While the pace of AI advancements continues to accelerate, adoption has not. New capabilities arrive weekly, yet pilots stall and outcomes lag. The technology isn't the problem—getting people to use it is.
This isn't a new challenge, but AI’s pace creates tension with human inertia. AI intensifies this tension because it touches everything: roles, workflows, org structures, and decisions. And rather than teaching our people how to align their work with the fields and screens of a system (as we’ve done with previous technology implementations), we are expecting them to figure out where it fits into the work they're already doing. That’s a different ask; it’s just not happening.
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The Prosci AI Integration Framework
One of the biggest challenges organizations face today is translating an enterprise-level AI initiative ("We are deploying generative AI tools across the organization") to the individual level ("How AI actually fits into the day-to-day work of individuals"). This translation process is essential for successful AI adoption. The pace of AI advancement can feel both exciting and overwhelming. For many professionals, it's unclear how AI actually fits into their daily work. When we give people prompt libraries and copy-and-paste use cases, we're essentially just giving them fish. The Prosci AI Integration Framework teaches them to fish—equipping them with a framework to make sense of their own personal integration of AI. × Is your AI initiative struggling to gain traction? The Prosci AI Integration Framework Prosci's AI Integration Framework is a human-centered model that helps individuals and organizations categorize their work into three distinct buckets—My Work (human-exclusive tasks), "With Me" Work (AI collaboration opportunities), and "For Me" Work (AI automation potential)—to make intentional decisions about when, where, and how to integrate AI into their daily responsibilities. By organizing responsibilities this way, individuals begin to see where AI adds value and where human strengths remain essential. This approach encourages confidence and control. It helps individuals and teams move from anxiety about AI to meaningful, intentional use. My Work: Human-Exclusive Tasks The first category is called "My Work." These are the tasks that must remain human. They rely on qualities that AI simply cannot replace: emotional intelligence, ethical judgment, real-time improvisation, and personal connection. Whether it's leading a team meeting, making a sensitive decision, or building trust with a client, these are the moments when your presence and judgment are non-negotiable. This category includes: Work that is emotionally complex or ethically sensitive Work that is improvisational or dependent on human presence Work that reflects personal values, relationships, or meaning Work that is routine, time-consuming, or draining Work that follows rules or repeatable steps Work that is important but not uniquely human Work that is easier, better, or faster with smart support Work that benefits from structure, synthesis, iteration, or polish Work that still needs you—but not every step These are the tasks that define the human side of work and will continue to belong to people, no matter how advanced AI becomes. This is often the work that drew people to their profession in the first place—the cultural alignment, the facilitation of high-stakes conversations, the building of trust and empathy. "For Me" Work: AI Automation Potential The second category is "For Me" Work. These are tasks that AI can take off your plate entirely. Think of the things you do repeatedly—routine, repetitive work that doesn't require your judgment or creativity. Examples include generating routine reports, organizing data, sending calendar reminders, or processing standardized information. This category includes: These are tasks that can be automated to save time, reduce errors, and free up your energy for more meaningful efforts. While it's tempting to focus heavily on this category because automation feels like a clear win, the most exciting transformation actually happens in the third category. "With Me" Work: AI Collaboration Opportunities The third category is "With Me" Work. This is where you collaborate with AI to do your job better. In this space, AI doesn't take over the task—it becomes a digital collaborator or partner. It helps you think faster, write more clearly, analyze more deeply, assimilate data more quickly, or brainstorm more creatively. You are still in control, but AI gives you a boost. Imagine drafting a report and asking AI to organize your ideas or exploring customer data and having AI suggest patterns to watch for. This category includes: These are tasks where AI becomes a digital collaborator, helping you deliver your work at a higher quality, in less time, with less strain, and ultimately with more enjoyment. This is the magic in the middle. Using the AI Integration Framework The most straightforward application is to list the three categories and have individuals or teams sort their tasks accordingly. The table below includes definitions and examples for each category. Category Definition Example Tasks My Work Tasks that require human presence, emotional intelligence, or ethical judgment Building trust through nuanced client relationships; Making judgment calls that balance logic, values, and emotion; Navigating ambiguity