What three studies tell us about automation in the workplace

One of the most popular topics we regularly tackle on this blog is automation, and the impact technology such as AI, Machine Learning and robotics is having, and will have, on the job market and the way we work.

In recent weeks, both in the US and UK, some interesting studies have been carried out on this hot topic by Pew Research Center and the Office for National Statistics (ONS), respectively. Meanwhile a survey entitled “Humans Wanted: Robots Need You” was conducted by recruitment company ManpowerGroup across 44 different countries, looking at the incorporation of bots into the working world and what this will mean for employees globally.

Dangerous and dirty

The Pew study examines the attitude of Americans in the light of increasing workplace automation, pulling together insightful charts and graphs from a range of public polls produced by the Center recently.

It concludes that while most Americans anticipate widespread disruption in the coming decades, few believe automation will affect their own job. Meanwhile, three quarters of Americans view job automation in a negative light with around half of respondents claiming automation has, to date, done more damage than good.

The general public is broadly supportive of automation replacing “dangerous and dirty” roles and is vehemently in favour (85 per cent) of seeing restrictions put in place to limit automation to only replacing jobs which are deemed too dangerous or unhealthy for humans.

Interestingly, when asked about whether the government or the individual should assume responsibility for helping workers who are displaced by the introduction of robots in the workplace, there was a split down the middle across party political lines.

1.5m jobs on the line

Meanwhile, across the pond, the Office of National Statistics (ONS) study states that 1.5m people in England are at high risk of losing their job. Having created a bot to analyse the jobs of 20m workers, ONS concluded that 7.4 per cent of these are at high risk of being replaced, with women assuming the highest risk by occupying 70 per cent of these roles.

There are some interesting correlations between these two studies. Both concur on the types of roles facing disruption — hospitality staff, retail assistants and sales workers top the high-risk list, while those working in medical professions and education are widely considered lower risk. Both studies also agreed that young people and part-time workers are particularly vulnerable to workforce automation.

Silver linings?

While these two studies paint an overwhelmingly bleak picture, the ManpowerGroup survey is, on the surface, far more optimistic in its outlook. The report’s overarching message is that humans and robots can coexist, and that automation needn’t be something to fear but something which will provide us with a wealth of new opportunities. It claims that 69 per cent of employers are planning to maintain the size of their workforce, while as many as 18 per cent actually want to hire more staff as a result of automation. To launch the study, Chairman and CEO of ManpowerGroup, Jonas Prising, said: “More and more robots are being added to the workforce, but humans are too.”

But if you scratch beneath the surface, the situation isn’t actually as peachy as they appear to want you to believe. The study states that “just” nine per cent of employers believe automation will lead to job losses. On paper that may not seem like a particularly high percentage, but in reality it is a very high number indeed.

Spin it how you want, automation will give with one hand and take away with the other, it will optimise some jobs and replace others, it will strike fear into some and have others in a state of excitable rapture. The world of work is changing around us as we live and breathe, and these interesting studies, however depressing they may be, offer useful insights and a valuable yardstick on the evolving attitudes of employers and workers during very uncertain times.

Last summer we discussed how automation is likely to affect different roles and tasks within the publishing ecosystem over the course of four blog posts. To find out how your job might be affected by the rise of the robots check out our The State of Automation series here: part 1, part 2, part 3, part 4.

Blockchain, Coming to a Computer Near You

Last year, Facebook was front page news when it came to light that Cambridge Analytica had obtained data on hundreds of millions of Facebook users through third-party apps. This week, Facebook CEO Mark Zuckerberg told ABC News that it is “still looking into” the claim that personal information for millions of users is easily available on Amazon.com Inc’s cloud servers. While Facebook is investigating this, what are users supposed to do? That is where blockchain might come into play.

Previously, I have written about blockchain and how it applies to publishers and content creation, but will this technology expand to help police look into how users interact with the internet and verify their identity as a whole? This week, while Zuckerberg was calling for Congress to regulate Facebook, PayPal invested in Cambridge Blockchain, a startup working to give individuals a way to own their own identity online. Akin to how blockchain allows bitcoin users to store value without a bank, blockchain may allow users to verify identity without an intermediary like Facebook.

