Between 70% and 95% of digital transformation initiatives fail to meet their objectives. That's not a typo. After years of effort and trillions spent globally, the failure rate remains stubbornly high across all industries.
For industrial organisations, the consequences are particularly severe. McKinsey found that traditional sectors such as automotive, oil and gas, and pharmaceuticals achieve success rates of just 4%-11%, far lower than those of digitally native industries.
The pattern is clear: treating digitalisation as a series of disconnected technology purchases doesn't work. What does? Thinking about the entire lifecycle.
Most industrial digitalisation efforts start the same way. A specific problem emerges: inefficient production planning, manual maintenance scheduling, and compliance reporting headaches. The response? Find a software solution that addresses that exact issue.
It's understandable. Point solutions promise quick wins. They're specialists, and they tackle clearly defined challenges without requiring organisation-wide change.
But here's what happens next: you solve one problem and create three new ones. Your shiny new maintenance software doesn't talk to your production management system. Data lives in silos. Teams use different platforms that don't integrate. Every new tool requires its own training, its own vendor relationship, its own ongoing management.
Before long, you're drowning in what one industry analysis calls "point solution fatigue", a sprawling collection of single-purpose tools that can't collaborate, can't scale, and can't give you the complete picture of your operations you actually need.
Lifecycle thinking means understanding that industrial digitalisation isn't about individual tools. It's about the complete journey from concept through production to ongoing operation and eventual decommissioning.
This process is also often described as creating a "digital thread"; the seamless tracking of information across the entire product and operational lifecycle based on a unified foundation. When one part of your operation changes, the impact ripples through connected systems automatically rather than requiring manual updates across disconnected platforms.
The factory of the future, according to Fraunhofer IESE's research on digitalisation in production, will be built twice: first virtually, then physically. Digital twins continuously fed with live data provide information about machine conditions throughout the entire lifecycle, making on-site interventions the exception rather than the rule.
This approach works because it acknowledges reality: your automation needs affect your training requirements. Your production data informs your maintenance schedules. Your compliance obligations connect to your operational processes. Pretending these things exist in isolation creates the very problems digitalisation is supposed to solve.
The failure to integrate isn't just an IT inconvenience. It's a strategic vulnerability.
Research on digitalisation in manufacturing systems found that companies typically focus on the benefits of new technologies while largely ignoring the assessment of the effort required across the full lifecycle. This results in implementations that look promising initially but create unexpected costs, resource consumption, and complexity over time.
Manufacturing facilities worldwide generate over 7.6 gigatons of CO2 emissions annually. When digitalisation efforts remain siloed, you miss the synergies that could drive both operational efficiency and environmental performance. Predictive maintenance powered by integrated IoT sensors and AI analytics doesn't just reduce downtime, it minimises waste and energy consumption when it's part of a connected system.
But when systems aren't integrated, you're left with what industry research calls "pieces of the puzzle scattered in different systems." You can't extract meaningful insights. You can't identify risks. You can't make informed decisions because you're constantly struggling to connect data from multiple sources.
Moving to lifecycle thinking doesn't mean ripping out everything you've built and starting fresh. It means being strategic about how new capabilities connect to existing operations.
Start by mapping your actual workflows across the digitalisation lifecycle. Where does information need to flow? What processes depend on each other? Which systems absolutely must communicate?
Then evaluate whether new solutions genuinely integrate or just claim to. Ask whether your providers can support you across the full digitalisation lifecycle - from automation implementation through digital enablement to workforce capability building - or whether you'll need to coordinate multiple specialists who've never worked together before. The integration burden shouldn't fall on you.
Most importantly, stop thinking about digitalisation as a series of isolated technology decisions. Think about it as building connected capabilities that will scale and adapt as your needs evolve.
Industrial digitalisation is complex. But the solution isn't more complexity through an ever-growing collection of disconnected tools.
Companies that treat digitalisation as a lifecycle journey are the ones beating those dismal failure statistics. They're not just implementing technology. They're building the foundation for sustained operational excellence.
The question isn't whether your organisation needs digitalisation. It's whether you're approaching it in a way that will actually deliver results five years from now.