At Allianz insurance, Lemonade isn’t just a beverage on hand in the boardroom it’s the future of direct- to-consumer insurance sales. Lemonade is an app that uses artificial intelligence (AI) and machine learning (ML) to enable insurance buyers to purchase insurance and file claims in minutes without human interaction. At the Klopotek Publisher’s Forum conference in Berlin last week, Klaus Driever, Head of Strategic Digital Initiatives for Allianz AG, presented their version of digital transformation. Allianz is not a publisher (in the traditional sense) but similarities between insurance and publishing exist: Long-evolved legacy processes, evolving B2C models and operating pressures including efficiency and profitability.
Allianz is a legacy insurance company with a long history, but they are self-aware enough to understand that the way they operated in a digital world is ineffective in a customer-centric world. The same is often said about the publishing industry, which suggests that the application of machine learning and artificial intelligence to publishing processes and procedures could have the same growth effect as Lemonade has had at Allianz.
At most publishing houses, content acquisition, development and production are poorly run departments lacking efficiency and effectiveness. In the process of managing reams of submissions for a limited number of publications or lists, most submissions are ignored or only cursorily reviewed over periods which can exceed months or years. Once a submission is accepted for publication, the writing, editorial and production cycles are deemed efficient if measured in months, (and not necessarily excessive if measured in years). Once published, the publication could fall flat due to poor or unintelligent marketing and promotion. The process is frequently obscure to all participants and rejected authors receive little or no feedback on their submission. Artificial intelligence applied to these processes could be revolutionary.
As I noted in a previous post (I Robot), many AI solutions are entering the marketplace, but I am not yet aware of any real data on the benefits being seen from these solutions. But, generally speaking, the adoption of AI is having a material impact in other ‘non-technology’ environments. For example, in hiring (typified by lots of resume submissions), AI applications have been able to significantly reduce the time spent on matching applications to job specifications. And machine intelligence has been shown to predict the likelihood of job success of specific applicants. Similarly, Hilton has shortened the average time it takes to hire a candidate from 42 days to five with the help of HireVue, an AI startup. Shortening cycle times can have a material impact on revenues and Allianz spoke about this positive aspect of their Lemonade implementation: They also believe their B2C solutions will generate incremental sales based on ease of use for consumers.
Implemented initially for life insurance, Lemonade will soon be a full-service provider for all Allianz consumer insurance products. Cycle time is important in publishing as well: Many years ago, Macmillan Computer Book publishing was able to produce a new computer reference title in two weeks versus their standard eight-week process. Each week shaved off the standard production process was worth $100K in revenue. AI and ML will achieve the same kinds of improvements but without the “brute force” brought to bear by Macmillan and, at the same time, apply continuous improvement to processes (producing even more gains).
Imagine an AI-based app like Lemonade for publishing submissions which asks simple questions about authors and their work, asks them to describe the work and to detail their process and/or research approach. Authors also submit their work via the app. In the background, the AI technology can interpret each these inputs and place the results against a ‘success or fit’ graph. Works can be rejected not only because they aren’t very good, but also because they are not a fit for the publisher. Additionally, feedback can be communicated back to the author for their interpretation and use. Initially, this process for a publisher could serve to funnel publishable materials to acquisition editors but, given the potential of AI and ML (and current examples from other industries), there is every reason to believe humans could eventually be excluded from most of the editorial and production process.
Once implemented within publishing workflows more improvement initiatives will emerge. And frankly, they must because exerting any additional productivity or optimization out of current processes is simply impossible and will never support the breadth of transformation required for reinvention. Having implemented Lemonade, Allianz has also seen opportunities for new products which may not have been available without the AI solution and attendant customer insights. Other companies have expanded AI use from recruitment screening to staff diversity, hiring and staff retention.
The publishing process is a symphony of inefficient tasks, activities and procedures that is heavily reliant on people for execution. While this may make publishing a quaint, friendly industry it also makes it prime for reinvention and rethinking. Publishing has emerged from the digital book ‘revolution’ largely intact without huge dislocations, but that will not be the case once AI and ML are commonplace in the industry. Publishing’s end product will be similar to those created today, but future publishing processes will be unrecognizable next to those in current practice. There is a revolution coming and, unless we anticipate it, not everyone is going to be able to make Lemonade.
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Michael Cairns is a business strategy consultant and executive. He
can be reached at michael.cairns@infomediapartners.com for project work
or executive roles.
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