Fail More

Reinforcement learning is one of the critical components of machine learning algorithms today. Every right prediction/action is rewarded, every wrong prediction/action is punished. Each reward/punishment tells the algorithm what behavior to repeat and what to stop. Over time , the algorithm works to maximise rewards.

What is critical here? In most situations, there are more wrong answers than right answers. There are more wrong steps and very few /or a single right step. What is key for the algorithm to learn fast? Fail more.

The more wrongs that you avoid ( based on past learning) , the closer you are to the right answer. While you might chance upon the right answer very quickly, the learning happens by finding the wrong ones and avoiding them in future.

Yet this behavior that is so intrinsic to us early in our childhood elludes us later in life. We try to look/plan /be prepared for 100% success at the first attempt . We are woefully unprepared for any other result. Failed project owners are crucified/vilified/taunted. Failed project teams try to hide under the table/ act as if they have committed a crime.

Without the wrong actions, there is no learning ( as we have seen from the reinforcement learning ). Instead of celebrating learning, we are forever scared of failures/ do not prepare for failures. What is worse is that we do not want to start a project if we are not 200% sure it will work.

While such conservatism worked in the past, it may not work now as every industry/business is getting disrupted from unkown quarters. ‘Fail Fast’ is not a buzzword and certainly not an optional!

Easier to Simpler

The next wave of growth will be from making complex things simple. Making simple things easier is done and dusted.

The explosive growth of telecom and internet have enabled the first set of apps to make the simple things easier. Ordering groceries, hailing a cab , booking a hotel room, buying a flight ticket…

Current generation of unicorns have done and dusted all these ideas. It’s time to leverage the new age technologies to simplify the complex. Leveraging technologies such as deep learning, blockchain, Quantum computing to simplify complex problems and make them as easy as making a request to Siri/ Alexa or touching your phone will give rise to next generation of unicorns.

This is easier said than done. It needs a wider different set of skills than before. Deep domain knowledge ,out of box thinking and risk taking, tackling the resistance of the incumbents' well financed self interests…

On the flip side returns are enormous. Unlike b2c platforms, B2B decision making is not strictly by the price and that enables for more profit making platforms from day 1 instead of ‘ buying the market ‘ at huge losses .

The complexities involved ,the value propositions, stakeholders for all quite different. So are the returns!

Half of Plan B and full Plan C

In most projects, Plan A never works.

What works is half of Plan B and full Plan C.

That's it.

Half of Plan B and full Plan C

For most projects, Plan A never works.

What works is half of Plan B and full Plan C.

That's it.

Beat the Machine with the Abstract

If you are wondering what skills can the machines not learn, at least in near future, here is one.

As a wise person said, ‘ Jobs of the past were Muscle. Jobs of the current are Brain. Jobs of the future are Heart’.

While algorithms are distilling movie story lines and even creating a movie from an algorithm generated story line, creative pursuits are still some thing that would keep you in good stead for the future.

One such key skills is Abstract Thinking. How does it matter ? For innovation, radical differentiation, problem solving and for many other situations, abstract thinking is critical. It is simply not about thinking at a higher level. It is about elements and their interaction at a level that you are able to explain a complex thing. It is abstract thinking that helps you find gaps/identify new solutions/think different and effect different outcomes.

How common is it? Not so common. If you get mired in details, it is difficult to abstract out and find the picture that you are missing. Abstract also demands that you be comfortable with the fuzzy, uncertain and the vague.

Typically you would find someone who is good with rational ( engineers, mathematicians, accountants, scientists…) or those good with the creative ( designers, painters, visualisers, advertising folks…) but not both. Today’s problems or challenges need both these approaches to make a difference.

It is also one of the skills that a machine may take time to ‘learn’. Or may be not!

The De-coupling Opportunity

This topic needs a bit of context and background.

When businesses generate cash surpluses, suddenly there is a demand by the investors/market to do something with the cash. You give in to the merchant bankers and buy businesses, or build new businesses ( or buy back shares if you have no ideas left. )

Over time, the original razor sharp specialist becomes a conglomerate ( which looks more like a shopping basket). And then the ‘Conglomerate Discount’ happens. It is the discount to the stock valuation for a firm which is spread across multiple businesses ( and hence has thin focus and poor core competency!) . A classic example is how GE was discounted by the stock market since the management attention is spread across multiple unrelated businesses.

Way back in 2000, Jack Trout exclaimed ‘ Differentiate or Die’. Unless you are sharpening your differentiation, you will lose in the market place .

On the other hand, given the shorter business cycles today, the obsolescence rates for current businesses are much faster. So businesses have to find new sources of growth replacing the older ones.

Those are both sides of the coin for the dear reader. The case to specialise and the case to diversify.

Now let us look at the de-coupling opportunity. As someone tries to reimagine an existing industry/market place, one way is to re-imagine the full business model and process. The other ( and perhaps cheaper/less risky) way is to de-couple and re-imagine ONLY a part of the business process. It does not bring radical changes to the market but it would still give the innovator enough incentive and opportunity.

Here are a couple of examples to understand ‘Decoupling’ opportunities.

A market place platform replaces a showroom of limited choices , with multiple choices and convenience. Thus it is decoupling the showroom part of the chain and making it significantly better.

A stock trading app is decoupling the broker/advisor based stock selection to knowledge enhancement and self service based investment management.

Why is it cheaper/less riskier? Because you are changing a portion of the process which makes customer/market adoption easier and your chance of success higher. It is also a prudent way to influence the market when you are small.