with human insight "With Me" Work Tasks that benefit from AI collaboration to improve quality, speed, or outcomes Drafting client communications with AI as a writing partner; Exploring ideas through AI-assisted brainstorming; Using AI to clarify thinking or structure presentations "For Me" Work Tasks that are routine, rule-based, and can be fully automated Logging work or time automatically; Filing emails, notes, and documents; Generating standardized reports Often, a change practitioner or leader can start this process independently, leveraging existing job descriptions and task inventories. By reviewing current work, you can uncover integration opportunities that the team may have missed. Additionally, facilitating this activity with teams using a collaborative, design-thinking approach can be engaging and clarifying. Additional Benefits of the AI Integration Framework Builds Personal Relevance Into Adoption One of the biggest hurdles in AI adoption is convincing people to integrate it into their daily workflows. Traditional tools offer a clear value proposition: "Follow this process, and efficiency improves." But generative AI doesn't operate on a one-size-fits-all value proposition. For an employee to embrace AI, they need to see their challenge reflected in the solution. The AI Integration Framework creates that personal connection by starting with the individual's actual tasks—not abstract capabilities. Shifts ROI From Process Optimization to Individual Empowerment Traditional tools optimize processes; generative AI unlocks human potential. In a CRM, every user performs tasks in a standardized way to generate predictable outcomes. But generative AI is like having a personal productivity partner—one that adjusts to your working style, helps you think more clearly, and removes blockers as they arise. If organizations only focus on system-wide use cases, they miss the deeply human layer of value that AI brings. The ROI is not just in speeding up workflows; it's in improving the quality of individual outputs, reducing mental fatigue, fostering creativity, and freeing up time for strategic work. The framework also enables clearer adoption metrics. When you've defined which tasks belong in "With Me" Work versus "For Me" Work, you can measure whether people are collaborating with AI on the right activities—not just whether they've logged into a tool. Sparks Curiosity and Experimentation When AI integration begins with people's tasks instead of tool functionality, they become naturally curious about its potential. They start experimenting. They ask questions like, "What else can this tool do for me?" That curiosity is the engine of adoption and innovation. When AI is framed around rigid use cases, experimentation shrinks. People use the tool only in predefined ways, and the real potential—found in those unscripted, unexpected moments—remains untapped. The AI Integration Framework opens space for discovery by putting the individual's work at the center. Scales Adoption Through Individuals, Not Just Systems The long-term success of AI adoption doesn't just depend on systems integration—it depends on integration into daily work. Individuals who discover transformative moments with AI become advocates. They share stories, show results, and build excitement. Those breakthrough moments happen when AI feels personal, not procedural. When someone experiences, "This tool just helped me solve a problem I've been stuck on for days," they don't need a mandate to keep using it. They become self-motivated ambassadors. Extending the AI Integration Framework Once we understand the framework for the individual, we can extend it to teams and the enterprise. The same three categories apply—but the language shifts from "Me" to "Us." Our Work (Human Exclusive): Work that requires team connection, trust, and dialogue to move forward "With Us" Work (AI Collaboration): Work that benefits from AI-synthesized inputs and co-creation "For Us" Work (AI Automation): Work that is procedural or status-based and can run without manual effort Teams use AI to solve bigger problems in news ways, while collaborating and connecting like never before. Enterprises integrate AI to better deliver on their mission to the customer, constituents, and stakeholders. At each level, the framework gives groups a shared language for deciding where AI fits—and where it doesn't. This extension allows organizations to move from individual productivity gains to enterprise-wide AI integration, infusing AI capabilities into the value streams and operations that drive organizational success. Human-Centered AI Integration The Prosci AI Integration Framework is more than a categorization tool—it's a perspective on the relationship between humans and AI. It turns AI from an external force into a partner, a digital collaborator that people can shape and apply in their own context. By focusing on tasks instead of tools, and collaboration instead of replacement, the framework helps individuals and organizations unlock the full potential of AI. It puts you in the driver's seat, deciding when and where and how to bring AI into your work. At its core, this shift is not about technology—it's about people. People don't adopt tools because they're told to; they adopt them because they feel equipped, supported, and excited. The AI Integration Framework creates the conditions for that to happen—one interaction, one task, and one human at a time.
