as leveraging predictive analytics, redefining training approaches, and leading with emotional intelligence, experienced professionals can harness the power of AI while maintaining a people-first approach. The future of change management lies not in replacing humans with technology but in combining the strengths of both for greater impact. As we move further into this era of transformation, change manager who adapt their mindsets, skillsets, and approaches will be at the forefront of driving successful organisational change—one that balances innovation with humanity.
With complex, high-stakes change environments, change leaders know that success hinges on more than just strategies and frameworks. It rests on the ability to transform behaviours into habits—turning deliberate, effortful actions into automatic routines. After all, the core of change is largely the result of a series of behaviour changes. Here we delve into the psychology and practice of habit formation in organisational change, offering actionable insights for senior change leaders.
The Foundation: Belief Fuels Change
Change begins with belief. Stakeholders must believe that change is not only necessary but achievable—and that they themselves are capable of adapting. This foundational belief can be especially elusive in organisations with a history of failed initiatives. Skepticism and fatigue are common barriers.
Leaders play a pivotal role in cultivating belief. They must demonstrate that change is possible through a series of small, achievable wins. For instance, consider a team resistant to adopting a new project management tool. Instead of mandating full adoption from day one, leaders might first encourage the team to use the tool for a single task or project. As the team sees the benefits—improved collaboration, streamlined processes—their belief in the tool and their ability to adapt grows.
Creating belief also involves transparent communication. Leaders need to articulate why the change is necessary and how it aligns with the organisation’s goals. When stakeholders understand the “why,” they are more likely to commit to the “how.”
Additionally, addressing past failures openly can help rebuild trust. Leaders can acknowledge previous shortcomings while emphasising what will be different this time—whether it’s stronger leadership commitment, improved resources, or a more phased approach. By creating an environment where past lessons inform current actions, belief becomes more attainable.
Social Reinforcement: The Power of Community
Humans are inherently social creatures, and the behaviours of others significantly influence our own. This is why social reinforcement is a cornerstone of successful change initiatives. Change champions and team leaders serve as visible examples of the desired behaviours, demonstrating both commitment and success.
Stories are particularly powerful in this context. When change champions share their experiences—challenges faced, strategies employed, and victories achieved—it reinforces the idea that change is possible for everyone. For example, in a digital transformation initiative, a frontline employee who shares how a new system simplified their workflow can inspire others to give it a chance.
Social reinforcement also fosters accountability. When team members see their peers embracing new behaviours, it creates a sense of collective momentum that is hard to resist. Positive peer pressure can become a motivating force, pushing individuals to align with group norms and expectations.
Furthermore, leveraging social proof through team recognition can amplify the impact. Publicly celebrating individuals or teams who exemplify desired behaviours not only rewards them but also encourages others to follow suit. Recognition initiatives, such as “Change Hero of the Month,” can spotlight efforts that align with organisational goals, building a culture of reinforcement and inspiration.
From Behaviour to Habit: The Mechanics of Routine
Turning behaviours into habits involves repetition and reinforcement. According to a 2006 study from Duke University, as much as 40% of our daily actions are based on habit. This underscores the importance of embedding new behaviours deeply enough that they become second nature.
The habit loop, as popularised by Charles Duhigg in The Power of Habit, consists of three components:
Cue: A trigger that initiates the behaviour.
Routine: The behaviour itself.
Reward: The benefit or satisfaction derived from the behaviour.
Let’s apply this framework to a customer complaints initiative. Suppose the goal is to enhance customer satisfaction by encouraging consultants to proactively address complaints. The cue might be specific language from a dissatisfied customer. The routine could involve logging the complaint, initiating a structured conversation, and offering next steps. The reward? The consultant feels confident they’ve resolved the issue and improved the customer’s experience. Over time, this routine becomes habitual, reducing the cognitive load required to execute it. This is also why sufficiently forecasting and estimating the effort and load required as a part of change adoption is critical in initiative planning.
