generative AI Archives

Written by Diem-Quynh Do on Digilah (Tech Thought Leadership).

My Bio:

Hi, I am Quynh. I am currently a 3^rd year Communication Studies major at Nanyang Technological University. Back in my first year of university, generative AI was largely an unknown topic. Fast forward to the present, it is the central point of every discussion about the use of technology in communication and media. Witnessing this swift change is the motivation for me to reflect on my experience with these tools and ponder the question: Is generative AI the future of my industry?

Read my research article below:

Generative AI (Gen AI) took center stage as the prominent buzzword of 2023. This technology revolutionized every single industry for its capacity to quickly generate various types of content including text, images, audio, and more. Its extraordinary capacities led to significant workforce changes and stood behind some of the major layoffs in the past year. That said, how is Gen AI transforming the advertising landscape, and where does it stand in an industry where it does what it does best – generating text and images?

The buzz around Generative AI

Gen AI first existed in the ’60s century in the form of chatbots. However, it was not until recent years that they became widely accessible and adopted by internet users worldwide. While Gen AI can assist all sectors in their day-to-day tasks, the implication of the current technology, which is to generate various types of media, remains the most relevant in the advertising industry.

In their original forms, text generators stand as a revolutionary tool for communication specialists and advertisers with their ability to churn out ad copy and refine written pieces in no time. Image generators are also widely utilized and proven to be a cost-effective and time-efficient alternative.

But the potential of Gen AI does not stop there. Big players in the tech industries are also bringing generative AI into their products. Adobe Photoshop, the household name for all professional creatives, introduced their AI-powered Generative Fill in early 2023. This feature was widely celebrated, as it helps to significantly reduce the time spent on creating and editing media assets.

Adobe Photoshop Generative Fill (Source: Adobe Support)

Canva, catering to non-professionals, also incorporated AI to swiftly assist users in discovering templates along with the AI image generator and other capabilities.

Canva’s Magic Studio, powered by AI (Source: Canva)

Google also introduced a new Gen AI tool for their Google Performance Max partners. This innovative tool will aid advertisers in asset creation, ad copy generation, and more.

Google’s AI-powered ads tool (Source: Google)

These tech giants saw a trend in advertisers’ habits: a desire for tools that facilitate swift content creation, allowing more time for other meaningful tasks. It is also a strategic move to cut the budget on outsourcing designers and writers and produce content in-house with the help of AI.

The confined box of AI tools

While Gen AI exhibits extraordinary capabilities, it fundamentally remains a product crafted by humans and acquires knowledge through the datasets fed into the system. Therefore, its generating capacity is limited by what is available in the training dataset.

A prominent example is a trend that blew up on TikTok. Taking advantage of the new image-generative feature of ChatGPT, users create prompts that ask AI to generate different sets of images. All went well until a user took a step further and asked ChatGPT to generate an image of a hamburger without cheese. To our surprise, AI failed at this simple task despite the creator’s effort to rephrase and work around the instruction.

The user is asking AI to create a hamburger without cheese (Video: https://www.tiktok.com/@teddywang86/video/7315228986072681733)

The internet community went wild, with many joking about how “they said AI will take over the world”. But what is left after a good laugh is the acknowledgment that AI’s capacity is limited. As most hamburgers in the training dataset have cheese, AI faces difficulty creating one without it.

Generally, Gen AI tools’ capability is limited by specific tasks assigned to them and the quality of the data used for their training. They can only perform certain tasks or only have the knowledge up until when it was trained. Those who use Gen AI in their work must have a good understanding of the limits of this technology, use it to assist only in relevant tasks, and double-check the quality of the work.

How far is AI from us?

Just as advertising professionals must undergo training and years of experience to excel at their job, Gen AI also requires training to adapt to the specific tasks that it is assigned. It is undoubted that with enough time and investment, Gen AI will become even better at producing results and catering to specific industries, including advertising.

But in this specific case, the learning curve might be steeper for AI than it is for humans. The supporting arguments often center around two key aspects: flexibility and originality.

Text AI generators, most prominently ChatGPT, are known for their limited flexibility in writing. While they can be instructed to adopt different styles, some users remain unsatisfied or frustrated as the result does not precisely meet their expectations.

A simple instruction to ‘energize’ a piece of text may return a copy that is oversaturated with emojis and buzzwords. The absence of ‘empathy’ in current AI technology also hinders their ability to produce written pieces that effectively address the intricacies and nuances of communication tasks.

The conversation surrounding originality often involves copyright infringement. This concern originated from the fact that third-party material might have been used in the process without permission. While referencing past artworks is a common practice in the creative industry, the added touch of human creativity somewhat mitigates these concerns.

