The use of AI in advertising is redefining the way media and marketing work, improving strategies, and enabling brands to make smarter decisions. The word “artificial intelligence” (AI) can be used as an umbrella term for a wide range of machines that learn.
Some learn with the help of humans, while others learn all by themselves. Thus, AI technologies perform certain cognitive tasks like humans or better. AI-powered machines can read and understand text, see and identify images, detect and understand sounds, physically move around obstacles, and sense their environment.
Everyday applications of AI
For example, Gmail and Google Docs now use AI to read your input and understand it well enough to recommend what to type next with Smart Compose. Facebook uses AI to identify who is in your photos and then recommends who you should tag. Tesla’s self-driving cars use AI to identify obstacles and (hopefully) drive safely and effectively.
Siri on your iPhone uses artificial intelligence (AI) to understand your voice commands and give you answers that make sense. Smart home technology, such as Nest, uses AI to detect changes in its field of view and then take action based on what it senses.
Over the past few years, AI has moved from being a branch of computer science to an everyday piece of technology that almost all consumers carry in their pockets daily, such as a smartphone with Siri, Bixby, or Google Assistant voice input. In recent years, the advertising and media industries have become increasingly interested in AI technologies.
The growing role of AI in advertising and media
While AI applications in the advertising and media industry are still in their infancy, there is huge potential for the technology to shape the next generation of advertising and media. Machine learning is already impacting the advertising ecosystem.
Real-time bidding (RTB), the buying and selling of online advertising space in real-time, is the best example of this. With the help of AI, self-learning algorithms are used for the execution of online campaigns that allow advertisers to critically identify the potential user. This further helps in delivering personalized ads to each user and getting them to take the desired action.
The growing demand for rich digital user experiences has increased the demand for AI in the media and advertising industry. Through continuous learning from user behaviour and actions, media and advertising companies focus on optimizing ad campaigns, email marketing, and website content customization.
The impact of AI on programmatic advertising
In addition, the growth of the global AI market in this industry could be fuelled by programmatic advertising. AI effectively connects advertisers with publishers by automating the process of buying and selling ad inventory.
Market growth has benefited from the trend towards effective voice-based search technology. Companies are trying to use AI to introduce the next generation of input methods into their services and products. Media companies are adding AI technologies as an integral part of their product development lifecycle, driven by the proliferation of smartphones.
Through contextual relevance, AI offers media companies a new competitive advantage. This enables them to connect the right users with the right content at the right time. As such, the global AI market for media and advertising looks set to grow rapidly.
Global Trends in AI Adoption
Globally, robust technology and IT infrastructure in the US and Europe is driving demand for AI solutions in the media and advertising sectors. Due to the huge growth opportunities in the media industry and the emergence and rapid adoption of predictive technologies by major media and advertising companies in these markets, the Asia-Pacific market is expected to grow at the fastest rate over the same forecast period.
While AI is enabling large media and advertising agencies to serve cognitive ads and integrate voice-based support into their campaigns, there is still room for small and medium-sized agencies to adopt AI as a key functional and operational part of their business.
The future of AI in advertising and media
Companies are buying AI technology to have a more proactive understanding of users. Consumers will soon be able to better communicate and engage with content through talking apps and chatbots as AI becomes more widely adopted in these industries.
Some of the major players in the global advertising and media AI market include IBM, Microsoft, Google, NVIDIA, Intel, Sentient Technologies, and Numenta.
The many and varied use cases for AI in advertising
The use of AI in advertising platforms
While users may not always see or interact with AI, it is critical to the infrastructure that underpins digital advertising products on many platforms. Modern programmatic platforms often use AI to buy, sell, and place ads in real time.
Ad exchanges all use AI to manage buying and selling in real-time. This includes programmatic exchanges, third-party networking, and social media advertising. AI also dictates how the campaign budget is spent, who sees the ads, and how effective the whole campaign is.
AI applications in performance and spend optimisation
One of the key use cases for AI in advertising is performance optimization. Machine learning algorithms are used to analyze how your ads perform on specific platforms and then suggest how to improve performance.