While PayPal surely see this as something its users can benefit from for online financial transactions, this technology could have wider implications that provide safe interactions online for users of all kinds and change online communication and collaboration in a remarkable way. When you consider how many different corporate entities own our data — from banks to retailers, social media networks to airlines — we can see just how exposed we all are to data infringements, cyber-attacks, identity theft and fraud, especially as we don’t actually know just how robust and secure these companies’ data infrastructures actually are. As blockchain applications proliferate the marketplace we should start to see this balance redressed and consumers taking back control of their data. Though it’s still too early to tell what might happen in the future until the technology is used, this investment by PayPal should give users some peace of mind that they can protect themselves from identity theft in the future.

Why Preprint repositories are essential to academic work: A Case Study

There is a lot of talk about peer review and how it can be made better, but unfortunately, a lot of this happens at a level of abstraction that makes it easy to miss more modest changes that can go a long way. 

For example, a common way of proceeding in certain sciences is the pre-publication review, according to which manuscripts are uploaded online for open discussion before official peer review and journal acceptance, giving the community at large an opportunity to review results and methods. The advantage of such a process is that it makes the peer selection process far more transparent, but on the downside does not allow for anonymity for either author or reviewer. The downside might seem like it clearly isn’t worth it, since anonymity is accepted as an obvious virtue. But a real life case-study indicates why it might be worth the price.

A recent, real-life example of how a larger pool of peers might be more effective than two anonymous peer reviews can be found in a recent incident surrounding an arXiv submission. arXiv.org is a site for the submission of pre-prints of papers in Science and Math. In 2018, two researchers from the prestigious Indian Institute of Science, Dev Kumar Thapa and Anshu Pandey, posted a paper at arXiv, where they claimed to have discovered an instance of superconductivity at room temperature in “a nanostructured material that is composed of silver particles embedded into a gold matrix”. If true, this could have been a game-changer for material science and really, all of society since we could theoretically transfer electricity without any loss.

This pre-print caught the eye of a Postdoc at MIT, Brian Skinner, who probed into the data a little more and found some odd features:

Skinner wrote up his observations and posted it on arXiv himself. The story was quickly picked up on various sites, including Nature, Scientific American, and Wired. The authors, for their part, seem to have dug in their heels and have not admitted to any wrong doings.

Most relevant for the broader point about opening up peer review is that Skinner is not an expert in the field of superconductivity, so he probably wouldn’t have been a potential reviewer for the paper in question at all! And his decision to “zoom in closely” on the data isn’t a standard method for vetting papers, so if the pre-print hadn’t been posted somewhere relatively public, this discrepancy would have gone unnoticed, and the paper would have been published. The best case scenario then would be retraction.

Of course, there is the lingering question of whether such a model could be extended outside certain sciences. For example, it has been pointed out that medical journals might resist this because making results public prematurely might impede the ability to get proper press attention after full publication. And there are questions about whether the lack of anonymity at the preprocess stage would effectively do away with anonymity since the authors will already be known from the pre-print. So this is far from a knockdown argument. But I suspect one reason pre-prints aren’t more popular is simply that many people outside the sciences haven’t heard of them, but that at least can be addressed easily enough.

Trending now — AI ethics

In a significant move this week, Google announced the formation of an external global advisory council designed to offer “guidance on ethical issues relating to artificial intelligence, automation and related technologies”.

The Advanced Technology External Advisory Council (ATEAC) will consist of eight leading academics and policy experts from around the world, including former US deputy secretary of state William Joseph Burns, the University of Bath’s computer science professor Joanna Bryson and mathematician Bubacarr Bah, who will meet for the first time in April and on a further three occasions throughout the year.

This move doesn’t necessarily represent a sea-change in the tech giant’s policy and attitude towards AI ethics - the company had already established internal councils, panels and review teams to confront the challenges posed by AI and related technologies. Last June it published its seven guiding AI principals, outlining its approach towards the adoption of AI. However, notably it is the first time Google has sought worldwide expertise on AI to inform its overall strategy, and it will be interesting to see how this development impacts the company’s future business decisions, which have often come under a great deal of criticism.

Google is not the only tech powerhouse looking at ethics and how it goes about adopting, investing in and incorporating AI innovations. Perhaps coincidentally, just a day before Google launched the external advisory council at the MIT Technology Review's EmTech Digital conference, Amazon had revealed a collaboration with the National Science Foundation and a $10m cash injection to help develop systems based on fairness in AI. Meanwhile over at Microsoft, Harry Shum, executive VP of its AI and Research Group, had also announced at the very same conference that it will be adding “an ethics review focusing on AI issues to its standard checklist of audits that precede the release of new products”.

The discourse around AI, particularly coming from the heavy hitters in Silicon Valley, has certainly changed, that much is clear. And whether this is down to pressure being applied on these firms to adopt a less gung-ho and more measured approach as they slog it out on the AI innovation battlefield, remains to be seen.

