By Hiba Hassan, head of the design and visual communications department, SAE UAE
Artificial intelligence is making its way into many fields. If a task is data-driven, AI can perform it faster and more efficiently; data is the lifeblood of AI systems. For example, Software-as-a-Service (SaaS) companies use AI to streamline workflow and predict topics for content creation. Financial services companies can standardise content language, personalise messaging, and improve writer productivity with AI. Healthcare companies can use AI to automatically generate content about health conditions, improve operational efficiency, and optimise existing healthcare content. E-commerce companies can use AI to test landing pages dynamically, predict ad content, automatically generate product descriptions, and recommend content and products based on consumer preferences. AI excels at automating tasks with standardised-repeatable steps, making it suitable for predictable and repetitive workflows.
Artificial Intelligence or AI is “The science of making machines smart.” says Demis Hassabis, Co-Founder and CEO of Google DeepMind. “Smart” means that the machine makes its own way toward achieving an end goal. Machine learning represents an essential subfield of artificial intelligence technology, enabling AI tools to accomplish tasks autonomously and gain knowledge through experience. AI learns with each goal attempt. As a result, AI unlocks exponential performance gains over time, making it more effective than traditional software.
AI has three core applications: language, predictions, and vision. Language AI includes understanding and generating written and spoken language, natural language generation, voice recognition, natural language processing, text analysis and summarisation, and sentiment analysis. An example of language AI is Gmail’s Smart Compose feature, which predicts the next sentence when composing an email. Prediction AI allows for predicting future outcomes based on historical data and continuously improves predictions through machine learning. It includes personalisation, pattern recognition, forecasting, and recommendation. An example is the weather app on the phone predicting rain in a specific location and notifying the user. Finally, Vision AI involves analysing and understanding data from images and videos, including computer vision, facial recognition, emotion detection, video generation, and image generation. Facial recognition technology used to unlock iPhones is an example of vision AI.
The internet and programmatic advertising have allowed us to reach consumers across numerous digital platforms and target them based on demographic and behavioural data points. However, humans need to improve at managing the vast amounts of data and ad variations that result from this. That’s where AI comes in, allowing companies to allocate and adjust advertising budgets, find new audiences, create hyper-personalised ad content, and much more. As a result, the use of AI in advertising is rapidly increasing, with advertisers using it to segment audiences, create ad creatives, test, and improve ad performance, and optimise spend automatically in real time. This is leading to remarkable results in language and prediction modules. For example, Vanguard, a leading investment firm managing $7 trillion in assets, used AI language platform Persado for personalised advertising. The company’s heavily regulated Vanguard Institutional business faced advertising restrictions and was limited to running ads only on LinkedIn. With Persado’s AI, Vanguard hyper-personalised its ads and tested them at scale to find the best approaches, leading to a 15per cent increase in conversion rates that would have been impossible without AI. Automatic recognition, photo tagging done by mobile phones, and virtual reality effects used to sell the experience of trying before you buy are examples of how Snapchat and Amazon use vision AI in advertising products on their platforms.
Some of the popular AI tools in the marketing and advertising scene are the famous ChatGPT, copy.ai, and Grammarly. Other notable ones include Persado, which personalises ad language to increase conversion rates. OneScreen optimises content and ads shown to audiences. Pathmatics brings advertising transparency and insight by providing performance analysis and competitive intelligence across channels. Albert analyses data across ad accounts and customer databases to target, run and optimise ad campaigns. Finally, GumGum uses computer vision technology to help advertisers place ads in the most visible spots across the web.
While AI has revolutionised the way we approach marketing and advertising, it’s important to note that it can’t take over these fields’ creative and artistic aspects. While it can analyse data, optimise campaigns, and even generate some forms of content, it can’t replace the creativity and strategic thinking of human marketers and advertisers, which is still essential for the success of any marketing campaign. Michael Tripp, who had welcomed AI in the 1st ever-AI-scripted Lexus ad of 2018 said, “I’m very optimistic that it will complement and augment the creative process and not undermine or replace. We’re fully committed to man plus machine.” Tripp also mentioned, “Maybe we should call AI the creative teammate.”