From automating routine workflows to unlocking new growth opportunities, AI models have the potential to redefine entire industries. But here is the uncomfortable truth: many enterprise AI projects fail to deliver meaningful impact.
Recent studies paint a stark picture. MIT reports that 95% of generative AI projects are not delivering significant value, while S&P Global notes that 42% of companies abandoned most of their AI initiatives in 2025.
Is the technology to blame? We say no. The failure often stems not from AI’s potential but from the approach. At Prosci, we know that successful transformation requires more than just code and data. It requires a strategic focus on the people side of ...
Project Management in Manufacturing Projects
Manufacturing organizations operate in one of the most demanding project environments there is. Production schedules are tight. Margins are thin. Safety, quality, and regulatory requirements leave little room for error. At the same time, manufacturers are under constant pressure to modernize by introducing new technologies, optimizing processes, integrating acquisitions, and responding to shifting customer and market demands. With so many moving internal and external pressures, project management for manufacturing is not optional. It is the backbone that enables manufacturers to plan, coordinate, and deliver complex initiatives while keeping operations running. That said, it is often asked to carry more weight than it can bear on its own. Prosci’s proven change management methodology maximizes project success. × Break Through Project Roadblocks: A Project Manager's Guide to Success What Is Manufacturing Project Management? Manufacturing project management is the application of project management tools, principles, and techniques to initiatives that create, improve, or transform manufacturing operations. They introduce new equipment, processes, and ways of working that affect engineering, operations, supply chain, quality, IT, and commercial teams at the same time. When one group changes, every other group feels it. When done well, it helps organizations in the manufacturing industry bring order to complex, high-stakes operational environments by: Defining scope, timelines, and budgets Coordinating resources across functions and sites Managing risk, quality, and compliance Delivering technical solutions efficiently and predictably Project Management Methodologies Used in Manufacturing Manufacturing organizations use a range of project management methodologies to manage complex initiatives, each offering different strengths depending on the nature of the work, level of uncertainty, and operational constraints. Traditional (Waterfall) Project Management Waterfall project management follows a linear, sequential structure, moving through defined phases from planning to execution and closure. This approach provides clear documentation, predictable timelines, and strong control, which makes it well-suited for manufacturing projects with stability requirements, such as equipment installations, facility expansions, or regulatory-driven initiatives. However, its rigidity can become a drawback when changes are required midstream, as late-stage adjustments often introduce delays and additional cost. Lean Project Management Lean project management focuses on maximizing value by minimizing waste. It emphasizes efficiency, continuous improvement, and the elimination of non-value-adding activities. Lean works especially well for process optimization efforts, production flow improvements, and quality initiatives. While highly effective for operational improvements, it is less suited for large-scale transformations that require extensive upfront coordination or cross-functional redesign. Agile Agile project management prioritizes flexibility, collaboration, and incremental delivery through short cycles of work. This methodology enables teams to respond quickly to changing requirements and incorporate feedback throughout execution, which can improve outcomes in product development or digital initiatives. In manufacturing environments, however, agile can be challenging to apply where physical assets, fixed schedules, or regulatory constraints limit how frequently changes can be made. Six Sigma and DMAIC Six Sigma and the DMAIC framework take a data-driven approach to improving quality and reducing defects. By defining problems, measuring performance, analyzing root causes, implementing improvements, and controlling results, DMAIC supports precision and consistency in manufacturing operations. This methodology is most effective when addressing process variation or quality issues. Hybrid Hybrid approaches combine Waterfall’s structured planning with Agile’s flexibility and iterative execution. In manufacturing, this means using disciplined front-end planning to define scope, safety, quality, and compliance requirements, while applying Agile practices to iterate, test, and refine solutions as conditions evolve. This balance allows organizations to maintain the rigor needed for complex operations while still adapting to shifting requirements, technology changes, and operational constraints. Hybrid methodologies are particularly effective for large, cross-functional initiatives that span both physical and digital environments, such as smart factory programs, automation deployments, or ERP implementations. The Challenges of Project Management in Manufacturing Even with experienced project teams and well-chosen methodologies, manufacturing companies face complex projects and challenges that are fundamentally different from those in other industries. Tightly coupled systems, shared resources, and the need to unfurl projects alongside live operations can produce some unique hurdles, including: Supply chain and material dependencies Manufacturing projects often rely on specialized components, long lead times, and global suppliers, many of which sit outside the organization’s direct control. A single delayed part can ripple across engineering schedules, installation windows, training plans, and production readiness. These dependencies complicate planning, increase exposure to geopolitical and market volatility, and require project leaders to manage uncertainty continuously, not just at the start of the project. Production downtime Unlike office-based projects, project goals must be executed while protecting material output, safety, and customer commitments. Even short periods of downtime on the production line can carry significant financial and reputational consequences. This limits flexibility, compresses implementation windows, and raises the stakes for execution accuracy. Teams must often deploy solutions during maintenance windows or off-shifts. And because of the need to avoid downtime, there is little room for error. Cross-functional coordination Manufacturing projects demand tight alignment across engineering, operations, supply chain, quality, IT, and leadership. Each function brings different priorities, constraints, and success measures, yet progress depends on seamless handoffs between them. When coordination breaks down, so do those handoffs. Engineering completes a design before operations are ready to run it. IT deploys systems that frontline teams are not prepared to use. Quality requirements surface after implementation, forcing rework and revalidation. Without disciplined coordination and shared ownership, decisions stall, teams optimize locally instead of systemically, and completed projects fail to deliver sustained performance. Quality, compliance, and safety requirements Manufacturing organizations operate under rigorous internal standards and external regulations, often across multiple regions. Changes that appear minor in scope can have significant downstream impacts on product quality, regulatory approval, or worker safety. These requirements constrain how quickly teams can adapt and elevate the importance of disciplined change control, documentation, and stakeholder alignment. Process improvement initiatives. Continuous improvement is essential for competitiveness, yet improvement projects require people to change deeply ingrained routines and habits. A new workflow, digital tool, or automated manufacturing process can only deliver value when it is adopted consistently across shifts and sites. Without reinforcement, improvements may struggle to take hold in daily operations, limiting the project’s ROI. Best Practices for Successful Project Management in Manufacturing Many project management principles apply across industries, but manufacturing environments demand a more disciplined, operationally grounded approach that does not compromise safety, quality, or customer commitments. The most successful project managers know this and adapt to the realities of the shop floor. Aligning projects with production schedules and business goals. Manufacturing projects must be planned in the context of live operations. Aligning initiatives with production cycles, demand forecasts, and strategic objectives ensures the project schedule and project budget reflects operational constraints, so teams can manage potential risks and reduce costs associated with unplanned downtime or rushed implementations. Integrating change management early and unifying team objectives. Projects succeed when technical execution and people adoption are addressed together from the start. Early