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 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


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

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.

A More Efficient System: A Look Ahead at 2019

Last year on the blog, we highlighted several ways in which technology is influencing and changing content industries. From newspapers to book and journal publishing, music to fine art, technology is speeding up processes, streamlining workflow, helping with discovery, creation, and fact-checking content, and improving the way we reach customers. What we also discussed is how, in many ways, these changes will impact those working within these industries.

As we look ahead to 2019, I want to emphasize some of the key changes we can anticipate this year to help prepare for the future of publishing and align our industry better with the changes that are going on in the world around us.

Artificial Intelligence

Even though we see artificial intelligence in our day-to-day lives, there continues to be some knee-jerk wariness on the part of publishers. Because Artificial Intelligence is uncharted territory, publishers aren’t alone. In a survey of some 979 technology pioneers, innovators, developers, business and policy leaders, researchers and activists conducted in the summer of 2018 by Pew Research, the experts predicted “networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities.”

Though AI on a larger, global scale should be taken in a slow, plodding way to ensure proper implementation and protection for the humans involved in each industry, on a smaller scale in publishing, AI can help improve workflow and systems, allowing for humans to engage in fewer mundane, time-consuming tasks and more high-level, creative pursuits.

Workflow Solutions and Automation

AI and machine learning are used to help automate some repetitive tasks in publishing. Last year, we ran a series on the State of Automation, which was highlighted by The Bookseller, outlining how automation is being implemented in the world around us—from retail sites to healthcare—and how it will impact the publishing industry on a granular level. Though automation is still very much in its infancy in publishing, it has the potential to be one of the more disruptive changes in the foreseeable future.

By automating many systems, some departments, such as production, editorial, and rights may have radically different workloads and responsibilities. By automating some of these systems, we could free up these departments to expand their roles into new, creative areas.

Blockchain in Publishing

Blockchain became the hot topic last year. Blockchain is a decentralized, digitized series of information blocks shared in a peer-to-peer network and the technology behind the cryptocurrency Bitcoin. For academic publishers, blockchain seems to be the most viable way to chart research, peer review, and dissemination of information.

Just last week, Dutch publishing consultant Sebastian Posth released a paper entitled “What Is Blockchain: Why and How Should the Industry Care?” comparing the shift to blockchain and cryptocurrencies as “significant as the shift that happened with the emergence of the internet.” Posth illustrates how blockchain can help publishers and other media with piracy, payment, expanding the ability to reach customers better, but blockchain will also “confront publishers with new, inherent obstacles and questions: about identity and governance; about laws and regulations; about transactions and revenue models; about crypto-currencies and currency-conversions; about crypto-economics and financial incentives; about censorship and borders – and a lot of things they might have never thought of before.”

AI in publishing production - Simply a question of “when”

Book publishing has always been an industry of tight margins. Particularly in the world of the printed book, publishers have always found themselves at the mercy of overheads which are well beyond their control. From paper and ink costs to fluctuating global currencies and transportation outlays, publishers’ profits have traditionally ebbed and flowed based on external factors, and we haven’t even mentioned challenges with the retailing environment and evolving reading habits.

This was a major concern among many of the C-level executives I met with at the Frankfurt Book Fair in October, and then more recently at other conferences in the US and UK. On the one hand they were happy and defiant that the printed book had apparently held strong in the midst of challenging trading conditions while having fought off a range of disruptive elements in the marketplace, but on the other they were showing a growing concern about rising costs associated with physical product, and evidently feeling the pinch.

Lean and mean

Books are an expensive business. However, most of the people I spoke to at Frankfurt were insistent that passing these increasing costs onto the consumer was not an option they were willing to explore. Yet they were extremely keen to hear about ways in which technology trends such as AI could enable them to streamline efficiencies across the business to reduce operational expenditure.

More than ever, directors of publishing houses are looking for ways to make their organisations leaner and meaner, and to ensure that they’re not overspending and overstaffing, in an effort to recoup some of these spiralling overheads. And many are becoming increasingly aware of the fact that the new wave of technologies being made available will help them to do exactly that.