But is it realistic to expect the likes of Google to genuinely care about AI ethics in so far as they are prepared to start prioritising these issues above their own sizeable business interests? This week the general mood at the summit in San Francisco was sceptical. Rishida Richardson, director of policy research for the AI Now Institute, was quoted in Reuters as saying:Considering the amount of resources and the level of acceleration that's going into commercial products, I don't think the same level of investment is going into making sure their products are also safe and not discriminatory.” 

While AI ethics may now be at the forefront of the agenda at conferences such as EmTech Digital, companies are still not being held accountable by the necessary regulation and legislation to keep them in-check and ensure that their roll-outs are responsible and ethical. In the absence of a single, global regulatory body operating in the field of AI, large tech firms are pretty much left to their own devices to self-regulate and develop AI-driven products and services without any directives or consequence. It’s a dangerous situation, and one which has led to several high profile, real world incidents whereby AI-based innovations have been rushed through and members of the general public have paid the price.

If we want the tech giants to offer more than lip service and tokenistic gestures on ethics in AI, maybe now is the time the industry should consider introducing independent regulation to enforce ethics rather than just talk about them.

BISG and PageMajik Survey Shows Publishing Workflow in Need of Rethinking

This piece was originally published in the Publishers Weekly Book Brunch London Book Fair Show Daily

When the digital revolution began over a decade ago, publishers were forced to examine their decades- old way of doing business. The move to digital forced publishers to look for dramatic ways to improve efficiency and keep up with a market they struggled to recognize. Unfortunately, the processes that followed were often a digital version of an existing system, barely improving productivity and, in some cases, creating additional unnecessary work.

To learn more about pain points in the publishing workflow, PageMajik and Book Industry Study Group (BISG) last fall partnered on a survey of publishing professionals. The goal: identify issues and offer workflow solutions that would help both the industry and individual publishers.

The survey revealed that 17% of respondents spend 25–50% of their time doing repetitive tasks, while 47% of respondents said repetitive tasks take up 10–25% of their time. Of those repetitive tasks, 58% of respondents felt that some of those tasks were avoidable. And, over half the respondents also said they could be more effective in their jobs if repetitive work was eliminated.

Among the largest time-wasting activities, according to respondents, were updating metadata, providing the same information in multiple reports, tracking projects in various formats, and outlining assignments.

A system that automates some of these processes would provide publishers with both efficiency and time. In turn, those publishers could focus on higher-level product development and related strategic work, such as acquisition, design, and promotion.

The conversation about workflow best practices doesn’t end with the survey or this article. On March 28th in New York, the Book Industry Study Group (BISG) will host a meeting focused on cloud-based workflows. Structured as an interactive, two-hour workshop, the program will solicit even more information about the challenges publishers and the book industry face.

PageMajik is also continuing to explore these challenges and share its views on how to address them. For more information about the survey or to discuss your particular workflow challenges and how we might help, please visit me at the PageMajik booth at Stand #3B08.

Jon White is the Global Vice President of Sales & Marketing at PageMajik.

Marshall Cavendish Education launches pilot with PageMajik

Leading Singapore-based education publisher Marshall Cavendish Education will be piloting PageMajik’s publishing workflow-based Content Management System. The roll out will happen in stages, upon the successful completion of the pilot.

Marshall Cavendish Education produces more than 400 curriculum-based titles each year, and, working with PageMajik, the publisher’s authors, editors and designers will be able to work together on one intuitive platform to improve collaboration, streamline workflows, and assist in meeting deadlines.

Richard Soh, Manager of Publishing Systems and Administration at Marshall Cavendish Education commented: “We are very excited about working with PageMajik. We anticipate that the product will dramatically improve the way we produce and publish content across the organisation, bringing more speed and efficiency into our publishing processes.”

Ashok Giri, CEO at PageMajik stated: “Marshall Cavendish Education has a magnificent history and heritage in education publishing and we are delighted to be working with the company to implement our product across their business. We are really looking forward to this collaboration and are confident that the PageMajik system will bring about positive change to the way Marshall Cavendish Education develop and produce content.”

 

About Marshall Cavendish Education

A subsidiary of Times Publishing Limited, Marshall Cavendish Education is the leading provider of distinctive K–12 educational solutions in Singapore, providing Singapore schools with innovative, high-quality content and solutions. 

