As experienced B2B marketers know, a personal approach is central to winning long-term business. That is why leading companies use strategies like ABM, one-to-one email marketing, and custom online advertising campaigns. 

These strategies usually require significant time and resource commitments, making them difficult to scale effectively. However, artificial intelligence (AI) technology is emerging as a game-changer. One that is reshaping personalisation as we know it.

And now AI is opening up new possibilities for hyper-personalised online advertising.

By harnessing customer data and automating processes, these tools can help marketers deliver the custom, omnichannel experiences B2B buyers crave. This more tailored type of online advertising enhances the customer experience and also helps drive business growth.

For instance, according to McKinsey's B2B Pulse 2023 survey, winning B2B companies are using AI to go beyond account-level outreach. They can now tailor content and experiences to the unique needs, preferences, and behaviours of individual decision-making roles.

McKinsey's research shows that companies investing in AI-powered personalisation capabilities are growing market share. Roughly half of the survey respondents who invested in personalisation tools experienced growth in market share. And 77% of companies using direct one-to-one strategies increased market share.

So, let’s explore how B2B businesses are using AI-powered advertising to better connect with potential customers.

The Current State of AI in Advertising

A huge challenge for effective B2B advertising is accurately targeting ads across an omnichannel landscape.

The problem with earlier forms of digital advertising automation is that they used rule-based models. You had to explicitly outline campaign parameters – search terms, blocked publishers, run times, etc. You also had to create a large inventory of online ad assets to appeal to different audience segments. In addition, you had to track and manually update online ad campaigns. And if you forgot a step, like adding negative keywords, ad budget could be wasted on low-quality traffic.

In contrast, AI advertising uses machine learning and natural language processing (NLP) algorithms. These features allow AI automation to mimic the way humans behave and learn. More importantly, AI advertising platforms can analyse performance based on past results and automatically improve accuracy.

AI algorithms process mind-boggling amounts of data in real time. This ability means they can generate predictive insights into buying behaviour. They can identify patterns and correlations that are nearly impossible for humans to detect manually.

For example, AI tools can determine the probability of a user taking a specific action. Such predictions help them display ads at the right time, making campaigns more effective and actionable.

By leveraging AI technology, B2B marketers can:

• Elevate digital advertising by optimising ad placement to deliver highly targeted messaging.

• Offer rich media advertisement formats that invite potential customers to interact with ad content.

• Improve advertising ROI by effectively using ad resources and optimising customer experiences.

Major advertising platforms have been using AI tools for a while. Google's Performance Max, for example, uses AI-enhanced automation to deliver relevant ads across different channels. Likewise, Facebook uses machine learning to determine the quality and estimated action rate of native advertising.

AI-powered strategies help companies win clients hungry for tailored, omnichannel B2B buying experiences. But with the arrival of generative AI, what is about to happen will make current AI advertising look like nothing.

The Rise of Generative AI

Previously, AI advertising tools could only recombine existing ad elements to make new assets. It could choose a headline from one ad, an image from another, and pick a call-to-action from a third. It could rearrange these parts to create an optimised fourth ad. 

However, human creators still had to provide the initial material for the AI to work with.

Generative AI models go beyond remixing existing ads. When given a prompt like ‘create display advertising for X product,’ it can produce original content like text, images, and video. It uses patterns from a large data set to construct a response. After the initial training period, it can build new ads without additional human input.

The Potential and Challenges of Generative AI in B2B Advertising

This new form of AI technology offers several business benefits.

• Personalised Ad Campaigns. The ability to leverage user data to create on-the-fly content helps brands target extremely specific audience segments. This precision leads to higher conversion rates, reduced ad waste, and better return on investment (ROI).

• Efficient Content Creation. Automating the production of visuals, ad copy, and other assets can streamline campaign development and be cost-effective. Ad content can be altered in real-time, allowing campaigns to quickly adapt to changing audience preferences.

• Enhanced Creativity. Creative professionals can use AI-generated content as a starting point to explore new ideas and overcome content fatigue. It also enables advertisers to test concepts more quickly, resulting in fresh and compelling ad campaigns.

Paired with programmatic advertising, generative AI can rapidly produce and deliver large volumes of tailored content. It offers the alluring possibility of bespoke advertising at scale.

However, the rise of AI-generated content has also sparked debates around authenticity, ownership, and security.

Ethics and Authenticity 

AI is a computer programme. It lacks moral/ethical awareness and does not care if what it produces is inaccurate or offensive. Indeed, AI content can have unintended, even harmful, consequences if not designed and deployed with clear guidelines. 

It could represent a potential minefield, particularly in industries that must adhere to strict industry regulations, such as healthcare or banking.

Intellectual Property 

How developers acquire training data for generative models is a murky subject. Also, copyright laws have not yet caught up with the technology. 

As such, it can be unclear who owns the content generated by AI. Is it the platform that developed the tool? Or the brand that provided the ad prompt? 

What happens if an advertising platform trained their AI using unethically obtained data? Will businesses that use it to advertise their products or services be on the hook for infringement?

Data Security and User Privacy

Currently, most generative tools do not guarantee data privacy. If a business enters sensitive information into an AI tool, that programme may use that information unpredictably. This fact makes generative AI a thorny issue for companies that must ensure client privacy or confidentiality.

Despite these challenges, generative AI holds immense potential for the advertising industry. However, brands must navigate carefully. Striking the right balance between marketing personalisation and user trust will be essential.

The Role of Major Ad Platforms in Promoting AI

The adoption of generative AI by major search, social media advertising, and e-commerce platforms signals an important shift towards automated, customised digital advertising experiences. 

Google Ads recently announced new AI solutions to enhance personalisation and campaign automation. Key features include:

• Auto-creative: This feature uses generative AI to automatically create multiple variations of ad creative elements. For example headlines, descriptions, and images generated within the platform.

• Performance Max Integration: This capability will help advertisers automatically generate and scale custom assets across different Google properties.

• Conversational Campaign Creation: This tool integrates NLP capabilities into Google Ads to jumpstart search ad campaign creation. With it, Google Ads can use a designated landing page to automatically generate relevant campaign assets (keywords, headlines, descriptions, images, etc.).

Meta (formerly known as Facebook) is also developing advanced AI capabilities for its advertising service. Recently, they introduced an ‘AI Sandbox’ for testing new AI-powered tools for their social media platforms. While specific details are not available at the time of writing, features include text variation, background generation and image outcropping.

LinkedIn is another platform that has thrown its hat into the ring. On June 7, 2023, they debuted an AI-generated ‘Copy Suggestions’ feature. According to the announcement, it will use ‘advanced OpenAI GPT models' to leverage data from your LinkedIn Page and Campaign Manager to help simplify campaign building. 

Other players in digital advertising are following suit and developing advanced AI offerings. According to Forbes, Amazon is hiring a team to develop artificial intelligence tools for merchants on the Amazon platform. Meanwhile, Microsoft is currently testing how to monetise its smart chat API. 

Looking to the Future of Advertising with 1827 Marketing

AI technology is opening up new opportunities in advertising. It's enabling us to combine technology and creativity to offer hyper-personalised marketing strategies and experiences on an unprecedented scale.

However, this opportunity comes with challenges. As we explore the benefits of AI, we also need to navigate ethical implications, data security, and transparency. These aren't roadblocks, but important checkpoints. They can guide us to build better, safer and more effective AI policies that respect both the brand and the audience.

Ready to explore the future of advertising? Contact 1827 Marketing to find out how we can help you develop flexible, tech-ready B2B advertising strategies.