How soon is now?

Several months ago, on this blog, we explored how automation is likely to impact various different roles within publishing. We concluded that where technologies such as AI would likely have the most significant short-term impact would be within the production and editorial departments and that we can expect publishers to start rolling out AI-based technologies into their workflows within the next two years or so.

Some in the industry were quick to dismiss this prediction, stating that they just couldn’t see publishers implementing AI-driven technologies, in any part of the business, in the immediate future. However, judging from these exchanges in Frankfurt I am now more convinced than ever that leaders are already prepared to take a long, hard look at how technology can help them to optimise their business processes.

First past the post

Earlier in the year we suggested that by introducing machine learning into the publishing workflow, particularly across pre-production and editorial departments, publishers can free up around 40 per cent of the time that is spent on manual tasks. In my mind this is a conservative figure, especially when you consider how much production resource is put into formatting, layout, typesetting and proofing - all highly automatable tasks which machine learning-driven technology can undertake.

The technology is there, and the business case has been identified. Now that publishing leaders are starting to really take a keen interest in how this technology works and how it can be applied across their organisations to boost efficiencies, make savings and drive revenue, it’s not a question of “if” but “when” and “how far” they want to go with it. Either way, the production department will certainly be the first to witness AI in action, and the benefits of this transition will be immediately felt all around the business.

Last Chance to Participate in Our Workflow Survey

We have partnered with the Book Industry Study Group (BISG) on a survey of publishing professionals to tell us where they struggle for time in their daily work lives — what work takes up the most time? what could you focus on if you had unlimited hours? how do you see the future of your role in publishing?

In talking to publishing professionals about their jobs, we hope to better understand where the industry is going and how we can provide solutions for challenges we face in our daily work lives.

We are closing the survey at the end of the year and we would love to hear from you. Please go here to tell us what your challenges are and how we can help you.

Discovery, Efficiency, and Better Research Tools: How PageMajik Can Work for Libraries

Open access and the recession have changed the landscape of library budgets and usage over the last 10 years. Library book and journal budgets have decreased; huge volumes of open access content exist, but there is no quality control or easy way to discover research; and the rise of new university presses publishing monographs, conference proceedings, and other content are trying to do so with the same staff and on a shoestring budget.

Last month, The Charleston Conference gathered together librarians, publishers, electronic resource managers, consultants, and vendors to discuss issues such as these and others, to chart a way forward, and to bring together companies who are working in that space to share some services that might be helpful to libraries as their roles continue to change.

The Charleston Premiers portion of the conference in which publishers and vendors showcase their newest and most forward-thinking products that may not be well known to the audience as a whole. The audience then votes to select their favourites in a variety of categories. We were pleased to have PageMajik selected as “Most Innovative Product” by the audience.

“For several years now the Charleston Premiers, which previews new and noteworthy products and innovations on the marketplace, has been gaining popularity at the Charleston Conference, particularly due to its fun, quick-fire pitching format and audience interaction,” said Anthony Watkinson, Director of the Charleston Conference. “This year delegates to the conference were particularly impressed by PageMajik’s pioneering approach towards improving publishing workflows and its innovative application of new tech such as AI, and I’d like to congratulate the company on winning our Most Innovative Product.”

PageMajik was developed out of our 40 years of experience working with publishers and libraries to understand the challenges that come with reduced budgets, small staffs, and vast amounts of information to sift through via open access.

What we discovered at the Charleston Conference was that there are many ways PageMajik can be useful to libraries. Most specifically, as libraries enter into the publishing side of the industry, using machine learning to tackle repetitive, time-consuming, expensive aspects of the publishing process, allows libraries and new university presses to free up 40% of the time spent on manual editorial and production tasks to focus on higher level work. Another, more traditional use of PageMajik is through the automatic meta-data tagging and analysis the system provides and which offers vastly improved discovery in the sea of content, cutting research time in half and making those research results more fruitful.