To support habit formation, organisations can utilise tools and reminders. For instance, automated notifications or visual aids like posters can reinforce cues and encourage consistent practice. Technology can also play a vital role by integrating habit-supporting systems, such as digital dashboards that track key behaviours and provide immediate feedback.
Habits are further strengthened when they are tied to personal values and aspirations. For example, a team member who values customer care will find it easier to embrace new routines that align with their intrinsic motivation. Aligning organisational habits with individual and collective values creates a powerful foundation for sustained change.
Scaling Change: Small Wins, Big Impact
Complex, large-scale changes can feel overwhelming. The key to success is to break these initiatives into smaller, manageable changes. Achieving these small wins builds momentum and confidence, laying the groundwork for tackling more significant challenges.
For instance, in an organisation shifting to remote work, a small initial change might involve standardising virtual meeting protocols. Once teams are comfortable with this, leaders can introduce more complex changes, such as remote performance management systems or asynchronous collaboration tools.
Small wins also provide measurable milestones. These visible markers of progress are crucial for maintaining stakeholder engagement and belief in the larger vision. Each success, no matter how minor, contributes to a sense of achievement that propels the team forward.
Moreover, small wins create opportunities for feedback and refinement. As each milestone is achieved, leaders can gather input to identify what’s working and what isn’t, ensuring continuous improvement. Feedback loops keep the change process agile and adaptive, responding to emerging challenges and opportunities.
Keeping the End in Sight: Navigating Obstacles
The journey of change is rarely linear. Delays, setbacks, and unforeseen obstacles are inevitable. To navigate these challenges, leaders must keep the end goal firmly in mind while celebrating progress along the way.
Regularly communicating achievements—both big and small—helps maintain focus and motivation. For example, if the ultimate goal is a 30% increase in operational efficiency, celebrating a 5% improvement early in the process can reinforce commitment and belief.
Visualisation tools such as roadmaps, dashboards, and progress trackers can also help teams see how their efforts contribute to the overall objective. This clarity reduces ambiguity and keeps everyone aligned. Leaders can further use storytelling to paint a vivid picture of the future state, inspiring teams to stay the course. This also helps to put human nuances and experiences into the data shown.
Equally important is maintaining flexibility. Leaders should be prepared to adjust timelines or approaches in response to new challenges without losing sight of the ultimate goal. This adaptability demonstrates resilience and fosters trust among stakeholders. Encouraging a mindset of learning and iteration can transform obstacles into opportunities for growth.
The Role of Measurement: Tracking Success
Measurement is integral to behaviour and habit formation. It provides objective data to assess whether changes are taking root and if progress aligns with strategic goals.
Metrics should be both quantitative and qualitative. For instance, in a customer satisfaction initiative, quantitative measures might include Net Promoter Scores (NPS) or resolution times. Qualitative data could involve customer feedback or employee reflections on their new routines.
Regularly reviewing these metrics allows leaders to adjust strategies as needed, ensuring that small changes cumulatively drive the desired outcomes. Dashboards and reporting tools can provide real-time insights, enabling data-driven decision-making.
In addition to tracking progress, measurement fosters accountability. When individuals and teams know their efforts are being monitored, they are more likely to remain committed to the change process. Transparent reporting also builds trust, showing stakeholders that their efforts are valued and impactful.
Alignment with Strategy: The Bigger Picture
In the midst of multiple concurrent changes, it’s easy for teams to lose sight of how their individual efforts support the broader strategy. Leaders must articulate this alignment clearly and consistently.
Consider an organisation undergoing a digital transformation while also pursuing sustainability goals. Leaders might connect the two by emphasising how digital tools reduce paper usage or improve energy efficiency. This alignment helps employees see the “bigger picture” and understand how their routines contribute to overarching organisational priorities.
