As the AI revolution gathers pace, machine learning is impacting almost every occupation and industry, with global spending expected to reach US$46 billion by 2020, according to IDC research.
Traditionally in the vanguard of early adopters, media organisations don't need selling on the advantages of automating workflows and using data analytics to obtain business insights.
AI also has the potential to optimise the content production process by providing executives with insights that can help them tailor their offerings more effectively and increase the profitability of new and existing content.
Stuck to the screen
Australians are voracious video consumers, with the average home now possessing 6.6 screens, including Internet-capable TVs, tablets, smartphones and high definition TVs, according to Nielsen's March 2018 Australian Video Viewing Report.
Individuals are using these devices to view an average of two hours, 27 minutes live and recorded television content each day.
The younger generation – Australians aged between 18 and 24 – spent around 22 additional hours a month watching video via desktops, laptops, smartphones and tablets.
Using AI to analyse the wealth of data surrounding Australians' viewing habits and preferences can provide content producers with valuable insights.
Here are three ways AI can be used to enhance content production.
Making content personal
It's a crowded market and video watchers are spoilt for choice. Making content that's original, creative and has oodles of audience appeal is the name of the game for producers. Traditionally, this has necessitated poring over past productions to try to gauge what has grabbed and kept viewers' attention and to ensure new content doesn't replicate old material.
AI can take some of the guesswork and intuition out of this imperfect process. It can be harnessed to help producers analyse and understand consumers' preferences and behaviours more deeply and deliver content that's more likely to match.
For instance, if users who watch Video A subsequently click through to Video B, it's possible to infer the productions have a common appeal to that cohort.
These patterns could feed into a powerful neural network of decision making which producers could use to personalise content and prioritise the creation of productions which are most likely to attract eyeballs in their key target markets.
Localise and Locate
Making quality content is challenging for producers. So too is ensuring it's exploited to its full potential. Localising content to cater to a range of international markets is one way to achieve a greater return on the initial production investment. With Microsoft, Google and Facebook all working to develop and enhance AI technology to accommodate language differences and nuances of speech, AI could soon be used to speed up the localisation process.
AI can also be employed to ensure producers leverage existing content wherever possible, rather than creating it anew. Locating relevant clips to supplement a bulletin or production can be a tortuous and time-consuming process if it entails sifting through mountains of archived footage. Using AI to organise and index these often-neglected assets can make it easier to repurpose, recycle and monetise them.
Monitoring live events
Live events are often a key differentiator for media organisations. They can also be fraught with technical difficulties and the stakes are usually high. One glitch or hitch and an entire event can be crippled. The process of monitoring and troubleshooting typically occurs in siloes, which can leave organisations scrambling to pinpoint and ameliorate problems.
AI could be employed as the engine of an early warning system, which could watch out for potential problems, such as unexpected spikes in viewer demand during live broadcasts, and alert technicians when a fix is needed.
