AI is widely used in advertising, but like any new technology, it requires careful handling. Here, we explore its advantages and challenges
Artificial intelligence (AI) is rapidly transforming advertising, bringing new levels of precision, scalability, and creativity to an industry long driven by gut instincts and demographic segmentation.
Indeed, an article in the Wall Street Journal recently reported that 94.1% of advertising revenue will be informed by AI by 2029.
From machine learning algorithms that fine-tune targeting in real time to generative AI that spins out fresh ad copy, AI-enabled solutions promise deeper consumer insights and higher returns on ad spend.
Yet, as with many disruptive technologies, adoption is not without risks.
Whether leveraging AI for automated ad placement, optimization, or personalized messaging, marketers must tread carefully.
Ethical concerns – such as privacy, bias, and transparency – cast a growing shadow, and poorly implemented AI strategies can undermine consumer trust or even cause reputational damage.
So, is AI in advertising a friend, a foe, or perhaps both?
In this article, we will explore AI’s role in modern advertising, discussing its main benefits, key challenges, and how B2B marketers can integrate it responsibly.
We will also delve into regulatory considerations and actionable best practices for mitigating risks while capitalizing on AI’s transformative potential.
Can AI achieve accuracy and relevance in campaigns?
One of AI’s biggest draws is its capacity to analyze vast amounts of data and derive actionable insights.
By sorting through consumer behavior trends, social media interactions, and demographic variables, AI systems can predict purchasing intent, segment audiences, and personalize content.
This is reinforced by industry uptake. According to a Statista survey, 55% of marketers said they were using AI for advertising, with another 40% looking to do so within the next year. But how accurate is it?
AI in programmatic advertising
AI-driven programmatic platforms like Google Ads or The Trade Desk harness machine learning to automate bidding strategies, allocate budgets efficiently, and optimize campaign performance – and report strong success off the back of the technology.
For example, according to Google Ads, AI-powered search ads helped one of its clients enjoy a 182% increase in signups and a 258% increase in clicks.
This efficiency is especially attractive for B2B companies, where longer sales cycles and high-value deals demand precision at every stage of the funnel. However, this success does not come without risks.
Data bias and the risk of misrepresentation
One major issue is that AI trained on historical data can reflect and perpetuate societal biases.
A famous example involves Amazon allegedly scrapping a sexist AI recruitment tool that, according to a report by Reuters, discriminated against female candidates because it was trained on historical data that skewed towards male applicants.
While Amazon declined to comment specifically, the story provides food for thought when considering how AI can inadvertently reinforce stereotypes or underrepresent key audience segments.
In advertising, such biases can manifest in unbalanced targeting (for example, certain demographics receiving fewer or even exclusionary ads), leading to reputational risks and missed market opportunities.
As an example, a study of Google Ads by Carnegie Mellon University found that women were shown ads for high-paying jobs significantly fewer times than men.
Keeping data fresh and relevant
Another hurdle is data freshness. AI algorithms relying solely on historical datasets may not accurately reflect changing consumer tastes or market conditions.
This can lead to model drift (also known as concept drift), whereby AI models are unable to respond to industry trends, product releases, or shifts in buyer behavior.
A real-world example of model drift is the case of real estate company Zillow and its failed implementation of an automated property valuation algorithm.
This analysis from The AI Journal reports that changes in the market and increased activity between initial testing and expansion resulted in overvaluations of over $500m. The sustained losses forced Zillow to lay off 25% of its workforce back in 2021.
The most comprehensive protection against model drift is the integration of real-time data streams into your AI models using online machine learning. Where this is not possible, the issue can be mitigated by combining information with contextual data or regularly updating a training data set. However, the truth is that any model relying on batch learning is exposed to a level of risk.
The role of generative AI in advertising
Generative AI offers an effective way to expedite the content creation process, enabling marketers to develop ad copy, visuals, and even videos at scale. Popular generative AI tools include Jasper, Adobe Firefly, Midjourney, ChatGPT, and Google’s Veo 2.
According to Hubspot, 84% of marketers believe generative AI boosts efficiency in content creation and 85% believe it improves quality of content.
Using AI for ad creatives
While using generative AI for creativity is a contentious issue, there’s no doubt it can help expedite certain processes. And when used right, it can provide an innovative angle for ad campaigns.
One brand that’s embracing the technology is Coca-Cola, which has received mixed reactions that perfectly illustrate the controversy surrounding the topic.
On one hand, the brand was met with outrage when it recreated its famous Christmas ad using AI. On the other hand, its ‘Create Real Magic’ campaign – a collaboration with OpenAI and Bain & Company that invited digital artists to produce ad creative using DALL-E and GPT-4 – was a great all-round success.
The latter illustrates how generative AI can foster higher interactivity and a sense of ownership among audiences, whether in B2B or B2C contexts.
The former, while arguably more successful in terms of attention, illustrates how strongly your audience may feel about generative AI – a reminder to proceed with caution.
AI content localization
For marketing campaigns spanning multiple regions, localized content can significantly boost relevance and engagement.
This study by CSA Research found that 76% of online shoppers prefer to buy products with descriptions in their own language with 40% declaring they would never buy from websites in other languages.
By combining generative AI with multilingual capabilities – such as integrations with ChatGPT or other translation APIs – marketers can adapt visuals and messaging to suit local cultural norms and linguistic nuances.
Quality control challenges
In theory, this sounds like a no brainer, but in practice it can often be a different story. Generative AI can produce flawed or misleading outputs, ranging from nonsensical text to images that unintentionally violate brand guidelines.
A real-world example of how AI hallucinations can cause problems was the case of a New York lawyer referencing non-existent cases in court after using ChatGPT for legal research. While not specifically linked to advertising, the case paints a worrying picture.
