By now, we are all swimming in a sea of AI. Every app, platform, or piece of software we use proudly claims to have incorporated some form of AI into its makeup. But what does “powered by AI” actually mean? Is it just a buzzword, or does it translate to real value for users?

Ad tech has always been driven by technological innovation. However, with the advancements of AI, and in our case specifically, machine learning, sales teams can take an entirely new approach to managing their clients' campaigns.

In this article, we break down what machine learning actually is, how it's being used in the ad industry, and how ShowSeeker is leveraging it to redefine campaign and order management.

More than AI

AI as a whole is not a new concept. Companies have used artificial intelligence for years, but its capabilities have evolved dramatically. While most people associate AI with generative AI—the technology behind text generation, image creation, and other content-producing tools—or with large language models that can recognize, understand, and generate human language (think ChatGPT), AI is much broader than that. Other branches, such as machine learning, serve entirely different purposes.

So, what exactly is machine learning?

AI and ML are often used interchangeably, but it's important to note the differences. At its core, machine learning is a branch of AI that uses mathematical analytics to find hidden patterns in streams of data. While that may seem like a relatively simple process, the key to the technology’s success ultimately relies on the state of the data. Grooming the data, training algorithms, and refining models is an ongoing process, one that requires precision, iteration, and deep industry knowledge—something we experienced firsthand.

When done correctly, ML can produce quality, magic-like data, and we’re seeing that manifest in media in numerous ways.

ML in the ad industry

The industry as a whole is benefiting from advancements in technology. Machine learning software redefines how advertisers reach the right audience, giving marketers access to a new era of data-driven automation that makes campaigns more impactful. They can segment audiences based on real behavior, interests, and engagement patterns. At the same time, campaigns can adjust in real time, optimizing placements and budgets for better performance and detecting and preventing issues like click fraud.  Additionally, through predictive analytics, campaigns can better prepare for outcomes using historical data to anticipate trends and refine strategies before they even begin. While this is a broad overview of how technology manifests itself in the industry, we’re implementing similar features into our OMS.

At ShowSeeker, we've applied some of these same principles to programming data, helping our users build more accurate, forward-looking schedules that keep them ahead of the curve.

How we’re using it

Last year, we wanted to address a common challenge amongst ad sales organizations and introduced Predictive Programming to Pilot®. Traditionally, programming data from providers such as Nielsen’s Gracenote is limited to a 56-day confirmed window. That’s a major challenge for sales teams creating campaigns months, or even a year, in advance.

Predictive Programming allows users to forecast and schedule programming data 12 to 14 months in advance by analyzing historical data. This means more accurate schedules, better-informed ad placements, and fewer last-minute surprises.

We designed it to showcase programming in three tiers:

• Predicted
• Projected
• Confirmed

The feature is structured to continuously improve through refined training and targeted adjustments, giving users a highly accurate view of future programming. This allows for confidence in scheduling regardless of the status of the particular programming being incorporated into the order.

Where we’re headed

When we started Pilot, we set out to solve a clear problem: help the media industry manage their campaign and order management in a more informed, faster, and simpler way.

We’re working towards integrating more features so teams can continue to build smarter and more accurate schedules. Machine learning opens up new possibilities for us in analytics, impression delivery, and viewership, to name a few. Stay tuned.

Wrapping Up

The next time a product claims to use AI, dig deeper. Think about what type of AI was used and the problem it is looking to solve. The most impactful use of AI stems from thoughtful implementation. For us, that means bettering our solutions for users. Through machine learning, we can add value to how teams create and manage their ad sales.