Machine Learning Value Propositions for the Midmarket

Machine Learning Value Propositions for the Midmarket
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by Sanjeev Kapoor 22 Oct 2019

Investments in Machine Learning (ML) and Artificial Intelligence (AI) are nowadays surging, as enterprises attempt to leverage these technologies in order to improve their competitiveness. However, a closer look at the market shows that the vast majority of these investments concern large enterprises and high-tech startups. In particular, large corporations take advantage of their size and wealth in order to deploy data-driven intelligence and stay ahead of their competitors. On the other hand, many startups are producing disruptive AI and ML related innovations, which makes them very attractive to investors like venture capitalists and innovation funds. Contrary to large enterprises and startups, companies in the midmarket seem to be more reluctant to adopt ML and AI. This is however bound to change, as most midmarket enterprises that have invested in ML are already reporting very positive and very promising results. The latter concern improvements in internal business processes, as well as the creation and roll out of novel products and services. Moreover, feedback from early adopters shows that ML can become an innovation enabler for the Midmarket in sectors like retail and finance.


Machine Learning Opportunities for the Midmarket

Here are some of the most representative ways in which Midmarket enterprises can benefit from Machine Learning:


Value Creation Guidelines

With so many exciting opportunities at hand, Midmarket enterprises should get prepared to pursue them. In this direction, they had better take into account the following best practices and guidelines:


In the next couple of years, it’s likely to see an increased number of Midmarket companies entering the machine learning game. There are certainly plenty of opportunities for them. However, these opportunities will come along with very stiff competition from other enterprises, including both large corporation and Small Medium Businesses (SMBs). In order to sustain this competition, Midmarket enterprises will have to excel in terms of data assets quality, business expertise and technical competencies. While this is a very hard and very demanding task, following the above listed guidelines could provide an essential boost in confronting the challenges.

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