Episode Transcript
In Episode 170, we introduced CAPED, our approach to handling large projects in regulated industries like BioTech, aerospace, telecom, and FinTech. Today, we’re diving deeper into Phase 1 of CAPED: **Strategic Planning**—and specifically, how it sets up a project for success in ways that traditional kickoffs fail to do.
A typical project kickoff aligns people on goals, reviews documentation, and lays out a plan. That’s fine, but here’s the problem: **traditional kickoffs assume predictability, when in reality, large projects are full of complexity.** In this episode, we’ll show you how CAPED embraces that complexity—so your project doesn’t fall into the usual estimation traps.
Before we dive in, if you haven’t watched our CAPED overview, check that out first. And visit [HumanizingWork.com/CAPED](http://humanizingwork.com/CAPED) to learn more.
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Let’s start with what Phase 1, **Strategic Planning**, has in common with a traditional project kickoff. There are a few basics—getting everyone aligned on the goals, reviewing any relevant customer or technology constraints, and talking through early solution ideas.
But here’s where CAPED takes a different approach. Instead of assuming we can analyze our way to a fully predictable outcome, we acknowledge that there’s **complexity involved**—and that means our first step has to be **mapping that complexity.**
In addition to the complexity map, the other big deliverable in phase 1 is an **estimate**—because leadership needs to make decisions about budget, timeline, and ROI. The way most teams try to generate estimates, though, is fundamentally flawed. They start by breaking the work into small pieces, estimating each piece separately, then adding everything up. If you’ve been involved in managing big projects, you’ve probably done this. I know I have.
At first glance, it seems logical. The problem is, it’s based on three flawed assumptions.
First, it assumes we already know everything that will need to be done. But large projects contain unpredictable elements, and the reality is, we **don’t** know all the details yet. The more we try to specify upfront, the more we create a false sense of certainty. It feels like we’ve thought things through, so we must be right! But in reality, some—probably a meaningful portion—of our detailed plans will turn out to be wrong.
Second, even if we had all the right pieces identified, we tend to **underestimate** how long each one will take. There’s something called **optimism bias**, which causes us to default to best-case scenarios when we estimate. We assume things will go smoothly, that there won’t be unexpected blockers, that everything will fit neatly together. But anyone who’s worked on a big project knows that’s almost never how it actually plays out.
And third, when we see a detailed plan, our brains tend to believe it’s **correct**, simply because of how much detail is there. It creates an illusion of certainty that isn’t earned. So now we have a plan that’s based on bad assumptions, underestimates how long things will take, and **feels** incredibly reliable even though it isn’t. That’s a dangerous combination.
So, what’s the alternative? Instead of relying on flawed decomposition-based estimates, we use an approach called **Reference Class Forecasting**, developed by Bent Flyvbjerg.
This method doesn’t try to predict everything from scratch. Instead, it looks at past similar projects and asks, **“How long did they actually take?”** By using real-world data instead of wishful thinking, we get a far more accurate estimate, and once we’ve built up a good dataset, this process is also much faster than trying to estimate from the ground up.
But there’s another key piece of CAPED Phase 1. Once we have our estimate, we need to figure out **where the biggest risks are.** And to do that, we systematically identify the assumptions we’re making—especially the ones that could cause real problems if they turn out to be wrong.
We use six categories to do this. We look at our assumptions about the customer’s **outcomes**, their **pains**, and their **willingness to pay** for a solution. We also examine assumptions about whether our solution will actual deliver on those customer outcomes, **our ability to deliver**, whether we can do it **profitably**.
From there, we sort those assumptions by how **important** they are—meaning, if we’re wrong, how big of a problem does that create?—and by how much **evidence** we have to support them. The ones that are **high-risk and low-evidence** become our focus.
Next, we bring in the **Cynefin framework** to help us determine the best way to gain evidence. Some assumptions can be figured out through analysis—they’re **complicated** but knowable. Others are **complex**, meaning the only way to validate them is through **experimentation and learning**. This is especially true for anything related to customer behavior.
Strategic Planning is complete when we’ve mapped the complexity and created a Reference Class Forecast. That gives us two critical things going into Phase 2: first, a realistic estimate for making decisions, and second, a clear sense of what we need to focus on to reduce risk.
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### **Closing & Call to Action (1-2 min)**
To wrap up, CAPED Phase 1, **Strategic Planning**, does two big things. It replaces unreliable, decomposition-based estimates with **Reference Class Forecasting**, which is backed by real-world data. And it systematically identifies and prioritizes **the assumptions we need to test**, so we don’t discover that our plan is untenable late in the project.
So, if you’re kicking off a big project soon, ask yourself: **Are we treating this as predictable? Or are we accounting for complexity?** That one question could save you from a lot of costly mistakes.
If you found this helpful, let us know in the comments. And if your organization is working on a large, complex project and could use expert guidance, visit [HumanizingWork.com](http://humanizingwork.com/) to schedule a conversation.
Thanks for watching! In the next episode, we’ll break down **how CAPED Phase 2–Active Planning takes a cue from great animation studios and architects, bringing agile iteration into the planning stage of a big initative.** See you then!
