Life after one view of change
Journey in deriving one view of change
We sat down with change whiz Ben Szonyi to understand his journey in deriving one view of change.
Ben is a senior change leader with extensive business improvement experience across the globe. Ben has also held program change lead roles, most recently at Bupa, where he was accountable for designing and delivering large scale, operating model change programs, which included introducing an enterprise view of change to enable strategic planning and decision-making.
Ben, tell us about what started the journey to derive the one view of change at Bupa? What was the pain you were trying to solve?
The main trigger for requiring an enterprise view of change was that the anecdotal evidence was suggesting our people were feeling change fatigue due to a large number of disassociated projects in train or on the roadmap, yet the impact on our people wasn’t a key criteria in the decision making process. To solve this we initially tried simple techniques like graphically displaying the projects we were running centrally from a program office on a Gannt style plan, however this didn’t enable us to see the change programs the business were doing to themselves. This meant at no point in time did we understood the current or future collective impact our people were facing, meaning we were at risk of overloading and ultimately failing to deliver the expected outcomes.
What process did you guys go through?
The first key step was gaining buy-in from our executive committees for the need to change.
Next, once we diagnosed the challenge outlined above, we went about investigating internal and external options for providing an enterprise view of change that also aligned to ur new change management framework. Our ideal solution was to include not only change impacts but also our peoples’ change readiness and not duplicate what was presented in existing PMO reports. Unfortunately we were not able to find this solution at the time and as a result put our focus into a pragmatic and viable internal solution that leveraged existing tools, i.e. SharePoint and MS Power BI. The idea was that once we had an internal solution made and the right operating model to support it, we would investigate more robust external solutions.
What worked well and met the business needs?
The part that worked best from an internal solution was leveraging existing tools meant people were familiar with them and they were cost effective. This also meant we had the ability to continually improve after each iteration based on the feedback of the users.
The other success was the buy-in from our business partners who were very responsive when it came to providing their data points and utilization of the reports.
What didn’t go so well?
The biggest challenge was gaining buy-in from the internal change team when it came to entering the baseline data (e.g. initiative, impact level by business area and key dates) from their detail change impact assessments as they didn’t see the benefit to them. Once they understood the benefit was for their business stakeholders, they started to get onboard.
Was there anything personally challenging from your perspective?
The most challenging aspect was the time and effort each month to run it, mainly the chasing of data and the manual effort to generate the extracts, load, analyse and report.
If you had to advise others who are about to take a similar journey what would you recommend?
With more developed products in the market now like The Change Compass, if I had my time again I would partner with one of these companies to not only get an off the shelf solution but also one that has learnt from other organisations’ mistakes. This would also mean that you could have a more automated solution. Also, don’t underestimate the time and effort required to gain buy-in from not only your stakeholders, but also your change managers/ agents by ensuring you have a clear WIIFM story.
Based on your experience, what do you see to be the next phase of development for change management?
After working in Marketing more recently, I feel that the key for change management is to treat change initiatives like marketing campaigns where you are clear about the target audience, their needs and measurable outcomes by use of data and a continuous improvement approach. The more we can make change a science and not just an art, we will gain more respect from our stakeholders by demonstrable positive impact.
The evolution of change management
Change management has transformed dramatically over decades, evolving from reactive crisis responses to sophisticated, data-driven strategies that predict and shape organizational transformation. Understanding this evolution equips practitioners with insights to navigate modern complexities like digital acceleration, regulatory pressures, and workforce expectations.
This guide traces key milestones in change management development, examines the shift toward strategic data integration, and explores emerging AI-driven capabilities that redefine practitioner roles. Practitioners gain practical frameworks to apply these insights in today’s fast-paced environments.
How Has Change Management Evolved Over Time?
Change management began as structured responses to organizational disruption but matured into proactive disciplines leveraging data and technology. Early approaches focused on resistance management; modern practices emphasize prediction, measurement, and continuous adaptation.