5 Levels of Change Management Maturity
Organizations are facing larger and more frequent changes in the current economic climate. A changing marketplace, empowered workforce and technological advancements have created an environment where change is now a part of everyday business. In this environment, organizations are beginning to recognize the importance of building the competency to rapidly and successfully change. Prosci’s Change Management Maturity Model, based on benchmarking research, describes the varying levels of change management capabilities across organizations. The maturity model has five levels, ranging from no change management to organizational competency. Each level involves more attention and management of the people side of change. Below is a detailed explanation of each level as well as the action steps your organization can take to move to the next level of the model. The article concludes with research data on Maturity Model levels from Prosci's Best Practices in Change Management – 11th Edition. Level 1: Ad Hoc or Absent Change Management At Level 1 of the organizational change management maturity model, project teams are not aware of change management and do not consider it as a formal approach for managing the people side of change. Use at the Project Level: Change management is applied on a project only as a last resort when employee resistance jeopardizes the success of the project. Level 1 integration between project management and change management Change management is reactive and an add-on to the project. No integration with project management takes place at the beginning of the project. Projects at this level can have one or more of the following characteristics: Project leadership is focused only on the technical side of the project including funding, schedule, issue tracking and resource management Communications from the project are infrequent and delivered on a need-to-know basis Employees find out about the change first through rumors and gossip rather than structured presentations Executive support is only evident through funding authorization and resource allocation; there is no active and visible sponsorship People managers have little or no information about the change and have no change management skills to coach their employees through the change process Employees react to change with surprise and can be very resistant Productivity slows and turnover increases as the change nears full implementation Steps for moving to Level 2 Attend change management training, purchase change management tools and resources, or engage change management consultants Apply change management to isolated projects and use change management techniques to help projects that are currently experiencing resistance to change Level 2: Change Management on Isolated Projects In Level 2, elements of change management begin to emerge in isolated parts of the organization. The effort to manage the people side of change is infrequent and is not centralized. Characteristics of this level are: A large variation of change management practices exists between projects with many different change management approaches applied sporadically throughout the organization; some projects may be effectively managing change while others are still in Level 1 There are elements of communication planning, but there is little sponsorship or coaching People managers have no formal change management training to coach their employees through the change process Change management is typically used in response to a negative event Little interaction occurs between the isolated project teams using change management; each new project “re-learns” the basic change management skills Level 2 change management and project management integration In Level 2, projects apply change management when resistance emerges or when the project nears implementation. Only isolated projects use change management at the beginning of their project. Some elements of communication planning occur early in the lifecycle. At this stage, change management is not fully integrated into project management. On projects that use change management, the project team is aware and knowledgeable of change management. In certain instances, a change management advocate can encourage the integration of change management and project management. Steps for moving to Level 3 maturity Create knowledge about the different change management initiatives used in the organization and begin research in change management best practices Create clusters of project teams applying change management principles Begin collection of knowledge and tools across the organization and celebrate change management successes Begin building support for using change management with executives and senior leaders who oversee multiple projects Level 3: Change Management on Multiple Projects At Level 3, groups emerge that begin using a structured change management process. Change management is still localized to particular teams or areas in the organization. Organizations at this level can have one or more of the following characteristics: Multiple projects are using structured change management processes, although these approaches and methodologies may be different Some elements of knowledge sharing emerge between teams in the organization; teams in some departments are sharing experiences and lessons learned While change management is applied more frequently, no organizational standards or requirements exist; pockets of excellence in change management co-exist with projects that use no change management Senior leadership takes on a more active role in sponsoring change and consider this role part of their responsibilities, but