In some cases, these platforms could use AI to intelligently automate the actions that you know you need to take based on best practices, saving you a significant amount of time.
In more advanced cases, some tools will automatically manage the performance and optimization of the ad spend, making decisions all on their own about how best to achieve the KPIs of the ad campaign.
AI in ad creation
Dynamic Creative Optimizer (DCO) is an AI-powered system that partially or fully creates ads based on what works best for campaign objectives.
This functionality is already available on many social media ad platforms and through third-party tools designed for premium publishers that use intelligent automation to build and serve ads based on the links the brands are promoting or the target audience, location, time of day, and more.
Future tools will use intelligent algorithms to create the ad copy itself. These systems use natural language processing (NLP) and natural language generation (NLG)—two AI-powered technologies—to create ad copy that performs as well as, or even better than, copy written by humans—in a fraction of the time and at scale.
AI in audience targeting
The targeting of the ad is just as important as the copy and the creativity of the ad. We have a healthy set of consumer data touch points with which to target audiences, thanks to data management platforms (DMPs). But it’s not efficient to do this manually.
That’s where AI can automate the process by looking at past audiences and ad performance, weighing this against the campaign KPIs and the real-time performance data that is coming in, and then identifying new audiences that are likely to engage with the ad.
AI technology in human resources
One industry that has been slow to adopt AI and ML technologies is human resources (HR). The one exception has been the implementation of applicant tracking systems (ATS) that use ML techniques to help screen potential hires.
As a result, an industry of AI-enabled services has sprung up to improve candidates’ chances of getting an interview and to save time for HR departments through the automation of CV screening.
The thing is, the rise in ATS adoption is just the tip of the iceberg, and a much wider adoption of AI and ML technologies in HR land is on the cards for the next couple of years.
Marketing tools powered by AI
As the world moves into an always-connected reality with the rise of the Internet of Things (IoT), businesses are realizing that there are more marketing channels to control than before. Using ML to modify and evolve marketing efforts over time, the only sensible solution is to carefully hand over the bulk of the ongoing work to AI-powered marketing systems.
From social media management to content marketing and everything in between, such tools are already available at every stage of the marketing industry. But maybe this is just the beginning. Companies are looking for ways to shift more of their marketing efforts to AI-powered solutions, having already seen how chatbots and AI call centres have influenced marketing decisions.
In some cases, depending on the buyer’s tone of voice and input, AI platforms will advise the salesperson on what to offer (more discounts, free items, etc.) during the phone negotiation. The machine learning (ML) systems have been fed with recordings of thousands of customer support conversations to provide real-world assistance.
Again, such an application needs to have a scale to make sense (an Amazon or a bank that has thousands of people working in a call centre or that has millions of transactions per day).
AI services in the financial sector
The adoption of AI and ML technology in the world of banking has been so immediate and so complete that it has given rise to a whole new business category of fintech. In particular, asset managers, hedge fund managers, financial advisors, and the entire banking sector are all-embracing technology.
At the ABA Agency, we see AI as being critical to the future success of the agency. Until recently, around 60% of the time of the bank’s growing army of data scientists was spent identifying and ingesting relevant data to train AI models. In addition to using its information, the ABA Agency draws data from around 7,500 external sources.
This meant that an expensive and relatively scarce resource was used inefficiently. A relatively new data platform developed by the bank, called OmniAI, helps it to feed relevant data into its models more quickly (think of data warehousing as a prerequisite before embarking on an ML project).
Not only does OmniAI enable data scientists to quickly obtain practical raw data based on their models, but it also automatically verifies that the data being used is compliant with various regulations (such as data protection).
AI in Advertising
In conclusion, some of the expectations built up around AI may be smoke and mirrors. How certain vendors in Silicon Valley and elsewhere have suddenly become AI vendors remains to be seen.
A lot of companies have somehow transformed themselves from being a data management company or a workflow processing company to an AI company in a single year. How did they do it?
Strong people, leadership, compliance, and support across the group are required to deliver tangible value from AI. AI also needs accurate data to work with; otherwise, it’s like buying an Aston Martin and not having any petrol (it will cost you money, but it won’t get you anywhere).
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