Key evolutionary phases include:
- 1950s-1970s: Foundations in Behavioural Science
Kurt Lewin’s three-stage model (unfreeze-change-refreeze) established foundational principles. Focus remained on human psychology and overcoming resistance through communication. - 1980s-1990s: Structured Frameworks Emerge
John Kotter’s 8-step process and Prosci’s ADKAR model provided systematic approaches. Emphasis shifted to leadership alignment and stakeholder engagement. - 2000s: Enterprise Integration
Change management embedded within project management methodologies like PMI and Agile. Organizations recognized change as a distinct discipline requiring dedicated resources. - 2010s-Present: Data and Analytics Integration
Rise of change portfolio management and adoption metrics tracking. Practitioners began measuring outcomes beyond activities, using dashboards for real-time insights.
This progression reflects growing recognition that successful change requires both human-centered approaches and rigorous measurement.
What Drove the Shift to Strategic Change Management?
Several forces accelerated change management’s maturation:
Digital Transformation Pressures
Rapid technology adoption created simultaneous change waves across organizations. Traditional sequential change approaches proved inadequate for multi-project environments.
Regulatory and Compliance Demands
Increasing scrutiny required demonstrable evidence of change adoption and risk mitigation, pushing practitioners toward measurable outcomes.
Workforce Expectations
Millennial and Gen Z entrants demanded transparency, purpose alignment, and visible progress tracking in change initiatives.
Portfolio Complexity
Organizations managing 10+ concurrent changes needed centralized oversight, leading to change portfolio management practices.
Measurement Maturity
Advancements in HR analytics and adoption metrics enabled practitioners to demonstrate ROI and secure executive support.
These pressures transformed change management from a support function to a strategic capability directly influencing business outcomes.
The Rise of Data-Driven Change Management
Modern change management integrates operational data, adoption metrics, and predictive analytics to guide decision-making.
Strategic Change Data Management
Organizations now maintain centralized repositories tracking change saturation, adoption rates, and portfolio capacity. This enables executives to balance change demands against organizational readiness.
Adoption Metrics Evolution
Beyond activity tracking, practitioners measure micro-behaviours, feature utilization, and sustained proficiency. Real-time dashboards replace periodic reports.
Portfolio Optimization
Data reveals change overlaps, capacity constraints, and high-risk initiatives. Practitioners allocate resources strategically rather than reactively.
Predictive Capacity Planning
Analytics forecast change bandwidth by department and role, preventing saturation and burnout during transformation waves.
This data foundation positions change management as a value-creating function rather than cost centre.
Implementation Frameworks and Best Practices in Modern Change Management
With the evolution of change management into a data-driven discipline, implementation frameworks have also advanced to incorporate strategic alignment, measurement, and agility.
Established Frameworks Adapted for Today’s Environment
Kotter’s 8-Step Process
This enduring framework continues to provide a roadmap for leading change, emphasising urgency creation, coalition building, vision communication, and consolidation of gains. Modern adaptations integrate data points at each step to monitor engagement and effectiveness.
Prosci ADKAR Model
The ADKAR model—Awareness, Desire, Knowledge, Ability, Reinforcement—remains influential for individual change adoption. Data from assessments aligned to each dimension now inform targeted interventions.
Agile Change Management
Agile methodologies bring iterative feedback loops and rapid adaptation, suited for fluid business environments. Incorporating continuous data collection and analytics allows agile teams to pivot change strategies responsively.
Emerging Best Practices
- Integrate Change Management Early in Project Lifecycles: Position change activities alongside project planning for seamless alignment and impact maximisation.
- Embed Data Streams for Real-Time Insights: Utilise adoption metrics, sentiment analysis, and feedback channels to guide decision-making dynamically.
- Foster Cross-Functional Collaboration: Engage stakeholders and change agents across departments to build collective ownership.
- Leverage Technology for Automation: Automate repetitive change management tasks such as communications, survey distribution, and reporting, freeing capacity for strategic priorities.
- Prioritise Employee Experience: Tailor change approaches to diverse workforce needs, using data-driven personas and segmentation.
The Role of AI and Automation in Advancing Change Management
Artificial intelligence and automation are set to redefine how change practitioners operate, transforming strategic decision-making, engagement, and measurement.
AI-Powered Predictive Analytics
By analysing historic change data combined with organisational variables, AI models predict likely resistance points, adoption rates, and saturation thresholds. These insights enable pre-emptive strategies designed to smooth transitions.