The rapid scalability of such tools means mistakes can go viral before human oversight catches them. That’s why implementing structured review processes – like AI auditing tools, brand safety protocols, and human editorial checkpoints – is extremely important in minimizing the risk of reputational harm.
Risks of over-intrusiveness and brand damage
Personalisation
While AI excels at personalizing user experiences, hyper-personalisation does come with risks. Spotify Wrapped in 2024 was a high-profile example of a big brand missing the mark.
There is, of course, also the risk that personalisation can give insight into sensitive user information such as purchase or browsing histories.
Ad placement
The risk extends to where your ads appear. A Guardian investigation revealed that some programmatic ad placements have landed on extremist or highly controversial websites, tarnishing brand reputation.
For brands that rely heavily on trust and professionalism, appearing alongside inappropriate content can be especially damaging. Regular audits, negative keyword lists, and sophisticated content filters are essential in mitigating these risks.
AI audit committees
Implementing an AI policy that sets rules, boundaries and best practices is an important step in ensuring brand safety when utilizing AI in advertising, but it is surprising how few organizations have anything in place.
A 2023 survey from Deloitte showed that only 13% of respondents had a formalised AI oversight framework, but the importance of AI governance is growing at an alarming rate.
These internal councils can facilitate an ongoing dialogue among marketing teams, data scientists, and legal advisors to proactively spot potential issues and set clear boundaries for AI’s role in advertising.
Ethical concerns and regulatory challenges
Data privacy and consumer trust
Data privacy remains a top concern, especially when AI thrives on large data sets. Whether it’s collecting purchase histories, browsing patterns, or business interactions, brands need to handle data ethically and transparently.
A report by the UK Government shows that 57% of respondents see stolen information as the biggest risk surrounding the gathering and storage of digital data.
The fact that the same study found that “scary” was the most popular word people used to describe AI underscores the need for clear consent mechanisms and accessible privacy policies.
GDPR, FTC, and emerging regulations
In Europe, the General Data Protection Regulation (GDPR) sets stringent rules on data collection, consent, and user rights.
Companies that run afoul of GDPR face steep fines and reputational blowback. Meanwhile, the Federal Trade Commission (FTC) in the United States recently approved a final order against a tech company about deceptive AI practices.
Beyond these existing frameworks, the European Union has enacted the AI Act, which could impose further obligations on developers and users of AI systems. The legislation aims to classify AI applications based on their level of risk, potentially restricting certain uses in advertising.
While it may not have a huge effect on the day-to-day work of ad executives right now, savvy marketers should keep abreast of regulations to ensure compliance and foster consumer trust.
Responsible AI standards
Leading technology firms like Microsoft have introduced Responsible AI Principles, which include guidelines for fairness, reliability, safety, privacy, and inclusiveness.
These internal frameworks can serve as models for smaller organizations eager to establish robust governance around their AI activities. By adopting clear ethical guidelines – such as requiring sound reasoning behind AI decisions or offering user-friendly opt-out options – brands can pre-emptively address consumer concerns.
The future of AI in advertising
While there are some early concerns and bumps in the road, AI in advertising is undoubtedly here to stay. Here are a few of the most prominent emerging AI technologies that offer a glimpse into the next wave of advertising innovation:
Predictive analytics
Leveraging machine learning to anticipate behaviors like lead scoring, churn rates, and high-intent buying signals. By analyzing patterns in CRM data, website visits, and intent signals, marketers can deliver the right message at precisely the right moment in the buyer’s journey.
Virtual influencers
AI personas like Lil Miquela have amassed large social media followings and could provide brands with a controlled, AI-generated persona to interact with potential buyers.
Real-time dynamic creative optimization (DCO)
AI will automatically assemble ad elements (headlines, visuals, calls to action) based on user behavior and context, delivering hyper-relevant creative at scale.
AI-driven conversational ads
Brands can engage prospects through chat-like interfaces on LinkedIn or other platforms, guiding them toward product demos or case studies.
Advanced personalization
Future tools may analyze not just browsing history but also emotional cues like sentiment analysis to tailor messaging at an unprecedented level of detail.
Advanced fraud detection
As AI-driven advertising grows, so will AI-driven fraud. Innovations in fraud detection using machine learning could protect marketers from wasted spend and ensure ad impressions reach legitimate audiences.
By anticipating these developments, marketers can position themselves as early adopters, leveraging AI’s evolving capabilities to outmanoeuvre competitors and deliver higher value to clients.
Final thoughts
AI has already proven itself a formidable ally in advertising, unlocking insights, efficiency, and creative potential that were previously hard to imagine.
From predictive analytics that refine lead generation to generative AI tools that produce tailored, on-brand content in multiple languages, the opportunities for B2B marketers are vast.
Yet, AI’s integration comes with risks: algorithmic bias, data privacy issues, and the potential for reputational damage if poorly executed.
The path forward lies in balancing innovation with accountability. By incorporating human oversight, adopting ethical frameworks, and staying abreast of regulatory developments, brands can position AI as a trusted partner rather than a liability.
As AI continues to evolve – offering real-time dynamic creative, conversational ads, and advanced personalization – those who invest in responsible usage today will be best equipped to reap AI’s transformative rewards tomorrow.
In the end, whether AI is a friend or foe depends largely on how thoughtfully it is deployed. For marketers ready to embrace the future, the key is clear: pair cutting-edge AI with ethical boundaries and human judgment to ensure that, indeed, AI becomes your brand’s most valuable ally.
Subscribe to our newsletter
Get the latest articles and updates delivered straight to your inbox
Related articles