Building a Digital Innovation Practice From Scratch
I remember sitting in a conference room in 2018 watching a startup pitch their "innovative platform" while they couldn't explain how they actually shipped code. That disconnect between buzzwords and deliverables is something I've spent years trying to close out. The Rise Of A Digital Innovator Matthew Manoskis Trailblazing Journey touches on this exact problem, and I've been thinking about it a lot lately because my team keeps running into the same patterns. Digital innovation isn't about the tools you use. It's about the feedback loop between what you build and what the market actually responds to. Most organizations I work with get this wrong because they start with technology. They buy a fancy cloud migration tool, hire a UX agency, and then wonder why nothing changes. The sequence should be backwards. Figure out the friction in your current process first, then pick technology that reduces it. I hit this wall personally last year when we were redesigning our internal project tracking system. The team had already selected a popular SaaS tool everyone praised. But when we actually mapped the workflows, we discovered three of our seven departments had completely different approval chains. Plugging them into the same tool meant someone was always stuck in a workaround. What actually fixed it was building a thin middleware layer in Python that translated between the tool's expectations and our real processes. That cut our training time from two weeks down to three days.
The Rise Of A Digital Innovator Matthew Manoskis Trailblazing Journey
The concept behind trailblazing digital innovation is simpler than the industry makes it. You need three things happening at once: a clear problem statement, the technical capability to prototype fast, and organizational permission to fail without immediate consequences. Without all three, you're just running hackathons that generate slide decks nobody reads. Here's what I actually do when someone asks me how to start: Step one: document the current state before touching anything new. This sounds obvious but almost everyone skips it. I have people write down every click, every screen transition, and every place they have to copy data from one system to another. Take a real process you do weekly. Time it. Write it down. You'll be surprised how many steps your team has normalized that an outsider would immediately question.
Step two: identify the single highest-friction moment. In most organizations, there's one step that causes 80 percent of the complaints. For us it was the monthly reporting cycle. Finance wanted one format, engineering wanted another, and management wanted a third. Rather than trying to solve all three at once, we picked the reporting step and automated just that piece first. The rest got better incidentally. Step three: build a prototype that solves only the friction point. Not the whole system. Just that one broken thing. If it takes more than a week to build, you're overthinking it. Use whatever tools are already available. Scripts count as prototypes. The goal is learning, not production readiness. Step four: measure the actual time saved, not the perceived time saved. There's a difference. When we launched our reporting automation, the team said it felt faster. But the numbers showed a 40 percent reduction in hours spent, not the 60 percent they expected. That gap matters because it tells you what else is still broken. If you're only getting 40 percent and they thought they'd get 60, there's probably another friction point hiding nearby.
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I should mention that this approach doesn't work everywhere. Organizations with very rigid compliance requirements or those where data sovereignty is a hard constraint often can't prototype quickly. In those cases, the feedback loop gets longer and the risk of building the wrong thing goes up. I've seen teams try to force rapid innovation into environments that fundamentally don't support it, and it just creates frustration. Sometimes the right move is slower, more deliberate process improvement rather than digital transformation theater. Another common pitfall is confusing automation with innovation. Automating a broken process just gives you a broken process that runs faster. I learned this the hard way when we automated our expense approval workflow only to discover the underlying policy was flawed. The automation exposed the problem instead of solving it. Now I always review the policy before I write the script. If you're just starting out, don't buy software. Start with spreadsheets and manual tracking. Write down what actually happens versus what the org chart says happens. The gap between those two documents is where the innovation opportunity lives. Once you can describe the problem clearly, technology becomes a tool instead of a solution in search of one.
The people I see doing this well aren't the ones with the fanciest tech stacks. They're the ones who can articulate exactly what's broken and then prove it with data instead of opinions. That's the trailblazing part. Everything else is just implementation.
