Imagine it’s five minutes before a meeting. Your smartwatch, without prompting, sends you key points. While in the meeting, you take notes. Those notes are instantaneously absorbed by the system, then collated with relevant prior meetings, files and communications, in order to better prepare you for the next meeting.
Imagine a set of smart sensors placed throughout a city that feed data to deep-learning systems. The systems then predict where traffic jams might occur and recommend route changes to the trucking fleets that keep inventory moving and businesses running on schedule.
This is the promise of what I’m calling "self-learning software." We've already seen it take root in consumer applications, and in the coming years it will help enterprises work faster and smarter than they ever have before. Its potential to increase productivity is so great, in fact, that it will generate unprecedented growth in enterprise software.
Here are some initial thoughts on what I believe to be the dawn of an exciting and enormous market opportunity.
The first era of the enterprise software industry was on-premises software, otherwise known as "shrinkwrap" software. The software is installed and runs on dedicated databases and machines on the premises of the person or organization employing the software. This initial period in the industry was dominated by the likes of SAP, Oracle, PeopleSoft and IBM.
With the rise and growth of the web, the need for shared servers and greater connectivity within companies pushed businesses toward software run from remote locations. We’re currently in the midst of this second era of enterprise software, "software as a service," in which agile cloud computing has replaced locally installed applications. Big SaaS players include Workday, Salesforce and NetSuite.
I think that the next great era in enterprise software will be the era of self-learning software (or SLS). Born of the innovations of Big Data and possessed of a new net intelligence layer, self-learning software will have huge impacts on productivity across all departments of an enterprise. The effect will be so seismic that I predict that SLS will double the size of the enterprise software industry by 2030. But, first ...
I define SLS as enterprise software injected with machine learning — the branch of computer science that explores how to enable computers to learn from and make decisions based on data without explicit programming instructions. The basic building block of self-learning software is the ability for a system to learn based on experience, make inferences from disparate signals, and then take action in response to new or unforeseen events.
With much of today's enterprise software, a person is required to make inferences repeatedly based on any number of data points — validating positive compliance with security protocols, for example. But the complexities of business environments often cause employees to be cautious, or even feel paralyzed, when action is required.
Software that functions more autonomously liberates companies from having to codify the rules of engagement and escalation — meaning, if an employee can reach a conclusion once and then train an SLS machine how it reached such a conclusion, the machine will learn by itself how to arrive at similar outcomes in differing situations. The human team will, thereby, be empowered to act more quickly and confidently, and freed to focus on higher level problem-solving.
A colleague’s 65-year-old immigrant mother only recently discovered the joys of predictive text. Now, when she sends her children short messages on her iPhone, she loves being liberated from the tedium of having to peck out every single letter, or having to consult her Chinese-English dictionary to spell a word.