The Ultimate Sin in Business

...is to be unprofitable.

Sure, you'd say, that's your CFO "pedigree" talking, there must be larger sins than this.

So hear me out.

A bottom line (the profit) is easily dismissed as an accounting convention. Cash is king - they say. And it's true! But this accounting convention was not invented for the purpose of distracting cash-minded managers from what really matters. Profit is a long-term indication of a business's ability to turn out positive cash flow. Fail to deliver that and your cash-rich days will be over - sooner than you think.

So, beyond Finance 101, a red bottom line signals a dysfunctional business model. Persisting losses in a business lead to an accumulation of damages to the organization that become increasingly difficult to course-correct:

- the mindset of the organization moves from "who's building what" to "who's avoiding the next layoff round", which leads to high amount of unproductive political actions (lies, deceit, backstabbing) and to a very clear negative selection of personnel - only the weak ones (or whose only skill is organizational survival) are left, since the good ones are more likely to be hunted out of the organization;
- the increasing negative pressure for immediate results, coupled with a weakening control environment and a rationalization of what's acceptable to do in a chronically ill organization will lead to corruption, red tape and other forms of non-compliant behavior;
- beyond people, the asset base (tangible and intangible) will be eroded from lack of proper care and will continue to lose its capacity to accrue economic benefits to the business;
- there is an erosion of creditworthiness as well, which leads initially to higher costs from the supplier base (as everyone prices in the business's inability to pay on time), up to the breaking point of credit default and insolvency.

Some people may push some valid counter-examples, like start-ups - they are not yet profitable. But this may be not because of a dysfunctional business model:
1/ a start-up is a vehicle in search of a valid business model so they may not have found it yet;
2/ or its business model only gets validated at a certain scale

And of course, there's the unicorns, which are heavily-funded start-ups that are expected to prove their business model (and win big) at huge scales. But these are not yet businesses, they are just more sophisticated bets. Extremely few of them pay off big, some of them earn some money back, most of them nothing at all - isn't that the definition of a bet?

Throughout my career as a CFO, I have long been an enemy of waste - not surprisingly, my start-up actively works with visionary entrepreneurs and business leaders in fixing their most important waste baskets (inefficient and manual business processes, ossified enterprise landscapes). The results and the initial take off are amazing.

So isn't an unprofitable business model, just simply, the biggest form of waste? All the money thrown away, all the human talent drain, all the assets blown to pieces?

I believe so, but then how do we repent from this sinful behavior? :-)

There is no universal recipe, but the biggest building blocks are
1/ gain visibility over your waste baskets (how's your controlling, by the way?),
2/ fix the easiest/biggest waste baskets first (which may very well mean scaling back)

and then

3/ gradually heal your business model by putting in place the right people, processes and systems.

Sounds like standard economics textbook? You'd be surprised how few businesses go past step no 2/.

The secret to a sustainable, profitable business is execute 3/ every day so that you don't need to execute 1/ and 2/ every now and then.

Which could probably get translated as more righteous living leads to less repentance :-)

More to come...

Design Your Business End-to-End

The guy who invented the wheel was an idiot. The guy who invented the other three, he was a genius. 

Sid Caesar.

tl;dr:
an idea may be worth something, but a working business model around the idea is worth many orders of magnitude more.


How do you get from an idea to a working business model? 

Well, first you need to fully wrap your mind around the idea and think through as many perspectives as possible: who is your customer? what is the problem that you're solving? why you and why now? what's your particular competitive advantage? how is your basic business plan going to look like? what are your particular constraints and how do you plan to overcome them? This is how you design your business model from one end to another and make sure it "holds water".

Second, there's a lot of work involved, but that's for a different story. Let's get back to the end-to-end principle.

During my professional life, I have witnessed some resounding business model failures that can be entirely explained by the dismissal of the end-to-end design principle.

One that comes to mind is a proximity retail start-up in Romania who was built on a quite clever expansion model that would alleviate the investment capital burden and would ensure the P/L is scalable with the network expansion cadence. 

Yet, somewhere along the way, impatience or greed kicked in and the owner decided to start chasing some vanity metric (no. of new stores opened every month!) and of course this required a pivot of the business model into a very risky area, where more investment capital was needed (putting pressure on the working capital and triggering delays in payments to suppliers) and the P/L was immediately thinned by burgeoning network costs (triggering a swift departure from the committed budget). This led to a massive backlash from the suppliers and financiers and the retailer soon collapsed.

Choosing the wrong metric is of course not a capital sin in itself (everybody does it all the time), but the pivoting of the entire business model without a full end-to-end scenario play was what killed this ambitious project. Sadly, I joined too late - the turnaround was no longer possible without a massive capital injection, which turned out not to be realistic.

