AI Assistants Enter Advertising: What Marketers Need to Know Now

Artificial intelligence (AI) is transforming the marketing landscape, and its impact on advertising is becoming increasingly significant. AI assistants are now being integrated into various aspects of digital marketing, from creative development to performance tracking. Generative AI, in particular, is gaining traction across industries as a tool for boosting creative output and improving campaign effectiveness.

The use of generative AI in marketing extends beyond content creation. It’s now being used throughout the campaign lifecycle, from concept development to testing ideas across multiple channels at scale. This technology enables marketers to work alongside AI during the creative production process, streamlining their workflow and saving time and resources.

With the help of AI, marketers can test more ideas and tailor campaigns to a wider range of contexts. For instance, Google Ads uses AI to generate relevant headlines and descriptions for its system to test, helping deliver more targeted ads and ultimately finding the best combination. This raises an important question: as AI makes creative production cheaper and faster, does the competitive advantage shift from making content to understanding what ideas are worth producing and scaling?

As AI systems become increasingly capable, marketers’ roles will lean towards deciding which ideas are worth developing rather than manually creating each asset. Teams can focus on sharing brand voice and creative directions while ensuring any AI-generated work is unique, relevant, and aligned with their organization’s goals.

Traditional paid search differs from AI ads in several ways. Paid search typically relies on the search query to drive ad relevance, whereas AI ads bring a more conversational layer to this process. For example, ChatGPT ads consider multiple signals, including context and intent, landing page information, title copy, advertiser-provided hints, and user experience data.

The distinction between traditional paid search and AI advertising is crucial for marketers. AI ads can use the conversational context around a customer’s needs when determining ad relevance. This creates new challenges for marketers as brands may need to think beyond keywords and consider users’ questions, needs, and existing use cases to provide relevant recommendations and purchases.

CPC (Cost per Click) and attribution in AI advertising are also becoming increasingly complex issues. OpenAI offers both CPM (Cost per Mille) and CPC buying options for ChatGPT ads. However, the challenge lies in attributing value before the click occurs. For instance, AI assistants could influence consumers by answering queries and comparing products before they visit an advertiser’s website.

This results in more difficult attribution as marketers may only see a single referral click or no click at all if the transaction occurs within the AI platform. This raises another question: should AI platforms receive credit for generating clicks or influencing decisions that lead to purchases?

The quality of product data will become increasingly important as AI assistants are integrated into product discovery. Brands must ensure their product feeds contain accurate information on aspects such as pricing and specifications, equipping AI systems with improved insights for comparing or recommending products.

Amazon has already demonstrated the importance of product data in its shopping assistant, Alexa for Shopping (formerly Rufus). Using Amazon’s product catalogue alongside conversational context, Alexa For Shopping can answer questions about products, compare options, and make recommendations. According to Amazon, ‘Rufus is an expert shopping assistant trained on Amazon’s product catalogue and information from across the web.’

Startup company Gravity is reportedly working with Best Buy and Target to develop an agent-to-agent advertising system where AI agents exchange product recommendations using real-time catalogues from advertisers. Gravity co-founder Zach Oldham noted that this system ‘helps enrich the buying decision.’

The integration of AI assistants into marketing raises concerns about brand-safety risks, particularly when it comes to AI-generated answers. It’s possible for an AI assistant to misrepresent a product or provide inaccurate information alongside an ad, creating new challenges for marketers.

A study examining 55,393 Google searches found that over 11% of claims made in AI summaries were unsupported by their cited sources. This suggests even credible sources may not guarantee accurate AI-generated summaries. Brands face the concern of having less control over how their businesses are represented through AI assistants.

As AI agents begin to interact with other agents on behalf of businesses, marketers will have limited visibility into how recommendations are made. This raises questions and concerns around transparency and accountability in agent-to-agent advertising.

A paper titled ‘Auditable Agents’ looked at distinguishing accountability from auditability. The research argues that no agent system can be accountable without auditability, identifying five requirements: action recoverability, lifecycle coverage, policy checkability, responsibility attribution, and evidence integrity.

If AI agents make advertising decisions, marketers must understand why a certain product was recommended, what influenced this decision, and who is ultimately responsible if something goes wrong. This requires more detailed oversight into how those systems operate.

AI governance has become an essential business priority for organizations looking to scale their use of AI while managing associated risks. Companies with robust frameworks and oversight in place are better positioned to adopt AI responsibly.