Clarity is particularly important when behaviours differ across teams. For example, proactive listening might be a critical behaviour for customer-facing teams, while cross-functional collaboration could be the focus for back-office teams. Leaders need to explain how these distinct behaviours interconnect and drive the overall strategy.
Furthermore, aligning behaviours with the organisation’s values can deepen commitment. When employees see how their actions reflect core values, they are more likely to internalise and sustain the desired changes. Leaders can leverage organisational storytelling to create a compelling narrative that unifies diverse initiatives under a shared vision.
Practical Steps for Change Leaders
Start Small: Identify a single behaviour to change and build on early successes.
Leverage Social Influence: Empower change champions to share stories and model behaviours.
Embed Habits: Use the habit loop (Cue, Routine, Reward) to make new behaviours automatic.
Celebrate Progress: Recognise achievements, no matter how small, to maintain momentum.
Measure Impact: Regularly track progress against clear, relevant metrics.
Communicate Alignment: Ensure teams understand how their efforts contribute to the overall strategy.
Be Transparent: Share challenges and adjustments to build trust and credibility.
Provide Resources: Equip teams with the tools and training needed to succeed.
Reinforce Continuously: Ensure that reinforcement mechanisms
Transforming behaviours into habits is the cornerstone of sustained organizational change. By fostering belief, leveraging social reinforcement, and breaking complex changes into manageable steps, change leaders can build a culture where new behaviours become second nature. With clear goals, consistent measurement, and strategic alignment, these habits will not only endure but also drive lasting success.
Sustaining change requires patience, persistence, and a deep understanding of human behaviour. By focusing on the incremental steps that lead to lasting habits, senior practitioners can guide their organizations through even the most challenging transformations—one habit at a time.
Change adoption is the heart of every change practitioner’s work. It’s the primary measure of whether a change initiative truly succeeds, yet, surprisingly, many organizations still fail to adequately track, measure, and manage change adoption. Without a clear understanding of how well end-users are adopting the change, it’s nearly impossible to gauge the initiative’s real impact on the business. Change adoption must be both intentional and managed, not just assumed.
If you search for change adoption on Google the top articles seem to refer to the same things. These include transition preparation, communication, training and support. The top 2 articles are by Whatif and Walkme and seem to emphasise the importance of in-app training products they offer. The Prosci article emphasise the ADKAR model on the other hand.
While common strategies for change adoption—such as communication, training, and support—are essential, these are foundational steps and not the complete formula for sustained adoption. There’s a nuanced spectrum of factors that contribute to adoption, including the type of change, the stakeholders, the organization’s capacity for change, measurement metrics, and performance management. The following insights explore these core factors and share practical strategies, bolstered by real-world examples, to help change practitioners improve adoption rates across their organizations.
1. Understanding the Type of Change
The nature of the change plays a significant role in determining how to drive adoption. A change can range from a simple update in process to a fundamental shift in behaviour, and this range requires different approaches:
– Simple Changes : Minor changes, like a new software feature or a small process tweak, may only need a basic communication update. For instance, consider an HR team implementing a new self-service portal for employees to access their pay stubs. In this case, a simple email announcement explaining how to access the feature, along with a short tutorial video, might be all that’s required to ensure adoption.
– Complex, Behavioural Changes : For more complex changes that impact behaviours or workflows, adoption strategies need to be more involved. Imagine an organization implementing a new performance review system that shifts from annual reviews to ongoing, quarterly feedback sessions. This type of change isn’t just procedural—it demands a shift in how employees and managers think about performance. Here, communication alone won’t be sufficient. It requires ongoing training, leadership modeling, reinforcement through feedback loops, and alignment with performance metrics. Regular team meetings can serve as a platform for leaders to showcase the change, while role-playing sessions can help embed the new behaviours.
Analogy : Think of the change type as similar to cooking different dishes. For a quick salad, all you need is the right ingredients and a bowl to toss them in. For a complex dish like a soufflé, you’ll need precise measurements, specific tools, and careful monitoring to ensure it doesn’t collapse. The type of change similarly determines the level of preparation and intervention required.
