“If it can be automated economically, it will be.”
– Authors Thomas Hayes Davenport and Julia Kirby
By now you’ve probably read at least some of the dozens of articles and reports about the upcoming tsunami of jobs (and careers) likely to be lost to automation, artificial intelligence (AI), smart machines and their related capabilities.
Some forecasts have suggested that from 15% to 50% of all jobs will potentially go the way of card catalogs and print indexes in the next two decades.
Is your LIS job likely to be among them? Possibly.
Does that mean your career will disappear as well? Not necessarily, but it will require some proactive thinking and planning.
According to LibGig Recruiting Director Brad Rogers:
“The reality is that the current hype around automation is far ahead of today’s jobs and employer needs for knowledge and information management workers. That doesn’t mean you shouldn’t think ahead. In the long run, for people who continuously enhance and update their skills and demonstrate their value, the workplace of the future may provide even greater opportunities.”
The future of knowledge-work automation
To get a good sense of what the future might hold for information professionals in an automated workforce, check out Only Humans Need Apply: Winners & Losers in the Age of Smart Machines (HarperCollins, 2016), by Thomas H. Davenport and Julia Kirby.
Davenport and Kirby have been immersed in information-work environments for decades. Davenport is well known to LIS professionals for his previous works, Working Knowledge [2000, Harvard Business Review Press, with Laurence Prusak] and Big Data at Work: Dispelling the Myths, Uncovering the Opportunities [2014, Harvard Business Review Press]). So when they assert that any type of routine activity that can be codified or reduced to a set of “if-then” steps using rules and algorithms, will be, it rings true.
Based on this assessment, how can LIS professionals begin to prepare for reconfigured roles? Part of this will be identifying what aspects of our existing roles are likely to be automated, and what aspects will still be human-driven.
How you can add value to AI and other new technology
We’ll need to ask how the skills we have (or are learning) can help us add value to smart-machine processes and functions. Other questions will come from envisioning our work and its value to our various constituencies:
- What can we bring to the game that smart machines won’t be able to replicate?
- What might we do if we had more time and bandwidth, thanks to smart machines taking over mundane, time-consuming activities?
- What new skills do we need to teach and learn in order to view smart machines as an opportunity for doing something different and better, rather than as a threat to marginalize us?
Happily, Davenport and Kirby have done a large part of the preliminary work necessary to figure out how we should think about our adaptive strategies.
Characteristics of jobs that could be automated
According to the authors, following are the ten job characteristics most likely to suggest it’s time to start thinking about your next career move:
- Automated systems currently in the marketplace could do at least some aspects of your job.
- Your job responsibilities don’t include a high level of physical touch or manipulation.
- Your responsibilities do include a lot of basic content transmission.
- They also include basic content analysis, like compiling routine statistical reports.
- Your LIS work involves coming up with answers to questions that are based on processed data.
- You’re the LIS version of a quant; that is, your work is based on doing quantitative analysis.
- A portion of your information or library work tasks can be simulated or performed virtually.
- Your work is highly routine; as Davenport and Kirby describe this element, “Consistency of outcomes and the criteria on which they’re based is key.”
- The work you do produces data for decision support at executive levels.
- At least some aspect of your LIS work is based on mastering and applying well-defined, rigid rules and processes.
Unfortunately, it seems that almost every type of knowledge or information work involves at least one, if not several, of these criteria. And given financial pressures, the reality of “if something can be automated successfully and economically, it eventually will be” is increasingly familiar… automated check-out, anyone?
Defending and developing your career strategy
Davenport and Kirby suggest that the smart move will be to develop and position your skills in relation to the obvious strengths of our new work partners, i.e., smart machines. The authors describe this positioning as augmentation—a process by which “humans and computers combine their strengths to achieve more favorable outcomes than either could alone.”
Five potential types of augmentation to consider:
- Step up. This approach is based on your ability to take a big-picture approach to things and understand how to interpret and respond to that big picture.
- Step aside. Let machines do what they do best (computing and automated processes) and step in to be the people-interaction intermediary. As we all know, in some situations, nothing can replace human engagement.
- Step in. Consider this the computer-overlord role—your responsibility will be to “understand, monitor, and improve” how the systems and their component parts work to provide value to the organization.
- Step narrowly. Some jobs occupy such a narrow niche, it’s unlikely anybody would be willing to invest in automating them. Develop an expertise in these areas and your job opportunities may not be extensive, but they also may not be automated out of existence.
- Step forward. This role is for those able to combine a deep understanding of the technology and its capabilities with a strategic sense of how technology solutions can be designed to meet business or organizational goals.
How automation may affect your career
There are several ways automation could affect your job.
The first is that certain aspects of multiple LIS jobs are automated. This will enable an individual worker to take on additional tasks. The good news of this scenario is that you can be more productive for your organization, and possibly learn new skills. The bad news: fewer information professionals will be needed to complete the same amount of work, further contracting a tight job market.
The second is that entire categories of jobs become automated. In this case, entire professional specializations could cease to exist. Two questions here: What specializations are most likely to lend themselves to complete or nearly complete automation? And what alternative, less automation-sensitive LIS career paths can potentially displaced workers bridge into?
The third is based on the hope that we bring out our best stuff for a more positive outcome. Through brainstorming, trial-and-error design thinking and innovation, we’re able to figure out which of Davenport and Kirby’s suggested augmentation strategies apply for LIS work and professionals that will be of value to our customers, clients, and colleagues. Stepping up? Stepping aside? Stepping in? Stepping narrowly? Stepping forward? Or some combination?
Positioning to pivot your LIS career
Each of these strategies has much to recommend it and would open up new opportunities for LIS professionals. But all are also pivots from our existing expectations, environments, and grad school curricula.
The one strategy that definitely won’t work is to hope for the best, and leave it at that.
The smart move for all knowledge workers: hope for the best, plan for the worst.
What plan do you need to put in place to make sure that if—and some would say when—information work does become highly automated, you’re ready to pivot into your new way of adding value with your LIS skills.
Or, you could make a complete switch to one of the careers most commonly identified as safe from being automated out of existence: electricians, plumbers, hairdressers, and the like.
- Davenport ,Thomas H. and Julia Kirby. Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. HarperBusiness, 2016. 288p. ISBN 978-0062438614.
- Ford, Martin. Rise of the Robots: Technology and the Threat of a Jobless Future. Basic Books, 2015. 352p. ISBN 978-0465059997.
- Frank, Malcolm, Paul Roehrig, and Ben Pring. What to Do When Machines So Everything. Wiley, 2017. 256p. ISBN 978-1119278665.
- Hess, Edward D. and Katherine Ludwig. Humility is the New Smart: Rethinking Human Excellence in the Smart Machine Age. Berrett-Koehler, 2017. 224p. ISBN 978-1626568754.
- Susskind, Richard and Daniel Susskind. The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford University Press, 2016. 256p. ISBN 978-0198713395.
- Willyerd, Karie and Barbara Mistick. Stretch: How to Future-Proof Yourself for Tomorrow’s Workplace. Wiley, 2016. 272p. ISBN 978-1119087250.