Scaling (while maintaining your sanity)

Published on
February 2, 2015
Chris Taylor
"Ideas are only valuable when applied."
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Here at Actionable, 2015 is off to a substantial start. We’ve grown our team by 25%, our Actionable Consultant community by 30%, and revenues are roughly double what they were last January. Growth is exciting, but it can also be overwhelming. To maintain my own sanity (and some semblance of a “balanced” life), I like to keep two models firmly in mind:

The first was introduced to me by Roger Martin in his book The Design of Business. What Roger calls “The knowledge funnel” I fondly think of as “the light at the end of the tunnel model”.


The gist is this:

Growth (in almost anything I can think of) consists of 3 distinct phases: Mystery, Heuristic & Algorithm.

When embarking on anything new, you are, by default, tackling a Mystery. You don’t know exactly what the specific problem is, the challenges that will arise or how you’re going to overcome them. It’s one of the reasons why starting a business can be so risky ~ you don’t know what you don’t know and there’s no guarantees that you’ll have the skills/resources/connections to overcome the unforeseen challenges down the road. The goal, when you’re in the mystery phase, is to move as quickly as possible to the Heuristic phase.

You’ll know you’re in the heuristic phase when you can recreate positive results with some degree of regularity. You can’t easily explain how you do what you do, nor do you have systems in place to scale it (yet), but it works. You’re gaining traction. The goal at this point (should you wish to scale) is to try to define the specifics of how it works such that you can create a process around it and/or teach other people how to do it.

Once the process has been documented, repeated by other people and validated, you’ve entered the Algorithmic phase of the process. The light at the end of the tunnel. Once you’re at the algorithm phase the goal is optimization; i.e., tweaking and refining. There’s a law of diminishing returns at this stage, but also (typically) you’re highest level of potential profitability. This is the stage where you, personally, can start to look at outsourcing, automating or delegating.

What helps keep me grounded during times of rapid growth is bucketing each task into one of the three stages.

“Where are we along the spectrum in regards to component X?”

Each thin slice of your business has it’s own path through the knowledge funnel, and it can be crippling to assume that they’re all at the same place. Additionally, you can’t jump over any phase – trying to institute too much process/procedure while still in the Mystery phase is more often than not a massive waste of resources. I remember one of the earliest builds of the technology side of Actionable Workshops; we spent way too much time and money building something based on wild guesses and assumptions. Most of it is now in the great coding scrap-yard in the sky. It was too soon.

Alternatively, spending too much time in Heuristic – while good for your ego (“I’m the only one capable of doing this”) – stunts your growth.

Which brings me to the second model – Eric Ries’ Build-Measure-Learn loop, better known as MVP (Minimum Viable Product), as introduced in The Lean Startup. While typically used to describe early stage software development, I’ve found the Build-Measure-Learn process to be invaluable in moving any component of the business from one stage of the knowledge funnel to the next.

“Based on what we know now, what hypothesis can we test?
How will we measure success?”

Whether it’s handing a process or area of responsibility over to a colleague, clarifying new marketing copy or attempting to understand how that one-off project went so well, the quest for understanding drives things further down the knowledge funnel. The Build-Measure-Learn loop is a handy way to remember to always be testing. Always be clarifying, reviewing and documenting.

“Growth is fun,” in the recent words of one of my colleagues. Make it last with a little bit of structure.