integration of change management clarifies not only what will be delivered, but how people must work differently to achieve performance targets. Aligning project leaders, functional managers, and frontline supervisors around shared success measures (e.g., safety, quality, throughput, and adoption), manufacturers reduce resistance, prevent misalignment between design and operations, and avoid late-stage surprises that delay implementation. Using standardized project templates and governance. Standardized charters, schedules, risk registers, and communication plans provide consistency across projects and sites, which is essential in manufacturing environments where work is tightly coupled and resources are shared. These tools enable disciplined risk management by making potential hurdles visible early, assigning clear accountability for monitoring and mitigating them, and ensuring risks are addressed as part of project execution. Tracking the right performance metrics. A project may go live on time, yet still fail to improve performance if the new process is not adopted or does not deliver the expected operational results. Tracking operational KPIs such as overall equipment effectiveness (OEE), cycle time, throughput, and cost variance alongside traditional project measures connects project delivery to outcomes. These indicators reveal whether changes are improving safety, quality, and productivity across shifts and sites. Conduct post-project reviews and capture lessons learned. Formal reviews at project close create a disciplined feedback loop that strengthens manufacturing execution over time. Examining not only what was delivered but also how adoption occurred across business sectors highlights systemic issues that impact future projects. Embrace a culture of continuous change. Manufacturing environments are constantly evolving. Organizations that treat change as a one-time event struggle to sustain improvements, while those that embed continuous change into their operations are better positioned to deliver long-term results. Integrating Change Management in Manufacturing Project Management Manufacturing excellence depends on delivering the right solutions and ensuring people adopt and use them effectively. Project management and change management work well together, providing essential structure for this outcome. Project management focuses on designing, building, and delivering the technical solution, whether that is new equipment, digital platforms, process improvement,s or production capabilities. Change management, on the other hand, focuses on the people side of the same effort. That includes preparing, equipping, and supporting employees to embrace, adopt, and use the change. When these disciplines operate together, manufacturing organizations move beyond project delivery to sustained operational performance. Prosci’s ADKAR® Model provides a practical framework for planning and tracking adoption alongside technical delivery. Research shows that when ADKAR is aligned with the stages of project management, organizations increase the likelihood that these initiatives will achieve their intended results. How to Integrate Change Management With Project Management Facilitate smooth transitions by defining clear scope and requirements for both the solution and adoption. Project scope must extend beyond technical specifications to include who must change, how work will change, and what proficiency is required to achieve performance targets. Impact and readiness assessments clarify expectations early and prevent misalignment that otherwise surfaces as delays, safety risks, or reworks. Establish continuous stakeholder communication as a disciplined workstream. Structured communication ensures the right messages are delivered by the right leaders at the right time. In practice, this looks like executives aligning direction, project leaders clarifying progress, and people managers explaining the initiatives and their impacts to frontline teams. Manage resistance proactively through people managers and targeted support. Resistance in manufacturing typically arises from risk. Operators, technicians, and supervisors worry about whether a change will compromise safety, increase defects, slow production, or put their jobs at risk. When people are unsure how a new process, system, or piece of equipment will affect their ability to do good work, they often become hesitant and wind up using workarounds or simply disengaging. Change management surfaces these concerns early through impact and readiness assessments, then equips people managers to address them directly. Supervisors become trusted coaches who can explain why the change is happening, what it means for each role, and how success will be measured. Align schedules with production cycles and organizational culture. In manufacturing companies, project timelines cannot be set in isolation from operations. Change activities must be planned around maintenance windows, shift patterns, regulatory inspections, and peak production periods. Ongoing dialogue between project leaders, operations managers, and frontline supervisors keeps plans realistic and allows for rapid adjustment when conditions change. Integrate people risks into project risk and change control processes. People-related risks are often treated as secondary to technical risks, yet they are just as likely to derail performance. Readiness gaps, training shortfalls, supervisor alignment issues, and resistance can keep people from accepting a change even when the technical solution is sound. Structured change control ensures that scope changes are evaluated for both technical and people impacts, preventing well-intended adjustments from creating hidden adoption or safety issues downstream. Use data to guide adoption and performance decisions. Performance data explains what is happening. Adoption data explains why. By tracking utilization and proficiency alongside operational KPIs, leaders gain clarity on whether performance gaps stem from technical issues, insufficient training, unclear expectations, or resistance to change. Manufacturing Excellence Through Project and Change Integration Manufacturing projects succeed when execution and adoption move together. Strong project management provides the structure needed to plan, coordinate, and complete complex initiatives, but lasting results depend on how well people adopt new processes, technologies, and ways of working. In a manufacturing environment, safety, quality, and consistency are non-negotiable. Prosci helps manufacturing organizations intentionally manage the people side of change, so teams can streamline execution, reduce resistance, and translate new processes, technologies, and ways of working into sustained, measurable performance improvements. Frequently Asked Questions Why is project management in manufacturing important? Project management in manufacturing is critical because it brings structure and coordination to complex, high-risk environments. Effective project management helps organizations streamline execution, deliver initiatives on time, within budget, and to required quality and safety standards while minimizing disruption to live operations. Without strong project management, operational inefficiencies quickly compound. What does a manufacturing project manager do? A manufacturing project manager plans, coordinates, and oversees initiatives that impact production, processes, or systems. This includes managing project schedules, available resources, budgets, risks, and cross-functional dependencies. They also monitor project status, resolve constraints, and help teams track progress to ensure initiatives remain aligned with operational and business goals. What is the best approach for project management in manufacturing? There is no single best approach for every initiative. Many manufacturers rely on hybrid methodologies that combine structured planning with the flexibility to adapt as conditions change. This allows teams to maintain control while responding to production constraints, supply chain variability, and evolving requirements. How does change management impact project outcomes in manufacturing? Change management directly influences whether project outcomes are realized after delivery. In manufacturing, value is created only when people consistently adopt new processes, systems, or behaviors on the plant floor. Integrating change management helps reduce resistance to these things, prepares them to adopt them, and helps them use them well. What are common barriers to change in manufacturing projects, and how can they be addressed? Common barriers include unclear communication, resistance driven by uncertainty, misalignment with production schedules, and a lack of manager support. These challenges can be addressed through early engagement, clear and consistent communication, realistic planning aligned with operations, and structured support for people managers who play a key role in adoption. What is an example of manufacturing project management? A common example is implementing an enterprise resource planning (ERP) system to improve production planning, supply chain visibility, and data integration. While the technical system may be delivered successfully, realizing its full value depends on how well employees are prepared, trained, and supported to use it consistently in daily operations.
