When people talk about a “digital transformation”, they typically focus on a few parts of the process – and a lot of the cool technology. In this series, you will hear about the five building blocks that a Great Digital Business should include – and a (possibly) surprising idea about where that focus truly belongs.
70-80% of digital transformations fail. Not because the technology doesn’t work, but because the pieces never connect. The 5 Building Blocks is a digital transformation framework that shows you where AI actually creates value – and why it’s all about how information flows.
Roughly every 20 years, a technology wave reshapes business. ERP automated operations (1960s-1980s). The Internet connected customers (2000s). IoT enabled smart products (2010s). AI is the fourth wave – transforming how expertise flows through teams. Unlike previous waves, AI disrupts knowledge hierarchies and challenges who controls information in your organization. Understanding this pattern reveals why AI implementation requires organizational change, not just technology deployment.
The best AI implementations I’ve seen started with something unexpected: companies running 15-year-old ERP systems. Not the newest platforms – old, stable systems sitting on decades of clean operational data. Your ancient ERP might be your biggest AI advantage.
Industrial B2B companies know more about their customers than Amazon ever could – what they manufacture, how their production runs, when equipment needs service. The problem? That intelligence is trapped in systems designed to process transactions, not generate insights.
That $2M IoT investment isn’t failing because of technology. It’s failing because your operational data is fragmented and your customer intelligence is siloed. Here’s how the companies who actually succeed approach product intelligence.
Every technology project comes down to two things: getting information into systems or getting information out. Understanding this pattern changes how you approach digital transformation – and determines whether AI creates value or just complexity.
The real AI skills gap isn’t about finding data scientists. It’s about bridging the gap between people who hoard knowledge and people who need it, between employees protecting their value and organizations that need teams to work differently.