Overcoming the Enterprise Differentiality Paradox


The low cost of replication with digitized products has long been known to be seriously disruptive in the music and publishing industries. However, the conflict between low cost replication of business software, rigid architecture, and customer differentiation relative to the broader economy is not generally well understood, although vitally important.

When enterprise software matured to the point of early adoption in the 1970s, it provided a substantial competitive advantage to those in a position to leverage wisely. Followers of the early adopters did so largely due to the perception by decision makers—based on compelling evidence—that adoption was essential to their future, and so the great commoditization cycle in enterprise software had begun. In the early 1980s a very similar pattern began with front office software, captured primarily by Microsoft, with commoditization slowly gaining strength in the 1990s.

While front office software could be neatly packaged and sold in storefronts or through consultants for desktop installation, back office systems for operations required on-premise customization and integration with incompatible systems, meaning a very large investment with ongoing maintenance, and high costs for adaptation. Within a few years, custom applications were increasingly sold as products, exploiting the nearly free replication costs, but this benefit was not without cost as wider adoption of back-office products soon began to erode the competitive advantage; the commoditization cycle was in motion.

In addition to the direct impact of commoditization on the software industry, which is well understood in business and finance, another dynamic was occurring that was considerably less visible and largely misunderstood, which is the impact on organizations when the vast majority worldwide are using the same operating and decision systems. As software systems became more ubiquitous, competitors in industry after industry were increasingly using similar systems, which began to extend the software industry’s commoditization to customers, and by extension the broader economy. This dynamic extension of commoditization evolved in conjunction with globalization and consolidation of industries over the past two decades. Indeed, the commoditization of business software acted as a catalyst to global consolidation of industries as business software customers were increasingly competing on price rather than value added differentiality. Competing on price alone with reasonably good management dictates outcomes that are primarily due to scale, particularly when automation systems are very similar between competitors; not quite singularity in the enterprise given the current maturity of technology, yet trending in that direction. Lack of differentiation does not a robust, durable economy make; competing on scale and price alone is a race to the bottom for everyone but market leaders.

This situation may at first appear attractive for business software vendors and their investors, particularly market leaders, as managing a commodity that has become essential can be a lucrative annuity. Dancing with complexity in technology, however, contains considerable risk of broken toes, particularly with software, which is anything but a finite resource. History has proven that substantial risk exists for incumbents that commoditize their customers.

Enter the Internet and Web, followed by cloud computing, and much smarter mobile adoption. Taken together with advances in hardware, software, semantics, analytics, and organizational systems, the opportunity to introduce truly adaptive enterprise computing in near real-time has finally arrived. It’s been a long voyage; one full of discovery and adventure which I hope will prove to have been worthy of our patience.

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About Mark Montgomery
I am a technologist, serial entrepreneur, business consultant, recovered VC, and inventor with interests that are both broad and deep across multiple disciplines, including organizational management, computing, communications, economics, sociology, science and nature, among others. For the past several years I have been founder and CEO of Kyield, which offers a distributed operating system for achieving optimal yield of executable knowledge across large data networks. The patented AI system core acts to unify networks with adaptive data tailored to each entity with continuous predictive analytics designed to significantly reduce ongoing costs while accelerating productivity, and generally make life more satisfying and productive for knowledge workers and their organizations. We provide popular free white papers, use case scenarios, and other information at http://www.kyield.com .

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