Project Management in Higher Education
Higher education institutions are no strangers to complexity. But today’s environment is different. Enrollment pressures, regulatory shifts, digital transformation, cybersecurity demands, AI integration, and evolving student expectations are converging at once, forcing universities and colleges to execute more large-scale initiatives than ever before.This is not temporary turbulence. According to EDUCAUSE research, 69% of higher education institutions continue to experience “a great deal” or “some” change and disruption across campus. As the volume of change rises, so do the stakes. Yet across higher education, a familiar pattern persists. Technically sound projects struggle to deliver their intended value. A new system goes live, but adoption lags. Units create workarounds. Stakeholders disengage. Timelines extend. Expected efficiencies fail to materialize. The issue is rarely the project plan itself. More often, it is institutional readiness. Universities operate within decentralized governance structures, deeply rooted traditions, distributed sponsorship models, and multiple definitions of success. Faculty autonomy, layered decision-making, and identity-based affiliations across schools and departments introduce dynamics that traditional project management alone was never designed to address. For institutional leaders and portfolio executives, this creates a new mandate. Project management in higher education must evolve beyond coordinating tasks and timelines toward building organizational readiness, strengthening engagement, and translating execution into measurable value realization. It must move from delivering outputs to enabling sustained transformation outcomes. × Overcome the 4 most common project management challenges What is project management in higher ed? Project management in higher education shares many technical similarities with other sectors. Initiatives require defined scope, timelines, budgets, resource coordination, and risk management. Institutions implement enterprise systems, modernize infrastructure, launch academic programs, and redesign processes just as corporations do. But the institutional environment in which these projects unfold is fundamentally different. Shared Governance: Unlike most corporate environments, higher education institutions operate within distributed governance models. Authority spans boards, presidents, provosts, deans, faculty senates, and department chairs. Academic leaders often don’t collaborate and retain significant autonomy, particularly around curriculum, research priorities, and faculty matters. Decentralized Decision-Making: Universities typically function as federated systems. Colleges, schools, research centers, medical facilities, and administrative divisions operate with distinct budgets, cultures, and priorities. Many leaders and employees identify more strongly with their college or department than with the institution as a whole. Project management in this environment requires navigating identity, influence, and distributed authority. Communication strategies must reflect timely, local realities. Sponsorship must extend beyond a central executive voice to trusted leaders across the institution. Long Approval Cycles: Higher ed values deliberation. Budget allocations, system selections, policy revisions, and academic program changes often move through layered review processes involving faculty committees, advisory groups, and regulatory bodies. These extended cycles shape both timelines, deadlines, and expectations. By the time a decision is finalized, conditions may have shifted. Stakeholders may also experience consultation fatigue or competing priorities. An effective project team anticipates extended decision paths, aligns milestones with governance calendars, and sustains momentum across longer time horizons. Importance of project management in higher education In these mission-driven institutions facing resource constraints and rising expectations, disciplined project execution directly affects institutional performance. Here’s why it matters. Strategic Alignment Institutions operate around clearly defined missions, whether it’s student success, research excellence, community impact, or financial sustainability. Structured project management connects initiatives directly to those priorities, reducing ambiguity and helping leaders collaborate and focus resources on work that advances long-term objectives and improves productivity. Navigating Multi-Stakeholder Environments Faculty, students, administrators, trustees, regulators, donors, and external partners often have overlapping interests and differing expectations. Project management provides a framework for clarifying roles, reconciling priorities, and maintaining alignment across these diverse groups. Resource Optimization and Budget Constraints Project discipline supports data management, realistic scoping, careful financial planning, and ongoing monitoring to prevent cost overruns or stalled initiatives. In constrained environments, efficient allocation becomes a competitive advantage. Organizational Efficiency From infrastructure development to curriculum redesign to enterprise system implementations, higher education projects often involve complex, cross-functional work. Structured management reduces duplication, clarifies accountability, and improves coordination across units. Enhancing Stakeholder Value Projects are ultimately measured by the value they create. Whether through improved facilities, modernized systems, expanded learning opportunities, or streamlined services, effective project management strengthens institutional credibility and stakeholder confidence. Impact on Student and Faculty Experience Every major initiative eventually touches the academic mission. When projects are executed effectively, students experience smoother processes and better support systems. Faculty benefit from tools and structures that enable teaching and research rather than adding friction to daily work. In short, disciplined project management strengthens institutional resilience. It aligns execution with mission, clarifies goals, and positions institutions to adapt in a rapidly changing landscape, all while improving collaboration and communication. Common project management challenges in higher education Even with disciplined planning, projects in higher education face structural and cultural barriers that complicate execution. Many challenges stem not from technical complexity, but from institutional dynamics and human response to sustained change. The most common obstacles include: Managing Change Fatigue and Stakeholder Resistance Institutions often run multiple initiatives simultaneously across departments and campuses. When projects feel uncoordinated or cumulative impacts are not visible, faculty and staff experience change fatigue. Resistance increases, engagement declines, and adoption slows. Addressing the people side of change early reduces these risks and improves long-term outcomes. Lack of Formal Project Management Training Project leadership in higher education frequently falls to faculty members or administrators whose primary roles lie elsewhere. Without shared methodologies or formal training, practices vary widely across units. Inconsistency leads to inefficiencies, unclear expectations, and avoidable rework. Manual Reporting and Tracking Processes Many institutions rely on manual reporting systems or fragmented tools to monitor progress. These approaches increase the likelihood of errors, delay visibility into emerging risks, and limit portfolio-level insight. Digital tools and standardized reporting structures improve transparency and decision-making. Unclear Authority Structures Distributed governance can blur accountability. When roles and decision rights are not clearly defined, projects stall. Leaders may hesitate to act without consensus, even when timelines require forward movement. Committee-Based