For 60 years, Marshall Cavendish Education has constantly developed solutions.to ensure educational excellence and has earned the approval of the Ministry of Education, Singapore.

Headquartered in Singapore, Marshall Cavendish Education has offices in Hong Kong, China, Thailand, Chile and the United States. The brand is also recognised worldwide for its work in ensuring excellent educational standards and for continuously raising the quality of learning around the world, inspiring students and educators to learn and teach more effectively.

For more information, please visit www.mceducation.com.

 

About PageMajik 

We are a 40-member team comprising experienced industry professionals and tech wizards with relevant domain experience in both the publishing and the software development side. Our core team has worked with the publishing industry for a combined 10 decades and has been able to use the experience to develop a truly revolutionary product. We listen to the needs of our customers, and incorporate forward-facing ideas into the development of our solution. Our product is ever-changing as we are constantly trying to improve the experience for our users.

For more information, please visit www.pagemajik.com.

Scorecards as a Method to Tackle Submission Overloads

Information is easy to think of all-at-once, as though it were a single fluid somewhere on the internet. But when we start thinking about its materiality, we are forced to consider how it is processed in discrete quantities through multiple nodes. For publishing specifically, a feature that is simultaneously obvious and somehow under-appreciated is that the massive amounts of academic output we make use of depends on the labour of actual editors. This involves having to sift through submissions and make calls about whether to reject them, who to request reviews from, decide how to react to the reviews received, and make a final judgement on whether to reject, accept, or recommend re-submission.

This dependence on human editors with limited time means they act as gatekeepers to which manuscripts get the green light and which remain locked away in private drawers. One academic philosopher calculates that even if we make the conservative estimate of a steady number of 10,000 papers submitted every year, this dwarfs the 2,000 or so number of spaces available for publishing. This will mean 8,000 unaccepted in the first year which scholars try to publish the next year too, which means 18,000 submissions competing for 2,000 slots. And then 26,000, and then 34,00. A staggering number of submissions will have to be dealt with.

What’s worse, the calculation above assumed that there was a fixed number of submissions every year, and we know this isn’t true — as we’ve written before, an estimate from Lutz Bornmann and Ruediger Mutz in their 2014 paper Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references, there seems to be an increase in overall submissions of 8–9 per cent every year.

Editors cannot look at more than one submission at a time, no matter how much they wish they could. Delays are to be expected, but if submissions are made during the delay itself, then this hardly solves the problem. I’m sure editors use a number of strategies to try to deal with this problem, but I suspect that a fairly common outcome (intentional or otherwise) is differential attention paid to articles based on whether the editor knows the author or topic, whether the writing style is sophisticated, etc. In other words, there is already bound to be heuristics and rules of thumb to sift through submissions made. This isn’t meant to be criticism of editors, but an acknowledgement that our inability to process large amounts of information simultaneously means that we need methods to order information in processable ways. This is a perfect place for introducing AI.

Acknowledging that editors already have a variety of preferences means seeing that they are quite likely differ systematically with disciplines and idiosyncratically with personal taste. The system offered to score submissions will not be one that simply scores every paper according to pre-set metric, but can involve multiple customizable factors that include number of previous author submissions and the number of times the previous works were cited, relevance of the title and key terms to the discipline, the similarity of the topics discussed with articles previously published in that particular journal, etc. And the specific weight each of these factors should get in the score can also be set.

At first glance, this might seem like too coarse-grained a tool because we can think of all kinds of papers we might like which might have been ranked low by some of these metrics. For example, new academics will be at a disadvantage if previous citations are taken into account, work that breaks new ground will be set back because its topics might not match existing trends, and non-prosaic titles may suffer if they lose out in favour of titles which are more to-the-point (consider how the Historian Simon Schaffer, for example, has a paper on ship design hilariously titled “Fish and Ships”). These are real and serious concerns.

But there are three reasons I still think scorecards should be adopted anyway, First, as I’ve tried to emphasize, many of these tests are already being used by editors now. For example, submissions by celebrated academics are treated vastly differently compared to unknown grad students. This system just makes this explicit, and so holding the system to a higher standard to human editors seems unfair. Second, this making explicit of standards can force academics to coordinate publicly about what exactly they will look out for in submissions, possibly even making the entire process more transparent instead of the black box it so often is. Third, as submissions increase, editors already are going to have to choose where to focus attention. The question is whether they choose to opt for a procedure of looking at submissions in order of submission or randomly, or according to some specifiable metric.

