The adoption of artificial intelligence by marketing departments has permanently altered the practice, and CMOs feel compelled to leverage the technology, in any way possible. But layering a few AI apps can only get them part of the way, says Tal Jacobson.
Marketers need to integrate native AI into their infrastructure in a way that can transform their planning and execution of marketing campaigns, rather than just layer off-the-shelf AI apps or agents to save time on mundane tasks or crunch reams of data, says Jacobson, CEO of the adtech company Perion.
“Saving time is helpful, but the real value of AI is improving campaign performance and efficiency at scale,” says Jacobson.
When AI is used only for reporting or workflow automation, it helps teams move faster, but when it is embedded into campaign execution, it can continuously optimize key performance drivers and creative delivery, Jacobson explains. The technology can dynamically adjust bids to maximize outcomes, optimize supply paths to reach higher quality inventory, and use dynamic creative optimization to deliver the most relevant creative variations to different audiences.
These optimizations run continuously while campaigns are live, helping marketers capture performance gains that would be difficult to manage manually, Jacobson adds. The ultimate effect is not just impacts on productivity, but also a stronger performance and better ROI.
“For marketers, the difference comes down to insights versus action,” Jacobson says. “One approach tells you what might work. The other actively works to improve performance while campaigns are running.”
Many platforms use AI to generate dashboards, recommendations, or insights based on post-campaign data, and those tools can help inform marketing decisions; but they still leave the staff needing to make the adjustments manually. On the other hand, if AI is embedded directly into the execution layer of the marketing platform, it can automatically make those optimizations – adjusting bids, shifting budgets, optimizing supply paths, and improving targeting – in real time, while campaigns are live, Jacobson explains.
“This enables brands to manage campaigns more efficiently and achieve stronger performance from their media spend, even as media environments become increasingly siloed,” says Jacobson.
For example, he notes the Perion One platform connects planning, buying, creative, and optimization into a single system. AI analyzes performance signals across channels continuously and activates changes in real time, whether deploying smart bidding strategies for media buying or creative optimization through dynamic creative. It helps marketers run smarter media campaigns at scale across channels, taming the complexity of a fragmented advertising environment, he says.
“Interface Vs. Infrastructure”
Simple AI solutions have their place, says Jacobson. Chatbots and AI assistants are transparent, low-cost, and purpose-built, which makes them an adequate tool for direct human interactions such as customer service, support, and simplifying workflows.
Simple interfaces help marketers interact with systems more easily, while intelligent systems underneath handle complex tasks such as real time optimization, and creative decisioning. Ultimately, AI works best when both layers exist, says Jacobsen.
“The right solution depends on the problem you are trying to solve,” he says. “The distinction isn’t simple vs. sophisticated, it’s interface vs. infrastructure. Simple AI handles the conversation. Infrastructure AI handles the consequences.”
One of the consequences of the increased use of AI has been a heightened concern over the data AI-based platforms use to train their models. Developing data trust is a big part of the conversations between marketing departments and adtech partners before they deploy these tools.
“In advertising today, one of the most important shifts is the move toward trusted first-party data environments,” says Jacobson. Major platforms and retail media networks, which can draw data from real interactions and purchase behavior, hold the strongest consumer signals to reach audiences built on high-quality data while maintaining privacy standards, says Jacobson. The role of companies like Perion is to activate those signals responsibly, he adds.
“It is about building the technology layer that can work with trusted data environments and translate those signals into better campaign performance,” Jacobson says. “As the industry evolves, the companies that win won’t own the most data. They’ll be the best at using it across multiple channels simultaneously.”
AI-Native Infrastructure
Perion has made the same AI-related adjustments marketers are dealing with today, Jacobson notes. The company had a long track record of providing advertising technology before the current AI explosion, and has found ways to adopt the technology to improve their offerings.
The company launched Outmax, an AI agent that can be layered onto media platforms such as YouTube, The Trade Desk, and Meta to allocate spend, manage pacing, and optimize outcomes in real time.
“We have moved beyond simply using AI to becoming AI-native infrastructure,” says Jacobson. “Outmax now serves as the connective tissue across the entire lifecycle.” The agent links planning, creative and performance measurement across all digital media, including connected TV, digital out-of-home and retail media networks.
“By embedding Outmax directly into the execution layer, we’ve automated the high-stakes variables: bidding, budget allocation, and supply path optimization,” Jacobson explains. “This allows campaigns to navigate the fragmented ecosystem and adapt in real time, letting marketers scale performance without the friction of manual intervention.”
But moving away from manual processes doesn’t mean AI is a way to replace human input. “I see it as the greatest unlock for human creativity we’ve ever had,” says Jacobson. When AI-native infrastructure handles the transactional part of executive bids and budgets by the millisecond, it frees the marketing team to focus on the proverbial “magic,” of the practice, he says.
“We are moving into an era where your strategy isn’t limited by your manual bandwidth, but only by the strength of your ideas,” says Jacobson. “The most successful brands of the next few years won’t be the ones with the biggest data science teams; they’ll be the ones who use AI-native execution to give their teams the freedom to be bold again and build storytelling that accelerates real growth to their brands.”
