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Imagine the work day of an insurance agent just a few decades back before emails, before the Internet, before the digitized record archives. From issuing a new insurance to processing a claim, even the most routine of activities would take days.

There would have been little to no automated processing available to handle the typical scenariosand as a result there would have been an excessively high demand on the more expert agents to handle more complex claims, leading to a higher percentage of human errors. 

Why Is Automation Relevant to Me?

This article from 1983, published just at the cusp of the full computerization of insurance industry, demonstrates the promises of that particular round of automationdigital revolutionand challenges of that process as it was unfolding. Today, the insurance companies, even in the remotest outposts of the interconnected world, benefit from that inevitable transformation. A single human agent can process a significantly higher number of claims every day with a greater degree of accuracy in assessments. By automating the typical claims of common patterns, the company can focus their expertise on handling the more complex and unique scenarios with greater diligence and rigor.  

As a result, insurance companies can offer more affordable insurance to a large group of consumers, and can provide creative solutions to complex insurance needs for the non-typical consumer base.   

This is a win-win situation not only for the insurance companies and the consumers, but society at large benefits from a stable insurance ecosystem and the comprehensive services it facilitates ubiquitously. 

Technological revolutions bear this distinctive feature of building a more resilient ecosystem of businesses and consumers in the wider context of the global economy that larger segments of society then participate, contribute and benefit from.        

Artificial Intelligence and the revolutionary changes that it is ushering in can be seen through the lens of such past technological revolutions: steam engine to electricity to the digital revolutions. Each industrial revolution brought in a higher degree of automation, and subsequently cycles of Automation-Efficiency-Innovation-Automation opened up new possibilities for growth and prosperity.   

Automations made possible by Artificial Intelligence offer us with similar opportunities around monitoring and managing networks and devices of business owners, opportunities that will lead to greater innovations by the MSPs—and will ensure MSPs can offer a wider array of affordable services to larger numbers of businesses. As more and more businesses digitize their infrastructure, MSPs can then enable them to avail the benefits of managed IT services.  

What is Artificial Intelligence?

The various technological innovations and breakthroughs that are powering this “fourth industrial revolution” get grouped under the term Artificial Intelligence. It is important to understand the technology landscape that characterizes “AI,” outside of the fascination and philosophical discussions it inspires in culture and in collective imagination.   

The term “Machine Intelligence” or even “Machine Competence” describe those innovations more accurately, and they all describe what essentially are software programs: a set of computational instructions written by humansor written by another set of instructions that are written by humansbeing executed on silicon chips designed by humans.    

“Machine Learning” (ML) is the most promising and predominant category of these programs. The classical definition of Machine Learning remains relevant and accurate: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”   

That is, a specialized program that gets better at performing it tasks more data it sees without having its instructions to be rewritten every time. It mimics one specific aspect of human intelligence—learning—but only very superficially, and accurate description of that process will be competence without cognition 

Why is Machine Learning So Successful?

The conceptual foundations of ML have been around for nearly fifty years. And whereas ML techniques like Neural Network was confined to academic esoterica even a decade back, its spectacular success in recent years, especially in the business domains that involve recognizing patterns in audio-visual data, is due to two technological breakthroughs: (a) the capacity to store, exchange and process vast amounts of data especially through Cloud platforms and (b) highly concurrent computation at Microprocessors enabled by the GPU (Graphics Processing Unit) and TPU (Tensor Processing Unit). 

The ability of an ML “model” (the program that makes predictions based on new data) to recognize relevant patterns improve as the model sees more data. And the speed of such “learning” becomes faster as we make chips that are more efficient in making large number of computations in parallel.  

AI/ML is excellent for identifying common enough patterns from even very noisy data.  

Stay tuned for Part 2 of this blog series to learn what it takes for MSPs today to leverage AI and machine learning for business transformation. 

Want to explore the power of automation firsthand? Watch a demo of Continuum Command!