Automated Change Interventions
Chatbots and virtual assistants can deliver personalised communications, FAQs, and training modules at scale, maintaining consistent messaging and freeing practitioners’ time for higher-value activities.
Natural Language Processing (NLP) for Sentiment and Feedback Analysis
AI-driven sentiment analysis of employee feedback, surveys, and collaboration platforms identifies emerging issues and morale trends faster than traditional methods.
Intelligent Dashboarding
AI enhances dashboards by correlating disparate data, highlighting risks, and recommending intervention actions. Customisable alerts notify change leaders of critical deviations in real time.
Augmented Decision Support
Machine learning integrates diverse inputs—financial, operational, human factors—to support scenario planning and optimise change portfolios, particularly in complex environments.
Preparing Change Practitioners for the Future
The evolving change landscape requires practitioners to blend traditional soft skills with digital and analytical capabilities. Key skill enhancements include:
- Data literacy and analytics interpretation.
- Familiarity with AI-enabled change tools.
- Agile methodology proficiency.
- Enhanced stakeholder engagement techniques leveraging virtual platforms.
- Continuous learning mindsets to adapt as technologies evolve.
Institutions and organisations should invest in upskilling programs and knowledge hubs supporting these competencies.
Key Takeaways for Change Practitioners
The evolution of change management offers clear guidance for practitioners navigating today’s complex landscape:
Embrace Data as a Strategic Asset
Shift from activity tracking to outcome measurement. Implement real-time adoption dashboards that correlate behaviours with business results, enabling proactive interventions.
Master Portfolio Management Discipline
Treat change as a finite resource. Establish governance processes to assess saturation, prioritise initiatives, and sequence delivery for maximum organisational capacity.
Build Cross-Functional Change Capabilities
Move beyond siloed project support. Embed change expertise within strategy, digital transformation, and HR functions for integrated execution.
Cultivate Continuous Learning Cultures
Position change practitioners as organisational learning facilitators. Use post-initiative reviews and trend analysis to build institutional knowledge.
Emerging Capabilities for Practitioners
AI-Augmented Decision Making
Leverage predictive models to forecast adoption risks and capacity constraints. Use sentiment analysis across communication channels to detect resistance patterns early.
Automation of Change Operations
Streamline repetitive tasks—status reporting, stakeholder mapping, communication scheduling—freeing capacity for strategic advisory roles.
Advanced Measurement Frameworks
Combine traditional metrics with micro-behaviour tracking and network analysis to understand influence patterns and adoption cascades.
Implementation Roadmap for Practitioners
Phase 1: Assessment and Foundation (0-3 Months)
- Conduct change maturity assessment across frameworks and capabilities
- Establish baseline adoption metrics for current portfolio
- Map organisational change capacity by department and role
- Build cross-functional change governance council
Phase 2: Data Integration and Optimisation (3-6 Months)
- Deploy centralised change portfolio tracking system
- Implement real-time dashboards with automated alerts
- Launch pilot AI sentiment analysis on feedback channels
- Standardise post-change review processes
Phase 3: Strategic Evolution (6-12 Months)
- Embed predictive capacity planning in annual cycles
- Scale successful automation across enterprise initiatives
- Develop practitioner upskilling academy
- Establish external benchmarking partnerships
Frequently Asked Questions (FAQ)
How has change management fundamentally evolved?
From reactive resistance management to proactive, data-driven portfolio disciplines that predict capacity and measure sustainable adoption.
What are the most important data capabilities for change practitioners?
Real-time adoption tracking, portfolio saturation analysis, predictive capacity modelling, and cross-initiative impact correlation.
How should organisations structure change governance?
Cross-functional councils with executive sponsorship, portfolio prioritisation processes, and dedicated measurement functions.
What skills will define future change practitioners?
Data analytics proficiency, AI tool fluency, portfolio strategy, systems thinking, and continuous learning facilitation.
Why is change portfolio management mission-critical now?
Concurrent digital, regulatory, and cultural transformations overwhelm traditional approaches. Portfolio discipline prevents saturation and maximises ROI.