no formal company-wide program exists to train project leaders, people managers or coaches on change management Training and tools become available to project leaders and team members; people managers now have the training and tools to coach frontline employees Level 3 project management and change management integration Change management is initiated at the start of some projects, with a large fraction still applying change management as a reaction to employee resistance during implementation. Teams who are successful at change management integrate change management with their overall project management methodology at the inception of the project, including communication and other change management plans. Steps for moving to Level 4 maturity Enlist executive support for applying change management on every project and for building change competencies at every level in the organization Select a common methodology that can be used throughout the organization and begin acquiring the tools and training necessary to roll out the common methodology Level 4: Organizational Change Management Standards In Level 4 of the Change Management Maturity Model, the organization has selected a common approach and implemented standards for using change management on every new project or change. Note: a common methodology does not mean a one-size-fits-all recipe; effective methodologies use repeatable steps, but they work best when tailored to the specific needs of every project. Organizations at this level can have one or more of the following characteristics: There is an enterprise-wide acknowledgement of what change management is and why it is important to project success They have selected a common change management methodology and are developing plans for introducing the methodology into the organization Executives, project teams and change leaders have access to training and tools, and people managers have formal training on their roles in change management There are individuals, groups or administrative positions dedicated to supporting change management efforts and building change management skills Executives assume the role of change sponsors on every new project and are active and visible sponsors of change Teams expect resistance and non-compliance in isolated instances, although some project teams may still not understand why they are using change management Adoption is not yet at 100% and the organization is in the process of building change management skills throughout the organization Level 4 project management and change management integration At Level 4, teams regularly use a change management approach from the beginning of their project, with change management work included in the planning phase of the project. As the project progresses, project management and change management continue to integrate to the point where they are not separable. Project teams follow both project and change management milestones. Steps for moving to Level 5 Create a formal position or staff group that is responsible for the effective deployment, training and improvement of change management competencies Correct non-compliance and analyze gaps in the organization that are not applying the selected methodology Level 5: Organizational Competency In Level 5 change management maturity, change management competency is part of the skill set of the organization. Organizations at this level can have one or more of the following characteristics: Effective management of change is an explicitly stated strategic goal, and executives have made this a priority Employees across the enterprise understand change management, why it is important to project success, and how they play a role in making change successful Change management is second nature, so commonplace that it is nearly inseparable from initiatives People managers routinely use change management techniques to help support a broad range of initiatives, from strategy changes to individual employee improvement The organization gathers data to enable continuous improvements to the common change management methodology, tools and training Extensive training exists at all levels of the organization Higher ROI, lower productivity loss and less employee resistance to change across the organization Level 5 project management and change management integration When organizations have developed a high level of change management competency, change management steps are completely integrated into project management, and change management work begins before the project kicks off. Planning and design phases have both project and change management elements and are viewed as standard practice. Change Management Maturity Model Benchmarking Research In Prosci's Best Practices research, participants identified where they were on the Change Management Maturity Model. Over half of participants (54%) fell at Level 1 (ad hoc or absent change management) or Level 2 (change management on isolated projects). Only 11% were at Level 4 or Level 5, where the organization had truly begun adopting organizational standards and building organizational competencies. Next Steps to Reach Organizational Change Management Maturity Moving up the Change Management Maturity Model improves how an organization operates and performs during times of change. There is a growing body of knowledge that shows a direct correlation between how well an organization manages the people side of change and how successful projects and initiatives ultimately are. As an organization sees examples of failed changes due to poorly managed change and successes due to effectively managed change, there is a greater sense of urgency related to moving up the Maturity Model.
