Automation as a Service Market to Reach $12 Billion Globally by 2026

Allied Market Research (AMR) has recently released a report suggesting that “global automation as a service industry” topped out at almost $2.1 billion in 2018 and will approach close to $12.5 billion by 2026.  Moreover, the CGAR (compound annual growth rate) is set to grow 25 percent between 2019 and 2026. 

Automation as a “service industry” sounds a bit like robots zipping around a restaurant serving you food, but realistically, it’s more subtle than that. It means that functions like software installation, maintenance, and support for business technologies  can be more efficiently carried out by machinery governed by autonomous systems that don’t require as much human maintenance. Think less robot waiter, and more a sink that unclogs itself while sorting the kind of dishes you are most likely to use hour to hour. 

The AMR findings have cited more than a few reasons for the rapid growth. Simplicity of doing business via the internet and adoption of the services technologies boosting growth of the global automation as a service market both make the list. This has all made the workforce cost less to maintain for businesses that use these technologies. 

Regardless, there have been so many changes regarding how we view things like privacy and data security that have stifled industry also . All AI and automation is predicated on the collection and sorting of user data. Restricting the flow of data, will naturally slow progress and some argue that is hurting the industry overall. In a 2017 report the InformationTechnology and Innovation Foundation noted that “A growing number of countries are making it more expensive and time consuming, if not illegal, to transfer data overseas. This reduces economic growth and undercuts social value.” 

Still, implementing AI becomes more difficult the more raw, untagged, unfiltered, and uncategorized data you have. So while privacy laws etc may not be lending to the AI boom, they could be helping to make long term implementation of AI more manageable by keeping the focus on quality of data mined ethically than boatloads of less useful data mined questionably.