Reshaping media with AI—the pros and cons

There are many claims about the potential of artificial intelligence (AI) to improve media planning and buying, which have created as much anxiety as excitement. The rise of generative AI tools, such as OpenAI’s ChatGPT and automated image and video software, has made reckoning with AI a necessity. 

That reckoning isn’t thoughtless though, as recent surveys show marketers are generally more cautious of AI today than they were a year ago.

Jumpstarting productivity

AI enthusiasts cite multiple potential benefits for media teams across the life cycle of a media campaign. At the highest level, AI promises to give people time to focus on strategy and big, transformative ideas, reduce complexity, improve optimization, and enable enhanced testing and modeling. 

AI also has the potential to help teams develop quicker and deeper insights and automate the generation of planning documentation—such as investment plans and campaign parameters, creative assets, and media placement selection—all based on audience-level insights.

Superhuman campaign optimization

Take, for example, the essential function of audience targeting. Today, media teams are starting to use generative AI to analyze customer data and third-party signals to provide segmentation recommendations that could transform campaign results. 

Generative AI is also helping with media planning, enabling media teams to respond more quickly to market shifts. Media planning is complex and requires knowledge of audience context, cultural trends, and media cycles.

AI simplifies this process, improves efficiency, and builds greater confidence in plans.

Producing markedly better outcomes than manual approaches, AI is also “always on,” meaning it can rapidly adapt to market changes around the clock and manage real-time bidding (RTB) and programmatic buys. 

Beyond optimization, AI is driving improvement in programmatic campaign performance, powering recommendation engines based on past performance, enhanced audience targeting, and more accurate insights across an enormous scale of data signals that would take years for humans to uncover. 

AI in the real world

Of course, strong results require strong inputs. Proper governance is needed to ensure data is clean, structurally sound, and properly integrated. 

Moreover, programmatic algorithms remain somewhat of a black box, with brands developing their own platform-specific strategies and bidding approaches. Only the largest brands will have enough data to even think about creating homegrown AI to support RTB. 

There are other early AI success stories. Virtual assistants have advanced beyond scheduling meetings and now serve as media planning and buying tools, like the AI-driven Omni Assist. This tool provides data-driven insights, notifications, and recommendations across the planning and buying workflow.

In this way, virtual assistants are helping media teams reduce discovery time and accelerate insights gleaned from client outcomes.

Taken as a whole, AI is already saving teams time while producing better results for clients and creating room for more human innovation. 

The challenge to ensure quality

Yet, barriers remain. To date, the responsibility to assess and overcome AI’s challenges and benefits has fallen on individual organizations. Many businesses are navigating the integration of AI into operations in real time. 

Leaders are leaning on their teams for insight into how it can be leveraged for growth, efficiency, and speed.

Despite this boots-on-the-ground input, most organizations lack the necessary alignment, investment, and infrastructure to build proprietary AI solutions. Instead, they scramble to source and secure larger industry players to activate their AI plans. 

Further complicating the issue is a pervasive sense of mistrust: Will relying on AI result in subpar outputs, such as display ads with fake-looking people, faulty or biased audience segmentation, or runaway spending? 

Given AI’s rapid development rate, steep learning curve, and data requirements, many in media are understandably hesitant to dive in headfirst.

4 principles for healthy AI integration

Here are four guideposts for teams that want to explore AI for media planning and buying:

  • Be curious but skeptical
    This is a very new space, with room for growth and improvement. Amazing AI applications are launched all the time, but too many businesses get distracted by shiny new objects instead of focusing on the most practical AI-driven applications for their unique organization. There’s a lot of hype—don’t believe it all.
  • Do your research
    Making smart decisions with the help of AI starts by making smart decisions about what kind of AI you should invest in. Tools like Omni Assist also help organizations plan and activate digital transformations that rely on AI, ensuring relevance.
  • Focus on data hygiene
    Clean, structured, and robust data is the key ingredient for turning any brand’s dreams of AI-driven transformation into reality. Make sure the data you use to feed your AI tools adheres to robust data governance frameworks.
  • Understand the reality of homegrown solutions
    With the proper resources, AI developed in-house can be meaningful, but it is typically time- and capital-intensive. Before launching an internal program, explore the possibilities for partnership, asking questions about opportunities to customize the solutions on offer.

The good news is that AI solutions are improving rapidly, including those that support integration and implementation, which will build confidence and encourage testing and learning. As AI evolves, media teams are creatively applying it to elevate their work.