It has to be remembered that this is only a sorting mechanism to decide the order in which the article should be read, and not intended to judge the quality of the article itself. There are still many questions and issues to address, but understood in this manner, it seems like a potentially vital tool to help deal with submission increases and regain some control.

Solving Indexing, one step at a time

Publishing is on the verge of exciting times. The promise of relatively new technology like machine learning, artificial intelligence, and Natural Language Processing makes it incredibly tempting to speculate on the new world we’ll soon be living in, including questions about which processes can be automated and whose jobs will be taken over. (We have even done some of the speculating ourselves, here and here).

While there is certainly a time for thinking carefully about large scale changes to our industry, I do fear that thinking only in terms of large scale changes makes us focus on the wrong questions — by constantly thinking in terms of abstractions and generalities, we can inadvertently ignore and fail to value the concrete.

Consider for example, the state of indexing. As any academic will tell you, indexing can be incredibly helpful for research. By listing major topics and the page numbers they are mentioned in, it allows readers to first decide whether a certain resource is what they are looking for by giving them a taste of the topics covered as well as a rough estimate of the extent to which they are addressed. And for research, a well-designed index enables people to narrow in on precisely the topic necessary, since obviously every resource cannot be read from scratch each time a paper or a book or a website entry needs to be written. The need for the index then is very real.

In addition, few people I talked to in publishing and in the world of academia think that the current indexing procedures work. A recent popular Twitter thread by historian and editor Audra Wolfe raised many issues I have been hearing about. She tweeted that professional indexers were essential for any academic who wasn’t knowledgeable about and competent at indexing, because otherwise the result was often “frustrating and unprofessional”.

In response, historian Bodie Ashton pointed out that early career researchers simply cannot hire indexers, and that if he had paid $7 per page for his first book, the indexing fee would have been a whole order of magnitude more than what he would have earned in royalties. Historian of technology Marie Hicks weighed in too, revealing that the turnaround time required by the publisher was too short to be able to hire an indexer. Moreover, they pointed out that it simply seemed unacceptable that anyone would need thousands of dollars to be able to produce an index that was professional.

I agree. This strikes me as a situation ripe for technological intervention— an indispensable job that costs too much and takes far too much time. The biggest obstacle to incorporating technology, however, is that expectations seem to skew too far in two directions. On the one hand, tech optimists seem to think we can come up with an indexing engine that will immediately replace professional indexers, saving them both time and money. Unfortunately, the work of indexing is not simply mechanical in a way that can be captured by a simple algorithm, but instead depends on skill that takes time to develop, and quite often also expertise in the discipline that the book belongs to. Unsurprisingly then, trying to replace human indexers wholesale results in unhappiness all around. Authors report being forced to live with clearly inadequate results or else having to redo the whole job themselves.

On the other hand, some people seem to over-correct and insist that indexing cannot possibly be improved, that we simply should accept the way things are. This kind of lapse into a fatalistic pessimism is sadly understandable. For some time now, there has been a standard story about how things play out: the unrealistic expectations of some about publishing tech leads to publishing tech advertising abilities they simply cannot deliver on, leading to disappointment all around. As this keeps repeating, of course publishers start to instinctively react to tech with skepticism. But given that there are real problems that need to be addressed — as the original tweets testify — this position isn’t sustainable either.

I believe the way out of this impasse is to recognise that this is in a very real way an artificial problem. Our talk of tech in terms of abstractions and generalisations only allows us to speak of progress in terms of binary states, as entirely successful or as entirely failing. Rather than fall for this, we need to stop asking whether a certain task can be automated or be performed by AI engines, and instead ask in what ways can tech actually help us, given where we are. Once we do this, we can start noticing that there are multiple products already that can assist indexing.

Keyword extractors that already exist may not be perfect but they can certainly generate a list of suggestions that can dramatically cut back on time, since authors or indexers will only need to remove unnecessary entries, add any left out, and tweak existing ones (for example, a case of synonyms or two different people with the same name accidentally classified as the same person). Statistical information about the frequency of terms can significantly ease indexing by showing the spread of a topic through the entire manuscript. And certain categories of keywords can be extracted better than others — proper names for example are far easier to identify than key concepts. And this is by no means the end of the line. I even predict engines intelligent enough to autogenerate keywords based on the kind of reader and subject area in the coming years.

Such a plan is undeniably ambitious, and will require quite a different fundamental attitude towards tech and change. But as one scholar wistfully writes about the task of indexing, an arrangement where publishers can take care of indexing well and quickly would be ideal. This can be made real, but only one step at a time.

