By Erwin Castellanos, General Manager, Adobe Advertising
When did advertising decide that simplicity and performance were incompatible concepts? This notion has given rise to a myth that has been quietly exhausting the people doing the real work: that to be effective, teams must accept a baseline level of chaos.
For years, the industry’s reaction to every new constraint has been to bolt on another layer: another vendor, another data set, another workflow, another “AI feature” that promises relief but arrives with new dashboards (and often more meetings).
Teams are overwhelmed. Everything keeps moving faster. Even high-performing organizations can feel like they’re sprinting just to stay in place. And the last year of AI “advancement” has produced more moving parts, not fewer.
2025 marked peak complexity in our industry. It was positioned as a breakthrough year. But for anyone in the trenches, it played more like a breaking point: more pressure, more steps, and less room to think.
Hard as it might seem amid the acceleration of everything, the biggest opportunity for advertisers in 2026 is a return to a quiet truth: Simplicity is not a retreat from performance. Done well, simplicity can unlock performance. It can also unlock relief: fewer fire drills, fewer handoffs, fewer places for budgets and attention to vanish.
Let’s look at three pivots that can help advertisers simplify their worlds while improving results. In the process, they also reduce effort, stress, and operational drag.
Let owned data do the heavy lifting.
Marketers are drowning in data, but they are also starving for usable truth. The most reliable path to both simplicity and performance is already in their hands: analytics insights from customers on their sites and apps, plus other first-party signals. When teams lean on owned data instead of rented signals, marketing becomes more controllable and easier to optimize with confidence.
Many organizations treat analytics derived from campaign results and on-site user behavior as the record of reality, while paid media planning and optimization happens in a parallel universe with a heavy reliance on third-party data. Site and app analytics provide a source of truth, but they’re rarely used to optimize paid media beyond some basic retargeting.
Brands have the ability to do more with their first-party data. Campaign and site analytics should not be treated like a post-game recap. They should be the source of truth that informs the buy, thereby driving improved performance and reducing costs. In addition, when the same success events used to run the business are used to steer media, fewer meetings are spent debating definitions and more time is spent improving outcomes.
When an advertiser anchors its paid media spend with insights from its own data, campaign targeting becomes simpler. Strong conversion tracking, on-site behavior signals, and audience insights derived from analytics and other first-party interactions can replace a meaningful share of third-party targeting over time. Early tests of more conversion-focused, context-aware models suggest a practical pattern: As models improve at finding likely converters, the need for external segments naturally declines. In practice, campaigns get simpler to build, signal paths get cleaner, optimization gets faster, and costs often drop because fewer third-party add-ons are required.
That is not just simpler. It is cheaper. Third-party data fees that look small in isolation become a tax on every impression. Reducing dependency on third-party data also reduces drag: fewer contracts, fewer plumbing projects, fewer “is this segment still valid?” debates. Performance and simplicity arrive together because the same move improves ROI and lessens the operational lift, giving teams back time for the work that actually moves results.
Treat fees as a design problem, not an accepted fixed cost.
Programmatic advertising delivers value through automation, but it shouldn’t behave like a toll road. Tech fees, resold inventory, duplicative auctions, and layered intermediaries can siphon meaningful budget away from working media. Complexity and fees tend to travel together. When advertisers strip out avoidable fees, they simplify the operation and put more budget back into working media.
The fix is not a blanket demand for cheaper CPMs. It is being more deliberate in how we approach the supply chain.
Supply path optimization helps buyers choose more efficient routes to inventory, cutting unnecessary hops and redundant auctions. Curation and “supply shaping” go further by improving the quality of what reaches the bidder, reducing the sprawl of near-identical supply sources.
The payoff is economic and operational. A cleaner supply path reduces fees and produces clearer reporting and faster learning cycles. Just as importantly, it reduces the daily friction: fewer escalations, fewer surprises, and less time spent deciphering where dollars went. Clarity and predictability translate into breathing room.
That said, talk is cheap when it comes to delivering simplicity and performance via supply. Agencies are holding their partners accountable, withdrawing spend from platforms that promise simplified performance but deliver opacity and hidden fees.
Demand practical AI, not just a shiny object.
So far, AI has often feigned simplicity in advertising rather than creating it. Many tools remove work in one place only to shift it elsewhere, especially when outputs require cleanup, rework, or senior review. That’s not simplification. It’s repackaged complexity.
That’s not to say the industry shouldn’t remain in hot pursuit of the efficiencies that AI can deliver. AI is a transformative force that can deliver tremendous value. But it is time that we get serious about pressure-testing the various tools. If a tool is promising to streamline processes, one must ask:
- Is the output high quality? If it’s low quality, it’s adding waste to the campaign.
- Is it measurably saving time or steps overall, and not just within one part of the process? If AI delivers an output that requires later scrutiny by a senior team member, it could actually be reducing efficiency by taking time away from high-value individuals.
- If you’re using AI to introduce capabilities, is it creating new complexity that makes it harder to understand the value being delivered?
The AI tools that are most valuable–practical AI tools–are the ones that remove steps and complexity (e.g., reducing external partner reliance, automating processes with high reliability, or seamlessly integrating insights that used to be added manually). At its best, practical AI also helps teams see what they would have missed, not just process what’s already in front of them faster.
Simplicity as the path to performance
These three shifts are not separate projects for marketing teams. They represent one philosophy: Advertisers need to simplify their programmatic media-buying programs and ensure more of their budget goes toward working media. For teams, that simplification looks like fewer systems to juggle, fewer partners to manage, fewer fees siphoning budget, and fewer downstream problems caused by upstream patchwork. More visibility. More control. More time for the work that actually matters.
Just because the ecosystem continues to become more complex, advertisers should not abandon hope for a simplified path to performance. They should treat the joint pursuit of simplicity and performance as a discipline: fewer unnecessary steps between intent and outcome, and fewer places for budgets and attention to disappear.