Approvals and Decision Delays Layered review processes are central to academic governance. While deliberation protects institutional integrity, extended approval cycles can slow momentum. Without clear planning around governance calendars and review pathways, projects risk timeline slippage and stakeholder frustration. Key project management methodologies used in higher education Selecting the right project management methodology in higher education requires balancing structure with adaptability. Institutional culture, governance complexity, stakeholder expectations, and project scope all influence which approach will be most effective. Waterfall methodology The waterfall methodology follows a structured, sequential process with clearly defined phases and milestones. This approach provides predictability and documentation — both valuable in higher education environments that require alignment with governance calendars, compliance standards, and regulatory oversight. Clear stage gates and defined deliverables can reduce ambiguity among stakeholders and create a visible sense of progress. For infrastructure projects, large enterprise system implementations, or initiatives with fixed regulatory requirements, Waterfall can offer stability and control. Agile methodology Agile methodologies prioritize flexibility, iteration, and continuous feedback. Work progresses in short cycles, allowing teams to adjust based on stakeholder input and emerging insights. This adaptability can be particularly valuable in academic settings where innovation and responsiveness are highly valued. Technology initiatives, student experience enhancements, and cross-functional improvements often benefit from iterative refinement rather than rigid sequencing. However, agility alone does not guarantee adoption. Without structured attention to stakeholder readiness, even iterative projects can encounter resistance. Hybrid methodology Hybrid approaches blend the structured planning of waterfall with the adaptability of agile. Institutions often use a phased governance structure for major milestones while allowing teams to iterate within those phases. For complex, institution-wide initiatives involving multiple stakeholder groups, hybrid models can provide both stability and responsiveness. Research consistently shows that hybrid approaches often strike the strongest balance between engagement and predictability. Integrating Prosci’s ADKAR Model into project methodologies Regardless of methodology, delivery does not equal transformation, because none inherently address whether faculty, staff, or students are ready to adopt new systems or behaviors. This is where structured, adaptive change frameworks become critical. The Prosci ADKAR Model reinforces five measurable outcomes — Awareness, Desire, Knowledge, Ability, and Reinforcement — that drive readiness and sustained adoption. ADKAR adapts across Waterfall, Agile, and Hybrid environments, integrating into iterative cycles and feedback loops rather than sitting outside them. Methodology shapes delivery. Readiness, engagement, and sustained adoption determine transformation impact. Change management and project management collaboration in higher education To strengthen execution across the institution, leaders must take deliberate steps to build change capability alongside project discipline. 1. Strengthen Institutional Readiness and Agility Elevating execution requires strengthening institutional readiness — the ability to absorb, adopt, and sustain change across multiple initiatives. When projects unfold simultaneously across decentralized units, cumulative impact shapes stakeholder experience. Without structured engagement and coordinated sequencing, fatigue increases and outcomes weaken. At the University of Virginia, change management was integrated directly into its University Project Portfolio framework. Project and change plans were developed together, creating greater coordination and reducing unaligned effort across units. 2. Establish Active and Visible Leadership Sponsorship Identify primary sponsors at the outset and define their responsibilities clearly. Sponsors should: Communicate the rationale for change. Reinforce priorities consistently. Model commitment in visible ways. Align messaging across leadership tiers. In decentralized institutions, build a sponsor coalition that reflects institutional structure. At Texas A&M, system-wide transformation required coordinated sponsorship across multiple universities and agencies, and the distributed leadership support strengthened local ownership and improved adoption. 3. Integrate Project and Change Planning From Day One Do not sequence change management after technical planning. Develop both plans in parallel. Align technical milestones with readiness milestones. Incorporate stakeholder impact assessments into scope definition. Engage likely concerns early and address barriers to adoption alongside system configuration. Integration prevents last-minute scrambling and reduces adoption risk. 4. Invest in Skill Development Across Roles Project execution in higher education often depends on leaders who were not formally trained in project or change management. Provide structured training for: Project managers Change practitioners Sponsors Middle managers Middle managers in particular require support, as they translate executive direction into daily operational reality. Equipping them with tools and clarity reduces resistance and increases consistency. At UC San Diego, investing in internal change capability strengthened institutional resilience beyond a single transformation effort. Best practices for effective project management in higher education Institutions that consistently deliver successful initiatives share common disciplines. These practices distinguish reactive project environments from institutions that execute with clarity and confidence. Adopt a Structured and Shared Methodology High-performing institutions standardize how projects are defined, governed, and evaluated. Whether using Waterfall, Agile, or Hybrid approaches, they apply consistent frameworks across units and integrate structured change management alongside technical planning. A shared methodology creates common language, clearer accountability, and repeatable success. Strengthen Stakeholder Engagement Effective project environments move beyond one-way communication. They map impacted groups early, clarify roles and expectations, and create structured feedback loops throughout the lifecycle. Transparent communication builds credibility, while early engagement reduces resistance and misalignment. Integrate Project and Change Management Delivery and adoption must progress together. Institutions that align technical milestones with readiness milestones reduce the risk of stalled implementation. Structured change practices — such as applying ADKAR to measure awareness, capability, and reinforcement — help translate project completion into sustained institutional value. Cultivate Active Leadership Alignment Visible, consistent sponsorship across governance tiers signals institutional priority. In decentralized environments, alignment among presidents, provosts, deans, and functional leaders strengthens momentum and reinforces shared direction. Leadership engagement must be sustained, not symbolic. Invest in Institutional Capability Long-term resilience depends on skill development. Institutions that build internal expertise in both project and change management reduce variability across initiatives. Training project leaders, sponsors, and middle managers fosters consistency and strengthens execution capacity over time. Monitor What Matters Schedule and budget metrics tell only part of the story. Leading institutions track readiness, engagement, system usage, and behavioral adoption alongside technical progress. Structured assessments help leaders evaluate alignment across leadership, delivery, and change enablement