How Legacy Infrastructure Kills New Ventures
Why your growth initiatives keep dying.
Most enterprises carve out capital for new ventures. They target high-growth markets that complement the core business. They staff up with smart people. They launch with fanfare.
Then the initiative dies quietly eighteen months later.
You can blame culture. You can blame execution. You can blame market fit. But there’s a deeper pattern: business model coupling. Companies try to run new business models on legacy infrastructure. Round peg, square hole. It doesn’t work.
Legacy Becomes the Liability
Most enterprises believe their infrastructure is an asset for new ventures. In reality, legacy systems create friction, bloated costs, and customer experiences that tank NPS before you hit year two.
The Widget CO problem.
Here’s the pattern. You’re a global construction widget company. Fifty years building market dominance. World-class at manufacturing, distribution, channel partnerships, and service. Strong margins. But growth has flatlined, and the CEO wants “innovation.”
A sharp team builds a cloud-based software stack. Game-changing product. The MVP crushes in early trials. So leadership launches it through the existing channel—leveraging fifty years of distribution muscle.
A year later, software revenue is 10% of plan. Leadership wants to kill it.
What happened?
You tried to sell SaaS like you sell widgets.
Widget CO is phenomenal at moving hard goods. They’ve optimized heavy tech stacks like SAP. Trained partners to maximize widget market share. Built supply chains that work at scale.
But SaaS is a different business model. Different economics. Different playbook. Different customer expectations.
Widget CO doesn’t know this playbook, so they customize a hybrid Frankenstein solution. The result: terrible customer experience, bloated costs, and unit economics that make no sense compared to benchmarked SaaS businesses.
The legacy becomes the liability.
Widget CO believes they’ll win because they have infrastructure, brand equity, and distribution. And sure—some of those assets help. But most create friction.
The legacy infrastructure was built to support channel partners, not sell directly to end users. Channel partners don’t know how to sell SaaS—they’re widget people. There are no implementation partners. Minimal tech support for customers. The service org can’t answer basic software questions.
NPS craters. The new product threatens the brand. Finger-pointing starts. Before long, the initiative gets shelved.
Fast forward three years: an external startup with an inferior product becomes a unicorn solving the same problem.
How to actually win at this.
Companies that approach new business models with humility can avoid this outcome. Here’s how Widget CO should’ve played it:
1. Use the actual playbook.
SaaS has well-defined business models. Don’t reinvent the wheel. Build from the ground up using proven SaaS economics—CAC, LTV, churn benchmarks, pricing models that work. Use frameworks like the Business Model Canvas to map it out before you build anything.
Don’t retrofit your legacy model and call it innovation.
2. Extract to survive.
New ventures need protection from the mothership. They can’t operate under the same KPIs, approval chains, and quarterly pressures as mature business units.
Create an internal or external venture structure with autonomy. Let them iterate and fail like a startup. Compensate for risk-taking, not for playing it safe. The good news: this can all be virtual. You don’t need a Silicon Valley office.
3. Build a modern tech stack.
Cloud platforms let you stitch together best-in-class capabilities with fewer resources. But don’t force new ventures to use legacy tech stacks designed for mature business models.
Your SAP instance is not the answer. Give new teams autonomy to build lean, proven stacks that match their business model.
4. Cherry-pick the good stuff.
Some legacy assets actually help. Brand recognition. Existing relationships. Distribution muscle—if used correctly.
Figure out which assets create value for the new model. Then structure internal systems to create win-win behaviors. Sales and channel partners can be useful if they’re designed into the business model with the right expectations and training.
Don’t assume everything transfers. Most of it doesn’t.
5. Study what actually worked.
Dig into the ventures that succeeded. What forces enabled those outcomes? What did leadership do differently? What constraints were lifted?
Repeat the elements that worked. Kill the rest.
6. Organize by business model, not business unit.
Some enterprises benefit from organizing around business models instead of traditional structures. Industrial products, retail, ecommerce, SaaS, services—each operates with different economics and different playbooks.
Forcing them under one P&L with shared KPIs creates misalignment and politics.
The takeaway.
Corporate ventures fail for many reasons. But business model incompatibility is the root cause nobody talks about.
Taking time upfront to research the business model and its requirements will save you eighteen months of expensive theater. You’ll either build something that works, or you’ll kill it early before burning capital and credibility.
Stop trying to force new business models into old infrastructure. It’s expensive, demoralizing, and predictable.
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Stop Incrementalizing Yourself to Death
Most people run from chaos. That’s a problem.
There’s a soft skill nobody talks about in transformation work: the ability to sit in disorder without panicking. It’s terrifying. It feels reckless. But it’s one of the fastest ways to cut through years of incremental bullshit.
Take the Twitter saga—whatever you think of Elon, there’s a method worth examining. He laid off half the workforce in one swing. Brutal? Absolutely. Chaotic? By design.