I have since then experienced several such "gut feel" business investment decisions but I was successful in stopping or blocking them before they could significantly hurt the business. 

While there is nothing wrong with "gut feel" decisions around great ideas, planning your entire business on the back of a napkin is crazy (and I have literally seen that!) - and most successful business models today require a bit more consideration than that.


So - carefully assess your business model and think it through - it will save you a great deal of pain and money later on, even if your business is not successful.


One might argue that "gut feel" is important when exploring completely new markets or business models. Yes, but then it is important to design the execution of your business model so that you start with something and grow as you learn, minimizing waste and risk (of which a new enterprise has plenty).

This is where the lean start-up thinking comes to rescue, with its famous Build-Measure-Learn cycle.

More on that, later.

The Curse of Complexity

"Learning is any change in a system that produces a more or less permanent change in its capacity for adapting to its environment"

Herbert Simon, The Sciences of the Artificial



Now more than ever, business tastes better if you put technology in it - the tasks get done faster, more accurately, employees can delegate more left-brain activities to their computing devices and can focus more on right-brain activities that nurture their humanity more. Who knows, they might even start to like their jobs.

Now let's shake off this rosy picture because, just like with any deep change, adding technology to a business model introduces its own set of challenges, like security, vendor lock-in, task commoditization etc.

But the biggest challenge is the added complexity layer. 

As the world around us explodes into a myriad of perspectives and new voices that were, until the advent of ubiquitous connectivity, unknown to us, our quest for technological advance exposes us more and more to this new, hyper-aware, world.

Let me give you an easy example:
1/ you build a website for your company...
2/ then you decide to turn it into a portal that manages customer and vendor workflows (appointments, customer service, MDM)...
3/ then you want to integrate those workflows into your IT landscape...
4/ then you want to start proactively managing your external stakeholders...
5/ then you realize you need social media connectors...
6/ then you start analyzing social feeds in order to manage your social media risk...
7/ then you decide that your website should really be a web app...
8/ then you want that app to be managed in the cloud...
9/ then you want to outsource the app development lifecycle...
10/ then you want to mitigate the vendor and cloud risks...

And of course you need people to manage all this new complexity and they need to interact in new ways that need to be managed as well...

This brings me back to my early days (when artificial intelligence -AI- was a bit more than an exciting academic topic) when I was researching AI techniques for the forecasting of financial time series. We were back then using as main tool a custom-built back-propagation neural network (NN) and we were struggling to optimally size the number of layers in order to give the NN the maximum predictive capability.

For those new to this, NN's are software programs that mimic the way the human brain learns (by altering the chemical information in the synapses - the links between living brain cells), by non-linear error-correction algorithms that alter the weights of the connections between software constructs that imitate brain cells. 

Nowadays, NN's have gone mainstream and are at the core of any pattern recognition software: OCR, photo and video recognition, voice recognition (Siri, Google Now, Cortana etc).

The training procedure for a NN is that you train it on a dataset, then you test it on another similar (but different) dataset, then you validate it on another similar (but different) dataset. The performance in the validation phase measures the NN's predictive ability.

As we built the NN, we could witness what was called in the literature "the curse of complexity" - as we added more layers, the NN just became dumber and dumber - the validation performance was dropping abruptly. This seems counterintuitive - you'd expect that, as you add more layers with more neurons and more connections, the NN gets smarter. 

It turns out that, when you have too many layers, the NN overlearns the training dataset, to the point of learning the data itself rather than the underlying non-linear equation that generated that data.

To draw an analogy to business, when you have too much complexity embedded in your business, your organization tends to learn best how to manage the current set of activities, but without being able to anticipate new business opportunities and without being able to closely follow the business strategy as set forth by its leadership.

So, how does a business entity reconcile the unnatural need for it to grow (i.e. increase complexity) with the natural need that it remains within the computational limits (or bounded rationality, as Herbert Simon calls it) of its decision making layer (i.e. contain complexity)?

There are no easy answers to this question - large corporations have gone through great pains in the 80's and 90's to keep the business manageable by establishing strict and rigid business rules, by delegating decision making abilities, and by deeply embedding business logic into systems and procedures. This approach, however, brings its own set of complexity, leading ultimately to ossification, which is the enemy of adaptation and economic survival.

My personal belief is that the answer revolves around the enterprise's ability to upgrade and pivot its business model based on the changing environment.

Enterprise strategists need to develop an uncanny ability to always ask themselves the excruciating question: 

"If I add this to my business, what else should I remove in order to keep it sane?"