In a world where technology’s influence on marketing and advertising has been nothing short of transformative and meteoric, the rise of generative AI is heralding a new era of creativity and efficiency. The way brands connect with their audiences is evolving rapidly, and at the forefront of this evolution lies the adoption of generative AI in ad creation. This groundbreaking development has the marketing and advertising industry abuzz, prompting a fundamental re-evaluation of roles and responsibilities.
As we delve deeper into this feature article, we will explore the seismic shifts taking place, uncover the challenges faced by marketers and advertisers, and reveal the extraordinary opportunities that await those willing to embrace this innovative technology.
“The roles for digital marketers will evolve and certainly not disappear,” remarks Shubit Rakshit, Business Director, FoxyMoron, [Zoo Media]. “While AI speeds up ad creation, marketers will play a pivotal role. Creative strategy remains their expertise, giving a heart and soul to the campaigns. Through data analysis, marketers derive insights that inspire ad optimisation. They still will be instrumental in creating audience segmentation, aligning the brand voice and forging meaningful connections with customers. Collaborating with AI specialists amplifies marketers’ impact in the digital marketing landscape,” Rakshit adds.
It is important that platforms ensure that the auto-generated ads comply with advertising policies and do not inadvertently violate any guidelines. Citing an example, Rakshit says, Google employs robust policy filters and machine learning algorithms to screen the ads and detect potential violations. Human reviewers also play a vital role in assessing complex cases and ensuring ad quality. “Also, Google regularly updates its policies to address emerging issues. Brands and advertisers can do their bit to support compliance by maintaining transparent messaging and adhering to guidelines in their business prompts,” he adds.
As to the impact on ad personalisation and targeting capabilities, Rakshit says that AI processes user data, unlocking deep insights for laser-focused targeting. “Advertisers can reach audiences with tailored messages, enhancing relevance and resonating with users. With AI-driven personalization, you can build tribes that trust your brand even more. It’s not just about chasing numbers but about creating enriching experiences that resonate with your audience.”
Though the use of AI in advertising is at a very nascent stage and has shown great results in creating images and infographics, its large-scale potential is still limited, feels Ankit Ahuja, Chief Strategy Officer IJCP – Group and Founder, Red Comet Films. According to him, natural language processing (NLP) plays a great role in creating personalised and engaging ad copy. “NLP is the bedrock of AI integration in advertising. When it comes to creating personalised and engaging ad copy, we can’t have AI tools churning out the same copy for similar brands. NLP is something that can help advertisers differentiate and use content generated through AI tools,” says Ahuja.
According to him, some specific used cases are:
- Predictive Analytics:AI can analyse historical data to predict future consumer behaviour and campaign performance, enabling advertisers to make data-driven decisions.
- Dynamic Creative Optimization (DCO): AI algorithms can dynamically generate and optimize ad creative elements based on user preferences and real-time data.
- Programmatic Advertising: AI-powered platforms automate ad buying and placement in real-time auctions, optimising targeting and budget allocation.
- Chatbots and Virtual Assistants:AI-driven chatbots and virtual assistants can engage with consumers, answer queries, and offer personalized product recommendations.
- Voice Search Optimization:AI-powered voice recognition technology enables advertisers to optimize campaigns for voice search queries and voice-activated devices.
On the key advantages of using AI in advertising campaigns compared to traditional approaches, Ankit Ahuja says that use of AI in advertising can be two-pronged. “We can use AI for creating a campaign like using tools for creating posters, logos, content, etc., and on the other hand, we can use AI for enhanced targeting, real-time analysis of campaign data. We can also increase campaign efficiency through automation and AI-driven tools to streamline the ad creation, placement, and reporting processes, saving time and resources. My personal favourite use of AI is personalisation at scale. AI enables the creation of personalised ad content and experiences for individual users or micro-segments, even at large scales.”
AI-powered algorithms have improved the effectiveness of ad placement and optimisation.
According to Ankit Ahuja, the ad space these days is very vast, but also very limited at the same time. “While we have lots of ad space opportunities, the lack of quality is an issue. At this time we need to ensure that ad placement is done for maximised results. Time is of the essence and AI is a true saviour in this space.”
According to him, AI can be used for the following to enhance the effectiveness of Ad placement:
- Automated Bid Management: AI algorithms optimise bidding strategies based on real-time data, improving ad placement and cost efficiency.
- Ad Placement Optimization: AI analyses contextual factors, user behaviour, and historical performance to determine the most effective ad placements.
- A/B Testing and Optimization: AI can automate A/B testing processes, rapidly iterating and optimizing ad variations to identify the best-performing ones.
