Mar 29 2023
Basis Technologies

Automation vs AI in Digital Advertising


Unless you’ve been doing nothing nowhere all at once for the past, say, 10 months or so, you know that artificial intelligence (AI) and automation have seized the marketing limelight.

Most of the current buzz centers around the potential business impact of new tools within the field of generative AI. But even before this boom, agencies and brands have been increasingly leaning into automation and AI technologies to unlock efficiencies and improve collaboration. One study found that 90% of marketers are using them to strengthen customer interactions, 89% are using them to enhance data integration, and 88% are using them to personalize the customer journey across channels. Other use cases include resolving customer identity, driving best offers in real-time, and bridging online and offline experiences. Indeed, marketing organizations today have so much disparate data at their fingertips and so many disparate systems to manage throughout any given campaign lifecycle that leveraging automated technologies to save time and cut costs is critical to unlocking success.

Automation and AI are spurring brands’ digital transformations, igniting conversations, and capturing headlines…but what exactly do we mean when we talk about “automation” and “AI”? The terms are often used interchangeably, but in order to make smart decisions about how to invest in them and grasp their possible impact, it’s important to know how they differ and where they fit into the marketing and advertising ecosystem. Here, we unpack all that, and more.

What’s the Difference Between Automation and AI in Digital Advertising?

The sometimes-synonymous use of these terms stems from the fact that automation is a broad category encompassing an entire class of technologies that includes AI itself. In advertising parlance, however, there are some key distinctions to be made.

The automation umbrella refers to a type of software that follows pre-programmed rules—it substitutes human labor across the campaign workflow, tapping into patterns to perform tasks that are repetitive and predictable. In the process, it enables scale that is virtually impossible to achieve without it.

AI, on the other hand, describes software designed to simulate human thinking—it is, as the name suggests, intelligent. Predicting outcomes under conditions of uncertainty is one of the most challenging aspects of digital advertising, and AI platforms can be a powerful ally in that fight by dynamically identifying and analyzing situations and crafting conclusions.

Simply put: automation works with data, while AI understands data.

How is Automation used in Digital Advertising?  

Digital advertisers use basic automation in an assortment of ways—for example, in leveraging programmatic media buying or eliminating incomplete or redundant lead information. But it can also help brands and agencies manage the myriad new channels and platforms that have caused media complexity to soar while sapping marketers’ time. Media buyers can use automation to simplify workflows and increase agility as they build out adaptable cross-channel experiences.

From streamlining planning processes, to optimizing ad spend in real-time, to centralizing all reporting, to reconciling financial data, automation can help marketing organizations tame the fragmented markets and accomplish things they never could with manual processes alone. And side note: this powerful technology can also enrich the employee experience by significantly reducing redundant and tedious tasks in favor of more meaningful, higher-level strategic work.

How is AI used in Digital Advertising?

Let’s move on to the topic of the moment.

It seems like barely a day passes without some marketing-related story about AI hitting front pages and homepages. The constant flurry—or, rather, blizzard—of new tools to hit the mainstream is whipping up an industry-wide frenzy.

OpenAI’s DALL-E 2 got the ball rolling when it burst onto the scene in late 2022. ChatGPT came next, gaining a million users in its first five days and reaching 100 million within just two months (making it the fastest-growing web platform ever). And GPT-4 has kept the hype going (and growing)—a tool proving adept at solving logic puzzles, building websites from a notebook sketch, creating business plans, telling jokes, and even passing bar exams. It’s also the technology powering Microsoft’s Bing Chat (you know, the one that went a bit crazy).

Of course, Mountain View was never going to sit back and let Redmond enjoy all the attention, so Google too has launched an API for its own language learning model (LLM), PaLM, alongside a number of other enterprise AI tools that it says will enable businesses to generate text, images, code, videos, and audio from simple natural language prompts. This includes Bard, Google’s chatbot, which runs on the company’s Language Model for Dialogue Applications (also known as LaMDA).

Big picture-wise, this is all game-changing stuff in the world of marketing. These new LLMs have accelerated the adoption of AI, and it’s easy to see where the advertising applications of these tools fit in. Brands and agencies are already reportedly using it to draft creative assets and brainstorm strategically, help refine consumer messaging based on online shopping habits, map out new marketing touchpoints, and scale their existing operations to do more with less.

It’s important to remember, though, that AI consists of so much more than just generative AI. Many aspects of digital advertising already leverage the technology to facilitate things marketers use regularly—think machine learning, behavioral marketing, contextual targeting, dynamic pricing, bid shading, and digital assistants.

Of all those areas, contextual targeting is probably the most widely adopted. Just over half (53%) of brand and agency marketers are currently using it, with another 33% planning to do so in the near future. This is no surprise considering the end of third-party cookies looms on the horizon. Since it doesn’t rely upon the use of personal data, contextual targeting neatly sidesteps identity issues, making it a sound (and cheaper) option for advertisers in this new privacy-forward future. It also adds an additional layer of filtering for page quality, helping to boost brand safety by avoiding lower-quality content and pages that don’t align with desired standards.

All said, the various applications of AI offer many of the same benefits as automation: They enable marketing organizations to build more robust actions that are regulation-compliant, all at a magnitude that would be implausible without them.

5 Best Practices for Using Automation and AI

Understand the Opportunity

If you’re not already leveraging automation and AI, start your journey by assessing the opportunity for your organization and establishing high-impact use cases. Laying out the capability and governance groundwork ahead of time is critical to implementing and utilizing the technology successfully.

Stay Balanced

Identify where you can find quick wins with the highest automation potential and then branch out. In parallel, develop a long-term vision for more comprehensive transformation that features automation and AI in your workflow and operating model.

Be Cautious with Generative AI

We’re still in the early days of generative AI and marketers can already use it to help with creative processes. But—and this is a big but—you must tread carefully. While the cost-savings this technology offers may be tempting, especially in today’s economic climate, advertisers that use it could find themselves embroiled in legal battles. Create guidelines around when, where, and how your organization adopts AI and ensure AI-generated content is always vetted by a human with relevant subject matter expertise.

Be Transparent with Your Employees 

The use of automation and AI can feel threatening to many marketers. But at least right now, the technology is at a stage where it’s there to help them do their jobs better and quicker. And who doesn’t want that? Marketing leaders should make sure they’re engaging their employees about how they’re using it and address any associated concerns they may have.

Start Experimenting

Developments in AI are poised to disrupt our industry. Changes will likely come slowly at first, but it’s essential to keep a close eye on them. Early adopters are more likely to reap the benefits down the line and late movers may struggle to keep up. Start testing and gain the advantage of early learning.

Automation vs AI in Digital Advertising—Wrapping Up

Automation and AI each have sweeping definitions that encompass a variety of complex technologies. Both are already indispensable tools for driving the kinds of large-scale, personalized, omnichannel advertising experiences that today’s consumers demand. It’s by no means essential for every marketer to be an expert in both, but looking into the future of digital media, it’s clear that automation and AI will have an enormous impact on our industry.


If you’re interested in learning more about how to incorporate automation and AI into your marketing but unsure of where to start, reach out to our Media Strategy & Activation Team. You can also check out AdTech Academy, which offers courses that cover an array of automation and AI-based topics, including algorithmic optimization, behavioral targeting, contextual targeting, machine learning optimization, programmatic advertising, and many others.

See Courses