There can be a lot of fear about the sorts of radical change that AI can foster in the way we work. Most of that fear is misplaced.
Here at Moogsoft, our whole mission is about helping companies make radical changes in the way they operate. This transformation also has implications for the way people work in the process of achieving the ultimate goals. Unsurprisingly, there can at times be some skepticism and even push-back from those affected.
Part of this resistance is due to the long-standing tech injunction: If it ain’t broke, don’t fix it.
(from User Friendly)
All of us who have spent any time in IT Operations have found ourselves elbow-deep in something, and wishing we could easily roll it back to the way it was before we decided to fix it. Whatever state it was in before we began taking it apart is suddenly looking not so bad after all.
Most IT Operations people are risk-averse for this very reason. When they evaluate a proposed change, they are not only comparing the desired benefits against the effort required, but adding a substantial margin for their own insurance. When IT professionals add this sort of “fudge factor” to a timeline, it’s important to understand that they are not padding estimates just for the sake of it, but because their experience indicates that it will be harder and take longer to achieve those benefits than the more hopeful and starry-eyed projections would appear to indicate.
From Risk Aversion To Change Aversion
Beyond this natural risk aversion, however, there is a second aspect to people’s fear of change, as exemplified in a recent study about the impact of digital transformation initiatives:
“The study found that the people most fearful of workplace change fall into the 18-24 age range. Employees who said they feared the least about their jobs being disrupted was the 55-plus age group.”
These fears about changes to people’s jobs are not new. Arguably, the goal of the entire field of computer science is to automate tasks that were previously carried out by humans. This ongoing transition by definition entails disruption of jobs that people do. If you don’t believe me, ask the next filing clerk or member of the typing pool that you meet.
The reason we in the IT industry can all look ourselves in the mirror is that, while we are indeed busily automating tasks away, most jobs worth doing do not consist of a single task — and so people, by and large, are not automated out of their jobs.
Mostly this idea has worked out well; companies recognize the value of having people who already know the ropes, and would rather find their existing employees new roles than have to recruit and train new people from scratch. Temporary shocks from the bursting of the dot-com bubble and from slow-downs in the wider economy were rapidly compensated for.
Your Job Is More Than Checkmarks On Your CV
Many technical roles are not the sort that can be filled by “off the shelf” candidates. One company I have been working with told me that it took them nearly three years to replace a senior person who had left them suddenly. What companies in that sort of position are looking to do is to retain their in-house talent, including by automating away the routine tasks that annoy and distract those valuable and experience people from doing their actual jobs.
The sorts of jobs that are most often defined by single tasks are the entry-level ones. I was lucky enough to start my career in organizations small enough that I had to jump around from task to task, learning new skills as I went, but in larger organizations there are a number of projects at any one time whose staffing is on the principle of “throw an intern or three at it.” Those newly-hired 18-to-24-year-olds are justifiably afraid of automation and AI taking away that single-task job before they gain enough experience to become less easily replaceable.
Here’s where the two ends meet. A report from Glassdoor on hiring patterns around AI highlights a list of new jobs — above and beyond the sorts of obvious AI-related jobs, such as software developer on a team building new algorithms — that are being created around these new technologies:
- AI copywriters, who are writing the copy used by AI customer service chatbots;
- Attorneys for AI groups, who are managing valuable AI intellectual property and legal issues;
- Technical sales directors, who are carrying AI innovations out into the field to connect these services with potential customers;
- AI analysts and strategy consultants, who are providing consulting and strategic advice for employers using and building AI technology;
- Marketing managers for AI groups, who are building awareness and a top-of-funnel customer base for companies offering AI technology as a product or
- User experience or “UX” designers for AI, who are creative talent tasked with building elegant and easy-to-use AI interfaces for customers;
- And AI journalists, covering news in the fast-moving deep learning and AI industry.
The New Way Of Working
These sorts of new jobs will suit both the young people just entering the job market without preconceived notions of what is possible, and the experienced veterans who can understand the human factors around how new technologies will be adopted. Between them, they are well-prepared to understand both the technologies and their uses and implications.
The new model of work will involve humans working alongside AI-powered technologies, not being replaced by them. The jobs that go away are the ones that require humans to behave like robots — and we are still a long way away from robots that are able to behave like humans, outside very narrowly defined scenarios.
All hype about AI aside, I think everyone agrees that its deployment will be disruptive — but the signs are that it can also be constructive, creating new jobs that have never existed before, building entire new industries that will bring new career paths with them.
About the author
Dominic Wellington is the Director of Strategic Architecture at Moogsoft. He has been involved in IT operations for a number of years, working in fields as diverse as SecOps, cloud computing, and data center automation.