- Ad Scheduling and Frequency Capping: AI algorithms optimize ad delivery timing and frequency to maximize engagement and minimize ad fatigue.
Explaining how AI contributes to enhancing audience targeting and segmentation in advertising, Ankit Ahuja said, “Stemming from personalisation at scale through AI tools, we are able to enhance audience targeting and segmentation. We derive the following benefits through the same:
- Advanced Data Analysis: AI algorithms can process vast amounts of data to identify patterns, behaviours, and preferences for precise audience targeting.
- Micro-Segmentation: AI can divide audiences into granular segments based on demographics, behaviour, interests, and intent, enabling tailored messaging.
- Lookalike Modelling: AI can analyse existing customer data to identify similarities and find new audiences that share characteristics with high-value customers.
- Dynamic Audience Profiling: AI continuously updates audience profiles based on real-time data, allowing for dynamic targeting and messaging adjustments.”
Generative AI tools, powered by technologies like OpenAI’s Language Model (LLM), have indeed made a significant impact in the field of marketing and advertising (M&A), points out Aakriti Bhargava, CEO & Co-Founder, Wizikey. “These tools, offered by companies like LinkedIn, Meta (formerly Facebook), Salesforce, and others, have made many time-consuming tasks like research and data analysis very easy. Let’s delve into the impact of LLM technology in M&A and how it has influenced human involvement. Simple use cases of Generative AI have been around for a while and include generating content, ad copies, research and data analysis. Let’s delve into complex use-cases.”
Continuing further, Bhargava says that while generative AI tools have undoubtedly streamlined the creative process, they haven’t eliminated the need for human involvement. The role of humans remains crucial in curating, fine-tuning, and ensuring the ethical and strategic aspects of AI-generated content, ad, or analysis, she adds.
“AI models are trained on vast amounts of existing data, which can introduce biases or limitations. Humans provide the expertise, creativity, and contextual understanding necessary to navigate these challenges. In the M&A domain, human involvement now focuses more on strategic decision-making, evaluating the generated options, and aligning them with brand values and marketing objectives and infusing it with their unique style, brand voice, and messaging nuances. They can also make sure the generated output complies with legal and regulatory guidelines, ensuring transparency and ethical advertising practices,” explains Bhargava.
At the forefront of martech revolution
There are specific tasks which AI can automate in the martech industry, and this can benefit marketers in myriad ways.
The power of vast data collection, real-time assemblance, and interpretation has made AI one of the key drivers of the martech revolution, says Neeraj Garg, Vice President & Head of Engineering, AGL (AdGlobal 360). He mentions about some areas where AI is being currently used:
- Personalisation:AI is being used to create personalised experiences for customers by analysing their data, behaviour, and preferences. This can help marketers deliver more targeted and relevant content, offers, and messages, which can increase engagement and conversions.
- Customer Segmentation:AI helps in analysing large volumes of customer data to identify patterns and create detailed customer segments. This enables marketers to deliver personalised content and targeted advertising to specific groups, increasing the effectiveness of marketing campaigns.
- Predictive Analytics:AI algorithms can analyse historical data to make accurate predictions about future customer behaviour, such as purchase intent or churn likelihood. This information allows marketers to optimise their campaigns and allocate resources more effectively.
- Content Creation:AI-powered tools can generate content, including blog posts, social media updates, and product descriptions. Natural Language Processing (NLP) algorithms enable these tools to understand context, tone, and style, producing high-quality content that resonates with target audiences.
- Chatbots and Virtual Assistants:AI-driven chatbots and virtual assistants have taken a leading role in giving rise to conversational commerce. It can engage with customers in real-time, answering their queries, providing recommendations, and assisting with purchases. These automated systems improve customer experiences, streamline interactions, and free up human resources.
- Sentiment Analysis:AI algorithms can analyse social media posts, reviews, and other user-generated content to gauge sentiment and understand how customers perceive a brand or product. This helps marketers monitor brand reputation, identify potential issues, and take proactive measures.
- Recommendation Engines: AI-powered recommendation engines use customer data and behaviour to suggest relevant products or services. By leveraging machine learning algorithms, these engines can provide personalised recommendations, increasing cross-selling and upselling opportunities.
- Budget optimisation:AI powered models like Next Best Action NBA can harness the power of data and behavioral analysis to recommend the correct action in terms of next best action which could be content personalisation/ targeting medium.
- Ad Optimisation:AI algorithms can optimise ad campaigns by analysing various factors, such as demographics, behaviour, and engagement data. This enables marketers to target the right audience, deliver ads at the most opportune times, and maximise conversion rates.
