The Intelligent Startup: A Primer
The Intelligent Startup: A Primer
How intelligent businesses control, predict, and adapt to change
Hey! It’s Andreas from Monetisation Matters, the home of in-depth articles and actionable insights on Strategy and Monetisation. Built for Founders, Product, and Product Marketing leaders as they navigate their $1m to $100m growth journeys.
This month’s article looks at what make a business intelligent; it’s structure and properties. A short primer on a complex topic.
What is intelligence?
Writing an article about what makes a business intelligent is tricky when there is no agreed definition. And no, IQ doesn’t count. Fortunately, there are a lot of intelligent people (whatever that means) studying the topic that we can learn from. Understand its properties then maybe we can operate more intelligently.
Competition is trying to steal your customers. New technologies threaten you with obsolescence. Governments change the rules of the game. Surviving and growing in hostile markets depends on your ability to take in information, select the best response from your range of possible actions, and adapt as the market changes.
On information and variety
You take the decisions that you have the information to take, and power is where information in the business resides. This is the “Redundancy of Potential Command Principle”. Timely inputs, in the right mix, and enough of it is needed to drive high quality decisions. To go beyond centralised command and control, information must flow. Vertical flows to maintain control; horizontally to ensure decision making centers are coordinated.
The right information isn’t much use if you don’t have any good actions to choose from. Which brings us to Ashby’s law; “Only variety absorbs variety”. A ‘controller’, (for our purposes a startup), facing a complex situation, must have a range of responses available which matches the complexity of the situation. Otherwise it won't be effective. An experienced chess player has more ‘variety’ than a novice. He is going to employ moves and combinations of moves that the novice doesn’t know how to respond to. Regarding a startup, there is a lot of variety in what customers need and want. To effectively serve the entire market the business would need to deploy a range of products, services and channels. One size fits all won’t cut it.
For any business, least of all a startup, ‘variety’ in the market is much larger than internal variety. What matters is the ability to match incoming variety with what you can cope with. We talk so much about ICP, product-market fit, and segmentation because it’s Ashby’s law at work. They are all concepts that deal with variety matching. Focus is how you cope with the variety in the market - concentrating on a small area where your capabilities match what is demanded of you. Giving you time to build capability to take on more.
Past, present, and future, all at once
It’s easy to fall into the trap of only thinking in terms of statics i.e. point in time assessments of topics like product-market fit. But growth is a dynamical process through time. Signs of intelligence through time would be to…
…control the present, to achieve a certain future outcome
…predict the future, given your actions, and those of others
…explain the past to better control the present, and predict the future
To explain what has happened, control what you are going to do, and predict the future, you need an internal representation of the world. You need a model. “Every good regulator of a system must be a model of that system” - the Conant-Ashby theorem. Intelligent organisations are therefore adept at building models that drive decision making. As an example, the revenue bow-tie by Winning by Design has become ubiquitous as it is an excellent model for B2B subscription businesses. Ditto the growth in RevOps which uses modeling extensively to build a system of control for go-to-market activities.
Common models/representations found in SaaS and Cloud businesses:
An ideal customer profile represents your best customers, providing clear guidance over who to target
Segmentation builds on one narrow ICP by breaking the market down into multiple segments, representing differences in needs
Customer lifecycle stages is a combined reflection over how customers buy, and how you will sell
Outbound sequences is a model of the series of steps most likely to lead to engagement and a first meeting
Sales forecasting leverages historical trends, conversion rates, capacity, and customer lifecycle stages to predict future bookings
Intelligent businesses deal with the past, present and future all at once. Looking back to explain why performance was what it was. Controlling the business to generate target performance now. And predicting what will happen. They aren’t separate phases but parallel processes. They must operate in parallel because everything is changing all the time. The faster a startup can adapt its models based on feedback, the better its control. Information flows combined with speed of adaptation become a catalyst for intelligence.
I have a need for speed
This is what John Boyd noticed in a completely different setting to business. John was a United States Air Force Colonel, fighter pilot in the Korean War and Commander during the Vietnam War. His key insight was that the ability to rapidly and accurately acquire and act on information is a source of competitive advantage. He gave us the OODA loop, a high level abstraction on how to do this systematically. It’s proven its effectiveness and universality, employed by fighter pilots, emergency response drivers, litigators and more.
OODA stands for Observe, Orientate, Decide, and Act. We’ve already touched on all the components of the approach: information; modeling; control; prediction; and feedback. At the heart of the approach is a model which drives decision making, and predictions over what is going to happen. Feedback is used to constantly update the model and improve how well it reflects the thing you’re trying to control.
Driving is something most of us can relate to and demonstrates OODA at work. You constantly scan the environment taking in information. Plan ahead based on where you want to go and any hazards you need to negotiate. Replan as things unfold in real time. Predict what other cars, bikes and pedestrians are going to do. Good drivers drive systematically. Emergency drivers learn the system of car control which is an OODA loop, enabling them to drive faster and safer.
Similarly, intelligent businesses operate systematically. Particularly in how they conduct customer discovery, build new features, and acquire and grow customers. Systematisation isn’t bureaucratic or rigid. Done right, it speeds up decision making and adaptation rather than slowing it down.
Strategy as the key differentiator
Finally, the picture wouldn’t be complete without addressing strategy. Sound strategy provides direction and intentionality to the models you build and the decisions you make. Humans are good at modeling, calculating, and drawing logical conclusions from a body of evidence. But our species is strategically inept. We slowly poison and destroy the environments we live in, we proliferate weapons that could lead to our extinction, and are brought to a standstill by a virus with a miniscule fraction of our complexity. If strategic incompetence is the norm, it becomes the greatest differentiator between intelligent and stupid businesses.
There are no playbooks for good strategy, but follow great strategists like Hamilton Helmer, Michael Porter, Richard Rumelt, Hiroyuki Itami, and common principles quickly emerge. Power. Fit. Information. Speed. Intelligent businesses make decisions that generate power over time. They stay focused and maintain fit by matching internal capabilities to external demands. They over-extend just enough to keep learning. And they are able to change at least as fast as the external environment.
I’ll end with how David Krakaeur, President of the Santa Fe Institute frames intelligence. Simply, it’s making a hard problem into an easy one. It has three dimensions to it which effectively captures the topics I’ve covered above:
Representation i.e. a model/encoding of the environment
Inference i.e. the ability to operate/compute.
Strategy i.e. using representations and inference to drive action with respect to some objective.
I enjoy writing, but it is a tremendous investment. If you enjoyed this article, please consider sharing it with your colleagues and friends so that it may grow. Thank you.