Business Scaling Framework
When it comes to business scaling, it is a matter of finding the simple building blocks that can go a long way. In short, these elements combined at scale don’t lose efficacy, but gain momentum.
That's easier said than done, as that “discovery process” will often involve a massive amount of interaction, to “stumble upon” these simple building blocks.
Thus, scaling means the ability to start small and create options; these options become bets, and by placing multiple “asymmetric bets,” you get very few picking up, and from there, you’ll see traction and momentum.
A core issue there, thus, is, for one thing, how we create these options.
Second, how do we place bets with chances to succeed?
And third, how do we scale the ones that worked?
The first principle of scaling is how small you need to go to create proper feedback loops while smoothing out the cost of error.
As you scale, you need two elements to inform you:
Are there enough error margins to keep experimenting without breaking the whole thing?
Do we get proper, tight feedback loops that inform the growth path toward optimal scalability?
For that matter, I’ve placed these elements on a matrix where the cost of error (from low to high) and feedback loop type (from loose to tight) inform the whole scalability strategy.
Indeed, the scalability of AI products is a critical factor in determining their success in the market.
In fact, as I’ve explained in business scaling, it is, at its core, a market discovery process that turns niches into mass markets.
Let’s get into the framework.
This is part of an Enterprise AI series to tackle many of the day-to-day challenges you might face as a professional, executive, founder, or investor in the current AI landscape.
