There is perhaps no greater trend in the tech world right now than artificial intelligence (AI). Specifically, generative AI, which can produce content based on prompts, is garnering a lot of attention from – and even causing fear within – media companies.

Generative AI makes content creation far easier, so media companies likely feel that they have to dive head-first into a future of AI-generated articles and TV shows.

The truth is that there is a far easier, even safer way for the media industry to dip its toes into the AI waters and prove out that it’s a worthwhile investment. The best place to start is with using AI to optimize advertising across media, whether that’s a website, an app, a CTV channel, streaming platform, or linear TV network.

A quick start with AI

It’s not breaking news that media companies need to adapt to remain competitive and constantly find new ways to create efficiencies and cut costs while remaining adaptable to industry trends. AI is getting attention because it promises to be an incredibly effective way for media companies to accomplish these goals.

Ad optimization is probably the easiest, fastest way to a return on your AI investment, largely because there are so many different ways that AI can be applied to the advertising process. These are just a few of the use cases for AI across a media business.

Personalization

When machine learning algorithms analyze user behavior and preferences, they can determine the ideal ad placement based on factors such as user demographics, interests, and browsing history. This personalization can lead to higher click-through rates and conversions for inventory, which allows a media company to charge a higher price and therefore drive more revenue. Similar models can be used for ad supported TV and CTV to optimize pod placement of advertisements in a TV show.

These same data signals can also fuel content recommendations, giving your audience a customized experience and driving longer interactions with a media brand.

Flexibility

Another application of AI is to track and analyze ad engagement. Ads that draw more interest from consumers are likely to perform better. Armed with this knowledge, media companies can adjust ad placement and content in real-time for maximum impact.

Insights

AI can analyze user-generated content, such as comments and reviews, to better understand user sentiment. This provides intel on whether ad placements need to be tailored to improve a negative experience.

Automation

AI’s greatest strength is automation, which can dramatically reduce the amount of time media companies spend running A/B tests on their ad formats and placements. Using AI, a company can run tests quickly and efficiently, helping determine the most effective strategy for their platforms. The ad creative itself can also go through a model to determine which ads will resonate with certain audiences.

Each of these use cases should excite media companies that are looking to increase efficiency and drive greater revenue. But the question still remains – where, exactly, should a company start? There are two key steps every operation should take.

1. Prioritize data collection

There’s an old saying, “garbage in, garbage out.” Even the most advanced AI is only successful when data is accurate and viable. In fact, we’re seeing hot consumer-facing AI products come under scrutiny because they generate inaccurate answers.

The first step any media company should take to effectively implement AI is to collect data on its audience. Most media companies already have this intelligence in spades, so the step becomes gathering it together in a usable fashion. Often, data can be siloed across different groups. It’s important to get buy-in across the company on any AI initiatives, so that the models have access to as much data as possible.

For most media companies, this data is generated by the users themselves, in the form of activity across the media properties, subscription sign ups, and ad interactions. This good, clean data provides insights into the types of ads audiences respond to, what times they’re most receptive to seeing an ad, and how they choose to react.

2. Select and apply algorithms

Armed with information about the audience, the next step is to use it. This is where media companies need to roll up their sleeves and better understand AI and Machine Learning. There are many different types of algorithms that can be used for ad optimization purposes, and media enterprises need to select the model that works best for their business and their goals.

These algorithms utilize historical data from previous campaigns as well as current campaign performance metrics in order to predict future performance based on similar past conditions. They learn over time, so with more data and time the models become more accurate at predicting future outcomes. Of course, most media companies don’t have the resources to build a model that specifically adheres to their business goals. Outsourcing teams to build models can further speed up adoption.

Again, AI for making content is getting all of the headlines, and it’s certainly valuable, but that’s not the place to start for most media companies. Ad optimization is an easy way to get started, and an easier way to start driving revenue and justify the costs of outsourcing algorithm development and technological updates. Now is the time to get on board. The next best time was yesterday.