2. Tailoring Strategies to Stakeholder Types
Understanding your end-users or stakeholders—those directly impacted by the change—is crucial. Each group will have different engagement channels and needs, which means you can’t rely on a one-size-fits-all communication plan. To drive adoption, you need to deliver information in ways that resonate with each audience.
– Identify Effective Channels : For example, one team may prefer to discuss updates in weekly meetings, while another may respond better to monthly town hall sessions. When a global retail company rolled out a new inventory management system, the change team customized its communication and training by region. Regional managers were empowered to communicate the changes in a way that suited their teams’ preferences, whether that meant team huddles, newsletters, or one-on-one conversations. As a result, the change was embraced much more readily because each team felt that the approach was tailored to their needs.
– Build Change into Routine Communication : To make the change part of the team’s daily workflow, leverage existing channels, like monthly business reviews or quarterly updates. For instance, if sales teams have weekly performance meetings, consider incorporating brief updates about how the change (such as a new CRM feature) can benefit their sales process, along with success stories from team members.
Analogy : Think of stakeholder engagement as similar to hosting a dinner party. You wouldn’t serve the same meal to every guest without considering their preferences. Similarly, change practitioners need to “serve” the change in ways that appeal to each stakeholder group’s tastes and communication preferences.
3. Aligning with Organisational Change Capacity
Change capacity—the organization’s ability to absorb and adopt change—is a critical but often overlooked factor. The timing of introducing new changes matters, especially when the change is complex. If an organization is already handling multiple projects or transformations, adding another initiative can result in resistance or “change fatigue.”
– Manage Competing Priorities : Suppose a financial services company is simultaneously upgrading its internal software, launching a new customer-facing app, and implementing a data security compliance initiative. Launching yet another change, like a new employee recognition program, may overwhelm employees, who may deprioritize it in favour of what they perceive as more urgent projects. Change practitioners should work closely with program managers to prioritize initiatives and strategically phase them to avoid saturation.
– Change Portfolio Management : Treat change initiatives as part of a portfolio. By actively managing this portfolio, you can ensure changes are introduced in waves that the organization can absorb. Regularly review the status of active changes with stakeholders to reassess the capacity and timing. This way, your adoption efforts won’t be diluted by other competing projects.
Analogy : Imagine trying to load groceries into an already-full refrigerator. Some items will fit, but others might have to wait. The same concept applies to organizational change capacity—only so much can fit into the organization’s “refrigerator” at once before things start falling out.
4. Defining and Measuring Adoption Metrics
Effective change adoption strategies hinge on clear metrics. Without defined adoption goals and measurement tools, it’s difficult to determine if users are actually embracing the change or merely checking boxes. Metrics will vary depending on the change and should be relevant to the behaviours or outcomes desired.
– Set Clear Adoption Metrics : For example, a company introducing a new collaborative software might measure adoption through the frequency of use, the number of shared documents, or the volume of cross-departmental activity within the platform. Each of these metrics helps track actual usage and determine if employees are using the tool to its full potential.
– Gauge Awareness, Willingness, and Competency : Assess and understand stakeholder readiness for the change at hand. Do they have the awareness, motivation and know-how for the new expected behaviours? Conduct regular surveys or feedback sessions to assess where teams are on the adoption curve. This approach can highlight areas where additional support is needed, such as more coaching or stronger reinforcement from leadership.
Analogy : Think of adoption metrics like the gauges in a car’s dashboard. Each gauge (speed, fuel, engine temperature) provides specific insights into the car’s overall performance, just as adoption metrics give insights into how well a change is taking hold within the organization.
5. Ongoing Performance Management for Sustained Adoption
Adoption isn’t a “one and done” effort. It requires continuous management, monitoring, and, ideally, integration into performance management. By tracking and reinforcing adoption metrics over time, organizations can keep the change front and centre and drive deeper, lasting adoption.