Blame Watson: Real AI vs. Fake AI

The phrase “Artificial Intelligence” has become ubiquitous over the last several years and we know where to place the blame — on IBM’s Watson. From predicting the weather to playing Jeopardy to diagnosing patients, Watson, and thus AI, appears to be everywhere and apparently can do anything. No longer the terror that is HAL from “2001: A Space Odyssey,” the new perception of Artificial Intelligence is that machines can and already do help humans with virtually anything.

Because of the excitement around AI and the possibilities through using this technology, many companies are blurring the lines of what AI means in order to capitalize on the recent trend with both investors and consumers. Unfortunately, much of those claims are smoke and mirrors, causing customers to buy into fake AI systems. In order to not be one of those sucked into this trap, we first must outline what AI truly is.

Artificial Intelligence implies using a combination of neural networks and machine learning that provide insight, analysis, and action without human interaction or direction. Useful and autonomous AI eliminates the need for human intervention and interaction; the machine does all the work for humans, it doesn’t just provide insights. For example, a true AI system could ingest massive amounts of data, provide analysis of said data, and take the next step to action on that analysis. Instead, what many systems and services use is “machine learning.”

Machine learning, while good, still requires human interaction to provide the structure and the continually revised set of rules the machine uses in order to “learn.” While many of these systems are very good, if a company is seeking to eliminate this work entirely from their human workforce’s to-do list, this system would not be able to do that.

So, how to tell if the system you’re considering is truly autonomous and thus worthy of the investment.

· Does it require a human to manage the system?

· Is it something that requires months of on-boarding?

· Does the system actually do the work for you or does it just provide suggestions for what you then have to do yourself?

Before you buy a system make sure that it will actually improve your workflow for the better, not add another difficult layer of work for you and your colleagues to manage. The benefit of using AI is always to improve on the speed in which work can be done, exceeding what a human can do. If your system is not providing that service, it may be time to rethink it.

2019: Year of the Workflow

Aside from the flu, dieting fads and Blue Monday, for many in the publishing industry January can only mean one thing – it’s time to implement plans and budgets for the year ahead. But as the marketing, sales, editorial, acquisitions and rights teams all bid against each other for more lines in the budget, grappling for a greater slice of the pie, how much is left in the pot for innovation, investment in technology and long-term strategic and visionary thinking?

The answer more often than not, as you might expect, is very little indeed. Decision makers in publishing have traditionally been very reluctant to prioritise investment in new technologies, replacing legacy systems and adapting workflows, sticking with the status quo as opposed to rocking the boat and causing inevitable short-term disruption and anxiety among employees.

Complete system overhauls are extremely rare in publishing, particularly in the larger houses where the scale of cost and disruption is much more prominent. This means companies are often locked into deals with suppliers for decades, leaving them lumbered with archaic solutions which haven’t necessarily adapted with the times to suit their needs. While it’s far from an ideal situation that many in the industry are still using 20th century technology on a day-to-day basis, it is unrealistic to expect publishers to take big, drastic steps in order to change things, especially during times of political and economic uncertainty.

But this doesn’t mean that publishers are turning a blind eye to technology and innovation. Last year we spoke to hundreds of business leaders across all sectors of the publishing world, many of whom were increasingly open to adapting their internal workflows in an effort to boost efficiency and stem loss of revenue.

Why workflows, you may ask? Well, one of the main issues has been that, while most publishers are producing books and journals across all formats, the workflows embedded throughout publishing companies are still primarily print-first models. This means that the processes in place for bringing ebooks, online journals and audiobooks to market are often the same for print products, which traditionally require much longer lead times. A case study by Gutenberg Technology, published in March last year, revealed the benefits of switching to synchronous print/digital or digital-first workflows, claiming that 47 per cent of time can be saved and as much as 30 percent of costs can be saved” if publishers were to adopt this modern way of working.

These are compelling statistics, which most CEOs are not taking lightly. In an industry where there is a constant struggle to keep costs down, profit margins are wafer slim and market forces are working against us, publishers can no longer afford any unnecessary wastage in their supply chains and internal workflows. Streamlining workflows and looking at how many tasks across the publishing business can be automated thanks to innovative new technologies is what industry leaders are now turning their attention to as strategy du jour.

So, while I don’t expect 2019 to be a year when publishers revolutionise the way they use technology and do business, I do believe it will be one where we take baby steps towards a smarter and more agile way of working. And technology will play a vital role in shaping the workflows publishers increasingly choose to adopt in the not so far future.

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