dimensions. Ultimately, project success is measured by value realization, not milestone completion. Align Projects With Long-Term Strategy Projects succeed when they are sequenced intentionally and clearly connected to institutional priorities. Portfolio oversight that balances ambition with capacity reduces initiative overload and mitigates change fatigue. Elevating execution in higher education Higher education institutions are navigating one of the most dynamic periods in their history and it requires robust execution capabilities. Project management remains essential via structured planning, disciplined resource allocation, stakeholder coordination, and financial oversight form the backbone of institutional progress. But structure alone is not sufficient. Universities do not change because a system goes live or a policy is approved. They change when faculty adopt new instructional models. When staff embrace new workflows. When leaders model new priorities. When students engage with new tools and services in meaningful ways. The complexity of shared governance, decentralized authority, long approval cycles, and multiple bottom lines requires more than traditional coordination. It calls for intentional integration of project management and structured change management. It calls for visible leadership alignment. It calls for institutional capacity to navigate change as a repeatable discipline rather than a one-time event. For institutional leaders, the mandate is clear: elevate execution by strengthening readiness and engagement across the enterprise. Align initiatives to mission. Balance stability with adaptability. Focus not just on completing projects, but on accelerating value realization and sustained institutional agility. Frequently asked questions What is strategic project portfolio management in higher education? Strategic project portfolio management in higher education is the coordinated oversight of multiple initiatives to align them with institutional priorities and available capacity. Rather than managing projects in isolation, institutions evaluate initiatives collectively — assessing cumulative impact, resource allocation, and alignment with mission. This approach helps reduce initiative overload, improve sequencing, and strengthen overall execution across academic and administrative units. Which project management methodologies are commonly used in higher education? Higher education institutions commonly use Waterfall, Agile, and Hybrid methodologies. Waterfall provides structured sequencing and clear milestones, which can be valuable in compliance-heavy or infrastructure projects. Agile supports flexibility and iterative improvement, particularly in technology and innovation-focused initiatives. Hybrid approaches blend structure and adaptability, making them well suited for complex, cross-functional transformations. What change management frameworks work well in higher education? Structured frameworks that address both institutional complexity and individual adoption are particularly effective. The Prosci ADKAR Model works well because it focuses on building Awareness, Desire, Knowledge, Ability, and Reinforcement at the individual level — an important consideration in decentralized, shared-governance environments. When integrated with project management practices, such frameworks strengthen adoption and reduce resistance across diverse stakeholder groups. How does change management support digital transformation in higher education? Digital transformation initiatives, such as ERP implementations, LMS upgrades, AI-enabled analytics, and data governance modernization, succeed only when technology investments are matched with structured efforts to build readiness, engagement, and sustained behavioral change. By focusing on adoption and value realization, institutions accelerate transformation outcomes rather than simply deploying new tools. How can higher education institutions measure project success rates? Project success in higher education should be measured beyond schedule and budget performance. Institutions should evaluate stakeholder adoption, training completion, system usage rates, and alignment with intended strategic outcomes. Tools such as structured project health assessments can help leaders evaluate leadership engagement, readiness, and overall alignment. Sustained behavior change and realized institutional impact provide the most meaningful indicators of success.
Editor's Choice
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.
Read story
Digital Transformation Made Real With Change Leadership
Everyone is racing toward digital transformation. But what separates those who talk about it from those who achieve it? The answer isn’t technology. It’s leadership. The promise is compelling: smarter operations, faster decisions, deeper connections. But transformation doesn’t succeed on tools alone. It demands bold vision, aligned leadership and cultures ready for change. Without these, even the best-funded initiatives stall. This article breaks down the trends shaping digital transformation—and more importantly, shows how to lead through them with clarity, coordination and a people-first mindset. What Is Digital Transformation? Digital transformation integrates technology across business functions, reshaping operations and customer experiences. The process involves reshaping business processes, company culture and customer experiences. But transformation only happens when people adopt new ways of working. The success of a new CRM or AI platform depends not on deployment, but on whether teams choose to engage, adapt and own the change. Digital tools create potential. People turn that potential into performance. Why Is Digital Transformation Important? Because the pace of change won’t slow down. Markets evolve. Expectations rise. And technology keeps pushing forward. If you delay, you risk falling behind. Digital transformation helps resilient, adaptive businesses stay ahead. It enables efficiency, allows for deeper customer relationships, and provides leaders with the clarity to make faster, smarter decisions. Here’s what the results may look like in practice: Operational efficiency – Intelligent systems and real-time analytics streamline day-to-day processes, reducing manual effort and freeing teams to focus on higher-impact work. Customer centricity – From personalized marketing to predictive support, digital transformation helps businesses understand and serve customers more effectively. Operational agility – A flexible digital foundation allows organizations to test new ideas, launch initiatives faster, and adapt to shifting market demands without major disruption. Competitive resilience – Digital maturity builds the structural flexibility and clarity leaders need to make smarter decisions and outpace competitors. Together, these capabilities position organizations to move with confidence—ready to pivot, grow, and lead in a constantly changing landscape. But staying ahead demands a clear view of what’s next. To lead effectively, organizations must understand the forces shaping the digital future and be ready to act. What’s Ahead: Trends in Digital Transformation Digital transformation trends will see the rise of AI-driven automation, hyper-connected ecosystems and the increasing importance of sustainability and cybersecurity. AI agents and spatial computing will also gain prominence. Let’s take a look at these trends in more detail: The rise of AI-powered technology AI is becoming the engine behind smarter, faster and more adaptive businesses. Organizations are embedding AI into core operations, transforming everything from customer service to supply chain management. The AI market surpassed $184 billion in 2024—a sharp increase of nearly $50 billion from the previous year. And the momentum isn’t slowing down. Projections show it could skyrocket to beyond $826 billion by 2030. This shift signals the transition from testing AI to actually putting