– Incorporate Adoption into KPIs : Align adoption goals with KPIs to maintain visibility. For example, if the goal is to increase the use of a project management tool, set a KPI that tracks project updates within the tool. Managers can be held accountable for meeting this KPI, incentivizing their teams to incorporate the tool into their workflow.
– Regular Check-Ins and Feedback: Use data-driven insights to adjust your strategy as needed. For instance, if certain teams lag in adoption rates, consider arranging tailored training sessions or conducting one-on-one interviews to understand the barriers they’re experiencing. Continuous feedback loops allow change practitioners to refine their approach based on real-time adoption data. Performance needs to be constantly nurtured, reinforced and managed. No ‘set and forget’ approach will work.
Analogy: Sustaining adoption is like maintaining a healthy habit. Just as regular exercise requires motivation, tracking, and routine check-ins to stay consistent, ongoing performance management helps ensure that change remains a part of the organizational fabric.
Data as the Catalyst for Improved Change Adoption
Data-driven insights are game-changers for change adoption. They enable change practitioners to move beyond guesswork and implement strategies with measurable, predictable results. By leveraging analytics, organizations can identify successful tactics based on stakeholder type, change type, and historical adoption patterns.
For example, by analyzing adoption data from previous projects, a technology company could discover that smaller, incremental training sessions worked better for developers than day-long sessions. This insight could inform future adoption strategies and improve the likelihood of success for similar changes.
Utilizing data to understand what drives adoption allows change practitioners to apply these learnings across the organization, achieving more consistent and reliable outcomes. Through correlation and prediction, organizations can anticipate which approaches will work best for each type of change and tailor their strategies accordingly.
This is exactly what we’ve been doing at The Change Compass. We’ve incorporated automation and AI to provide data insights that tell you what tactics and approaches work to maximise change adoption based on data. You can also drill into what works for particular stakeholders, business units and types of changes. Data insights can also inform what volume of change may stifle change adoption.
Designing change approach and interventions should not be guess work. So far, companies try to enhance their rates of change adoption success by hiring change management specialists, together with stakeholder feedback. However, the most senior stakeholder or those with the loudest voice in the room don’t always get the outcome. These are still based on opinions, versus what has proven to work based on data. Imagine the power of implementing this across the enterprise and the ability to avoid costly mistakes and mishaps in the tens (or hundreds) of millions of investments in change initiatives per annum.
Building a Culture of Adoption
Improving change adoption is not a one-time effort but an ongoing, intentional process that combines targeted communication, stakeholder engagement, capacity planning, performance tracking, and data-driven insights. By focusing on the unique aspects of each change, tailoring strategies to specific stakeholder groups, and continuously managing performance, change practitioners can significantly increase adoption rates. Ultimately, success lies in building a culture where change is not just accepted but actively integrated into the organization’s DNA.
When change adoption becomes a measurable, manageable, and data-driven process, practitioners can guide their organizations through change with confidence and clarity, transforming resistance into resilience and integration into innovation.
Measuring change adoption is one of the most important parts of the work of change practitioners. It is the ultimate ‘proof’ of whether the change interventions have been successful or not in achieving the initiative objectives. It is also an important way in which the progress of change management can clearly be shown to the project team as well as to various stakeholder groups. The ability to show clearly the progress of change outcome is critical to focus your stakeholders’ actions on the right areas. It is one of the key ways to ‘prove your worth’ as a change practitioner.
Measurement takes time, focus and effort. It may not be something that is a quick exercise. There needs to be precise data measurement design, a reliable way of collecting data, and data visualisation that is easily understood by stakeholders.
With the right measurements of change adoption, you can influence the direction of the initiative, create impetus amongst senior stakeholders, and steer the organisation toward a common goal to realise the change objectives. Such is the power of measuring change adoption.