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8 Ways AI-Driven Change is Different (And What Change Leaders Must Know)
Organizations investing millions in AI technology often fail to realize expected returns, not because of technical failures, but because they're applying traditional change management approaches to a fundamentally different type of transformation. What Prosci’s AI Research Reveals About AI Change Recent Prosci research studying 1,107 professionals across organizational levels reveals the scope of AI adoption challenges which organizations are facing. The data is striking: Prevalence of Human vs. Technical Challenges of AI Adoption Human vs. Technical Challenges – User proficiency emerged as the primary challenge, accounting for 38% of all reported AI implementation difficulties. This breaks down into learning curve challenges (22%), prompt engineering struggles (11%), and inadequate training (6%). Technical implementation issues account for only 16%. This represents a fundamental shift from traditional technology rollouts where technical challenges often dominate. The Trust Gap is Measurable – The research reveals significant trust disparities across organizational levels. Frontline workers report minimal trust in AI (+0.33 on a -2 to +2 scale), while executives demonstrate significantly higher trust levels (+1.09). Leadership Support Drives Success – Organizations with "very smooth" AI implementations show dramatically different leadership characteristics. They demonstrate strong leadership support (+1.65) compared to struggling organizations (-1.50). These numbers underscore what workshop participants have been telling us—AI change is fundamentally different, and traditional approaches aren't sufficient. 8 Key Differences in AI-Driven Change Over the past six months, we've conducted AI adoption workshops with hundreds of change practitioners across industries in North America. Through polling data and feedback from attendees who are experts in their organizations, eight distinct patterns have emerged that separate AI transformation from traditional change initiatives. 1. The "never-ending phase 2" challenge Traditional change management operates on defined phases with clear endpoints. AI adoption breaks this model. As one workshop participant put it, "AI changes so fast—what are we chasing?" Another described it as a "never-ending Phase 2." The technology evolves rapidly, new capabilities emerge constantly, and organizations must adapt their implementations in real-time. Your change management plans need flexibility and agility, not one-time delivery. Reinforcement becomes an active process of continuous readiness rather than a finite goal. Successful practitioners are building adaptive, modular change plans and coaching sponsors to maintain visibility over longer, less predictable timelines. Aligning The Prosci ADKAR Model to Iterative Changes 2. Security concerns reshape risk management AI introduces elevated risks that traditional change management rarely encounters. Workshop participants noted a "heightened level of security concern" where "individual responsibility and risk mitigation become more important." AI systems can inadvertently expose sensitive data, generate inaccurate information, or create new vulnerabilities. The consequences in sensitive contexts—healthcare, finance, legal—can be severe. This demands that risk management be integrated directly into every change management activity. Awareness campaigns must prioritize responsible behavior alongside tool adoption. Training programs need security-focused messaging woven throughout, not added as an afterthought. 3. Ethics and governance take center stage Unlike traditional technology implementations, AI decisions can perpetuate bias, generate misinformation, or impact people's lives in ways that aren't immediately visible. Workshop participants consistently raised "ethical and responsible use" and "ethical and bias concerns" as central challenges. Building awareness must explicitly include ethical considerations, not just operational changes. Sponsorship coalitions need to visibly model ethical behavior to set the organizational tone. Forward-thinking practitioners are creating visible feedback channels to identify and course-correct ethical risks early, integrating policy updates directly into knowledge-building activities. 4. The shift to individualized learning Traditional training approaches fall short with AI adoption. The technology demands personalized, self-directed learning to build sufficient literacy. As one expert noted, organizations need to "build competencies to ensure resilience and flexibility to engage in continuous learning." AI tools apply differently across roles, departments, and individuals. A marketing specialist might use AI for content creation, while a financial analyst applies it to data analysis. Generic training programs can't address this variety effectively. Successful practitioners are offering multi-path learning experiences: AI academies, peer-to-peer learning networks, and resource hubs that people can access based on their specific needs. 5. Scale and complexity demand enterprise thinking AI implementations often affect multiple departments simultaneously, without clear boundaries. Workshop participants described "the scale of it all—change, speed, etc." with "AI potentially having no limits." Traditional project-based change management approaches struggle with this scope. AI adoption requires enterprise-wide perspective, broader stakeholder impact assessments, and sponsorship coalitions of senior leaders. The complexity isn't just technical—it's organizational. AI implementations trigger cascading changes across business processes, decision-making frameworks, and organizational structures. 