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In my work with LIS students and practitioners throughout the country, we often focus on where the new jobs and career paths might be emerging – which is a smart, useful approach. But the corollary question, and especially important if you are considering building a specific LIS skill set, is where the disappearing jobs and career paths are likely to be.

Will automation affect LIS jobs? Without question. Perhaps a more realistic question to ask might be what aspects of LIS work are likely to be replaced by automation or robotic intelligence?

Because if recent studies are any guide, the question isn’t if automation will replace information work, but rather how soon, and by how much.

The future of knowledge work automation

Citing a McKinsey Global Institute research report “Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy,” in their Only Humans Need Apply: Winners & Losers in the Age of Smart Machines (HarperCollins, 2016), authors Thomas H. Davenport and Julia Kirby quote the report as noting that “we estimate that knowledge work automation tools and systems could take on tasks that would be equal to the output of 110 million to 140 million full-time equivalents (FTEs).”

Davenport is fairly well known to LIS professionals given his previous works, e.g., Working Knowledge (2000, Harvard Business Review Press, with Laurence Prusak) – the seminal textbook on Knowledge Management, and Big Data at Work: Dispelling the Myths, Uncovering the Opportunities (2014, Harvard Business Review Press).

The authors have been immersed in information-work environments for decades, and in Only Humans Need Apply they lay out a stark future for anyone engaged in any type of routine information activities that can be codified, or reduced to an automated set of “if-then” steps using rules and algorithms:

This is a theorem we will return to again and again in this book: If work can be codified, it can be automated. And there’s also the corollary: If it can be automated in an economical fashion, it will be. Already we’re seeing a rapid decomposition of jobs and automation of the most codifiable parts – which are sometimes the parts that have required the greatest education and experience. (p. 14)

A similar conclusion is reached by father and son authors Richard Susskind and Daniel Susskind in their examination of automation’s impact on employment, The Future of the Professions: How Technology Will Transform the Work of Human Experts (Oxford University Press, 2015). They write:

…in a ‘technology-based Internet society,’ we predict that increasingly capable machines, operating on their own or with non-specialist users, will take on many of the tasks that have been the historic preserve of the professions. We anticipate an ‘incremental transformation’ in the way we produce and distribute expertise in society. This will lead eventually to a dismantling of the traditional professions….Increasingly capable systems [and machines] will bring transformations to professional work that will resemble the impact of industrialization on traditional craftsmanship. (p. 2)

Although both books point out emerging opportunities within this new work landscape, Martin Ford’s Rise of the Robots: Technology and the Threat of a Jobless Future (Basic Books, 2015) is a bit more pessimistic:

It’s becoming increasingly clear…that robots, machine learning algorithms, and other forms of automation are gradually going to consume much of the base of the job skills pyramid. And because artificial intelligence applications are poised to increasingly encroach on more skilled occupations, even the safe area at the top of the pyramid is likely to contract over time. (p. 251)

All three books consider the same question: how do we prepare ourselves and our professions for a future where jobs are often, at their core, automated processes for which humans are basically the support staff?

And what does that mean to the LIS profession and its graduate programs?

Playing strategic defense

Davenport and Kirby suggest that the smart move will be to develop and position your skills in relation to the obvious strengths of our 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.”

Specifically, they recommend these five types of augmentation:

“Step up.”  This approach is based on our ability to take a big-picture approach to things and understand how to interpret and respond to that big picture.

“Step aside.”  In other words, let the machines do what they do best (computing and automated processes) and avoid what they do worst (as in, no people skills) by stepping in to be the people-interaction intermediary. As we all know, there are situations where 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.”  There are some jobs that occupy such a narrow niche that it’s unlikely any vendor 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 won’t 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.

Mapping LIS skills to a smart-machine future

Within the LIS profession, there are multiple ways we can 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.

We’ll also need to ask how the skills we have (or are learning) can help us add value to smart-machine processes and functions. What can we, as humans, bring to the game that smart machines won’t be able to replicate?

Another question will come from envisioning our work and its value to our various constituencies. What might smart machines, freeing us up from more mundane activities, allow us to do if we had more time?

What might smart machines enable us to do as they evolve that today is beyond our resources and bandwidth?

And what, if any, new skills do we need to teach/learn in order to embrace smart machines as an opportunity for adding value rather than as a threat to marginalize us?

Let’s start the conversation

I certainly don’t have answers, but I’m guessing that among our LIS associations, graduate programs, thought leaders, students, new professionals, and “big picture” thinkers we can at least start the conversation.

Perhaps a conference session or a white paper? A student-group research project or an association trends report? A vendor-sponsored survey or an annual assessment of the profession? The reality is that we’re going to need to engage multiple stakeholders to help shape our future, but I’m firmly convinced that we can figure this out in a way that supports healthy LIS employment. After all, we’re way smarter than those smart machines….