The Future of Advertising: Artificial Intelligence & Creativity
How will technology continue to change the advertising industry? An exploration of the intersection between advertising, artificial intelligence and creativity.
“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”
— Roy Amara, The Institute of the Future, 2006
Over the past three decades, everything has become interconnected and over half the world’s population are now online. Meaning that four billion people are spending on average six hours every day using internet-powered devices or services.
The speed at which technology is advancing has led some to predict an imminent “fourth industrial revolution”. The first revolution being steam power, the second electrical power and the third digitalisation.
The speed of advancement is most visible in the endurance of Moore’s Law. In 1965, Gordon Moore observed that the number of transistors that fit on a computer chip doubled every year, forecasting that computer power would continue to double biennially for the next ten years. This prediction sustained over 50 years of accuracy. Although it can be argued the law became a self-fulfilling prophecy driven by big corporations investing heavily to maintain the pace.
Ray Kurzweil built upon Moore’s Law with the “Law of Accelerating Returns” looking beyond circuits to wider trends in exponential computing growth, believing that in the 21st century 20,000 years of progress will be seen in just 100 years. Ultimately predicting that “within a few decades, machine intelligence will surpass human intelligence”. The point in time when advances in technology, particularly artificial intelligence, can create machines that are smarter than humans is known as the ‘Singularity’. At SXSW in 2017, Kurzweil predicted the Singularity to be feasible by 2045 supporting Schwab’s belief of the incoming fourth revolution “blurring lines between physical, digital, and biological worlds” or as Kurzweil sees it humans becoming “a hybrid of biological and non-biological thinking”.
Regardless of which revolution is unfolding, it is undeniable that rapid technological change is altering every aspect of human life — culturally, socially and economically.
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An Overview of Artificial Intelligence
Artificial Intelligence often gets labelled as a buzzword of the 21st century. However this isn’t the first time, AI has repeatedly been overestimated for decades. The term was first coined in 1955 by John McCarthy as “the science and engineering of making intelligent machines“ but after realising the limitations of computer hardware at the time research halted, not seeing the second burst of interest until the 80’s. Fast forward to now, and technology has reached a point where AI appears to be within reach, driven by key advancements:
Big Data: As devices have become connected and usage has grown, so has the data created. Data has seen an annual growth rate of over 50% since 2010 and “Data is to AI what food is to humans.”
Computing Power: Ongoing improvements in GPU computer capacity, ever decreasing costs for that power and the advent of cloud-based services, have begun to democratise AI, in turn boosting opportunities for mass research and development.
Deep Learning: A direct product of points one and two. Deep learning is a subset of machine learning, both of which are integral to AI progression. Machine Learning focuses on giving computers the ability to learn without being explicitly programmed. Deep learning expands on that, instead utilising our ever-expanding understanding of the human brain to create artificial neural networks that mimic the activity of neurons to better abstract information from large datasets.
“AI everywhere” was tipped as a megatrend over the next decade due to the position of multiple AI-related emerging technologies on the Gartner annual hype cycle report. However, for many of these technologies sitting at the top of the ‘peak of inflated expectation’, there is still a ‘trough of disillusionment’ to follow. As Amara’s Law states “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run”.
Nonetheless, “Silicon Valley loves a fad”, and the current rate of investment in AI might just propel it through inevitable disillusionment a little faster. Tech giants are investing significantly, for example, IBM’s Watson and Google’s DeepMind, but with a focus on their corporate strategies, there is a need for democratisation to ensure that potential benefits are created safely and distributed equally.
Technology & Advertising
Technology has changed the way consumers access information. Traditional linear media, such as TV, is being blindsided by the endless source of digital content accessible at the touch of a button. This shift in content consumption has brought about the “attention economy”, brands no longer have to only compete with each another to stand out, they need to compete just to be seen in the first place.
In 2016, digital advertising spend surpassed TV. Research showed 55% of TV advertising time was not paid attention to, in contrast, YouTube mobile adverts were 84% more likely to receive attention. The same study also showed “attention equals impact” with ad recall significantly correlated to the attention given. Although YouTube-owner Google funded this research, non-bias studies show similar trends.
In the attention economy, it is not just big brands vying for a look-in. Technology has democratised media meaning anyone with “a good strategy, an internet connection and basic web design skills is now capable of competing”. Teenagers filming videos on a bedroom webcam garner engaged audiences the size of which most brands could only dream of. This shift from passive consumers to active content creators gave rise to the age of influencer marketing, in turn requiring new laws to protect consumers from the blurred lines of influencer endorsements.
Kevin Kelly predicted the trend for the decentralisation of media will continue, reaching a point where every advert is embedded with a tracking code to know where it is viewed and precisely who by, meaning anybody with an audience could select adverts to place on their channels resulting in both parties profiting from the action.