it to work, and seeing real, measurable results. Here are some of the key AI technologies powering digital transformation: Generative AI – Gen AI improves creative and customer-facing workflows by generating text, images, code and more in real time. It helps personalize marketing campaigns, draft content, assist in product design, and even simulate business scenarios. Research shows that 40% of companies have already adopted Gen AI. AI-driven automation – Streamlines repetitive, manual tasks such as data entry, scheduling and report generation. Automating these time-consuming processes allows organizations to reduce errors, cut costs and free up time for employees to focus on higher-value work that requires human insight, like managing change. AI agents – Similar to chatbots but with more advanced capabilities, AI agents use natural language processing, machine learning and decision-making capabilities to perform tasks independently. They can manage calendars, handle complex customer service requests, monitor systems for anomalies, and take proactive steps. Adopting AI is one of the most complex and high-potential shifts organizations can face. Realizing its full benefits requires a deliberate focus on people, processes and change readiness. Cloud-native is the new normal Legacy systems are quickly becoming a thing of the past, with agile, scalable cloud solutions taking center stage. The shift is undeniable—nearly 92% of digital leaders say that their companies adopted cloud technology on a small or large scale. Here are some of the key approaches driving this transformation: Cloud-native applications – These are tools built specifically for the cloud environment. With cloud-native apps, organizations can scale faster, innovate more efficiently, and stay ahead of the curve. Multi-cloud strategies – These offer organizations the flexibility to combine multiple cloud services for their needs. They help businesses mitigate security risks by diversifying their cloud environment. Serverless computing – A cloud computing model where the cloud provider manages the infrastructure, such as servers and virtual machines. Developers can then focus on writing code, while the cloud provider handles all scalability and infrastructure concerns. Moving to the cloud requires a cultural shift that demands reskilling, collaboration across teams, and aligning leadership around a digital-first vision. The most successful organizations will be those that adapt quickly and foster an environment of continuous learning and innovation. Human-centric personalization Customers and employees now expect personalized, connected digital experiences that cater to their needs and behaviors. Research shows that 73% of customers expect better personalization as technology improves. This means that many organizations will focus on creating deeply relevant and intuitive interactions that anticipate what people want before they even ask. Here are the key technologies driving the future of personalization: Hyper-personalization – Takes customer engagement to the next level by delivering tailored experiences in the moment. This means offering the right content, products or services at the right time based on individual behaviors, preferences and context. Edge computing – Reduces latency by processing data closer to where it’s generated. This means faster, more responsive interactions—especially for real-time applications—enhancing user experience and reducing reliance on centralized data centers. IoT (Internet of Things) – Creates a network of connected devices that communicate and respond autonomously. With IoT, you can automate processes, track assets and deliver experiences that respond to real-world events in real time, creating a more dynamic and responsive environment. Leaders must ensure people know how to engage with and leverage these technologies. Providing the right training, fostering a culture of continuous learning, and aligning teams around a shared vision of personalization are key to achieving this. Improving security and data governance Security is increasingly becoming a strategic priority that impacts every part of the business. Protecting sensitive data and ensuring regulatory compliance are foundational to sustainable growth and building trust with customers. In fact, research shows that 76% of companies globally stated that cybersecurity was the leading priority for their IT initiatives. So, what technologies are leading the transformation? Zero trust architecture – An always-verified access model that assumes no one—inside or outside the organization—should automatically be trusted. Every user and device must continuously authenticate before accessing systems. This ensures robust protection against breaches. AI-powered threat detection – Traditional security measures often rely on static rules and human intervention, which can leave gaps in defense. In contrast, real-time, adaptive protection, powered by AI, continuously learns and evolves to detect and respond to threats before they escalate. To ensure these technologies are fully adopted, leaders must support employees in understanding and consistently following secure practices and integrate security into the company culture. The Prosci Methodology can help organizations manage this transformation. With its proven change management approach, you can drive culture-embedded, compliant transformation and make security and data governance integral to daily operations. Real-World Cases of Successful Digital Transformation Projects Companies that embrace new technologies are reaping the benefits, from improved operational efficiency to enhanced customer experiences. Here are three real-world examples of companies that have successfully done so: Proximus Proximus, Belgium’s largest telecommunications provider, spearheaded its digital transformation by piloting a focused, data-driven marketing initiative. Partnering with Digipolitans and Google, the company built a 12-week agile campaign around promoting Netflix subscriptions. By leveraging behavioral data and Google Marketing Platform tools, Proximus streamlined campaign delivery and personalized digital touchpoints. The results were striking: a sixfold increase in sales leads, a 14% conversion rate (up from 4%), and a 72% influx of new site visitors—all achieved with a smaller budget. The initiative also catalyzed long-term structural change across the organization. Siemens Siemens is a global technology company that specializes in industrial automation, digitalization and smart infrastructure solutions across sectors like manufacturing, energy and healthcare. The company is leading the charge in industrial technological innovation, unveiling breakthroughs in AI and digital twin technology at CES 2025. By digitizing its manufacturing operations with IoT, AI and digital twins, Siemens has transformed how it simulates, monitors and optimizes processes. The result: smarter solutions for clients, reduced downtime and greater supply chain transparency across its global footprint. Starbucks Starbucks is crafting the future of retail with AI. Through its “Deep Brew” initiative, Starbucks harnessed artificial intelligence to optimize inventory, streamline staffing and deliver hyper-personalized marketing. By embedding tech into every touchpoint, the company created smarter, more seamless customer and employee experiences, driving higher efficiency, more mobile orders and stronger loyalty program engagement. (Image Source) These real-world examples illustrate what’s possible when digital transformation is approached with clarity, alignment and a focus on people. Yet success stories are only part of the equation. Transformation at scale is rarely straightforward. Even the most prepared organizations encounter difficulties and uncertainty. Common Challenges of Digital Transformation Technology is only half the equation. Without the right leadership and support, even the most advanced solutions fall