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Why the future of content is autonomous

(Image credit: Image source: Shutterstock/Bakhtiar Zein)

A crucial aspect of engaging and retaining your audience is ensuring that the content they come into contact with speaks to their needs and is delivered in a way that they can easily understand. Relevancy and language are key. In fact, it’s practically hard-wired into our brains to filter out irrelevance – in all forms.

With customers demanding more unique online experiences than ever before, companies often struggle to implement the technology required to truly connect with their audiences, and deliver a consistent customer journey. Whether it’s addressing customers in their native languages, creating content that’s relevant to them or delivering it in the best way, there are so many elements for businesses and marketers to consider in this day and age.

Understanding that more can mean less

Unsurprisingly, many often fall into the trap of thinking that more content increases their chances of meeting these needs. Yet by increasing their output, not only do businesses use up valuable human resources, they run the risk of bombarding their audiences with too many choices and messages, whilst not catering to their very basic demands.

According to a recent 2018 Forrester study, today’s companies are struggling to handle the growing volume and velocity of content. Despite this, 93 per cent say that the amount of content they produce will only rise within the next two years. Evidently, both marketers and customers are feeling overwhelmed by the content they are encountering.  When we consider the pressure that this places on the individual to generate high quantities of writing, and the consequences having too many options has on audiences, we have to ask ourselves - is it really worth it?

In order to fight audience fatigue, businesses must think strategically about their content strategy, and accept that adopting a ‘less is more’ approach doesn’t necessarily mean compromising their creativity. In fact, it can mean quite the opposite. Without the pressure of generating new material at scale, businesses can win back some of their marketer’s precious time. And by adopting a machine-first approach, forward-thinking companies can optimise the digital experience by synthesising content on demand.

But what does ‘adopting a machine-first approach’ really mean?

It’s no secret that Artificial Intelligence (AI) has influenced a significant shift in the way that industries operate in recent years. A machine’s ability to learn from examples and use that information to inform future decisions, brings with it a number of opportunities. When we consider content as one example of this, Linguistic AI helps businesses understand, transform and create language with little human involvement once the initial AI training phase is complete. In this sense, AI enables the language to actually create itself.

By embedding machine learning technologies into the content supply chain, businesses are now able to create more relevant content at scale in a way that was previously impossible. Intelligent content that sees itself, knows itself, and fluidly combines and recombines is not only the future, it’s a present day opportunity.

How linguistic AI can assist content creators

Not only is Linguistic AI capable of creating language, it also helps assist marketers in their own content creation process. Leveraging data insights enables the algorithm to sift through vast volumes of content and connect a person with the most relevant and helpful information they need to add value to the material that they’re working on.

Combining the creativity of humans with the speed and agility of machines means less time spent on manual tasks like searching and managing content, and more on extracting value from it. In fact, it’s a no brainer. Why? Consider that data professionals spend more time on governing, searching and preparing data than capitalising on its benefits. According to IDC they waste an average of 14 hours per week because they are unable to find, protect or prepare data, and a further 10 hours on building information assets that already exist.

Man and machine fuelling the future of content

When change is a constant, we ask ourselves - how do we prepare for the future? We all feel the rapid pace of technological advancement, yet it’s sometimes easy to miss how these technologies can fundamentally change the nature of our problems, whilst we’re swept up in the excitement and uncertainty of it all. If we consider the wise words of Albert Einstein, we’re reminded of the value of stepping back and taking time to reflect on the roadblocks that stand in our way. "If I had an hour to solve a problem, I would spend 55 minutes thinking about the problem and five minutes thinking about solutions."

As an example, in a future where autonomous, shared cars are the norm, does it make sense to worry about having enough parking spots for future retail stores? Instead, perhaps a better question to answer would be - what do we do with the vast amounts of parking spaces that are currently taking up valuable space? How do we make the most of this opportunity and create something new in its place? It’s all about approaching the problem from a different mind-set.

Impacting the bottom line

If we think about training a machine in this way, so that it understands how to create and serve up the best, most relevant content at scale, whilst at the same time intelligently considering the most appropriate style and language for audiences, we start to see the impact Linguistic AI can have on the bottom line for both businesses and the industry at large.

Those companies that want to be prepared for the future of content, must wake up to the potential of Linguistic AI if they’re to be part of this wave of transformation, or get comfortable treading water. The future of your business could depend on your willingness to recognise the power and potential of Linguistic AI.

Mihai Vlad, VP of Machine Learning Solutions, SDL (opens in new tab)
Image source: Shutterstock/Bakhtiar Zein

Mihai Vlad is VP of Machine Learning Solutions at SDL. He is in charge of the Commercial Strategy for the Machine Translation division, and all the Machine Learning and AI solutions SDL is bringing to market.