The team at PageMajik prides itself on its innovative approach to radical improvement, increased speed and cost reduction within the editorial workflow. As we work with libraries more, we are eager to find other ways we can help improve their processes. For more information or to tell us your particular challenges, please go to

No Winter of Discontent in Newsrooms

As the days grow shorter and the nights grow longer, it’s beginning to feel a lot like winter. But, will this cold season mean cold feet when it comes to AI investment and roll outs, as some are predicting — or in other words will this be another “AI Winter”?

There is no denying that there is, still, a lot of hype around AI. And with this hype comes inevitable disillusionment when some of the bold statements, commitments and trials don’t pan out as expected.

Many industries and companies experience ‘AI fails’ when projects aren’t properly planned out, are rushed through, are done for the wrong reasons, are not scalable, or are not supported by the correct infrastructures. Recently, for example, the automotive industry was dealt a blow when deep learning powered self-driving car experiments didn’t go to plan, setting progress back years.

Peaks and troughs

These peaks and troughs of enthusiasm and disappointment are characteristics of pretty much every major technological disruption in history, and part and parcel of the hype cycle, a concept famously created by IT analysts Gartner, whose basic graphical illustration helps to explain this phenomenon.

Some industries, and some companies operating within them, are further along the AI hype cycle than others. Arguably book publishing is at the very beginning of this process, so yet to experience a “peak of inflated expectation” let alone a “trough of disillusionment” or “AI Winter”, for that matter.

Early adopting cousins

Interestingly, one of the most advanced and progressive industries for innovative AI applications is the newspaper and magazine publishing industry. Our cousins have been experimenting and rolling out machine learning initiatives since 2013 when the Associated Press became an early adopter, automating formulaic business and sports reporting.

Two years later the New York Times implemented an AI project called Editor to help journalists reduce labour-intensive tasks such as research and fact-checking. In 2016, the Washington Post trialled “robot journalism” at the Rio Olympics using Heliograf software, which analysed data and produced news stories. And last year Reuters launched its News Tracer product, which uses machine learning to sift through social media outlets for legit breaking news. Finally, just a few days ago, Quartz announced the launch of the Quartz AI Studio, a new tool to help journalists around the world use machine learning to report their stories.

Forced hands

There are good reasons why newsrooms in particular have been so quick to innovate and experiment with AI, arguably reaching the “Plateau of Productivity” on Gartner’s hype cycle long before others. The tumultuous, cash-strapped sector has faced severe disruption in the form of migration to digital, changing consumer purchasing and reading habits, and a complete shake-up of the traditional business and revenue models which had existed for years (so not too dissimilar from the evolution of book publishing, but at breakneck speed). Pew Research reported that in the space of just 10 years newsroom employment at US newspapers dropped by nearly a quarter. There has never been more pressure on editorial teams to work more efficiently and deliver more with less resources.

In the face of such extreme circumstances and weakening financial conditions for media publishers, AI is clearly seen as a knight in shining armour, helping newsrooms to work harder, faster and smarter. And it just so happens that journalism, not traditionally seen as a hotbed of innovation, is the perfect testing ground for AI projects.

Lessons to learn

So, what can the book publishing industry learn from its cousins and their early adoption of AI technologies, given that we potentially have the benefit of a slower curve of disruption? If we look at where AI is being introduced in newsrooms, we can see most of the implementations are launched to boost efficiencies. Not necessarily to replace journalists on any meaningful scale, but to assist them in their roles, and take care of the more mundane and repetitive aspects of their roles, so they can focus on bigger and better things.

As Uber, Tesla and others within the automotive industry are learning, ambitious AI and machine learning projects can be incredibly risk averse and long, frustrating processes. Yet, as many newsrooms can now attest, workflow-based AI projects, which are innovative while scalable, useful and well-grounded can be incredibly effective and make all the difference. It’s realistic that the book industry will start to see AI applications rolling out over the next few years, and judging from the experiences of our cousins, these AI rollouts will be most successful when embedded in our workflows.