The End-User Era is Changing The Way Companies Evaluate and Buy ML

The End-User Era is Changing The Way Companies Evaluate and Buy ML

Discover the end-user era, and how companies evaluate and buy machine learning software and applications.


The end-user era is upon us. It represents an important shift in the way organizations purchase business software, and it directly applies to Machine Learning (ML).


How executives made business software purchase decisions – Before the end-user era


In the old days, before the end-user era, business software buying decisions were made by a business executive, the CIO and the procurement department. The business executive would declare that a new software suite is needed to improve for example the company’s business efficiencies or its competitive edge. Several vendors would be invited to present their products, conduct demos, and present discounted multi-year offers which included not only the cost of the software licenses, but also installation, upgrades, support, training and maintenance. The CIO would make sure the software could live within the company’s existing IT environment, and evaluate how much hardware, which databases and other tools might be necessary to support this new software application. The procurement department would then evaluate the overall total cost of the new solution, and calculate how these capital expenses would affect the company’s balance sheet. In most cases, it made the final go/ no-go decision.


Needless to say, these purchasing decisions took several months to make. It took several months to set up the hardware, install and customize the software and train the end-users. It could take up to a whole year or more before the business executive would see the new software application up and running.


How end-users make purchase decisions today – The end-user era


Things are different today. The purchasing decision is made in just a few seconds, for free – by the end-users themselves. The end-users will try the software for free, ask other team members to join, and if the team finds that the software is easy-to-use, and that it actually improves the way the team works, they’ll ask their boss to purchase licenses. As Blake Barlett says: “Software just shows up in the workplace unannounced”. The end-user era is here, and it’s revolutionizing the software industry. Gartner estimated in a 2019 research report that by 2023, 40% of professional end-users will decide which business applications their companies should buy.


The COVID-19 pandemic has massively reinforced this movement according to a recent McKinsey report. “More than three quarters of buyers and sellers say they now prefer digital self-serve and remote human engagement over face-to-face interactions”. Even after lockdowns are lifted this new paradigm will continue to amplify, for two main reasons.


The first is that end-users more frequently than ever buy goods online with just a click or two. In the end-user era, they also live online: They Google for their research, go to Facebook for news, listen to music streaming services, watch their favorite shows and movies-on-demand, and exchange ideas plus share comments through all sorts of social media networks. These end-users are bringing this new way of interacting with technology to the business world.


The second is that more and more business software applications live in the Cloud. Companies no longer want to make expensive hardware purchases, nor worry about maintenance and upgrade costs. They’re also looking for more economical pay-as-you-go pricing models, instead of paying thousands of dollars up-front.


What are the implications for machine learning software?


The same applies to ML/AI. Setting up and deploying a robust and scalable ML application should not be complex nor costly. We saw this trend coming a few years ago when ML users, mostly the data engineers and data scientists, were involved early in the purchasing decision process. We therefore decided from the get-go that the ForePaaS Platform would be free-to-try, easy-to-learn and easy-to-use. It would have to be intelligent enough to automate all of the back-end technical tasks, including the data workflows, data storage, security, and the complex underlying Cloud services. With the ForePaaS Platform, users have set up their ML applications in just a few days, and deployed them in the Cloud with a single click. Welcome to the new ML end-user era!


For more articles on cloud infrastructure, data, analytics, machine learning, and data science, follow me Paul Sinaï on Towards Data Science.


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The image used in this post is a royalty free image from Unsplash.