- Marketing Automation:AI streamlines marketing processes by automating repetitive tasks like email marketing, lead nurturing, and campaign management. This frees up time for marketers to focus on strategy and creative aspects while ensuring consistent and timely execution of campaigns.
- Image and Video Analysis:AI-powered tools can analyze images and videos to understand visual content and extract valuable insights. For example, marketers can use AI to identify brand logos, track product placements, or analyse user-generated content related to their campaigns.
- Voice Search Optimisation:With the rise of voice assistants, AI helps marketers optimise their content for voice search queries. By understanding natural language processing and voice recognition, marketers can tailor their campaigns to be more voice-search-friendly and capture voice-driven traffic.
“As AI technology continues to evolve, we can expect to see even more advancements and opportunities for marketers to use AI to drive business success,” adds Neeraj Garg.
AI by design is meant to do things that humans do; invariably all the use cases defined above for AI would fall into the AI automation bucket, he says, adding, “In terms of adoption, it would be used for automating a lot of effort-intensive and data-driven activities. It could be data analytics, content curation, customer assistance using voice/ text bots, among others. There would be an element of assisted human supervision in context to areas like auto-generated segments, hyper personalisation, campaign and spend optimisation, etc.”
According to Garg, the automation of many day-to-day tasks, such as reporting, data entry, data cleaning, automated content distribution across multiple channels, etc., has substantially reduced redundancies. “One could even say that it has enhanced efficiency and effectiveness by automating repetitive and time-consuming tasks. Marketers now have more time to focus on critical responsibilities like devising creative marketing strategies and optimising campaigns. AI also enables marketers to respond more efficiently to crises. The faster they can track and identify emerging challenges, the quicker they can respond and avert potential threats,” he says.
According to Neeraj Garg, the potential future developments in AI and martech are vast and exciting. “It will likely be characterised by increased automation, enhanced personalisation, and data-driven decision-making. AI will continue to play a central role in transforming marketing processes, enabling marketers to create more targeted and engaging experiences for their customers. However, it is important to note that while AI can greatly enhance marketing efforts, human creativity, intuition, and judgment will remain crucial in developing effective strategies and building authentic connections with customers. Metaverse and AR/VR, once matured, would have a great influence on the marketing strategy,” he says.
The risks and concerns
A few of the potential risks are ad quality, policy compliance, lack of originality, audience perception, and data privacy, points out Shubit Rakshit, adding that in order to mitigate these risks, maintaining human oversight is crucial, especially for ensuring brand alignment and creative excellence.
“Testing AI-generated ads in smaller batches can help evaluate their performance before full-scale deployment. Continuously monitoring ad performance and user feedback allows for quick adjustments. Combining AI-generated creatives with human creativity can add a unique touch to the ads and address any potential lack of originality. Robust data governance practices ensure data security and compliance,” Rakshit adds.
When asked how AI differed from traditional ad creation methods, he explained that while traditional ad creation relies on manual efforts, generative AI automates the process. “It leverages advanced language models and machine learning algorithms to analyse data and generate various ad assets. This automation speeds up the process, enables personalisation at scale, and allows for real-time optimisation. Generative AI continuously learns and adapts to audience preferences, leading to more relevant and engaging ads,” he adds.
Speaking on any potential downsides or risks to relying heavily on AI in the martech industry, and how they can be mitigated, Neeraj Garg says that in future, we can expect AI to be more evolved and current challenges like data and algorithmic bias would be minimised. According to him, there are multiple concerns around loss of human touch and over-reliance on data stifling creativity. Some of the key challenges which would stay would be regulatory frameworks and the ethical side of AI.
“AI in martech relies heavily on collecting and analysing customer data. The misuse or mishandling of this data can lead to privacy breaches and security risks. To mitigate these risks, marketers should adopt robust data protection practices, comply with relevant regulations (such as GDPR or CCPA), and implement strong security measures to safeguard customer information. Transparency and consent in data collection and usage are essential to build trust with customers. As AI becomes more pervasive in martech, ethical considerations will become increasingly important. Marketers will need to navigate issues such as data privacy, algorithmic bias, and transparency. Responsible and ethical AI usage will be essential to build and maintain consumer trust.” he explains.
With regard to the challenges or ethical considerations advertisers should be mindful of when incorporating AI into their advertising strategies, Ankit Ahuja says, “With any kind of tech usage, the first ethical consideration that comes to mind is privacy and data protection. Advertisers must handle consumer data ethically, ensuring compliance with privacy regulations and obtaining proper consent. Also, AI algorithms should be monitored to avoid perpetuating biases and discrimination in targeting or ad content. Lastly, advertisers should provide transparency about AI’s use, data collection, and how algorithms influence ad targeting and delivery.”