Over the past year, AI has dominated headlines and industry stages with an unprecedented level of intensity. The conversation feels dramatic, sweeping and at times overwhelming. But here’s the truth my colleagues and I shared at the Google x Rise Executive Growth Summit: While AI sounds revolutionary, this isn’t our first encounter with it.
If you’ve been running digital media for the past decade, you’ve already met AI many times. It’s been influencing bids, creative and optimization long before today’s hype cycle.
At Rise, our omnichannel digital agency, we aren’t reacting to an AI revolution. We’ve been operating inside one for almost a decade. So before we get swept up in the noise, it’s worth grounding the conversation in a few realities (four, specifically) that bring AI back into focus and into context.
Reality #1: It’s been a long time coming
AI is the continuation of a long automation arc that advertisers have been navigating since 2016.
Smart Bidding, one of the earliest major machine-learning infusions, launched nearly a decade ago and from there, automation expanded into creative generation, product feed ingestion, audience expansion and millions of real-time bid adjustments happening silently in the background of every campaign.
Brands have been making decisions in machine-learning environments for years, and Rise has been developing the strategic and analytical frameworks required to manage them.
If this moment feels unfamiliar, remember: You’re not starting from zero. You already have the muscle memory to operate in automated systems — and far more preparation than the headlines suggest.
Reality #2: What’s new is scale, not strategy
While the industry’s latest AI developments represent a meaningful step forward, the fundamentals of effective media management haven’t changed.
AI can now optimize faster and across a wider range of signals. It can ingest more inputs, evaluate more possibilities and automate more tasks than ever before. But the strategic principles remain:
- Inputs still matter. What you feed the system (i.e., products, creative and audiences) still shapes outcomes.
- Strategic oversight still matters. AI doesn’t replace judgment; it accelerates the impact of good decision-making.
- Measurement still matters. Each channel has its own AI-driven tools, but none of them are integrated. To get a true attribution picture, you still need to manually evaluate performance across platforms. That’s just smart marketing.
Rise’s core process has always been designed for automated environments. So sure, AI may expand the playing field, but it doesn’t rewrite the playbook.
Reality #3: AI creates new challenges that strategy must solve
As automation expands, it doesn’t just enhance campaigns. It reshapes how they must be managed. These are the new challenges of AI for marketers:
- Performance eventually plateaus
Even the strongest AI systems reach a point where gains level off. Models learn quickly, but they also stabilize quickly and without fresh inputs or strategic shifts, growth doesn’t continue indefinitely. AI can improve efficiency, but it cannot produce perpetual lift on its own. - Transparency declines as automation increases
More agentic decisions mean fewer adjustable levers and fewer explanations for why performance changes. Teams lose access to the granular inputs they once relied on, which changes how accountability is established. - The absence of clarity creates operational tension
Marketers still need to understand why a campaign is working, not just that it is. AI does not supply that narrative. Human interpretation, business context and strategic decision-making remain essential for translating algorithmic output into meaningful action.
Reality #4: You don’t have to hand over the keys
Despite the pace of innovation, brands do not need to move their entire media program into fully automated systems. In many cases, doing so too quickly can make performance harder to interpret, harder to validate and harder to control. The goal is not to choose between AI and human oversight; it’s to introduce automation in a way that preserves clarity and protects outcomes.
A measured approach begins with small, structured tests, not full-scale adoption. This controlled methodology enables:
- Clear, side-by-side A/B comparisons
- Directional learnings that inform real decisions
- Maintained control over pacing, audiences and creative inputs
- Verified incremental value, not assumed improvement
In our experience, strategic, phased adoption consistently outperforms wholesale shifts. Thoughtful integration keeps brands in the driver’s seat, ensuring AI enhances performance rather than obscuring it.
How to ensure strategic action in AI adoption
AI will continue to accelerate, evolve and expand visibility gaps, but the brands that win won’t be the ones that adopt automation the fastest. They’ll be the ones that adopt it intelligently.
The path forward isn’t blind trust or total resistance. It’s disciplined testing. It’s intentional optimization. It’s independent measurement that confirms what the platforms can’t. And it’s a strategic framework that treats AI not as a magic solution, but as an increasingly powerful system that still requires human direction.
Want to continue the conversation? Let’s chat.
