Digital transformation remains one of the most talked-about priorities in manufacturing, yet failure rates continue to hover near 70% across the industrial sector. In this analysis, IIoT World examines the root causes behind stalled transformation initiatives, from misaligned leadership expectations and siloed technology deployments to the chronic underinvestment in workforce readiness. Whether you are a plant manager evaluating your first IIoT pilot or a VP of operations scaling enterprise-wide AI, this article offers a structured framework for diagnosing common pitfalls and implementing corrective strategies that deliver measurable, lasting results.
At Hannover Messe 2025, Friedrich Richter, Schneider Electric’s senior vice president of Services for Industrial Automation, didn’t just talk about digital transformation—he laid out why so many industrial players are still stuck in pilot purgatory. From small manufacturers to global enterprises, most are not struggling to start the journey; they’re struggling to scale it.
The reality? Over 70% of companies fail to scale their digital transformation initiatives. The reason isn’t a lack of vision—it’s a lack of structure, cadence, and operational know-how.
The Hesitation Trap
According to Richter, many small to mid-sized manufacturers are hesitant not because they lack ambition but because they’ve been burned before. Isolated use cases, disconnected tools, and tech-first approaches often lead to limited or no ROI—leaving leadership wary of doubling down.
But Richter is blunt: hesitation is a risk. Digital disruption is rewriting the industrial landscape. Citing a Harvard Business Review study, he notes that over half of the Fortune 500 companies since 2000 have disappeared due to digital disruption. For those who delay transformation, survival is not guaranteed.
From Pilots to Programs
What separates the few who succeed? It’s not just the tech—it’s the programmatic mindset.
Schneider Electric’s own smart factory journey didn’t succeed because of a single bold move. It succeeded because of a structured global program: aligning all factories under a unified operational model, standardizing KPIs, and empowering plant managers with a digital “core model” toolbox to scale effectively.
That core model—developed internally—is now the basis for Schneider’s consulting and transformation services. It includes:
- Digital maturity assessments
- Roadmap creation with proven use cases
- ROI modeling based on real-world experience
- Implementation support from design to deployment
Most importantly, Schneider stays involved beyond the strategy phase, unlike many external consultants. “We don’t just leave customers with slides,” Richter says. “We help turn value identification into value realization.”
Don’t Build a Monument—Build Momentum
One key misconception? That companies need to build a flagship “lighthouse” factory before scaling. Richter disagrees.
While lighthouse plants can generate attention—and even public funding in some regions—real impact comes from rolling out scalable, ROI-driven use cases across multiple sites quickly and efficiently. That’s where structured cadence matters most.
The People Side of Transformation
Technology can only go so far without people. In fact, Richter says operator adoption is one of the most underestimated factors in successful digital transformation.
In Schneider Electric’s own factories, some over 100 years old, workers have taken ownership of digital tools—customizing dashboards, requesting features, and integrating tech into their workflows. This wasn’t an accident—it was the result of:
- Building on lean, continuous improvement cultures
- Involving workers in the transformation process from the start
- Reskilling and repositioning roles to align with new digital capabilities
This human-centric approach varies by region. In markets like Europe and the U.S., where the industrial workforce is aging, engaging employees in transformation is a retention and productivity strategy. In regions with younger workforces, digital adoption tends to be faster, but still requires guided frameworks.
The Lesson for 2025: Think Like a Program
Asked for one takeaway that industrial leaders should remember in 2025, Richter is unequivocal: “Think like a program.”
That means:
- Elevating transformation to the C-level
- Structuring it across sites and regions
- Creating governance, cadence, and value-measurement mechanisms
- Focusing on scalable, high-impact use cases—not isolated experiments
In a world where digital disruption is not just coming but accelerating, treating transformation as a side project is no longer an option. Only structured, programmatic approaches will deliver results at scale.
Sponsored by Schneider Electric
About the author
Lucian Fogoros is the Co-founder of IIoT World
Related articles:
- Status of digital readiness for Industrial Transformation programs globally
- Industrial IoT as Practical Digital Transformation
FAQ
1. What is the most common reason digital transformation fails in manufacturing?
The most frequently cited cause is a lack of strategic alignment between technology investments and business objectives. Research from McKinsey and BCG consistently shows that approximately 70% of industrial digital transformation projects fail to reach their stated goals. The problem typically begins when organizations treat transformation as a technology deployment rather than an organizational change initiative. Without executive sponsorship, clear KPIs tied to operational outcomes, and cross-functional governance, even the most advanced IIoT or AI platforms will underperform. Successful transformations start with a defined business case and work backward to technology selection.
2. How can manufacturers improve the ROI of their digital transformation programs?
Manufacturers can improve ROI by starting with small, high-impact pilot projects that demonstrate measurable value within 90 days. Focusing on use cases such as predictive maintenance, energy optimization, or quality defect reduction provides clear before-and-after metrics. It is also critical to invest in data infrastructure early; without clean, contextualized data from the shop floor, advanced analytics and AI models cannot function reliably. Organizations that achieve the highest ROI typically allocate 30% to 40% of their transformation budget to people, including training, change management, and hiring data-literate operations staff.
3. What role does Industrial AI play in successful digital transformation?
Industrial AI serves as the analytical engine that converts raw operational data into actionable insights, making it a core enabler of digital transformation rather than an optional add-on. Applications range from machine learning models for predictive maintenance, which can reduce unplanned downtime by 30% to 50%, to computer vision systems for automated quality inspection. However, AI only delivers value when it is embedded into existing workflows and decision-making processes. Organizations that deploy AI in isolation, without integrating it into MES, SCADA, or ERP systems, often see minimal returns. The key is treating AI as a capability layer within a broader transformation architecture.
