By Becky Johnson, Advertising Week Podcast Host and Content Contributor
The conversation around artificial intelligence has become obsessed with intelligence itself. Every week brings a new model, a new agent, or a new breakthrough promising to automate work that once required teams of people. Yet beneath the excitement surrounding AI’s capabilities lies a less glamorous reality: intelligence alone does not create outcomes.
For marketers, agencies, media companies, and enterprise organizations, the real challenge is increasingly becoming connectivity.
An AI agent can analyze data, generate recommendations, and make decisions faster than any human. But if it cannot access the systems where that data lives, move information between platforms, or execute actions across a fragmented technology stack, its value quickly diminishes. The future of AI may look conversational on the surface, but underneath it depends on an increasingly complex network of APIs, integrations, governance rules, and infrastructure that most organizations rarely think about until something breaks.
As businesses race to build agentic workflows, the industry’s attention remains largely focused on the application layer. Organizations are investing heavily in developing AI assistants, automation tools, and intelligent agents capable of handling increasingly sophisticated tasks. What receives far less attention is the infrastructure layer required to make those systems operational at scale.
That disconnect is becoming more important as enterprises continue shifting from third-party data ecosystems to first-party data strategies. In the cookie era, information often moved through browsers and advertising platforms with relatively little direct coordination. Today’s environment requires systems to communicate directly with one another, creating a growing dependence on APIs and platform interoperability.
The result is that many organizations are discovering a hidden bottleneck in their AI ambitions.
The challenge is not that AI lacks intelligence. The challenge is that data remains trapped inside dozens of disconnected systems. Marketing organizations routinely operate across planning platforms, customer data platforms, analytics tools, activation environments, reporting systems, billing solutions, and media buying technologies. While each platform may function effectively on its own, generating business outcomes increasingly requires them to work together as a coordinated ecosystem.
This is where the industry’s understanding of AI may need to evolve.
Much of today’s discussion assumes that AI will replace software platforms. A more likely outcome is that software becomes infrastructure while AI becomes the interface. Instead of logging into multiple platforms throughout the day, marketers may increasingly interact through natural language prompts delivered to agents capable of orchestrating activity across dozens of systems simultaneously. The platforms themselves do not disappear. They simply move further into the background.
In that world, connectivity becomes mission-critical.
The organizations that gain the greatest advantage from AI may not be those deploying the flashiest tools or announcing the most ambitious agent strategies. They may be the companies that have invested in creating stable, scalable systems capable of moving data seamlessly between platforms. Intelligence can only be as effective as the information it can access and the actions it can perform.
That shift also introduces new operational realities. For years, companies have relied on internal engineering teams to build and maintain integrations between critical systems. While effective in the short term, that approach often creates a hidden tax on innovation. Engineers who should be focused on developing new products or capabilities frequently find themselves maintaining existing connections, troubleshooting broken APIs, or responding to platform updates that disrupt established workflows.
As AI adoption accelerates, the number of required integrations is only likely to increase.
Want to Learn More? Check out this podcast episode featuring Vitaly Pecherskiy, CEO of StackAdapt:
The next evolution of enterprise infrastructure may therefore be less about building more connections and more about creating smarter ones. Emerging concepts such as self-healing APIs point toward a future where connectivity layers can identify failures, diagnose root causes, and recommend or implement fixes automatically. Rather than waiting for a human engineer to discover that a connection has broken, systems themselves can monitor performance across entire ecosystems and respond in real time when disruptions occur.
For marketers, the implications are significant.
Campaign execution today often involves a series of manual processes spread across multiple systems. Planning, trafficking, reporting, optimization, invoicing, and performance analysis frequently require human intervention at every stage. AI agents have the potential to automate much of that operational workload, allowing teams to spend less time moving information and more time interpreting it.
This does not necessarily mean fewer marketers. It means different marketers.
The most compelling vision of AI in marketing is not one where humans are removed from the process. It is one where repetitive administrative tasks are increasingly delegated to agents, allowing people to focus on strategy, creativity, audience understanding, and business decision-making. An AI agent can optimize a campaign every few minutes if necessary. A human can decide why the campaign exists in the first place and what success should ultimately look like.
That distinction matters because the future of marketing will likely require both.
As organizations build more sophisticated AI ecosystems, governance and security will become equally important considerations. Intelligent agents capable of spending budgets, accessing customer data, and making operational decisions require the same guardrails traditionally applied to human users. Permissions, controls, and accountability frameworks will need to evolve alongside automation, ensuring that organizations maintain oversight while still benefiting from increased efficiency.
The companies best positioned for the next phase of AI adoption will therefore be those that recognize a simple truth: intelligence is only one part of the equation.
The headlines may continue to focus on models, agents, and automation. But beneath every successful AI strategy sits a foundation of connectivity, interoperability, governance, and trust. The organizations that get those fundamentals right will be able to move faster, automate more effectively, and unlock greater value from the technologies now reshaping the industry.
The future of AI may look like magic to the end user. Behind the scenes, however, it will be powered by something far more practical.
Better plumbing.