short. Misalignment, resistance and cultural inertia are the real barriers. Let’s examine the most common barriers that can stall digital transformation, and why overcoming them requires more than technology alone. Measuring return on investment (ROI) Unlike traditional investments, the ROI of digital transformation can be harder to quantify, especially in the early stages. Benefits like improved customer experience or greater agility are intangible or long-term, so how do you prove their financial impact? Organizations must define clear success metrics from the start, ones that go beyond immediate revenue and cost savings. This means tracking qualitative outcomes (like employee engagement or customer satisfaction) and quantitative results (such as efficiency gains or time-to-market improvements). ROI must also account for adoption metrics. Without widespread use, even the most advanced solutions won’t deliver value. Leaders need to monitor how well new technologies and processes are embraced across teams, not just whether they’re deployed. Prosci helps organizations put this into action with structured outcome tracking. Our change experts help organizations measure the real impact of change by linking adoption and usage to business results. Prosci Performance Levels Effective communication within organizations Transformation efforts often fail when vision and strategy aren’t well communicated across departments. Teams may work in silos, misunderstand objectives, or lack visibility into how their work connects to broader initiatives. To avoid these pitfalls, communication must be ongoing, cross-functional and directly connected to the "why" behind the change. It’s not enough to share updates. You need to create a communications plan so that every person in your organization understands the broader purpose of the change, their role in achieving it, and how each department’s efforts fit into the bigger picture. A well-designed communications plan ensures messaging is consistent, timely and aligned with business goals. The plan should outline: Key messages Target audiences Preferred communication channels Frequency of updates When communicating updates around change, it’s also important to consider who’s delivering them. Prosci research shows that people prefer to receive certain change messages from specific roles in the company: Preferred Senders of Messages During Change To ensure people are receptive to communication, it’s important to consider their preferences. Aligning communication with trusted voices helps leaders foster an organizational culture of transparency, strengthen credibility and inspire confidence across the organization. Cultural shift and adaptation Digital transformation requires a fundamental shift in mindset across the organization. One of the biggest challenges is bridging the cultural gap between the old way of working and the new approach required for transformation. Leaders must shift from directive management to transformational leadership. Simply implementing new systems or processes is not enough. You need to consistently demonstrate the mindsets and behaviors that set the tone for others to follow. Creating safe spaces for experimentation is also important. Innovation labs, pilot programs and internal communities allow teams to explore new tools and approaches, and fail forward without the risk of major disruption. This creates an environment where change becomes a constant opportunity, rather than a threat. To overcome these challenges, digital transformations need to be built on trust, clarity and engagement—from the C-suite to the front line. That’s where change management becomes the differentiator. Why Change Management Is The Missing Link Change management isn’t a side task—it’s the work. If people aren’t supported through the change, transformation is destined to fail. Without the right support, employees may be apprehensive about new systems, underutilize tools or revert to old habits, undermining the entire initiative. Digital transformation struggles when change is done to people, not with them. A structured, people-focused approach is critical to driving successful transformation. Structured, intentional change leadership aligns people with purpose, equips them for the transition, and significantly improves adoption. Prosci helps organizations navigate transitions smoothly, creating lasting adoption and securing long-term success. It’s the bridge that connects digital technology implementation with real, sustainable cultural shifts within the organization. Supporting people with the Prosci ADKAR Model At the heart of the Prosci Methodology is the Prosci ADKAR® Model, a proven model that breaks down individual change into five key elements: Awareness, Desire, Knowledge, Ability and Reinforcement. Prosci ADKAR Model These elements represent the building blocks of successful transformation on an individual level. By understanding where people are in the digital transformation journey, leaders can pinpoint specific barriers and provide targeted support, whether that’s through communication, training or coaching. The ADKAR Model also helps people understand why the change is happening, what’s in it for them, and how they’ll be supported through the transition. And when people see the purpose and benefits clearly, they’re more likely to engage, adopt and champion the change. Aligning at scale with the 3-Phase Process The ADKAR Model drives successful change on a personal level, but the Prosci 3-Phase Process scales that impact across the organization. It provides a structured framework to lead enterprise-wide transformation. Here’s an overview of the three phases: Phase 1 – Prepare Approach – In this phase, change leaders define success, assess readiness and build a tailored strategy for change. Phase 2 – Manage Change – In this phase, the team develops and delivers the bulk of change management activities, from communication and training to resistance prevention and assessments. Phase 3 – Sustain Outcomes – In this phase, the organization measures adoption, collects feedback and takes steps to ensure the change sticks. Guiding organizations through these phases help teams lead digital transformation initiatives with clarity, consistency and a sharp focus on their people. Prosci 3-Phase Process In the context of digital transformation, aligning at an organizational level is essential. It connects the dots between digital technology rollouts and human adoption, ensuring that new tools and systems are used to their full potential. Supporting change with Prosci technology To drive meaningful change at scale, you need the right tools. Prosci’s innovative technology solutions can make all the difference: Proxima is a web application that guides users through the Prosci Methodology to help them achieve change success. With built-in templates, tools and dashboards, Proxima keeps teams aligned and focused on what matters most—achieving successful outcomes and delivering measurable value. Kaiya™, Prosci’s expert change management AI tool, supports change leaders in real time, giving you instant access to change management insights, best practices and tailored solutions. Whether you’re building a communications plan or scaling across multiple initiatives, Kaiya helps you move faster, think smarter and extend your impact across the organization. Preparing for Digital Transformation in 2026 and Beyond Digital transformation offers immense promise, but it only delivers when led with clarity, conviction and a commitment to people. The organizations that succeed won’t be those with the flashiest tools, but those who treat change as a capability. That’s where Prosci comes in. With structured, flexible, and research-based approaches like the ADKAR Model and 3-Phase Process, Prosci equips organizations to lead with purpose, align their people, and make transformation stick. Because digital change isn’t just about moving fast. It’s about moving forward, together.