The myth of the change management curve
One of the most popular graphs in change management and often referred as the ‘change curve’ is the Kubler-Ross model. The model was specifically designed by psychiatrist Elisabeth Kubler-Ross to refer to terminally ill patients as a part of the book ‘On Death and Dying’. For whatever reason, it has somehow gained popularity and application in change management. Therefore, be very careful when using applying this model in a change context.
There is little research evidence to back this up even in psychological research. When applied in change management there is no known research that supports this at all. So be careful when you come across models such as this one that is simple and seem intuitively ‘correct’. On the other hand, there is ample research by McKinsey that for effectively managed initiatives and transformations, stakeholders do not go through this ‘valley of death’ journey at all.
If the ‘change curve’ is not the correct chart to follow with regard to change adoption, then what is the right one to refer to? Good question.
The ‘S’ curve of change adoption is one that can be referenced. It is well backed in terms of research from technology and new product adoption. It begins with a typically slow start followed by a significant climb in adoption followed by a flattened level at the end. Most users typically do not uptake the change until later on.
Here is an example of key technologies and the speed of adoption in U.S. households since the 1900s.
Source: HBR.org
With the different types of change contexts, the shape of the S curve will be expected to differ as a result. For example, you are working on a fairly minor process change where there is not a big leap in going from the current process to the new process. In this case, the curve would be expected to be a lot more gentle since the complexity of the change is significantly less than adopting a complex, new technology.
On the other hand, if you are working on many iterative agile changes, each iteration that impacts users may be a small S curve in themselves. Ideally, each iteration work together towards a greater piece of overarching change.
Going beyond what is typically measured
Most change practitioners are focused on measuring the easier and more obvious measures such as stakeholder perceptions, change readiness, and training completion. Whilst these are of value, they in themselves are only measuring certain aspects of the change. They can be viewed as forward-looking indications of the progress that supports moving toward eventual change adoption, versus the eventual change adoption.
To really address head-on the topic of measuring adoption, it is critical to go beyond these initial measures toward those elements that indicate the actual change in the organisation. Depending on the type of change this could be system usage, behaviour change, following a new process or achieving cost savings targets.
Project Benefit realization
It goes without saying that to really measure change adoption the change practitioner must work closely with the project manager to understand in detail the benefits targeted, and how the prescribed benefits will be measured. The project manager could utilise a range of ways to articulate the benefits of the project. Common benefit categories include:
Business success factors such as financial targets on revenue or cost
Product integration measures such as usage rate
Market objectives such as revenue target, user base, etc.
These categories above are objectives that are easier to measure and tangible to quantify. However, there could also be less tangible targets such as:
Competitive positioning
Employee relations
Employee experience
Product or solution leadership
Employee capability
Customer experience
There could be various economic methods of determining the targeted benefit objectives. These include payback time or the length of time from project initiation until the cumulative cash flow becomes positive, or net present value, or internal rate of return.
The critical aspect for change practitioners is to understand what the benefit objectives are, how benefit tracking will be measured and to interpret what steps are required to get there. These steps include any change management steps required to get from the current state to the future state.
Here is an example of a mapping of change management steps required in different benefit targets:
Project benefits targeted
Likely change management steps required
Change management measures
Increased customer satisfaction and improved productivity through implementing a new system.
Users able to operate the new system. Users able to improve customer conversations leveraging new system features. Users proactively use the new system features to drive improved customer conversations. Managers coaching and provide feedback to usersBenefit tracking and communications. Customer communication about improved system and processes Decreased customer call waiting time .
% of users passed training test. System feature usage rate. Customer issue resolution time. User feedback on manager coaching. Monthly benefit tracking shared and discussed in team meetings. Customer satisfaction rate. Customer call volume handling capacity.
Measuring behavioural change
For most change initiatives, there is an element of behaviour change, especially for more complex changes. Whether the change involves a system implementation, changing a process or launching a new product, behaviour change is involved. In a system implementation context, the behaviour may be different ways of operating the system in performing their roles. For a process change, there may be different operating steps which need to take place that defers from the previous steps. The focus on behaviour change aims to zoom in on core behaviours that need to change to lead to the initiative outcome being achieved.
