Mastering AI Transformation Through Project Management – Elmhurst University
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BY Eric J. Sanders, Ph.D. | BY Marc Bara, Ph.D. | 7 MIN READ
Every few years, a new wave of change sweeps through organizations with the same promise: adapt or become obsolete. In 2012, it was agile methodologies. In 2020, digital transformation. Now it is artificial intelligence. Organizations are racing to implement AI transformation initiatives, purchasing enterprise licenses, running prompt engineering workshops, and rebranding every internal process as “powered by AI.”
The urgency feels new, but the pattern is not. A decade of research from BCG, McKinsey and Harvard Business School tells a consistent story: approximately 70 percent of these transformation efforts fail. And the reasons have almost nothing to do with the technology itself. For project managers and organizational leaders, this is both a warning and an opportunity. The question is whether AI transformation will repeat the same cycle, or whether the lessons from past failures can finally break it.
The numbers are sobering. Boston Consulting Group’s analysis of more than 900 digital transformations found that only 30 percent achieved their goals (Forth et al., 2020). McKinsey’s research placed the success rate even lower in traditional industries, between 4 and 11 percent (McKinsey, 2018). These figures predate the current AI wave, but early indicators suggest AI transformation projects are following the same trajectory. As of 2024, roughly 40 percent of AI initiatives still get stuck in the scaling phase, never delivering enterprise-wide value.
Behind every failed transformation are wasted budgets, demoralized teams and lost competitive ground. For professionals pursuing careers in project management and change management, understanding why these initiatives fail is now a core competency, not an optional specialization.
The pattern from past transformations is clear: organizations treated digital change as a technology problem when it was fundamentally an organization development and change management challenge. The gap between buying new tools and actually transforming how an organization works is where most digital transformation failures occur. For anyone watching how companies are approaching AI today, the resemblance is hard to ignore.
Research reveals a critical distinction that most organizations miss. During the agile transformation era, experts identified the difference between “doing agile” (implementing ceremonies, roles and artifacts) and “being agile,” which meant fundamentally changing mindsets, decision-making structures and organizational culture. The first can be accomplished in months. The second requires years of sustained change management effort.
AI transformation is falling into the same trap. What most organizations call AI transformation today is really AI adoption, including the following:
None of this constitutes AI transformation. True AI transformation means restructuring decision-making for human-AI collaboration, redesigning work processes from first principles, and rebuilding how expertise and authority flow through an organization. Project management professionals who understand this distinction are essential to bridging the gap between adoption theater and real organizational change.
The ING Bank case, documented by Harvard Business School (2018), illustrates what successful transformation requires. In 2015, ING, a traditional Dutch bank with 52,000 employees, dismantled its hierarchical structure, eliminated functional departments and reorganized into 350 autonomous squads. The results were significant: development cycles dropped from 18 months to three to six months, mobile app satisfaction rose 20 percent, and ING became the top-rated mobile bank in the Netherlands.
But the crucial detail is the timeline: three years, not three months. ING changed power structures, focused on customer outcomes rather than internal efficiency, built internal capability instead of relying on contractors, and committed to a multi-year change management journey.
BCG found that organizations applying this kind of comprehensive approach to digital transformation achieved success rates of 65 to 80 percent, compared to the 30 percent baseline. McKinsey’s research confirmed that having more than 50 percent of internal employees on the project management team and planning for 24 to 36 months were among the strongest predictors of success. The evidence points in one direction: AI transformation requires sustained project management and change management commitment, not short-term technology deployment.
Research from Prosci, Kotter and many others converges on practical tips for project managers looking to improve AI implementation outcomes:
The evidence is clear: AI transformation failure is not a technology problem. It is a project management and organizational change problem. The organizations that succeeded in past transformation waves did so by addressing the core competencies of effective project management—organizational design, stakeholder alignment, skill development and reinforcement systems.
The good news is that decades of research on organizational development and project management provide a roadmap for what works. The challenge is whether organizations, and the professionals who lead them, will use it.
For those ready to develop the skills needed to lead AI transformation successfully, Elmhurst University’s Master of Project Management (MPM) program provides the foundation in change management, organizational leadership and evidence-based practice that these initiatives demand.
Elmhurst also offers a Graduate Certificate in Project Management, which consists of the first four courses of the MPM curriculum. You can stop there with the certificate, or you can continue with six more classes to complete the full master’s degree.
If you’re ready to lead change in your organization or position yourself for a new role, fill out the form below to learn more about the Elmhurst MPM program.
Forth, P., Reichert, T., de Laubier, R., & Chakraborty, S. (2020, October 29). “Flipping the Odds of Digital Transformation Success.” BCG.
Hiatt, J. (2006). ADKAR: A Model for Change in Business, Government and Our Community. Prosci Research.
Kerr, W. R., Gabrieli, F. & Moloney, E. (2018). Transformation at ING (A): Agile, Case 818-077, Harvard Business School.
Kotter, J. P. (1996). Leading Change. Harvard Business School Press.
McKinsey & Company. (2018, October 29). Unlocking success in digital transformations.
Eric J. Sanders, Ph.D. is the director of the Master of Project Management (MPM) program at Elmhurst University. He has a Ph.D. in organization development, an MBA in international business, an M.S. in economics, and a B.S. in psychology. Sanders has practiced as a consultant since 2004 with clients in financial services, manufacturing, health care and non-profits, and previously worked in retail sales and management for 20 years. Throughout his career, Sanders has led projects of various sizes, and for several years guided change management on multi-million-dollar IT system installation programs. His research and publication interests are in the characteristics and practices of scholar-practitioners, organizational culture, inclusion and agency, and change management.
Marc Bara, Ph.D. is a project management consultant, educator and researcher based in Barcelona, Spain. He is the founder of ProjectWorkLab, a consultancy and training company specializing in enhancing project management, digital transformation and agile frameworks. He holds a Ph.D. in telecommunications engineering and is a PMP-certified professional with more than two decades of experience in engineering, data management, and project leadership. Bara has published articles on technology, leadership and digital transformation in peer-reviewed journals and business magazines. His current research explores the intersection of artificial intelligence and project management.
Posted February 17, 2026
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