6. Navigating ambiguity in future states Traditional change management excels at moving from clearly defined current states to well-articulated future states. AI adoption challenges this model. Participants noted "no clear 'tomorrow' state" and difficulty "defining the future state clearly." AI capabilities evolve rapidly, and organizations can't predict exactly how they'll use the technology six months from now. The solution isn't to wait for clarity—it's to equip people to navigate ambiguity confidently. Practitioners are framing communication around progress markers rather than final destinations, reinforcing organizational purpose to anchor people even as tactics evolve. 7. New forms of resistance require new responses AI evokes distinct resistance that goes beyond typical procedural concerns. Workshop participants described "different and new types of resistance, more fear-based, around risks, unknown factors, loss of relevancy, and societal impacts." The fears are deeper and more personal. People aren't just worried about learning new processes—they're concerned about their fundamental relevance in an AI-enhanced world. Standard resistance management techniques aren't sufficient. Practitioners need to address emotional drivers, not just procedural hurdles. Building desire becomes harder because the perceived threat feels existential. 8. Reshaping roles and work dynamics AI significantly impacts roles, responsibilities, and workplace dynamics. Participants noted major implications for the "future of work and roles" with "knowledge and ability varying from team to team." This isn't just about learning new tools—it's about fundamental work redesign. AI changes how people spend their time, what skills they need, and how they create value. Practitioners are building future-state role maps showing how AI complements human capabilities and reinforcing an organizational narrative of partnership with AI rather than competition. Early Warning Signs and Success Indicators Our research reveals clear patterns distinguishing successful AI transformations from struggling ones. Organizations with "very smooth" implementations demonstrate dramatically different characteristics: The Experimentation Gap – Organizations with "very smooth" implementations strongly encourage trying new tools, while those "making progress with challenges" show moderate encouragement. Organizations struggling with implementation actually discourage trying new tools. This stands out as one of the strongest predictors of AI implementation success. Leadership and Cultural Alignment – Successful organizations demonstrate strong leadership support and organizational culture that actively supports AI-driven change. Data Openness Balance – Organizations with smooth implementations show higher data openness compared to struggling organizations, demonstrating the importance of balancing security with accessibility. Warning Signs to Watch For: Executives expressing high confidence while frontline workers show resistance Security concerns being treated as separate from change management Training approaches that don't account for role-specific AI applications Discouraging experimentation rather than fostering safe exploration Adapting Your Change Management Toolkit Traditional change management tools require thoughtful adaptation for AI adoption success. Our Prosci ADKAR Model remains relevant, but awareness-building must encompass ethical considerations and continuous learning expectations rather than just operational changes. Communication strategies need to emphasize progress markers over final destinations while addressing the measurable trust gap between organizational levels. Training approaches must shift from one-size-fits-all to personalized learning journeys that build adaptability skills alongside technical competencies. Perhaps most critically, sponsorship requirements expand beyond individual project sponsors to coalitions of senior leaders who can maintain visibility and model ethical AI behavior over extended, less predictable timelines. Preparing for Continuous AI Evolution AI adoption isn't a destination—it's an ongoing journey of organizational capability building. The most successful organizations treat AI change management as a core competency, not a project deliverable. This means building internal expertise in AI-specific change patterns, developing organizational agility for continuous adaptation, and creating cultures that embrace rather than resist AI-driven evolution. AI adoption success depends more on managing the human side of change than on the sophistication of the technology. For change practitioners willing to adapt their approaches, this represents both a significant challenge and an unprecedented opportunity to demonstrate the strategic value of expert change management. Make A Strategic Investment in Adoption AI adoption is more than a technical implementation—it’s a transformation in how your people work, innovate, and deliver value. The path to success requires deliberate strategies to engage employees, align leadership, and integrate AI into workflows. By partnering with Prosci, you’ll gain a trusted guide with the research, methodologies, and expertise to manage the people side of AI adoption effectively. With Prosci, your organization will not only achieve the full promise of AI but also build the change resilience needed to navigate future transformations. Partner with Prosci to unlock the full potential of your AI initiatives—and secure lasting competitive advantage in an AI-powered future.