But here’s what happened: the system broke in specific places. Fast. He learned in weeks what would’ve taken consultants eighteen months and millions in discovery fees to figure out. Which 100 roles actually mattered. Which systems were load-bearing. Which processes were theater.
You can hate the approach. But you can’t ignore the speed.
Disorder Uncovers What Matters
Most organizations study problems until the market moves. Controlled chaos cuts through years of planning by spiking the system and watching what actually breaks.
Chaos as a diagnostic tool.
Most organizations incrementalize themselves to death. They pilot programs. They form committees. They study the problem until the market moves and the problem changes.
Controlled chaos does something different—it creates variance fast. You spike the system, watch what breaks, and identify the vital few variables that actually move outcomes. The critical Xs that drive your most important Ys.
In transformation work, we do this deliberately. We dump everything into one space—a war room, a virtual whiteboard, whatever. All the diagnostics, all the analyses, all the conflicting data. It looks like a disaster. Clients panic.
That’s the point.
Ambiguity is a feature, not a bug.
We coach teams to work the chaos. Test hypotheses. Find connections nobody saw when everything was siloed. Build consensus around what success actually looks like—not what the strategic plan from 2019 says it should look like.
Slowly, patterns emerge. The puzzle assembles itself. A strategic path becomes visible that nobody could’ve planned from a conference room.
This is a form of productive suffering. Grinding through disorder hardens the outcomes. The strategy that survives chaos is antifragile—it gets stronger under pressure because it was forged under pressure.
When chaos doesn’t work.
If your business runs well and has solid foundations, don’t inject chaos. Incremental planning works fine when systems are healthy.
But if you’re dysfunctional? If you’re in rapid change and legacy processes are anchors? If every meeting ends with “let’s form a working group to explore this further”?
You don’t need more structure. You need controlled demolition.
The takeaway.
Get comfortable being uncomfortable. Chaos reveals truth faster than consensus ever will. The answers are already in your organization—they’re just buried under process, politics, and the fear of breaking things.
Sometimes you have to break things to find out what matters.
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From 20 Years Experience, 10 Lessons on Financial Forecasting
What twenty years of financial modeling taught me.
Early on, I built credibility through complexity. Multi-tab spreadsheets with hundreds of inputs, sensitivity analyses following statistical distributions, thousands of outputs, and forecasts stretching twenty years out.
I could command a boardroom with the stories these models told. I believed the numbers.
Then I checked the receipts. Short-term? Decent. Long-term? Garbage. Accuracy varied wildly by industry maturity, but the pattern held: complexity didn’t equal precision.
80/20 Rule
Also known as pareto’s principle: roughly 80% of effects come from 20% of causes. Find the vital few, ignore the trivial many.
A decade later, I stripped it all down.
Fewer inputs. Fewer assumptions. Leaned hard into the 80/20 rule—find the variables that actually move the business, ignore the rest.
The 80/20 principle comes from Vilfredo Pareto, an Italian economist who noticed that 80% of Italy’s land belonged to 20% of the population. The pattern showed up everywhere he looked. Today we call it the Pareto Principle: roughly 80% of effects come from 20% of causes. In financial modeling, that means most of your forecast accuracy comes from a handful of key drivers. Nail those, and the rest is noise.
The simpler models weren’t perfect. But they performed better. And teams could actually use them without a decoder ring.
The lesson: less is more. Here are ten things I wish I’d known earlier.
- Understand the goals of the model. Whether revenue, cash flow, market share, P&L, headcount, or volume, design a simple model focused on the core output. And resist the tendency to build the all-in-one model.
- Think modularly, for example, inputs, calculation engine, and outputs.
- Resist complex embedded formulas at first. Further, make it easy for others to edit and understand the logic.
- Use writing as an analogy to modeling. Work quickly to write a first draft, or MVM, a minimal viable model. And then begin an editing process until you create a structured and logical result.
- Subtractive is better than additive. Sculptors start with a block and chisel away until they execute their vision. I attempt to do the same. Start with knowns and chisel away based on confident subtractions. The additive approach, or bottoms-up, can provide a nice check and balance but tends to overshoot reality.
- Start with a shorter duration with realistic assumptions, then expand. Working backward from a target is valuable, but it often leads to disconnected results — so do both. Working the model from the goal also helps to assess input sensitivity.
- Show your results as scenarios; base case, moderate, and aggressive. Don’t fall in love with a single number or scenario.
- Focus on the inputs rather than argue about the outcomes — make it easy for reviewers to challenge inputs and see the results in real time.
- As confidence builds around the model, add new modules or consolidate formulas to reduce the size. Redo the editing step.
- Start from scratch with your models, or at the very least, start with other’s models that you trust and wholly understand.
Modeling anything takes a bit of art and science and most importantly – reps.
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If you like what you see, we think you’re gonna love what you hear. Book a first consultation with us, and together we’ll figure out how to make your life a little better.