All About Automation


Note: This blog is the fifth in a monthly co-authored series written by Charlotte O’Donnelly, Research Analyst at Blue Hill Research, and Matt Louden, Brand Journalist at MOBI. MOBI is a mobility management platform that enables enterprises to centralize, comprehend, and control their device ecosystems.

Lately, doom-and-gloom predictions about robot workers and how we’ll all be out of a job have dominated tech news headlines. While these stories are usually educational and entertaining to read, they’re not new.

In fact, workers have feared automation for nearly 300 years. Modern professionals share similar concerns with Industrial Revolution-era workers who protested the implementation of sewing machines, factory production, and steam engines, for example.

While today’s Artificial Intelligence (AI) and machine learning technologies have the potential to radically alter the way we work, don’t start boxing up your office just yet. Throughout history, new innovations have always created more jobs than they’ve eliminated: they’ve changed how we work, not whether we work.

Powered by People

While employees may initially view intelligent technology with suspicion, AI’s ability to make a task cheaper and quicker to complete increases the demand for skilled labor for tasks that are impossible to automate. Consider our Industrial Revolution examples above: after new technologies were implemented, a cloth weaver’s production increased 50-fold thanks to a 98% reduction in labor. This resulted in four times as many textile employees by 1900.

More recently, consider the retail banking industry. While ATMs reduced the average number of employees per branch early on, they also streamlined bank expenses. These cost savings were so dramatic that the number of urban bank branches rose by 43% from 1983 to 2004, more than making up for those initial job losses.

The goal of implementing automation isn’t to eliminate jobs; it’s to redefine and streamline them. While some workers may be required to learn new skills, they are overall much more likely to find jobs than lose them. A 20-year analysis of the American workforce found that employment grew significantly faster in occupations that relied on computers to enable employees to perform non-automated tasks more effectively.

Most experts believe large-scale job loss isn’t what enterprises should be worried about. According to the Organization for Economic Cooperation and Development, only nine percent of US jobs are at a high risk to be automated. What is cause for concern is that less than one tenth of a percent of the annual GDP is dedicated to helping people manage workplace changes—and this funding has declined over the last 30 years.

A Few Things to Remember

While each automated technology implementation is unique, there are a few universal problems your enterprise will likely need to troubleshoot. Here are five things to keep in mind when adopting AI and machine learning:

1.         You’re the Expert

Whether you’re building Robocop, R2D2, or Johnny 5, realize that even the most advanced automation system is just a tool. It’s your process—you’ve managed it for weeks, months, or years already. Make sure someone is constantly monitoring and managing the technology to ensure it’s satisfying your company’s needs.

2.         Actions Speak Louder Than Words

Employees are going to be afraid when they hear about AI and machine learning. Simply teaching them what these terms mean isn’t enough; if you want to get everyone on board, organizational leaders need to show a workforce how this technology has been used before and why it will make completing specific tasks easier.

3.         Fill Your Bandwagon From Top to Bottom

Even after explaining the advantages of automation, don’t expect complete employee buy-in right away. Workers need to see executives and IT leadership 100% behind a new technology before they’ll start to get comfortable with it.

4.         Open Minds and Doors at the Same Time

One thing’s inevitable—there’s going to be pushback. People seldom enjoy change, so prepare for this ahead of time and let your workforce know that they need to trust automation (even if it goes against intuition or traditional ways of doing business).

5.         Technology Can’t Do It Alone

Automated systems, AI, and machine learning speed up decision-making, but do not eliminate the need for human interaction. Technology is nothing more than a complement to your organization’s strategic thinking capabilities; humans must do the heavy lifting here.

As automation continues to grow in popularity, it’s important that companies embrace new technology for the right reasons. Your AI and machine learning capabilities will never actualize their potential if leveraged solely to replace employees.

About Charlotte O'Donnelly

Charlotte O'Donnelly is a Research Analyst at Blue Hill Research supporting written and research topics in mobility, IoT, and technology expense management. She is primarily responsible for surveying the market and reporting on significant trends and developments from market leaders in this space. Charlotte also supports the analysis, writing, and creation of client deliverables, multimedia assets, and internal initiatives. Prior to Blue Hill Research, Charlotte worked in mobile technology and financial services consulting. Charlotte has a background in business, technology, and law, and is passionate about the intersection of these subject areas.
Posted on May 31, 2017 by Charlotte O'Donnelly

Leave a Reply

Your email address will not be published. Required fields are marked *

Latest Blog

Blue Cedar Puts Mobile Application Security Far Ahead of MDM Apple iPhone X Highlights Enterprise Corporate-Liable vs. BYOD Conundrum Blue Hill - AOTMP 2018 Q1 Agenda

Topics of Interest

Advanced Analytics




Artifical Intelligence


Augmented Reality



Big Data


Business Intelligence



Cognitive Computing

Corporate Payments

Data Management

Data Preparation

Data Wrangling





design thinking


Emerging Tech

enterprise applications

Enterprise Mobility

Enterprise Performance Management

enterprise video

fog computing

General Industry



Hadoop World

Human Resources


IBM Interconnect




Information Builders


Internet of Things





legacy IT


Legal Tech

Log Data

Machine Learning

Managed Mobility Services


Mixed Reality


Mobile App Security

Mobile devices

Mobile Managed Services







Predictive Analytics

Private Equity



Questioning Authority

Recurring Revenue

Risk Management


Sales Enablement



service desk

Social Media



Supply Chain Finance

Switchboard Software




Telecom Expense Management




Unified Communications


USER Applications

User Experience

User Interface

video platform

Virtual Reality



Wearable Tech