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Individual Barriers to Change and What to Do About Them
The Prosci ADKAR Model is a framework for managing and understanding individual change. The model consists of five building blocks that must be achieved for change to be successful: Awareness of the need for change, Desire to participate and engage in the change, Knowledge of the skills and competencies needed to successfully change, the Ability to perform the necessary skills, and the Reinforcement to sustain the change. Progressing from one element of the model to the next is not constant or a given because individuals usually reach barrier points. Here's what to do about them. Addressing Individual Barrier Points in the ADKAR Model Consider each ADKAR element in the example profiles that follow. Each profile shows how far an individual has progressed in achieving the ADKAR elements on a scale of 1 to 5, with 1 being the lowest and 5 being the highest. Typically, scores less than 3 suggest that the individual needs additional work on that element. The "barrier point to change" is defined as the first ADKAR element with a score of 3 or less on this scale. For example, if you were to rate an employee with the scores shown in Profile 1 below, Awareness would be defined as the barrier point to change. The term "barrier point," as used here, means that you first must address this ADKAR element before moving forward in the model. For example, you would not want to send an employee to a training course to address Knowledge when the employee has no Awareness of the need for change or has no Desire to engage in that change. Profile 1: Barrier point at Awareness In our Best Practices in Change Management studies, research participants consistently identify lack of Awareness as the primary reason why employees and managers resist a change. Without Awareness of the need for change, individuals lack crucial pieces of information and block progress with change. When the barrier point is at Awareness or Desire, you will see little or no evidence that the change is taking place. This is the most obvious and important observation: change is not happening with this person. If the barrier point is Awareness of the need for change, you may see the person simply ignoring the change completely. They may pretend that no change is going on and simply continue with business as usual. If confronted, the person may question why the change is needed, or argue or debate the reasons for change. It is not uncommon for an employee to defend the current state, especially if they helped create the process or tools currently in use. Building Awareness is the first step in enabling a successful change. Awareness sets the foundation for helping individuals make personal choices about the change at hand. This first step requires effective communications from the sponsor of the change, as well as careful coaching by the employee's immediate supervisor. Employees need to know the nature of the change, why the change is happening, and how the change aligns with the direction of the organization. They need to hear these messages from people they trust. Some employees may need time to digest this information and internalize the business reasons for the change. Some employees may need to hear the message multiple times from different people. Profile 2: Barrier point at Desire As with lack of Awareness, you may first identify lack of Desire by noticing that the change is not taking place with an employee. When a person lacks Desire to change, you will observe the person disengaging from work, either partially or completely. If the change has a large impact on the person's day-to-day activities, you can expect them to become increasingly distracted or absent, or start to seek other work opportunities (this is where employee turnover begins). Some employees may openly resist the change. Others may find passive ways to resist such as garnering support from other employees, or by spreading misinformation or rumors about the change. In the worst case, an employee may attempt to sabotage the change by deliberately taking actions that disrupt or interfere with the change process. If confronted, employees at this stage of the ADKAR Model may show fear or uncertainly around the desired future state or become angry at being "forced" to change. It's not uncommon for such an employee to have low overall morale or a poor outlook. Desire is often the most difficult element to facilitate in another person. Any manager or sponsor attempting to help another person attain this element will be challenged because the factors causing lack of Desire are not always within the manager's control. For example, lack of Desire may be related to a personal situation outside of work or one's financial status. Therefore, the first step to building Desire is not to act but to listen. An effective change leader will first seek to understand the root cause of an employee's lack of Desire and explore all the facets of the change that may be impacting this individual. Because Desire to participate and engage in a change is ultimately a personal choice, the manager must be willing to address "what's in it for me" or WIIFM from the employee's perspective. Profile 3: Barrier point at Knowledge Knowledge is the third element in the ADKAR Model. Knowledge in