Read story
Overcome Resistance to ERP Systems Changes With ADKAR
Investing in Enterprise Resource Planning (ERP) software can unify your business information resources, improve productivity, and create other long-term benefits. However, adoption challenges and resistance often create unforeseen risks and detract from the ERP system's benefits. Here’s how the Prosci ADKAR® Model can help you succeed. ERP Systems and the Need for Change To gain efficiency and reduce long-term costs, large and small companies are transitioning to ERP systems. Businesses use the systems to manage and improve processes ranging from procurement and manufacturing to financial and human resources functions across the enterprise. All these efforts help eliminate waste, improve productivity, and increase employee satisfaction. Because an ERP system integrates data into a common database, data can flow easily between operations. As a result, the system eliminates data duplication, enhances data integrity, improves ease-of-use, and helps your teams overcome siloed operations. Integrated data and processes also give your managers and teams greater insight into decision-making from real-time information. Given your significant investment in time and resources, as well as the importance of the expected benefits from the project, it’s critical for your business to recognize and overcome resistance to ERP changes through effective change management. Resistance to ERP Systems and Other Adoption Challenges More than likely, your employees have grown accustomed to familiar legacy systems. Even with all the benefits offered through an ERP tool, the migration from familiar applications to a new system sets up a series of challenges for technical and people leaders. As impacted employees move from the comfort of the current state to the disruption of the transition state and ultimately to the future state, technical challenges often provoke people challenges that lead to resistance. As part of your planning for the ERP implementation, leaders and the project team should carefully establish the business requirements and expected benefits for the project. Without clearly established requirements and benefits, people who work with the system can create unrealistic expectations. In turn, unrealistic expectations often evolve into customization requests that increase the project scope and slow the schedule. During implementation, impacted people can struggle with completing their current work while learning a new system. Project teams need to focus on all the milestones for reaching the “go live” date while implementing technological, functional and process changes at a rapid pace. And the push to learn new skills, terms and processes elevates stress levels even further. Cascading Technical and People Challenges Each of these challenges increases project risk. The need for understanding and mitigating that risk underscores the need for building strong change management capabilities in your organization. If you don’t adequately manage those challenges, the technical and people challenges of an ERP implementation can lead to dissatisfied employees and other stakeholders, increased stress, and loss of trust. Fear and Resistance to Change During an ERP Implementation The changes caused by an ERP implementation often seem intensely personal and overwhelming to employees. Newly defined processes sometimes move tasks from one department to another or even lead to organizational restructuring. Some department workloads may increase while others decrease or shift to different areas. Each of those changes break apart comfortable working relationships. And all this occurs within a project schedule that seems to have a life and vocabulary of its own. The result is often fear and resistance. Fear of Change Certainly, the fear of change feeds resistance behaviors. With any shift to a different technology, fear of the unknown shapes how your staff responds to change. Migration to an ERP system amplifies this fear because no one sees the finished product until close to the project go-live date. Even though vendors and project leads work to assure teams that more efficient workflows and a more collaborative environment are coming—and ask for patience as the process unfolds—people who use the new system may not fully accept the assurances. Fear of the Unknown Implementing an ERP system creates the need for people to learn new skills, creating pressure to upskill and reskill within a limited time. People also need to stay productive with their current workloads while learning new skills, which stresses even the most experienced people. For mid-career employees in particular, the fear of learning new skills can increase stress, complicate decision-making, and even cause physical illness. Fear of Failure The fear of failure translates into reluctance to try new processes or practices. If people work around or otherwise avoid the root causes of their fear (i.e., the learning curve), the fear leads to other resistance behaviors. How the ADKAR Model Helps You Overcome Resistance to ERP Systems Effective change management is critical during ERP implementations because of the impact on everyday work and morale. From my vantage point, the Prosci ADKAR Model offers optimal alignment with an ERP implementation and helps individual people and similarly impacted groups move through the transition in a structured way. Because the ADKAR Model emphasizes the people side of change, people managers also become more aware of the complexity in the work people do, as well as the impact software development and testing has on it. Along with its focus on the people side of change, the ADKAR Model emphasizes project success. Change management through the ADKAR Model helps business leaders mitigate resistance, highlights the ability of employees to adopt the change, and then checks back to reinforce those abilities. The Prosci ADKAR Model Here’s how each ADKAR element applies when mitigating or overcoming resistance to ERP systems: Awareness Building Awareness helps mitigate resistance by answering the questions people have about the change and its impacts on their work. Implementing any change that cuts across organizational and cultural lines requires excellent communication from leaders, people managers and project leads. As you apply the ADKAR Model, you will also need to build Awareness of the need for implementing an ERP system and communicate information about the project itself. I have found that communication must flow vertically and horizontally with clear lanes for sharing and receiving information. Desire Building Desire addresses resistance by answering the questions people have during a change, including the business “Why” behind the change and personal “What’s in it for me?” The Desire to participate and support the change begins with business leaders delivering transparent communications about the reasons for the change and then advocating for the new vision. Successful ERP implementations depend on change practitioners gathering feedback from impacted groups, people managers quickly responding and addressing barriers, and sponsors encouraging people to participate and role modeling the right behaviors. Productive communication and active participation build alignment with the objectives of the ERP strategy. Knowledge Knowledge of how to adopt the ERP system lessens fears and doubts, and helps people prepare for their role as users in adopting the change. When considering Knowledge, change practitioners should advocate for ERP training programs that address both the technical aspects of the new system as well as any process and workflow changes people need to adopt and use. While your human resources department may need training about new recruiting and onboarding procedures, finance teams will need training that addresses accounts receivable and payable processes, invoicing, purchase requisitions, and end-of-fiscal year procedures. Along with specific training for staff in impacted departments, project leads must also consider how those and other procedures affect people and stakeholders, and the type of training that best serves their needs. Ability Giving people the opportunity to apply and demonstrate new skills acquired through training helps them prepare for the go-live date while building confidence in their Ability to implement the required skills and behaviors. Change practitioners can help your organization achieve its goals by ensuring that employees have the ability to adopt and apply the changes enabled by the ERP implementation. To build Ability, change practitioners should work with people managers to provide hands-on practice and coaching. As employees across the enterprise become proficient with the new ERP tools, they can help others learn new skills. Reinforcement Resistance occurs at all stages of change, and it’s common for people to develop workarounds or revert to old ways of working. Reinforcement enables you to help people stay the course through additional support and resistance management tactics. When reinforcing the ERP system change with the ADKAR Model, change practitioners should work with the project team to gather feedback from impacted groups about how they use ERP tools. Using insights from surveys and face-to-face meetings, the project team can make changes that help people do their work most effectively. When measuring performance, change practitioners often use scorecards that show progress towards implementing changes and realizing benefits. Preparing to Overcome Resistance to ERP Changes When implementing an ERP system, change practitioners need to start managing resistance at the project’s initiation and all throughout its lifecycle. The process of managing resistance begins with assessing the organizational readiness of your business for the change, usually in partnership with the project manager and vendor representatives. The process continues with identifying primary and secondary stakeholders, assessing impacts and risks, and engaging stakeholders before the ERP implementation. I ask that change practitioners also begin to address the ADKAR barrier points for staff impacted by the change before the implementation begins. When assessing the possible resistance to change, I encourage change practitioners to apply the Prosci 10 aspects of change impact. It’s a helpful tool for defining the change for individuals, addressing individual and group impacts, and as the basis for building adoption metrics. In addition, the aspects establish a framework for becoming more responsive to employee needs and improving engagement. Prosci 10 Aspects of Change Impact Reframing and Overcoming Resistance to ERP Changes Effectively understanding and managing resistance requires you to look at it through a different lens that removes negativity and blame. Reframing resistance in this way will help you better identify types of resistance and their root causes, and build the right tactics to help people move through their barriers. Your organization can realize even greater benefits from an ERP implementation by applying a robust change management methodology that incorporates the ADKAR Model. This ensures alignment between the organizational requirements for the ERP system and the needs of the people who are impacted by the change while ensuring a solid return on your ERP project investment.
Read story