How do AI capabilities enhance change effectiveness?
Predictive risk modelling, automated stakeholder engagement, real-time sentiment tracking, and intelligent resource allocation recommendations.
Click the below to download our infographic.
Using The Change Compass to improve change maturity
There are 5 major focus areas to improve change maturity.
1. Strategic change leadership.
Strategic change leadership is about how leaders of the organisation demonstrate personal responsibility, accountability and are able to rally the organisation around the change.
The Change Compass allows leaders visualise the impacts of change across the whole organisation. This includes the change impacts on business performance and capacity.
2. Business change readiness.
Business operations need to have a view of what change is coming down the pipeline and be able to influence the prioritisation and sequencing of changes being rolled out.
Data from The Change Compass helps business operations to manage operational challenges whilst delivering the change.
3. Project change management
This is about the changes being delivered within each project. Each change delivery needs to be considered and planned as a part of the overall change landscape and not in isolation.
The Change Compass helps stakeholders to visualise what each project is delivering and how this compared to other projects.
4. Change capability
Delivering change capability through experiencing each change can become a competitive advantage for organisations.
With visible data from The Change Compass, this is like having a step counter attached to the wrist. Suddenly, the business has a visible and measurable way to see changes being delivered.
This leads to focus, experimentation and continous improvement. All of these act to drive overall change maturity and business performance.
To read more about building Change Maturity click here.
Making business decisions with The Change Compass
Understanding the pace of change – Infographic
What gets measured gets managed
Managing change as a change driver – Infographic
Download the infographic here.
Practical agile for change managers – Part 1
Communications
A critical part of agile is being able to iterate and continuously improve in order to deliver an optimal solution. Rather than one large change release, an agile project would break this down into smaller releases. Each release will go through an iterative process to test, collect data, evaluate and use any learning to improve the next release.
If an agile approach is appropriate we should also adopt this same approach in how we deliver change management activities. This means that we should be running a series of experiments to test, learn, document and improve on how we deliver change to the organization.
This contrasts to how most change managers would approach developing and delivering the change approach. The standard approach is collecting various information about the change, talk to key stakeholders about the change, and then form a view based on previous experiences in terms of what change approach would work for this initiative. Then, this approach would be present to stakeholders to get their blessing before executing on the change approach.
Below is an example of planning to run experiments in an agile environment from Alex Osterwalder, the founder of Strategyzer. First is designing the experiment, shaping its hypothesis, and testing it, which involves looking at the outcome data, learning from the experiment and making any relevant decisions based on the outcome.
Referenced from Alexander Osterwalder.
In this first part of a series on practical agile applications for change managers we focus on communications.
Communicating for change is a critical part of managing change and is also one that can easily be tested using a series of experiments.
The Campaign Monitor has outlined a series of aspects in which emails can easily be tested. These include:
- Subject headlines
- Pre-header
- Date and time
- Call to action
- Content
Digital businesses also often conduct A/B Testing whereby 2 different sets of content are designed and delivered at the same time for the duration of the test. At the conclusion of the experiment we can then look at the results to see which one did better based on audience responses.
How do we measure communications experiments?
There are several ways to do this:
- Readership – For intranet pages, your corporate affairs rep can usually access readership statistics
- Surveys – Send surveys to the audience to ask for feedback
- Focus groups – Run small focus groups for feedback
There is one area in which corporate can better learn from digital businesses – using digital tools to measure and track communications. For example, you can send out emails promoting a new intranet page, and then check back to see how many users actually visited the site. The results may be helpful as an initial experiment before launching the email to a wider audience group to achieve maximum results.
There are plenty of external tools such as ActiveCampaign or Mailchimp where you are able to use features such as:
- A/B testing results
- Send emails are certain times or dates
- Automatic email responses
- Target particular segments
- View and click rates
In the following diagram you can see an example that it’s not difficult to build a drip-email series of interactions with your stakeholders based on their responses (or lack of).
It’s feasible to use these tools for a project where you can run a series of experiments and measure outcomes to support your change iterations.
Want to read more about agile? Visit our Ultimate Guide to Agile for Change Managers.
Why lots of functions think they are all experts in managing change
Click here to download this infographic.