How do we identify these behaviours in a meaningful way so that they can be identified, described, modelled, and measured?
The following are tips for identifying the right behaviours to measure:
Behaviours should be observable. They are not thoughts or attitudes, so behaviours need to be observable by others
Aim to target the right level of behaviour. Behaviours should not be so minute that they are too tedious to measure, e.g. click a button in a system. They also should not be so broad that it is hard to measure them overall, e.g. proactively understand customer concerns vs. what is more tangible such as asked questions about customer needs in XXX areas during customer interactions.
Behaviours are usually exhibited after some kind of ‘trigger’, for example, when the customer agent hear certain words such as ‘not happy’ or ‘would like to report’ from the customer that they may need to treat this as a customer complaint by following the new customer complaint process. Identifying these triggers will help you measure those behaviours.
Achieve a balance by not measuring too many behaviours since this will create additional work for the project team. However, ensure a sufficient number of behaviours are measured to assess benefit realisation
Measuring micro-behaviours
Behaviour change can seem over-encompassing and elusive. However, it may not need to be this. Rather than focusing on a wide set of behaviours that may take a significant period of time to sift, focusing on ‘micro-behaviours’ can be more practical and measurable. Micro-behaviours are simply small observable behaviours that are small step-stone behaviours vs a cluster of behaviours.
For example, a typical behaviour change for customer service reps may be to improve customer experience or to establish customer rapport. However, breaking these broad behaviours down into small specific behaviours may be much easier to target and achieve results.
For example, micro-behaviours to improve customer rapport may include:
User the customer’s name, “Is it OK if I call you Michelle?”
Build initial rapport, “How has your day been?”
Reflect on the customer’s feeling, “I’m hearing that it must have been frustrating”
Agree on next steps, “would it help if I escalate this issue for you?”
Each of these micro-behaviours may be measured using call-listening ratings and may either be a yes/no or a rating based assessment.
After having designed the right measurement to measure your change adoption, the next step would be to design the right reporting process. Key considerations in planning and executing on the reporting process includes:
Ease of reporting, you should aim to automate where possible to reduce the overhead burden and manual work involved. Whenever feasible leverage automation tools to move fast and not be bogged down by tedious work
Build expectations on contribution to measurement. Rally your stakeholder support so that it is clear the data contribution required to measure and track change adoption
Design eye-catching and easy to understand dashboard of change adoption metrics.
Design reinforcing mechanisms. If your measurement requires people’s input, ensure you design the right reinforcing mechanisms to ensure you get the data you are seeking for. Human nature is so that whenever possible, people would err on the side of not contributing to a survey unless there are explicit consequences of not filling out the survey.
Recipients of change adoption measurement. Think about the distribution list of those who should receive the measurement tracking. This includes not just those who are in charge of realising the benefits (i.e. business leaders), but also those who contribute to the adoption process, e.g. middle or first-line managers.
Example of change adoption dashboard from Change Automator
Measuring Adoption Across Initiatives
You may be driving multiple initiatives as a part of a large program or a portfolio of initiatives. The key challenge here is to establish common adoption measures that are apple-to-apple metrics comparisons across initiatives. Yes, each initiatives will most likely have different sets of what constitutes adoption. However, there are still common ways to report on adoption across initiatives such as overall percentage of adoption of identified adoption elements, or percentage of the number of milestones reached. You can also utilise manager reports of behaviours adopted, as well as system records of utilisation of certain features for example.
Understanding change adoption is not only helpful to understand what works for one initiative, it can also be a linchpin to help you scale change adoption across change initiatives across your whole portfolio. Talk to us to find out more about how The Change Compass can help you understand what change interventions leads to higher change adoption rates, through data. Using a data-led approach in deciphering what drives change adoption can truly drive successful change outcomes.
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