To conclude, the fact that Gen AI does not seem to have ‘creativity’ on its own will continue to pose concerns about its range of capabilities and the originality of the product created. Yet, Gen AI remains a powerful tool that can elevate human creativity by offering new ideas and bringing in new thought dimensions.

Most asked questions

Do AI tools have a confined set of capabilities?

Does ChatGPT always give satisfactory results?

How will Gen AI help the advertising industry to evolve with time?

Most asked queries

ChatGPT

Canva

Adobe Photoshop

Copyright infringement

Generative AI

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Written by Shivani Koul on Digilah (Tech Thought Leadership).

As marketers, we fundamentally learn about the 4 Ps of Marketing: Product, Price, Place, and Promotion. Later, three more Ps were added: People, Process, and Physical evidence. Whatever we do revolves around the customer.

AI will disrupt all the Ps in many ways going forward, and many times there is an argument: will that take jobs? My assumption is that it will take non-productive jobs and give space to marketers to work on something newer, something smarter.

This is the role of technology! To assist humans in making the best possible product, but never forgetting that “human touch”. Marketing is all about creativity, empathy, and understanding human behavior. Successful marketing needs originality and a creative spark only humans can possess.

Having said that, a 2020 Deloitte global survey of early AI adopters showed that three of the top five AI objectives were marketing-oriented: enhancing existing products and services, creating new products and services, and enhancing relationships with customers.

The main job of any marketer is understanding the need of the customer, matching it with the offerings like product and services, and persuading the customer to buy the product and service.

This looks simply, a 3-step process, but there are a lot of critical steps & information involved in-between where marketers must analyze a lot of data and tweak the marketing strategy accordingly.

Let’s discuss it here with some real-time examples and see how marketers are using AI to support them.

1. Content Marketing –

Currently, AI can help in customizing the marketing content like product information, email writing, blogs, marketing messages, and copywriting. All this can be done using open AI tools available and should work on simple prompts.

With machine learning, this data can be used further for creating sales pitch, cross-selling pitch, customer engagement, last-minute deal or offer by considering different variables like demographics of the target consumer, behavior, demographic, along with deep analysis of the impact of communication. Example AI tools.

2. Data Analytics –

Slicing and dicing of data was done earlier as well, but AI has taken it to the next level where one can get predictive analysis and prompts/suggestions to enhance the content/marketing campaign.

Customer data like preferences, engagement, status as in which ladder of purchase funnel the customer and CRM, and that gives space to marketers for redefining the market strategy. Example CRM tools.

3. Search Engine Optimization –

This has been a game-changer with real-time examples like Google, Netflix, and other search engines. This will help segmentation of customers and suggest target advertising. This has also helped marketers to leverage cross-functional platforms.

So, if you search for some product on Amazon and open any social media like Instagram or Facebook, you will see recommendations of the same or similar products on that platform as well. By doing this, marketers can enhance the recall value of products or services.

4. Placement of advertisement –

This has been very important: where to post an advertisement for the best ROI. AI has made it easy to target or place advertisements based on consumer data like purchase history, preference, and context of purchase.

Example Google/YouTube advertisement.

5. E-commerce and Digital Marketing –

AI has been widely used by e-commerce websites and digital marketing to reach out to the right customers, understand their needs and buying patterns, and automate marketing workflows and course correction of marketing efforts which otherwise would have taken a lot of resources. Example any AI-enabled fitness app.

AI can be a game-changer in many ways, but Human decision-making is typically reserved for the most consequential questions, such as whether to continue a campaign or to approve expensive TV ads.

Training your algorithm enough that it should give expected results, training algorithms with correct prompts, and above all, safety and security of data.

Data privacy is going to be of utmost importance for AI. Clear and transparent policies need to be drafted against data security and privacy.

I believe AI is like a child which needs to be trained to become a responsible assistant; hence humans have a greater role as to how they are raising this child to serve mankind.

As marketers continue to embrace AI technologies, they must strike a balance between innovation and ethical considerations, leveraging AI’s capabilities to enhance customer experiences while upholding trust and transparency.

References:

https://hbr.org/2021/07/how-to-design-an-ai-marketing-strategy

Most asked questions

What are the 4 P’s of marketing?

4 P’s of Marketing: Product, Price, Place, and Promotion. Later, three more P’s were added: People, Process, and Physical evidence.

What is the key to successful marketing?

Successful marketing needs originality and a creative spark.

How is machine learning serving the marketing industry?

With machine learning, data is used for creating sales pitches, cross-selling pitches, customer engagements, last-minute deals, or offers along with deep analysis of the impact of communication.

Most searched queries

Machine learning

CRM (Customer Relationship Management)

SEO (Search Engine Optimization)

ROI (Return on Investment)

Hello readers! Hope you liked what you read today. Click the like button at the bottom of this page and share insights with your colleagues and friends!

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