Is there a future for IoT software ?

You only find out who is swimming naked when the tide goes out. WARREN BUFFETT

A taxi driver has to complete the famous Knowledge, a test introduced as a requirement for taxi drivers in 1865. Taking three to four years to master — 400 routes, 25,000 streets and 20,000 places of interest, it has been proven that the cabbie has a larger hippocampus as a result of this deep knowledge they retained. Remember, Fred Housego, the cabbie who won Mastermind in 1980?

Then of course we know what happened next.

Uber arrived in town. The cabbie’s blood sweat and tears (knowledge pool ) became questionable when up against smartphones, GPS satellites and open APIs ( knowledge flows ).

It may be interesting for IoT software companies’ to reflect whether managing IoT scenarios is be heading the same way.

In 2009, an article from HBR called Abandon Stocks, Embrace Flows (https://hbr.org/2009/01/abandon-stocks-embrace-flows.html ) suggested that “If you knew something valuable, something nobody else could access, you had, in effect, a license to print money. All you needed to do was to protect and defend that knowledge and then deliver products or services based on that knowledge as efficiently and as broadly as possible.” ( Black cab driver ).

We all know that “as the world speeds up, stocks of knowledge depreciate at a faster rate. As one simple example, look at the rapid compression in product life cycles across many industries on a global scale. Even the most successful products fall by the wayside more quickly as new generations come through the pipeline faster and faster “( Uber taxi ).

Now think about IoT software .

IoT in its truest sense has two modes of engagement and influence. Either as a capability asset for business process optimization ( tools to manage predictive quality processes in a manufacturing plant using instrumentation, quality and business workflows ), or a change agent for market disruption ( platforms to develop smart AI systems to control an autonomous car using geo-information smart systems).

In a stock of knowledge situation, IoT software is built on decades of layering infrastructure platforms, devops command and control, programming skills, user interfaces, complex integration, device platforms and critical support models. Software for ERP, HR, PIM, CMS, EAM, PLM and others thrive in this environment.

Digitally mature industries are masters of acquiring software code to give them an edge — data compliance, production output growth, business process optimization, predictive quality measurement etc. The airline industry is perhaps the strongest example of this. Car manufacturers similarly. For these guys IoT software is considered as being tightly coupled to their product, inherently immersed in the overall cost of production and therefore, a cost of sale that is adequately compensated for all concerned.

Yet in a flow of knowledge situation, the role of software is becoming more loosely coupled from the end result. No longer dominant given the speed of obsolescene in knowledge ( here today gone tomorrow dark data ), the power is moving to the ‘smarts’ within the data, and the decision making capability of the user. Flows and channels are the new news here — not stocks of knowledge held within software domains.

Airline and car manufacturers’ now excel in their ability to combine stocks of knowledge whilst extracting value and edge from flows of knowledge. The shift to autonomous cars will be the biggest example of an industry that will continue to use tightly coupled software to manage product life-cycle, whilst extracting new business revenues from deep exploitation of flows of knowledge adjacent to their product — social sentiment, environmental events, retail moments. All loosely coupled.

Software companies that operate in narrow domains of knowledge stocks, should be therefore wary of the impact from industries’ that are moving to differentiate from leveraging flows of knowledge.

Stocks or flows? More questions than answers.

How do we strike the right balance in managing stocks and flows? How do we decide which flows of knowledge are most valuable? How can we make money when the success is now managed in the flow, and not the capability? Are the skills in the right place and if not, what’s the plan? Most importantly, how do we continue to reassess these choices as the world continues to shift around us?

Will we keep developing software with the cost model wrapped round licensing recognition and revenue collection, or will we also see a move to new ways of generating monies? Especially given the ever increasing obsolescence meter of under-utilized software and dwindling silo skills within once dominant software domains.

And will it finally take a crypto currency approach to be the oil to grease the revenue collection flywheel for software companies’ to reinvent some of their offerings and fight back against the risk of becoming obsolescent?

One thing is clear. No one can afford to sit back and reflect on their perceived value, without risking the day that when the tide does indeed go out, will we be the naked one??

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