this context means understanding how to change, having the skills and training on the new tools or processes, and understanding the new roles or responsibilities required to change. Recall that the first observations we made around lack of Awareness or Desire were that the change was not happening with the employee. When someone lacks Knowledge or Ability, the opposite is true. The first observation is that an employee is trying to change. But when an employee lacks Knowledge, you will observe honest attempts at making the change happen, which often don't work out. These employees will often say they don't know what to do or they lack the necessary skills. You can expect frequent questions and increased demands on managers' and co-workers' time. Typically employees who lack Knowledge about how to change but try anyway are troubled by mistakes and rework, and therefore become frustrated and discouraged. They may even develop low confidence and increasing fear of making more mistakes If the barrier point at Knowledge is not resolved, the cost of change increases. This uses additional resources, and employees may become so frustrated that they give up. Ways to address the barrier point of Knowledge include training on the change process itself (enabling employees to recognize what's happening and take control of their own situation), training on the new tools and process, and direct coaching from the employees' immediate supervisors or managers. Profile 4: Barrier point at Ability Does attaining the Knowledge regarding a change automatically lead to having the Ability to change? No, but it's a common misconception among change practitioners. The element of Ability is when "walking the walk" becomes reality. Whether from a physical disability, mental block, function of time, or lack of resources, it's possible that an individual may have the Awareness of, Desire for and Knowledge to change, but may not be able to perform the change. An example is an individual who takes golf or swimming lessons but may not be a proficient golfer or swimmer. Until the majority of employees successfully attain the Ability element, the change will not begin yielding desired outcomes. As with Knowledge, the first observation you might make when an employee lacks Ability is the employee trying to change. In this case, you can expect them to take longer to perform the necessary tasks. Productivity will be low. Employees at this barrier point seek constant help from their manager or co-workers. Some of these employees may feel disappointed in their own performance, and they can become upset over the mistakes they make. Left unattended, these employees may attempt to find work-arounds that are easier for them, even if these work-arounds do not align with the change. It's not uncommon for an employee who started with strong levels of Awareness, Desire and Knowledge, but who lacks Ability, to revert to a lack of Desire if they come to believe they won't be successful in the new environment. To address a lack of Ability, managers must ensure that their employees receive the full amount of coaching they need to master the new skills and processes. Equally important, employees must be allowed time to practice until they become proficient at the change. Subject matter experts and mentors are great assets for helping employees in this state. Some organizations even implement a help desk to give employees someone they can talk to immediately when they need assistance. Profile 5: Barrier point at Reinforcement Even though Ability is the element at which employees demonstrate the change and make it real, Reinforcement is the final essential element in the ADKAR Model for individual change. When employees lack recognition, reward and Reinforcement for the change, you can expect a decline in their enthusiasm and energy level around the change. In some cases employees will simply revert to old ways of doing work. When a person feels unrecognized, they may believe that no one cares or is paying attention. They may feel uncompensated for all the hard work they have done to achieve the change. Any employee who feels unappreciated is unlikely to perform at their best. Because change is a process that occurs over time, we may need to spend as much time reinforcing the change as we did building Awareness of the need for change. Tactics for reinforcing change include engaging the primary sponsor and the employees' direct supervisors to provide recognition and Reinforcement to employees, and celebrating successes both publicly and privately. Compensation and performance measurement systems also need to be aligned with the change. Integrated ADKAR Elements Although barrier points at each element can have consequences, the relationships between ADKAR elements are important because the five building blocks are sequential. A barrier point at Awareness can affect Desire, and gaining Knowledge is a key component in attaining Ability. A sagging or low level of any one element can pull down other elements, while an increase in any element through targeted change management efforts can positively contribute to helping individuals attain other elements of the ADKAR Model. Identify barriers to change in your current project when you attend the Prosci Change Management Certification Program.
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