There are two broad ways advertisers look to capture this sort-after consumer attention:
Pay For It: Buy digital media space to push content in front of the eyes of a specific or sizeable audience
Earn It: Create content that is so compelling consumers want to seek it out and share it
Ultimately, the backbone of success for both is the utilisation of quality consumer data, knowing exactly who to reach and how best to engage them. Brands have been collecting data on their consumers for years, but advances in machine learning are now allowing for this information to have a much greater potential than ever considered possible.
Artificial Intelligence & Advertising
Advertising gains attention when evoking emotion in the intended audience. In the past, predicting the success of a campaign before it went live was almost impossible. Large-scale audience data analysis was costly, labour-intensive and arguably not entirely reliable. As Henry Ford is famously misquoted as saying “If I had asked people what they wanted, they would have said faster horses”.
However, A.I. makes prediction easier, it supports better decision-making. It is a tool that can interact with every single consumer simultaneously while recording and analysing every minute detail of that interaction. An AI can potentially know a consumer’s digital-self, better than they know it themselves.
This article will explore two key areas of advertising that AI is disrupting:
How content is created
How content is catered to the consumer
Disruption One: How Content Is Created
Creativity, defined as “the use of imagination or original ideas to create something”, is the crux of good content. By this definition, it is arguable whether an AI could ever be capable of true creativity as it has always learnt from a human’s original dataset. Although the same argument is often applied to human creativity, Austin Kleon is a proud activist of the mantra that “nothing is original”.
Nonetheless, AIs have been trained to create art for decades, but only recently has mainstream media’s interest peaked, for example, AI-created music videos, paintings and movies. AI was even pitted against a human Creative Director to construct an advert with 46% of the public believing the human one to be AI-generated. On inspection, many of these headline-grabbing stories still involve significant human support, but many believe in a future where “complete automation may be possible”.
As an assistant, AI is already primed to help in almost every area of content creation; this has become known as ‘Augmented Intelligence”, where AI takes on an assistant rather than lead creative role, giving time back to human creatives rather than replacing them. There are examples of augmented intelligence tools for every stage of the advertising agency process:
1. Research & Insights
Picasso Labs: Creative analysis AI which reviews brand, audience and competitor content to provide insights
Automated Insights: Digests big data into readable narratives using natural language generation software
Lyrebird: Creates audio from small samples with the ability to generate scripts in seconds, plus include emotion
Persado: AI-generated copy that uses data and personalisation to create bespoke email subject lines and social media ads
Adobe Sensei: Powers intelligent features across Adobe products to assist designers in video and image production
3. Account Management
Albert: A full AI marketing platform automating media buying and targeting across multiple digital channels
Lobster: Allows specific searching and licensing of social media imagery
Disruption Two: How Content Is Catered to Consumers
“Technology is humanity’s accelerant” with it brings a constant state of flux, everything becomes iterative rather than final. In advertising, this provides an immense opportunity for continuous improvement of live content. For example, Facebook allows A/B testing of content using their split testing ad formats.
Generative content and machine learning create possibilities for personalisation that would have previously been impossible. In the past, agencies created a singular idea that was most appealing to the most amount of consumers. However, AI means that one incredible idea can now come with an array of sister ideas, each tweaked to cater personally for every consumer it could reach. Although generative content creation is yet to catch-up with the data to provide one-off bespoke experiences, brands have begun to experiment with creating multiple versions of content to test on groups of consumers. In addition, start-ups have sprung up offering AI-automated personalisation and dynamic video content.
For decades, Hollywood and the media have humanised AI conjuring up visions of a far-off future, for example. Cameron’s ‘Terminator’ or Garland’s ‘Ex-Machina’. Futurist Arthur Clarke’s third law stated “any sufficiently advanced technology is indistinguishable from magic” and for many, that is how AI feels.
However, that future has already begun “computers are ubiquitous and smart enough to handle our most important tasks. We’re surrounded by A.I. and machines that do things for us”. Perhaps seen most clearly in everyday devices such as personal smartphone assistant Siri, smart-home controller Nest and soon-to-launch Tesla self-driving cars. AI is already making decisions for us daily.
It is time to move away from seeing AI as a substitute for humans and instead look further ahead to the value it can drive in enabling new goods, services and innovations. However, it is essential the ethical implications are not forgotten (see follow-up article: “Ethical Considerations for The Future of Advertising”). Rapid technological improvement is often driven by greed. Everyone wants to be the first to the post to reap the money and glory that follows, meaning the most critical thing technology is supposed to serve, its people, can be quickly abandoned.
Afterword: This article was originally written as coursework required for the ‘Hyper Island’ Digital Management Masters. A Harvard referenced version and/or complete Bibliography is available on request.